{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Regression with Amazon SageMaker Linear Learner algorithm for Taxi ride fare prediction\n", "_**Single machine training for regression with Amazon SageMaker Linear Learner algorithm**_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "\n", "This notebook demonstrates the use of Amazon SageMaker’s implementation of the Linear Learner algorithm to train and host a regression model to predict taxi fare. This notebook uses the [New York City Taxi and Limousine Commission (TLC) Trip Record Data] (https://registry.opendata.aws/nyc-tlc-trip-records-pds/#) to train the model. We are not using the whole dataset from above but a small subset of the dataset to train our model here. You will download this subset of data in below steps.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## Setup\n", "\n", "\n", "This notebook was tested in Amazon SageMaker Studio on a ml.t3.medium instance with Python 3 (Data Science) kernel.\n", "\n", "Let's start by specifying:\n", "1. The S3 buckets and prefixes that you want to use for training data and model data. This should be within the same region as the Notebook Instance, training, and hosting.\n", "1. The IAM role arn used to give training and hosting access to your data. See the documentation for how to create these. Note, if more than one role is required for notebook instances, training, and/or hosting, please replace the boto regexp with a the appropriate full IAM role arn string(s)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [] }, "outputs": [], "source": [ "# cell 02\n", "import os\n", "import boto3\n", "import re\n", "import sagemaker\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [] }, "outputs": [], "source": [ "# cell 03\n", "role = sagemaker.get_execution_role()\n", "sess = sagemaker.Session()\n", "region = boto3.Session().region_name\n", "\n", "# S3 bucket for training data.\n", "# this will create bucket like 'Sagemaker--'\n", "data_bucket=sess.default_bucket()\n", "data_prefix = \"1p-notebooks-datasets/taxi/text-csv\"\n", "\n", "\n", "# S3 bucket for saving code and model artifacts.\n", "output_bucket = data_bucket\n", "output_prefix = \"sagemaker/DEMO-linear-learner-taxifare-regression\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Before running the below cell make sure that you uploaded the nyc-taxi.csv file in Sagemaker Studio, provided to you, in the same folder where this Studio notebook is residing.\n", " \n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " fare_amount vendor_id pickup_datetime dropoff_datetime passenger_count \\\n", "0 18.0 CMT 01/11/12 1:18 01/11/12 1:35 1 \n", "1 10.0 CMT 01/11/12 1:18 01/11/12 1:28 1 \n", "2 35.5 CMT 01/11/12 1:18 01/11/12 2:16 1 \n", "3 5.5 CMT 01/11/12 1:18 01/11/12 1:22 1 \n", "4 10.5 CMT 01/11/12 1:18 01/11/12 1:27 1 \n", "\n", " trip_distance pickup_longitude pickup_latitude rate_code \\\n", "0 5.4 -73.984519 40.779776 1 \n", "1 2.4 -73.996082 40.753302 1 \n", "2 5.4 -73.970535 40.799144 1 \n", "3 1.1 -73.956560 40.771124 1 \n", "4 2.7 -73.959062 40.771722 1 \n", "\n", " store_and_fwd_flag dropoff_longitude dropoff_latitude payment_type \\\n", "0 N -73.947342 40.764681 CRD \n", "1 N -73.985783 40.727865 CSH \n", "2 N -73.957026 40.770164 CSH \n", "3 N -73.960994 40.757343 CRD \n", "4 N -73.967998 40.800170 CRD \n", "\n", " surcharge mta_tax tip_amount tolls_amount total_amount \n", "0 0.5 0.5 3.80 0.0 22.80 \n", "1 0.5 0.5 0.00 0.0 11.00 \n", "2 0.5 0.5 0.00 0.0 36.50 \n", "3 0.5 0.5 1.62 0.0 8.12 \n", "4 0.5 0.5 2.85 0.0 14.35 \n" ] } ], "source": [ "# cell 04\n", "import boto3\n", "FILE_TRAIN = \"nyc-taxi.csv\"\n", "# s3 = boto3.client(\"s3\")\n", "# s3.download_file(data_bucket, f\"{FILE_TRAIN}\", FILE_TRAIN)\n", "\n", "import pandas as pd # Read in csv and store in a pandas dataframe\n", "\n", "# df = pd.read_csv(FILE_TRAIN, sep=\",\", encoding=\"latin1\")\n", "df = pd.read_csv(FILE_TRAIN, sep=\",\", encoding=\"latin1\", names=[\"fare_amount\",\"vendor_id\",\"pickup_datetime\",\"dropoff_datetime\",\"passenger_count\",\"trip_distance\",\"pickup_longitude\",\"pickup_latitude\",\"rate_code\",\"store_and_fwd_flag\",\"dropoff_longitude\",\"dropoff_latitude\",\"payment_type\",\"surcharge\",\"mta_tax\",\"tip_amount\",\"tolls_amount\",\"total_amount\"])\n", "print(df.head(5))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 24998 entries, 0 to 24997\n", "Data columns (total 18 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 fare_amount 24998 non-null float64\n", " 1 vendor_id 24998 non-null object \n", " 2 pickup_datetime 24998 non-null object \n", " 3 dropoff_datetime 24998 non-null object \n", " 4 passenger_count 24998 non-null int64 \n", " 5 trip_distance 24998 non-null float64\n", " 6 pickup_longitude 24998 non-null float64\n", " 7 pickup_latitude 24998 non-null float64\n", " 8 rate_code 24998 non-null int64 \n", " 9 store_and_fwd_flag 13789 non-null object \n", " 10 dropoff_longitude 24998 non-null float64\n", " 11 dropoff_latitude 24998 non-null float64\n", " 12 payment_type 24998 non-null object \n", " 13 surcharge 24998 non-null float64\n", " 14 mta_tax 24998 non-null float64\n", " 15 tip_amount 24998 non-null float64\n", " 16 tolls_amount 24998 non-null float64\n", " 17 total_amount 24998 non-null float64\n", "dtypes: float64(11), int64(2), object(5)\n", "memory usage: 3.4+ MB\n" ] } ], "source": [ "# cell 05\n", "df.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### We have 18 features \"fare_amount\", \"vendor_id\", \"pickup_datetime\", \"dropoff_datetime\", \"passenger_count\", \"trip_distance\", \"pickup_longitude\", \"pickup_latitude\", \"rate_code\", \"store_and_fwd_flag\", \"dropoff_longitude\", \"dropoff_latitude\", \"payment_type\", \"surcharge\", \"mta_tax\", \"tip_amount\", \"tolls_amount\", \"total_amount\" in the dataset\n", "\n", "Lets explore the dataset" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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fare_amountpassenger_counttrip_distancepickup_longitudepickup_latituderate_codedropoff_longitudedropoff_latitudesurchargemta_taxtip_amounttolls_amounttotal_amount
count24998.00000024998.00000024998.00000024998.00000024998.00000024998.00000024998.00000024998.00000024998.00000024998.00000024998.00000024998.00000024998.000000
mean12.3514481.7054963.273141-72.57105339.9919961.040243-72.52584539.9613980.3642690.4982001.0989530.10898914.421859
std10.2210201.3109853.54144210.0677935.5181090.32297510.2232845.6212740.2233490.0299481.9499090.77798211.451719
min2.5000001.0000000.000000-76.0896030.0000001.000000-76.0895730.0000000.0000000.0000000.0000000.0000003.000000
25%6.5000001.0000001.270000-73.98774540.7433901.000000-73.98850940.7321850.0000000.5000000.0000000.0000008.000000
50%9.5000001.0000002.180000-73.97511740.7572371.000000-73.97499440.7553050.5000000.5000000.0000000.00000011.000000
75%14.0000002.0000003.850000-73.95747740.7711641.000000-73.95440940.7716010.5000000.5000001.7000000.00000016.500000
max243.0000006.000000100.0000000.00000041.0434875.0000000.00000043.2166671.0000000.50000037.50000017.900000243.000000
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" ], "text/plain": [ " fare_amount passenger_count trip_distance pickup_longitude \\\n", "count 24998.000000 24998.000000 24998.000000 24998.000000 \n", "mean 12.351448 1.705496 3.273141 -72.571053 \n", "std 10.221020 1.310985 3.541442 10.067793 \n", "min 2.500000 1.000000 0.000000 -76.089603 \n", "25% 6.500000 1.000000 1.270000 -73.987745 \n", "50% 9.500000 1.000000 2.180000 -73.975117 \n", "75% 14.000000 2.000000 3.850000 -73.957477 \n", "max 243.000000 6.000000 100.000000 0.000000 \n", "\n", " pickup_latitude rate_code dropoff_longitude dropoff_latitude \\\n", "count 24998.000000 24998.000000 24998.000000 24998.000000 \n", "mean 39.991996 1.040243 -72.525845 39.961398 \n", "std 5.518109 0.322975 10.223284 5.621274 \n", "min 0.000000 1.000000 -76.089573 0.000000 \n", "25% 40.743390 1.000000 -73.988509 40.732185 \n", "50% 40.757237 1.000000 -73.974994 40.755305 \n", "75% 40.771164 1.000000 -73.954409 40.771601 \n", "max 41.043487 5.000000 0.000000 43.216667 \n", "\n", " surcharge mta_tax tip_amount tolls_amount total_amount \n", "count 24998.000000 24998.000000 24998.000000 24998.000000 24998.000000 \n", "mean 0.364269 0.498200 1.098953 0.108989 14.421859 \n", "std 0.223349 0.029948 1.949909 0.777982 11.451719 \n", "min 0.000000 0.000000 0.000000 0.000000 3.000000 \n", "25% 0.000000 0.500000 0.000000 0.000000 8.000000 \n", "50% 0.500000 0.500000 0.000000 0.000000 11.000000 \n", "75% 0.500000 0.500000 1.700000 0.000000 16.500000 \n", "max 1.000000 0.500000 37.500000 17.900000 243.000000 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# cell 06\n", "# Frequency tables for each categorical feature\n", "for column in df.select_dtypes(include=['object']).columns:\n", " display(pd.crosstab(index=df[column], columns='% observations', normalize='columns'))\n", "\n", "# Histograms for each numeric features\n", "display(df.describe())\n", "%matplotlib inline\n", "hist = df.hist(bins=30, sharey=True, figsize=(10, 10))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### As we can see that store_and_fwd_flg column doesn't have much variance in it ( as 98% of the column values are N and 2% are Y) hence this column won't have much impact on target variable ( fare_amount ). Also from our domain knowledge we can see that payment_type column value doesn't impact on trip fare hence we can drop both of these features from dataset" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 24998 entries, 0 to 24997\n", "Data columns (total 16 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 fare_amount 24998 non-null float64\n", " 1 vendor_id 24998 non-null object \n", " 2 pickup_datetime 24998 non-null object \n", " 3 dropoff_datetime 24998 non-null object \n", " 4 passenger_count 24998 non-null int64 \n", " 5 trip_distance 24998 non-null float64\n", " 6 pickup_longitude 24998 non-null float64\n", " 7 pickup_latitude 24998 non-null float64\n", " 8 rate_code 24998 non-null int64 \n", " 9 dropoff_longitude 24998 non-null float64\n", " 10 dropoff_latitude 24998 non-null float64\n", " 11 surcharge 24998 non-null float64\n", " 12 mta_tax 24998 non-null float64\n", " 13 tip_amount 24998 non-null float64\n", " 14 tolls_amount 24998 non-null float64\n", " 15 total_amount 24998 non-null float64\n", "dtypes: float64(11), int64(2), object(3)\n", "memory usage: 3.1+ MB\n" ] } ], "source": [ "# cell 07\n", "df = df.drop(['payment_type', 'store_and_fwd_flag'], axis=1)\n", "df.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### we can see that in the dataset there are 2 features 'pickup_datetime' and 'dropoff_datetime' which depict when ride started and when did it end. As we know that taxi fare is highly dependent on duration of the drive hence as part of feature engineering we will create a feature which will calculate ride duration using these features" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "0 1020.0\n", "1 600.0\n", "2 3480.0\n", "3 240.0\n", "4 540.0\n", " ... \n", "24993 540.0\n", "24994 420.0\n", "24995 600.0\n", "24996 1200.0\n", "24997 420.0\n", "Name: journey_time, Length: 24998, dtype: float64" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# cell 08\n", "df['dropoff_datetime']= pd.to_datetime(df['dropoff_datetime'])\n", "df['pickup_datetime']= pd.to_datetime(df['pickup_datetime'])\n", "df['journey_time'] = (df['dropoff_datetime'] - df['pickup_datetime'])\n", "df['journey_time'] = df['journey_time'].dt.total_seconds()\n", "df['journey_time']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### after creation of 'journey_time feature' we can drop 'pickup_datetime' and 'dropoff_datetime' features" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 24998 entries, 0 to 24997\n", "Data columns (total 15 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 fare_amount 24998 non-null float64\n", " 1 vendor_id 24998 non-null object \n", " 2 passenger_count 24998 non-null int64 \n", " 3 trip_distance 24998 non-null float64\n", " 4 pickup_longitude 24998 non-null float64\n", " 5 pickup_latitude 24998 non-null float64\n", " 6 rate_code 24998 non-null int64 \n", " 7 dropoff_longitude 24998 non-null float64\n", " 8 dropoff_latitude 24998 non-null float64\n", " 9 surcharge 24998 non-null float64\n", " 10 mta_tax 24998 non-null float64\n", " 11 tip_amount 24998 non-null float64\n", " 12 tolls_amount 24998 non-null float64\n", " 13 total_amount 24998 non-null float64\n", " 14 journey_time 24998 non-null float64\n", "dtypes: float64(12), int64(2), object(1)\n", "memory usage: 2.9+ MB\n" ] } ], "source": [ "# cell 09\n", "df = df.drop(['dropoff_datetime', 'pickup_datetime'], axis=1)\n", "df.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### As you can see that vedor_id is still a categorical feature and we need to chage it to float ( using dummuies 0) so that dataset can be passed to Liner learner algorithm" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 24998 entries, 0 to 24997\n", "Data columns (total 16 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 fare_amount 24998 non-null float64\n", " 1 passenger_count 24998 non-null int64 \n", " 2 trip_distance 24998 non-null float64\n", " 3 pickup_longitude 24998 non-null float64\n", " 4 pickup_latitude 24998 non-null float64\n", " 5 rate_code 24998 non-null int64 \n", " 6 dropoff_longitude 24998 non-null float64\n", " 7 dropoff_latitude 24998 non-null float64\n", " 8 surcharge 24998 non-null float64\n", " 9 mta_tax 24998 non-null float64\n", " 10 tip_amount 24998 non-null float64\n", " 11 tolls_amount 24998 non-null float64\n", " 12 total_amount 24998 non-null float64\n", " 13 journey_time 24998 non-null float64\n", " 14 vendor_id_CMT 24998 non-null float64\n", " 15 vendor_id_VTS 24998 non-null float64\n", "dtypes: float64(14), int64(2)\n", "memory usage: 3.1 MB\n" ] } ], "source": [ "# cell 10\n", "df = pd.get_dummies(df, dtype=float)\n", "df.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Split the dataframe in train, test and validation" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "tags": [] }, "outputs": [], "source": [ "# cell 11\n", "import numpy as np\n", "\n", "train_data, validation_data, test_data = np.split(df.sample(frac=1, random_state=1729), [int(0.7 * len(df)), int(0.9 * len(df))])\n", "train_data.to_csv('train.csv', header=False, index=False)\n", "validation_data.to_csv('validation.csv', header=False, index=False)\n", "test_data.to_csv('test.csv', header=False, index=False)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "tags": [] }, "outputs": [], "source": [ "# cell 12\n", "boto3.Session().resource('s3').Bucket(data_bucket).Object(os.path.join(data_prefix, 'train/train.csv')).upload_file('train.csv')\n", "boto3.Session().resource('s3').Bucket(data_bucket).Object(os.path.join(data_prefix, 'validation/validation.csv')).upload_file('validation.csv')\n", "boto3.Session().resource('s3').Bucket(data_bucket).Object(os.path.join(data_prefix, 'test/test.csv')).upload_file('test.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "---\n", "Let us prepare the handshake between our data channels and the algorithm. To do this, we need to create the `sagemaker.session.s3_input` objects from our [data channels](https://sagemaker.readthedocs.io/en/v1.2.4/session.html#). These objects are then put in a simple dictionary, which the algorithm consumes. Notice that here we use a `content_type` as `text/csv` for the pre-processed file in the data_bucket. We use two channels here one for training and the second one for validation. The testing samples from above will be used on the prediction step." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "training files will be taken from: s3://sagemaker-us-east-1-404735392408/1p-notebooks-datasets/taxi/text-csv/train\n", "validtion files will be taken from: s3://sagemaker-us-east-1-404735392408/1p-notebooks-datasets/taxi/text-csv/validation\n", "test files will be taken from: s3://sagemaker-us-east-1-404735392408/1p-notebooks-datasets/taxi/text-csv/test\n", "training artifacts output location: s3://sagemaker-us-east-1-404735392408/sagemaker/DEMO-linear-learner-taxifare-regression/output\n" ] } ], "source": [ "# cell 13\n", "# creating the inputs for the fit() function with the training and validation location\n", "s3_train_data = f\"s3://{data_bucket}/{data_prefix}/train\"\n", "print(f\"training files will be taken from: {s3_train_data}\")\n", "\n", "s3_validation_data = f\"s3://{data_bucket}/{data_prefix}/validation\"\n", "print(f\"validtion files will be taken from: {s3_validation_data}\")\n", "\n", "s3_test_data = f\"s3://{data_bucket}/{data_prefix}/test\"\n", "print(f\"test files will be taken from: {s3_test_data}\")\n", "\n", "output_location = f\"s3://{output_bucket}/{output_prefix}/output\"\n", "print(f\"training artifacts output location: {output_location}\")\n", "\n", "# generating the session.s3_input() format for fit() accepted by the sdk\n", "train_data = sagemaker.inputs.TrainingInput(\n", " s3_train_data,\n", " distribution=\"FullyReplicated\",\n", " content_type=\"text/csv\",\n", " s3_data_type=\"S3Prefix\",\n", " record_wrapping=None,\n", " compression=None,\n", ")\n", "validation_data = sagemaker.inputs.TrainingInput(\n", " s3_validation_data,\n", " distribution=\"FullyReplicated\",\n", " content_type=\"text/csv\",\n", " s3_data_type=\"S3Prefix\",\n", " record_wrapping=None,\n", " compression=None,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training the Linear Learner model\n", "\n", "First, we retrieve the image for the Linear Learner Algorithm according to the region.\n", "\n", "Then we create an [estimator from the SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/api/training/estimators.html) using the Linear Learner container image and we setup the training parameters and hyperparameters configuration.\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "382416733822.dkr.ecr.us-east-1.amazonaws.com/linear-learner:1\n" ] } ], "source": [ "# cell 14\n", "# getting the linear learner image according to the region\n", "from sagemaker.image_uris import retrieve\n", "\n", "container = retrieve(\"linear-learner\", boto3.Session().region_name, version=\"1\")\n", "print(container)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training job DEMO-linear-learner-taxifare-regression-21-13-56\n", "CPU times: user 77.1 ms, sys: 17.7 ms, total: 94.8 ms\n", "Wall time: 134 ms\n" ] } ], "source": [ "%%time\n", "import boto3\n", "import sagemaker\n", "from time import gmtime, strftime\n", "\n", "sess = sagemaker.Session()\n", "\n", "job_name = \"DEMO-linear-learner-taxifare-regression-\" + strftime(\"%H-%M-%S\", gmtime())\n", "print(\"Training job\", job_name)\n", "\n", "linear = sagemaker.estimator.Estimator(\n", " container,\n", " role,\n", " input_mode=\"File\",\n", " instance_count=1,\n", " instance_type=\"ml.m4.xlarge\",\n", " output_path=output_location,\n", " sagemaker_session=sess,\n", ")\n", "\n", "linear.set_hyperparameters(\n", " epochs=16,\n", " wd=0.01,\n", " loss=\"absolute_loss\",\n", " predictor_type=\"regressor\",\n", " normalize_data=True,\n", " optimizer=\"adam\",\n", " mini_batch_size=1000,\n", " lr_scheduler_step=100,\n", " lr_scheduler_factor=0.99,\n", " lr_scheduler_minimum_lr=0.0001,\n", " learning_rate=0.1,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "After configuring the Estimator object and setting the hyperparameters for this object. The only remaining thing to do is to train the algorithm. The following cell will train the algorithm. Training the algorithm involves a few steps. Firstly, the instances that we requested while creating the Estimator classes are provisioned and are setup with the appropriate libraries. Then, the data from our channels are downloaded into the instance. Once this is done, the training job begins. The provisioning and data downloading will take time, depending on the size of the data. Therefore it might be a few minutes before we start getting data logs for our training jobs. The data logs will also print out Mean Average Precision (mAP) on the validation data, among other losses, for every run of the dataset once or one epoch. This metric is a proxy for the quality of the algorithm.\n", "\n", "Once the job has finished a \"Job complete\" message will be printed. The trained model can be found in the S3 bucket that was setup as output_path in the estimator." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:sagemaker:Creating training-job with name: DEMO-linear-learner-taxifare-regression-21-13-56\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2023-03-21 21:14:01 Starting - Starting the training job...\n", "2023-03-21 21:14:36 Starting - Preparing the instances for training.........\n", "2023-03-21 21:15:53 Downloading - Downloading input data...\n", "2023-03-21 21:16:22 Training - Downloading the training image......\n", "2023-03-21 21:17:28 Training - Training image download completed. Training in progress...\u001b[34mDocker entrypoint called with argument(s): train\u001b[0m\n", "\u001b[34mRunning default environment configuration script\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:46 INFO 139712331487040] Reading default configuration from /opt/amazon/lib/python3.7/site-packages/algorithm/resources/default-input.json: {'mini_batch_size': '1000', 'epochs': '15', 'feature_dim': 'auto', 'use_bias': 'true', 'binary_classifier_model_selection_criteria': 'accuracy', 'f_beta': '1.0', 'target_recall': '0.8', 'target_precision': '0.8', 'num_models': 'auto', 'num_calibration_samples': '10000000', 'init_method': 'uniform', 'init_scale': '0.07', 'init_sigma': '0.01', 'init_bias': '0.0', 'optimizer': 'auto', 'loss': 'auto', 'margin': '1.0', 'quantile': '0.5', 'loss_insensitivity': '0.01', 'huber_delta': '1.0', 'num_classes': '1', 'accuracy_top_k': '3', 'wd': 'auto', 'l1': 'auto', 'momentum': 'auto', 'learning_rate': 'auto', 'beta_1': 'auto', 'beta_2': 'auto', 'bias_lr_mult': 'auto', 'bias_wd_mult': 'auto', 'use_lr_scheduler': 'true', 'lr_scheduler_step': 'auto', 'lr_scheduler_factor': 'auto', 'lr_scheduler_minimum_lr': 'auto', 'positive_example_weight_mult': '1.0', 'balance_multiclass_weights': 'false', 'normalize_data': 'true', 'normalize_label': 'auto', 'unbias_data': 'auto', 'unbias_label': 'auto', 'num_point_for_scaler': '10000', '_kvstore': 'auto', '_num_gpus': 'auto', '_num_kv_servers': 'auto', '_log_level': 'info', '_tuning_objective_metric': '', 'early_stopping_patience': '3', 'early_stopping_tolerance': '0.001', '_enable_profiler': 'false'}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:46 INFO 139712331487040] Merging with provided configuration from /opt/ml/input/config/hyperparameters.json: {'epochs': '16', 'learning_rate': '0.1', 'loss': 'absolute_loss', 'lr_scheduler_factor': '0.99', 'lr_scheduler_minimum_lr': '0.0001', 'lr_scheduler_step': '100', 'mini_batch_size': '1000', 'normalize_data': 'True', 'optimizer': 'adam', 'predictor_type': 'regressor', 'wd': '0.01'}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:46 INFO 139712331487040] Final configuration: {'mini_batch_size': '1000', 'epochs': '16', 'feature_dim': 'auto', 'use_bias': 'true', 'binary_classifier_model_selection_criteria': 'accuracy', 'f_beta': '1.0', 'target_recall': '0.8', 'target_precision': '0.8', 'num_models': 'auto', 'num_calibration_samples': '10000000', 'init_method': 'uniform', 'init_scale': '0.07', 'init_sigma': '0.01', 'init_bias': '0.0', 'optimizer': 'adam', 'loss': 'absolute_loss', 'margin': '1.0', 'quantile': '0.5', 'loss_insensitivity': '0.01', 'huber_delta': '1.0', 'num_classes': '1', 'accuracy_top_k': '3', 'wd': '0.01', 'l1': 'auto', 'momentum': 'auto', 'learning_rate': '0.1', 'beta_1': 'auto', 'beta_2': 'auto', 'bias_lr_mult': 'auto', 'bias_wd_mult': 'auto', 'use_lr_scheduler': 'true', 'lr_scheduler_step': '100', 'lr_scheduler_factor': '0.99', 'lr_scheduler_minimum_lr': '0.0001', 'positive_example_weight_mult': '1.0', 'balance_multiclass_weights': 'false', 'normalize_data': 'True', 'normalize_label': 'auto', 'unbias_data': 'auto', 'unbias_label': 'auto', 'num_point_for_scaler': '10000', '_kvstore': 'auto', '_num_gpus': 'auto', '_num_kv_servers': 'auto', '_log_level': 'info', '_tuning_objective_metric': '', 'early_stopping_patience': '3', 'early_stopping_tolerance': '0.001', '_enable_profiler': 'false', 'predictor_type': 'regressor'}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:50 WARNING 139712331487040] Loggers have already been setup.\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:50 INFO 139712331487040] Final configuration: {'mini_batch_size': '1000', 'epochs': '16', 'feature_dim': 'auto', 'use_bias': 'true', 'binary_classifier_model_selection_criteria': 'accuracy', 'f_beta': '1.0', 'target_recall': '0.8', 'target_precision': '0.8', 'num_models': 'auto', 'num_calibration_samples': '10000000', 'init_method': 'uniform', 'init_scale': '0.07', 'init_sigma': '0.01', 'init_bias': '0.0', 'optimizer': 'adam', 'loss': 'absolute_loss', 'margin': '1.0', 'quantile': '0.5', 'loss_insensitivity': '0.01', 'huber_delta': '1.0', 'num_classes': '1', 'accuracy_top_k': '3', 'wd': '0.01', 'l1': 'auto', 'momentum': 'auto', 'learning_rate': '0.1', 'beta_1': 'auto', 'beta_2': 'auto', 'bias_lr_mult': 'auto', 'bias_wd_mult': 'auto', 'use_lr_scheduler': 'true', 'lr_scheduler_step': '100', 'lr_scheduler_factor': '0.99', 'lr_scheduler_minimum_lr': '0.0001', 'positive_example_weight_mult': '1.0', 'balance_multiclass_weights': 'false', 'normalize_data': 'True', 'normalize_label': 'auto', 'unbias_data': 'auto', 'unbias_label': 'auto', 'num_point_for_scaler': '10000', '_kvstore': 'auto', '_num_gpus': 'auto', '_num_kv_servers': 'auto', '_log_level': 'info', '_tuning_objective_metric': '', 'early_stopping_patience': '3', 'early_stopping_tolerance': '0.001', '_enable_profiler': 'false', 'predictor_type': 'regressor'}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:50 WARNING 139712331487040] Loggers have already been setup.\u001b[0m\n", "\u001b[34mProcess 7 is a worker.\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:50 INFO 139712331487040] Using default worker.\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:50 INFO 139712331487040] Checkpoint loading and saving are disabled.\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:50 INFO 139712331487040] Create Store: local\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:50 INFO 139712331487040] Scaler algorithm parameters\n", " \u001b[0m\n", "\u001b[34m[03/21/2023 21:17:50 INFO 139712331487040] Scaling model computed with parameters:\n", " {'stdev_label': \u001b[0m\n", "\u001b[34m[10.6103325]\u001b[0m\n", "\u001b[34m, 'stdev_weight': \u001b[0m\n", "\u001b[34m[1.3070091e+00 3.5066948e+00 1.0515575e+01 5.7811842e+00 3.1933939e-01\n", " 1.0605734e+01 5.8308725e+00 2.2492871e-01 2.8557034e-02 1.9322473e+00\n", " 7.1918929e-01 1.1728467e+01 2.0943656e+05 4.9696580e-01 4.9696580e-01]\u001b[0m\n", "\u001b[34m, 'mean_label': \u001b[0m\n", "\u001b[34m[12.458818]\u001b[0m\n", "\u001b[34m, 'mean_weight': \u001b[0m\n", "\u001b[34m[ 1.7095456e+00 3.2892518e+00 -7.2441216e+01 3.9915714e+01\n", " 1.0407274e+00 -7.2412895e+01 3.9899578e+01 3.6159089e-01\n", " 4.9836370e-01 1.0816537e+00 9.5663652e-02 1.4496091e+01\n", " 1.8879422e+04 5.5500013e-01 4.4500005e-01]\u001b[0m\n", "\u001b[34m}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:50 INFO 139712331487040] nvidia-smi: took 0.032 seconds to run.\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:50 INFO 139712331487040] nvidia-smi identified 0 GPUs.\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:50 INFO 139712331487040] Number of GPUs being used: 0\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433470.4392855, \"EndTime\": 1679433470.4393241, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"Meta\": \"init_train_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 12000.0, \"count\": 1, \"min\": 12000, \"max\": 12000}, \"Total Batches Seen\": {\"sum\": 12.0, \"count\": 1, \"min\": 12, \"max\": 12}, \"Max Records Seen Between Resets\": {\"sum\": 11000.0, \"count\": 1, \"min\": 11000, \"max\": 11000}, \"Max Batches Seen Between Resets\": {\"sum\": 11.0, \"count\": 1, \"min\": 11, \"max\": 11}, \"Reset Count\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}, \"Number of Records Since Last Reset\": {\"sum\": 0.0, \"count\": 1, \"min\": 0, \"max\": 0}, \"Number of Batches Since Last Reset\": {\"sum\": 0.0, \"count\": 1, \"min\": 0, \"max\": 0}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433470.899643, \"EndTime\": 1679433470.8997183, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 0}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.36113863417681524, \"count\": 1, \"min\": 0.36113863417681524, \"max\": 0.36113863417681524}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433470.8998072, \"EndTime\": 1679433470.8998258, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 1}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.4953607105928309, \"count\": 1, \"min\": 0.4953607105928309, \"max\": 0.4953607105928309}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433470.8998811, \"EndTime\": 1679433470.8998983, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 2}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5418412673052619, \"count\": 1, \"min\": 0.5418412673052619, \"max\": 0.5418412673052619}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433470.8999548, \"EndTime\": 1679433470.8999665, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 3}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.46846175967945775, \"count\": 1, 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{\"validation_absolute_loss_objective\": {\"sum\": 4.260128125, \"count\": 1, \"min\": 4.260128125, \"max\": 4.260128125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.023077, \"EndTime\": 1679433471.0230942, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 18}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 3.997765673828125, \"count\": 1, \"min\": 3.997765673828125, \"max\": 3.997765673828125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.023159, \"EndTime\": 1679433471.0231752, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 19}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 4.105459326171875, \"count\": 1, \"min\": 4.105459326171875, \"max\": 4.105459326171875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.0232806, \"EndTime\": 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2.605124462890625, \"count\": 1, \"min\": 2.605124462890625, \"max\": 2.605124462890625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.0235128, \"EndTime\": 1679433471.0235302, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 23}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 2.46235888671875, \"count\": 1, \"min\": 2.46235888671875, \"max\": 2.46235888671875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.023593, \"EndTime\": 1679433471.0236099, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 24}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.528740625, \"count\": 1, \"min\": 5.528740625, \"max\": 5.528740625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.0236723, \"EndTime\": 1679433471.0236893, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 25}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.439909375, \"count\": 1, \"min\": 5.439909375, \"max\": 5.439909375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.0237517, \"EndTime\": 1679433471.0237696, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 26}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.487525390625, \"count\": 1, \"min\": 5.487525390625, \"max\": 5.487525390625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.0238333, \"EndTime\": 1679433471.023851, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 27}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.6837927734375, \"count\": 1, \"min\": 5.6837927734375, \"max\": 5.6837927734375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.0239182, \"EndTime\": 1679433471.0239365, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 28}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.78493134765625, \"count\": 1, \"min\": 5.78493134765625, \"max\": 5.78493134765625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.024045, \"EndTime\": 1679433471.0240636, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 29}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.78302353515625, \"count\": 1, \"min\": 5.78302353515625, \"max\": 5.78302353515625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.0241299, \"EndTime\": 1679433471.024147, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 30}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.88792412109375, \"count\": 1, \"min\": 5.88792412109375, \"max\": 5.88792412109375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.024203, \"EndTime\": 1679433471.0242198, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 31}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.73386728515625, \"count\": 1, \"min\": 5.73386728515625, \"max\": 5.73386728515625}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:51 INFO 139712331487040] #quality_metric: host=algo-1, epoch=0, validation absolute_loss_objective =1.662186962890625\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:51 INFO 139712331487040] #early_stopping_criteria_metric: host=algo-1, epoch=0, criteria=absolute_loss_objective, value=1.4689070556640624\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:51 INFO 139712331487040] Epoch 0: Loss improved. Updating best model\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:51 INFO 139712331487040] Saving model for epoch: 0\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:51 INFO 139712331487040] Saved checkpoint to \"/tmp/tmpr40k0f3g/mx-mod-0000.params\"\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:51 INFO 139712331487040] #progress_metric: host=algo-1, completed 6.25 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433470.4396222, \"EndTime\": 1679433471.032084, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 29498.0, \"count\": 1, \"min\": 29498, \"max\": 29498}, \"Total Batches Seen\": {\"sum\": 30.0, \"count\": 1, \"min\": 30, \"max\": 30}, \"Max Records Seen Between Resets\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Max Batches Seen Between Resets\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}, \"Reset Count\": {\"sum\": 3.0, \"count\": 1, \"min\": 3, \"max\": 3}, \"Number of Records Since Last Reset\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Number of Batches Since Last Reset\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:51 INFO 139712331487040] #throughput_metric: host=algo-1, train throughput=29527.60748265163 records/second\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.4835372, \"EndTime\": 1679433471.4835935, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 0}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11402796621883617, \"count\": 1, \"min\": 0.11402796621883617, \"max\": 0.11402796621883617}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.4836705, \"EndTime\": 1679433471.483684, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 1}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.35958431827320775, \"count\": 1, \"min\": 0.35958431827320775, \"max\": 0.35958431827320775}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.483743, \"EndTime\": 1679433471.4837587, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 2}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.40265861960018384, \"count\": 1, \"min\": 0.40265861960018384, \"max\": 0.40265861960018384}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.483812, \"EndTime\": 1679433471.4838283, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 3}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.33471733901079964, \"count\": 1, \"min\": 0.33471733901079964, \"max\": 0.33471733901079964}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.4838881, \"EndTime\": 1679433471.4839053, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 4}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.13498261620016658, \"count\": 1, \"min\": 0.13498261620016658, \"max\": 0.13498261620016658}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.4839683, \"EndTime\": 1679433471.4839835, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 5}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.1278264496747185, \"count\": 1, \"min\": 0.1278264496747185, \"max\": 0.1278264496747185}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.484031, \"EndTime\": 1679433471.484047, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 6}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.10793425526338465, \"count\": 1, \"min\": 0.10793425526338465, \"max\": 0.10793425526338465}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.4841042, \"EndTime\": 1679433471.4841201, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 7}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.13256121197868795, \"count\": 1, \"min\": 0.13256121197868795, \"max\": 0.13256121197868795}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.4841735, \"EndTime\": 1679433471.4841893, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 8}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.39147532564051013, \"count\": 1, \"min\": 0.39147532564051013, \"max\": 0.39147532564051013}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.4842534, \"EndTime\": 1679433471.48427, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 9}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.43347521613625917, \"count\": 1, \"min\": 0.43347521613625917, \"max\": 0.43347521613625917}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.484332, \"EndTime\": 1679433471.484349, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 10}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.313895897360409, \"count\": 1, \"min\": 0.313895897360409, \"max\": 0.313895897360409}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.4844048, \"EndTime\": 1679433471.4844222, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 11}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.3564782319910386, \"count\": 1, \"min\": 0.3564782319910386, \"max\": 0.3564782319910386}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.484485, \"EndTime\": 1679433471.4845016, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 12}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11643587538775275, \"count\": 1, \"min\": 0.11643587538775275, \"max\": 0.11643587538775275}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.4845574, \"EndTime\": 1679433471.4845743, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 13}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.1534874590705423, \"count\": 1, \"min\": 0.1534874590705423, \"max\": 0.1534874590705423}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.4846303, \"EndTime\": 1679433471.4846458, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 14}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.14860651397705077, \"count\": 1, \"min\": 0.14860651397705077, \"max\": 0.14860651397705077}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.4847023, \"EndTime\": 1679433471.4847193, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 15}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.135654759126551, \"count\": 1, \"min\": 0.135654759126551, \"max\": 0.135654759126551}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.4847836, \"EndTime\": 1679433471.4848015, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 16}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.4211196719898897, \"count\": 1, \"min\": 0.4211196719898897, \"max\": 0.4211196719898897}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.4848578, \"EndTime\": 1679433471.484874, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 17}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.3794910224465763, \"count\": 1, \"min\": 0.3794910224465763, \"max\": 0.3794910224465763}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.484928, \"EndTime\": 1679433471.4849439, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 18}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.35183292703067554, \"count\": 1, \"min\": 0.35183292703067554, \"max\": 0.35183292703067554}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.4850037, \"EndTime\": 1679433471.4850202, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 19}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.36060266292796417, \"count\": 1, \"min\": 0.36060266292796417, \"max\": 0.36060266292796417}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.485071, \"EndTime\": 1679433471.4850864, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 20}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.1973949759988224, \"count\": 1, \"min\": 0.1973949759988224, \"max\": 0.1973949759988224}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.4851496, \"EndTime\": 1679433471.485167, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 21}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.17700425854851218, \"count\": 1, \"min\": 0.17700425854851218, \"max\": 0.17700425854851218}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.485223, \"EndTime\": 1679433471.4852395, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 22}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.17611054499009077, \"count\": 1, \"min\": 0.17611054499009077, \"max\": 0.17611054499009077}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.4852958, \"EndTime\": 1679433471.4853125, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 23}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.20058974770938648, \"count\": 1, \"min\": 0.20058974770938648, \"max\": 0.20058974770938648}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.4853692, \"EndTime\": 1679433471.4853854, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 24}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5528786800608916, \"count\": 1, \"min\": 0.5528786800608916, \"max\": 0.5528786800608916}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.4854295, \"EndTime\": 1679433471.4854393, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 25}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5488127692727481, \"count\": 1, \"min\": 0.5488127692727481, \"max\": 0.5488127692727481}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.4854877, \"EndTime\": 1679433471.4855034, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 26}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5486333438648897, \"count\": 1, \"min\": 0.5486333438648897, \"max\": 0.5486333438648897}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.4855568, \"EndTime\": 1679433471.485573, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 27}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5596135218003216, \"count\": 1, \"min\": 0.5596135218003216, \"max\": 0.5596135218003216}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.485635, \"EndTime\": 1679433471.4856517, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 28}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5572790276022518, \"count\": 1, \"min\": 0.5572790276022518, \"max\": 0.5572790276022518}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.4857123, \"EndTime\": 1679433471.4857297, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 29}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5600958862304688, \"count\": 1, \"min\": 0.5600958862304688, \"max\": 0.5600958862304688}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.4857943, \"EndTime\": 1679433471.4858115, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 30}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5640369549919577, \"count\": 1, \"min\": 0.5640369549919577, \"max\": 0.5640369549919577}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.485873, \"EndTime\": 1679433471.4858892, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 31}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5617008379767923, \"count\": 1, \"min\": 0.5617008379767923, \"max\": 0.5617008379767923}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:51 INFO 139712331487040] #quality_metric: host=algo-1, epoch=1, train absolute_loss_objective =0.11402796621883617\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.5945845, \"EndTime\": 1679433471.5946393, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 0}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.8457241333007812, \"count\": 1, \"min\": 0.8457241333007812, \"max\": 0.8457241333007812}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.5947232, \"EndTime\": 1679433471.5947368, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 1}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 3.11748173828125, \"count\": 1, \"min\": 3.11748173828125, \"max\": 3.11748173828125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.5947957, \"EndTime\": 1679433471.5948126, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 2}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 3.49285048828125, \"count\": 1, \"min\": 3.49285048828125, \"max\": 3.49285048828125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.5948703, \"EndTime\": 1679433471.5948815, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 3}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 2.85618017578125, \"count\": 1, \"min\": 2.85618017578125, \"max\": 2.85618017578125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.5949335, \"EndTime\": 1679433471.5949507, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 4}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.7494934814453125, \"count\": 1, \"min\": 0.7494934814453125, \"max\": 0.7494934814453125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.5949934, \"EndTime\": 1679433471.5950024, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 5}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.843162060546875, \"count\": 1, \"min\": 0.843162060546875, \"max\": 0.843162060546875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.5950353, \"EndTime\": 1679433471.5950482, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 6}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.8296254760742188, \"count\": 1, \"min\": 0.8296254760742188, \"max\": 0.8296254760742188}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.5951045, \"EndTime\": 1679433471.5951202, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 7}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.6742811401367188, \"count\": 1, \"min\": 0.6742811401367188, \"max\": 0.6742811401367188}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.5951822, \"EndTime\": 1679433471.5951989, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 8}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 3.430368994140625, \"count\": 1, \"min\": 3.430368994140625, \"max\": 3.430368994140625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.5952537, \"EndTime\": 1679433471.5952697, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 9}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 3.765533056640625, \"count\": 1, \"min\": 3.765533056640625, \"max\": 3.765533056640625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.5953288, \"EndTime\": 1679433471.5953453, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 10}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 2.740255029296875, \"count\": 1, \"min\": 2.740255029296875, \"max\": 2.740255029296875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.5953977, \"EndTime\": 1679433471.5954137, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 11}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 3.2054419921875, \"count\": 1, \"min\": 3.2054419921875, \"max\": 3.2054419921875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.5954778, \"EndTime\": 1679433471.5954957, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 12}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.8540639892578125, \"count\": 1, \"min\": 0.8540639892578125, \"max\": 0.8540639892578125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.5955563, \"EndTime\": 1679433471.5955737, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 13}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.20416640625, \"count\": 1, \"min\": 1.20416640625, \"max\": 1.20416640625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.5956345, \"EndTime\": 1679433471.5956507, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 14}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.3007739990234375, \"count\": 1, \"min\": 1.3007739990234375, \"max\": 1.3007739990234375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.5957139, \"EndTime\": 1679433471.5957308, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 15}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.9220379638671875, \"count\": 1, \"min\": 0.9220379638671875, \"max\": 0.9220379638671875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.5957916, \"EndTime\": 1679433471.595809, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 16}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 3.7006935546875, \"count\": 1, \"min\": 3.7006935546875, \"max\": 3.7006935546875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.595862, \"EndTime\": 1679433471.5958772, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 17}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 3.36485888671875, \"count\": 1, \"min\": 3.36485888671875, \"max\": 3.36485888671875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.59593, \"EndTime\": 1679433471.5959458, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 18}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 3.091339599609375, \"count\": 1, \"min\": 3.091339599609375, \"max\": 3.091339599609375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.5959904, \"EndTime\": 1679433471.5960007, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 19}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 3.139783251953125, \"count\": 1, \"min\": 3.139783251953125, \"max\": 3.139783251953125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.596027, \"EndTime\": 1679433471.5960343, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 20}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.2801742919921875, \"count\": 1, \"min\": 1.2801742919921875, \"max\": 1.2801742919921875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.5960796, \"EndTime\": 1679433471.5960956, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 21}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.3917083984375, \"count\": 1, \"min\": 1.3917083984375, \"max\": 1.3917083984375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.5961561, \"EndTime\": 1679433471.5961716, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 22}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.446954296875, \"count\": 1, \"min\": 1.446954296875, \"max\": 1.446954296875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.5962954, \"EndTime\": 1679433471.596318, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 23}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.4708493408203125, \"count\": 1, \"min\": 1.4708493408203125, \"max\": 1.4708493408203125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.5963786, \"EndTime\": 1679433471.5963902, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 24}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.54116748046875, \"count\": 1, \"min\": 5.54116748046875, \"max\": 5.54116748046875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.596422, \"EndTime\": 1679433471.5964353, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 25}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.57100634765625, \"count\": 1, \"min\": 5.57100634765625, \"max\": 5.57100634765625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.596495, \"EndTime\": 1679433471.5965118, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 26}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.56472783203125, \"count\": 1, \"min\": 5.56472783203125, \"max\": 5.56472783203125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.5965672, \"EndTime\": 1679433471.5965846, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 27}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.54545908203125, \"count\": 1, \"min\": 5.54545908203125, \"max\": 5.54545908203125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.5966332, \"EndTime\": 1679433471.59665, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 28}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5644478515625, \"count\": 1, \"min\": 5.5644478515625, \"max\": 5.5644478515625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.5967038, \"EndTime\": 1679433471.596721, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 29}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5990462890625, \"count\": 1, \"min\": 5.5990462890625, \"max\": 5.5990462890625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.5967777, \"EndTime\": 1679433471.596794, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 30}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5034091796875, \"count\": 1, \"min\": 5.5034091796875, \"max\": 5.5034091796875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.5968518, \"EndTime\": 1679433471.5968676, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 31}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.49375859375, \"count\": 1, \"min\": 5.49375859375, \"max\": 5.49375859375}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:51 INFO 139712331487040] #quality_metric: host=algo-1, epoch=1, validation absolute_loss_objective =0.8457241333007812\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:51 INFO 139712331487040] #early_stopping_criteria_metric: host=algo-1, epoch=1, criteria=absolute_loss_objective, value=0.6742811401367188\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:51 INFO 139712331487040] Epoch 1: Loss improved. Updating best model\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:51 INFO 139712331487040] Saving model for epoch: 1\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:51 INFO 139712331487040] Saved checkpoint to \"/tmp/tmpo3nd_5qd/mx-mod-0000.params\"\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:51 INFO 139712331487040] #progress_metric: host=algo-1, completed 12.5 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.0323973, \"EndTime\": 1679433471.6048958, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 46996.0, \"count\": 1, \"min\": 46996, \"max\": 46996}, \"Total Batches Seen\": {\"sum\": 48.0, \"count\": 1, \"min\": 48, \"max\": 48}, \"Max Records Seen Between Resets\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Max Batches Seen Between Resets\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}, \"Reset Count\": {\"sum\": 4.0, \"count\": 1, \"min\": 4, \"max\": 4}, \"Number of Records Since Last Reset\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Number of Batches Since Last Reset\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:51 INFO 139712331487040] #throughput_metric: host=algo-1, train throughput=30555.895913678014 records/second\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.067183, \"EndTime\": 1679433472.0672414, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 0}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.06638977185417624, \"count\": 1, \"min\": 0.06638977185417624, \"max\": 0.06638977185417624}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.0673227, \"EndTime\": 1679433472.0673358, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 1}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.27402876730526193, \"count\": 1, \"min\": 0.27402876730526193, \"max\": 0.27402876730526193}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.0673866, \"EndTime\": 1679433472.0673985, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 2}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.31069405050838694, \"count\": 1, \"min\": 0.31069405050838694, \"max\": 0.31069405050838694}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.0674593, \"EndTime\": 1679433472.067474, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 3}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.24920083797679227, \"count\": 1, \"min\": 0.24920083797679227, \"max\": 0.24920083797679227}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.0675364, \"EndTime\": 1679433472.0675523, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 4}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.061439568687887754, \"count\": 1, \"min\": 0.061439568687887754, \"max\": 0.061439568687887754}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.0676079, \"EndTime\": 1679433472.0676239, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 5}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.08339708732156192, \"count\": 1, \"min\": 0.08339708732156192, \"max\": 0.08339708732156192}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.0676744, \"EndTime\": 1679433472.0676901, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 6}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05517498465145335, \"count\": 1, \"min\": 0.05517498465145335, \"max\": 0.05517498465145335}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.0677536, \"EndTime\": 1679433472.067771, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 7}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.07639901239731732, \"count\": 1, \"min\": 0.07639901239731732, \"max\": 0.07639901239731732}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.067835, \"EndTime\": 1679433472.0678527, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 8}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.307764747170841, \"count\": 1, \"min\": 0.307764747170841, \"max\": 0.307764747170841}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.0679164, \"EndTime\": 1679433472.0679333, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 9}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.3337815533806296, \"count\": 1, \"min\": 0.3337815533806296, \"max\": 0.3337815533806296}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.067998, \"EndTime\": 1679433472.0680163, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 10}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.2415706625545726, \"count\": 1, \"min\": 0.2415706625545726, \"max\": 0.2415706625545726}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.0680776, \"EndTime\": 1679433472.0680943, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 11}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.28601741297104777, \"count\": 1, \"min\": 0.28601741297104777, \"max\": 0.28601741297104777}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.0681603, \"EndTime\": 1679433472.0681787, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 12}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.0711173075507669, \"count\": 1, \"min\": 0.0711173075507669, \"max\": 0.0711173075507669}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.0682442, \"EndTime\": 1679433472.0682619, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 13}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.07892969131469726, \"count\": 1, \"min\": 0.07892969131469726, \"max\": 0.07892969131469726}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.0683205, \"EndTime\": 1679433472.068337, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 14}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.08637335833381204, \"count\": 1, \"min\": 0.08637335833381204, \"max\": 0.08637335833381204}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.068401, \"EndTime\": 1679433472.06842, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 15}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.08029043422025793, \"count\": 1, \"min\": 0.08029043422025793, \"max\": 0.08029043422025793}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.0684817, \"EndTime\": 1679433472.0685003, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 16}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.3332387300379136, \"count\": 1, \"min\": 0.3332387300379136, \"max\": 0.3332387300379136}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.0685644, \"EndTime\": 1679433472.068583, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 17}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.3026786355411305, \"count\": 1, \"min\": 0.3026786355411305, \"max\": 0.3026786355411305}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.068648, \"EndTime\": 1679433472.0686653, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 18}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.2755025939941406, \"count\": 1, \"min\": 0.2755025939941406, \"max\": 0.2755025939941406}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.0687299, \"EndTime\": 1679433472.0687482, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 19}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.28049537568933824, \"count\": 1, \"min\": 0.28049537568933824, \"max\": 0.28049537568933824}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.0688124, \"EndTime\": 1679433472.0688305, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 20}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.1339563679414637, \"count\": 1, \"min\": 0.1339563679414637, \"max\": 0.1339563679414637}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.0688953, \"EndTime\": 1679433472.0689137, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 21}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.13597667828728172, \"count\": 1, \"min\": 0.13597667828728172, \"max\": 0.13597667828728172}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.0689707, \"EndTime\": 1679433472.0689878, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 22}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12860464163387522, \"count\": 1, \"min\": 0.12860464163387522, \"max\": 0.12860464163387522}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.0690424, \"EndTime\": 1679433472.06906, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 23}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.1424602185417624, \"count\": 1, \"min\": 0.1424602185417624, \"max\": 0.1424602185417624}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.0691128, \"EndTime\": 1679433472.06913, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 24}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5519907406077665, \"count\": 1, \"min\": 0.5519907406077665, \"max\": 0.5519907406077665}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.0691876, \"EndTime\": 1679433472.069205, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 25}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5536307157628676, \"count\": 1, \"min\": 0.5536307157628676, \"max\": 0.5536307157628676}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.0692716, \"EndTime\": 1679433472.0692894, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 26}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.553936092601103, \"count\": 1, \"min\": 0.553936092601103, \"max\": 0.553936092601103}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.0693514, \"EndTime\": 1679433472.069368, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 27}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5495149213005515, \"count\": 1, \"min\": 0.5495149213005515, \"max\": 0.5495149213005515}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.069422, \"EndTime\": 1679433472.0694387, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 28}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5566332756491268, \"count\": 1, \"min\": 0.5566332756491268, \"max\": 0.5566332756491268}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.0695014, \"EndTime\": 1679433472.0695188, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 29}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5536139131433824, \"count\": 1, \"min\": 0.5536139131433824, \"max\": 0.5536139131433824}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.0695841, \"EndTime\": 1679433472.0696023, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 30}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5535672858743107, \"count\": 1, \"min\": 0.5535672858743107, \"max\": 0.5535672858743107}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.069668, \"EndTime\": 1679433472.069685, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 31}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5547154181985294, \"count\": 1, \"min\": 0.5547154181985294, \"max\": 0.5547154181985294}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:52 INFO 139712331487040] #quality_metric: host=algo-1, epoch=2, train absolute_loss_objective =0.06638977185417624\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.1925476, \"EndTime\": 1679433472.1926024, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 0}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.2484336669921876, \"count\": 1, \"min\": 1.2484336669921876, \"max\": 1.2484336669921876}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.1926854, \"EndTime\": 1679433472.1926992, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 1}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 2.4676431640625, \"count\": 1, \"min\": 2.4676431640625, \"max\": 2.4676431640625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.1927392, \"EndTime\": 1679433472.192755, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 2}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 2.82099990234375, \"count\": 1, \"min\": 2.82099990234375, \"max\": 2.82099990234375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.192809, \"EndTime\": 1679433472.1928248, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 3}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 2.233633203125, \"count\": 1, \"min\": 2.233633203125, \"max\": 2.233633203125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.1928773, \"EndTime\": 1679433472.1928914, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 4}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5012408386230469, \"count\": 1, \"min\": 0.5012408386230469, \"max\": 0.5012408386230469}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.1929524, \"EndTime\": 1679433472.1929686, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 5}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.155798828125, \"count\": 1, \"min\": 1.155798828125, \"max\": 1.155798828125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.193026, \"EndTime\": 1679433472.1930425, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 6}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.7123684326171875, \"count\": 1, \"min\": 0.7123684326171875, \"max\": 0.7123684326171875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.193092, \"EndTime\": 1679433472.1931074, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 7}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.882204736328125, \"count\": 1, \"min\": 1.882204736328125, \"max\": 1.882204736328125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.193169, \"EndTime\": 1679433472.193186, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 8}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 2.820050927734375, \"count\": 1, \"min\": 2.820050927734375, \"max\": 2.820050927734375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.1932416, \"EndTime\": 1679433472.1932576, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 9}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 3.0226798828125, \"count\": 1, \"min\": 3.0226798828125, \"max\": 3.0226798828125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.193311, \"EndTime\": 1679433472.1933272, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 10}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 2.1766214599609377, \"count\": 1, \"min\": 2.1766214599609377, \"max\": 2.1766214599609377}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.1933687, \"EndTime\": 1679433472.1933837, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 11}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 2.599691552734375, \"count\": 1, \"min\": 2.599691552734375, \"max\": 2.599691552734375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.193433, \"EndTime\": 1679433472.193444, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 12}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.595556884765625, \"count\": 1, \"min\": 0.595556884765625, \"max\": 0.595556884765625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.1934903, \"EndTime\": 1679433472.1935062, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 13}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.6279733154296875, \"count\": 1, \"min\": 0.6279733154296875, \"max\": 0.6279733154296875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.193539, \"EndTime\": 1679433472.193552, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 14}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.879441357421875, \"count\": 1, \"min\": 0.879441357421875, \"max\": 0.879441357421875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.1936014, \"EndTime\": 1679433472.1936166, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 15}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.688953759765625, \"count\": 1, \"min\": 0.688953759765625, \"max\": 0.688953759765625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.1936731, \"EndTime\": 1679433472.1936896, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 16}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 3.012805078125, \"count\": 1, \"min\": 3.012805078125, \"max\": 3.012805078125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.1937518, \"EndTime\": 1679433472.1937692, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 17}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 2.724896923828125, \"count\": 1, \"min\": 2.724896923828125, \"max\": 2.724896923828125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.1938186, \"EndTime\": 1679433472.1938345, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 18}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 2.47720400390625, \"count\": 1, \"min\": 2.47720400390625, \"max\": 2.47720400390625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.1938727, \"EndTime\": 1679433472.1938825, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 19}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 2.523497021484375, \"count\": 1, \"min\": 2.523497021484375, \"max\": 2.523497021484375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.193938, \"EndTime\": 1679433472.1939542, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 20}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.404729541015625, \"count\": 1, \"min\": 1.404729541015625, \"max\": 1.404729541015625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.194006, \"EndTime\": 1679433472.1940427, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 21}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.241302734375, \"count\": 1, \"min\": 1.241302734375, \"max\": 1.241302734375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.1940994, \"EndTime\": 1679433472.1941166, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 22}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.1417757934570312, \"count\": 1, \"min\": 1.1417757934570312, \"max\": 1.1417757934570312}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.1941624, \"EndTime\": 1679433472.1941729, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 23}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.3439627197265624, \"count\": 1, \"min\": 1.3439627197265624, \"max\": 1.3439627197265624}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.1942284, \"EndTime\": 1679433472.1942391, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 24}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5563146484375, \"count\": 1, \"min\": 5.5563146484375, \"max\": 5.5563146484375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.1942906, \"EndTime\": 1679433472.1943045, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 25}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.57263876953125, \"count\": 1, \"min\": 5.57263876953125, \"max\": 5.57263876953125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.1943583, \"EndTime\": 1679433472.1943736, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 26}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.564612109375, \"count\": 1, \"min\": 5.564612109375, \"max\": 5.564612109375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.194426, \"EndTime\": 1679433472.194443, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 27}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5674208984375, \"count\": 1, \"min\": 5.5674208984375, \"max\": 5.5674208984375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.1944938, \"EndTime\": 1679433472.1945093, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 28}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5464943359375, \"count\": 1, \"min\": 5.5464943359375, \"max\": 5.5464943359375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.1945598, \"EndTime\": 1679433472.1945755, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 29}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.58159990234375, \"count\": 1, \"min\": 5.58159990234375, \"max\": 5.58159990234375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.1946282, \"EndTime\": 1679433472.1946437, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 30}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.56011533203125, \"count\": 1, \"min\": 5.56011533203125, \"max\": 5.56011533203125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.1946983, \"EndTime\": 1679433472.1947143, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 31}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.6193755859375, \"count\": 1, \"min\": 5.6193755859375, \"max\": 5.6193755859375}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:52 INFO 139712331487040] #quality_metric: host=algo-1, epoch=2, validation absolute_loss_objective =1.2484336669921876\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:52 INFO 139712331487040] #early_stopping_criteria_metric: host=algo-1, epoch=2, criteria=absolute_loss_objective, value=0.5012408386230469\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:52 INFO 139712331487040] Epoch 2: Loss improved. Updating best model\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:52 INFO 139712331487040] Saving model for epoch: 2\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:52 INFO 139712331487040] Saved checkpoint to \"/tmp/tmpplfwr4w5/mx-mod-0000.params\"\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:52 INFO 139712331487040] #progress_metric: host=algo-1, completed 18.75 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433471.6051993, \"EndTime\": 1679433472.202506, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 64494.0, \"count\": 1, \"min\": 64494, \"max\": 64494}, \"Total Batches Seen\": {\"sum\": 66.0, \"count\": 1, \"min\": 66, \"max\": 66}, \"Max Records Seen Between Resets\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Max Batches Seen Between Resets\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}, \"Reset Count\": {\"sum\": 5.0, \"count\": 1, \"min\": 5, \"max\": 5}, \"Number of Records Since Last Reset\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Number of Batches Since Last Reset\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:52 INFO 139712331487040] #throughput_metric: host=algo-1, train throughput=29288.378335710448 records/second\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.6384017, \"EndTime\": 1679433472.6384814, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 0}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.10899765306360581, \"count\": 1, \"min\": 0.10899765306360581, \"max\": 0.10899765306360581}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.6385655, \"EndTime\": 1679433472.6385791, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 1}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.21316348266601562, \"count\": 1, \"min\": 0.21316348266601562, \"max\": 0.21316348266601562}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.6386373, \"EndTime\": 1679433472.638654, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 2}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.25072243095846736, \"count\": 1, \"min\": 0.25072243095846736, \"max\": 0.25072243095846736}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.63871, \"EndTime\": 1679433472.6387212, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 3}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.19083919480267694, \"count\": 1, \"min\": 0.19083919480267694, \"max\": 0.19083919480267694}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.6387768, \"EndTime\": 1679433472.6387935, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 4}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.049377120410694794, \"count\": 1, \"min\": 0.049377120410694794, \"max\": 0.049377120410694794}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.6388466, \"EndTime\": 1679433472.638862, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 5}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.07381570569206687, \"count\": 1, \"min\": 0.07381570569206687, \"max\": 0.07381570569206687}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.6389182, \"EndTime\": 1679433472.6389349, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 6}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.06795999392341165, \"count\": 1, \"min\": 0.06795999392341165, \"max\": 0.06795999392341165}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.6389828, \"EndTime\": 1679433472.6389983, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 7}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.15247786465813132, \"count\": 1, \"min\": 0.15247786465813132, \"max\": 0.15247786465813132}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.6390474, \"EndTime\": 1679433472.6390626, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 8}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.2490208309397978, \"count\": 1, \"min\": 0.2490208309397978, \"max\": 0.2490208309397978}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.639123, \"EndTime\": 1679433472.6391392, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 9}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.26916262278837316, \"count\": 1, \"min\": 0.26916262278837316, \"max\": 0.26916262278837316}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.6392002, \"EndTime\": 1679433472.6392171, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 10}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.18533363073012407, \"count\": 1, \"min\": 0.18533363073012407, \"max\": 0.18533363073012407}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.6392808, \"EndTime\": 1679433472.6392972, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 11}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.22423204399557675, \"count\": 1, \"min\": 0.22423204399557675, \"max\": 0.22423204399557675}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.6393561, \"EndTime\": 1679433472.6393719, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 12}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.09067258004581227, \"count\": 1, \"min\": 0.09067258004581227, \"max\": 0.09067258004581227}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.63943, \"EndTime\": 1679433472.6394465, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 13}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.06251952070348403, \"count\": 1, \"min\": 0.06251952070348403, \"max\": 0.06251952070348403}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.6394994, \"EndTime\": 1679433472.6395154, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 14}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.10055344682581284, \"count\": 1, \"min\": 0.10055344682581284, \"max\": 0.10055344682581284}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.639571, \"EndTime\": 1679433472.6395872, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 15}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.08350472102445715, \"count\": 1, \"min\": 0.08350472102445715, \"max\": 0.08350472102445715}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.639653, \"EndTime\": 1679433472.6396706, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 16}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.27304605371811813, \"count\": 1, \"min\": 0.27304605371811813, \"max\": 0.27304605371811813}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.6397302, \"EndTime\": 1679433472.639748, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 17}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.24389575823615578, \"count\": 1, \"min\": 0.24389575823615578, \"max\": 0.24389575823615578}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.6398046, \"EndTime\": 1679433472.639821, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 18}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.22149788172104778, \"count\": 1, \"min\": 0.22149788172104778, \"max\": 0.22149788172104778}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.639884, \"EndTime\": 1679433472.6399016, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 19}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.22596274701286764, \"count\": 1, \"min\": 0.22596274701286764, \"max\": 0.22596274701286764}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.639963, \"EndTime\": 1679433472.6399794, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 20}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.1205827870088465, \"count\": 1, \"min\": 0.1205827870088465, \"max\": 0.1205827870088465}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.640037, \"EndTime\": 1679433472.6400535, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 21}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.13042452643899358, \"count\": 1, \"min\": 0.13042452643899358, \"max\": 0.13042452643899358}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.6401122, \"EndTime\": 1679433472.6401286, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 22}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12378413435992072, \"count\": 1, \"min\": 0.12378413435992072, \"max\": 0.12378413435992072}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.6401846, \"EndTime\": 1679433472.6402016, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 23}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11974148963479435, \"count\": 1, \"min\": 0.11974148963479435, \"max\": 0.11974148963479435}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.6402657, \"EndTime\": 1679433472.6402826, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 24}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5522439216164982, \"count\": 1, \"min\": 0.5522439216164982, \"max\": 0.5522439216164982}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.6403463, \"EndTime\": 1679433472.640363, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 25}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5520102969898897, \"count\": 1, \"min\": 0.5520102969898897, \"max\": 0.5520102969898897}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.6404276, \"EndTime\": 1679433472.6404462, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 26}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5517112319048714, \"count\": 1, 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\"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 29}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5539023904239431, \"count\": 1, \"min\": 0.5539023904239431, \"max\": 0.5539023904239431}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.6407263, \"EndTime\": 1679433472.6407435, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 30}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5538080408432905, \"count\": 1, \"min\": 0.5538080408432905, \"max\": 0.5538080408432905}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.640801, \"EndTime\": 1679433472.6408167, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 31}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5537935252470129, \"count\": 1, \"min\": 0.5537935252470129, \"max\": 0.5537935252470129}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:52 INFO 139712331487040] #quality_metric: host=algo-1, epoch=3, train absolute_loss_objective =0.10899765306360581\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.7538285, \"EndTime\": 1679433472.753886, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 0}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.7408075805664063, \"count\": 1, \"min\": 0.7408075805664063, \"max\": 0.7408075805664063}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.75398, \"EndTime\": 1679433472.753999, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 1}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.9043967529296875, \"count\": 1, \"min\": 1.9043967529296875, \"max\": 1.9043967529296875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.7540865, \"EndTime\": 1679433472.754107, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 2}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 2.272879931640625, \"count\": 1, \"min\": 2.272879931640625, \"max\": 2.272879931640625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.7541564, \"EndTime\": 1679433472.7541733, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 3}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.6708952880859376, \"count\": 1, \"min\": 1.6708952880859376, \"max\": 1.6708952880859376}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.7542303, \"EndTime\": 1679433472.7542467, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 4}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5891940551757813, \"count\": 1, \"min\": 0.5891940551757813, \"max\": 0.5891940551757813}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.7542949, \"EndTime\": 1679433472.7543108, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 5}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.3875408508300781, \"count\": 1, \"min\": 0.3875408508300781, \"max\": 0.3875408508300781}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.7543633, \"EndTime\": 1679433472.754381, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 6}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.9519273559570313, \"count\": 1, \"min\": 0.9519273559570313, \"max\": 0.9519273559570313}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.7544355, \"EndTime\": 1679433472.7544532, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 7}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.707306591796875, \"count\": 1, \"min\": 1.707306591796875, \"max\": 1.707306591796875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.7545037, \"EndTime\": 1679433472.7545214, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 8}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 2.2449153564453126, \"count\": 1, \"min\": 2.2449153564453126, \"max\": 2.2449153564453126}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.7545683, \"EndTime\": 1679433472.7545862, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 9}, \"Metrics\": {\"validation_absolute_loss_objective\": 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{\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 12}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.6409091796875, \"count\": 1, \"min\": 0.6409091796875, \"max\": 0.6409091796875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.7548263, \"EndTime\": 1679433472.7548435, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 13}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.6152512451171875, \"count\": 1, \"min\": 0.6152512451171875, \"max\": 0.6152512451171875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.7548919, \"EndTime\": 1679433472.7549093, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 14}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.1116362548828125, \"count\": 1, \"min\": 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\"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 17}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 2.1896291259765626, \"count\": 1, \"min\": 2.1896291259765626, \"max\": 2.1896291259765626}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.7551801, \"EndTime\": 1679433472.7551963, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 18}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.9972560791015626, \"count\": 1, \"min\": 1.9972560791015626, \"max\": 1.9972560791015626}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.7552497, \"EndTime\": 1679433472.755267, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 19}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 2.030113134765625, \"count\": 1, \"min\": 2.030113134765625, \"max\": 2.030113134765625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.7553124, \"EndTime\": 1679433472.7553282, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 20}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.302498095703125, \"count\": 1, \"min\": 1.302498095703125, \"max\": 1.302498095703125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.75538, \"EndTime\": 1679433472.7553964, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 21}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.3809199462890624, \"count\": 1, \"min\": 1.3809199462890624, \"max\": 1.3809199462890624}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.7554483, \"EndTime\": 1679433472.755465, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 22}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.2733880859375, \"count\": 1, \"min\": 1.2733880859375, \"max\": 1.2733880859375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.7555227, \"EndTime\": 1679433472.7555394, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 23}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.283225634765625, \"count\": 1, \"min\": 1.283225634765625, \"max\": 1.283225634765625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.7555852, \"EndTime\": 1679433472.7556014, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 24}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.55823740234375, \"count\": 1, \"min\": 5.55823740234375, \"max\": 5.55823740234375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.7556634, \"EndTime\": 1679433472.755681, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 25}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.55382333984375, \"count\": 1, \"min\": 5.55382333984375, \"max\": 5.55382333984375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.7557397, \"EndTime\": 1679433472.7557576, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 26}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5647654296875, \"count\": 1, \"min\": 5.5647654296875, \"max\": 5.5647654296875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.7558017, \"EndTime\": 1679433472.7558181, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 27}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5578802734375, \"count\": 1, \"min\": 5.5578802734375, \"max\": 5.5578802734375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.7558718, \"EndTime\": 1679433472.7558887, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 28}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.54656708984375, \"count\": 1, \"min\": 5.54656708984375, \"max\": 5.54656708984375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.7559354, \"EndTime\": 1679433472.7559526, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 29}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.58221416015625, \"count\": 1, \"min\": 5.58221416015625, \"max\": 5.58221416015625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.7560055, \"EndTime\": 1679433472.7560222, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 30}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.57232197265625, \"count\": 1, \"min\": 5.57232197265625, \"max\": 5.57232197265625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.7560737, \"EndTime\": 1679433472.7560904, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 31}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.57136162109375, \"count\": 1, \"min\": 5.57136162109375, \"max\": 5.57136162109375}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:52 INFO 139712331487040] #quality_metric: host=algo-1, epoch=3, validation absolute_loss_objective =0.7408075805664063\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:52 INFO 139712331487040] #early_stopping_criteria_metric: host=algo-1, epoch=3, criteria=absolute_loss_objective, value=0.3875408508300781\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:52 INFO 139712331487040] Epoch 3: Loss improved. Updating best model\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:52 INFO 139712331487040] Saving model for epoch: 3\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:52 INFO 139712331487040] Saved checkpoint to \"/tmp/tmpcoc7s4oe/mx-mod-0000.params\"\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:52 INFO 139712331487040] #progress_metric: host=algo-1, completed 25.0 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.2028117, \"EndTime\": 1679433472.7644827, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 81992.0, \"count\": 1, \"min\": 81992, \"max\": 81992}, \"Total Batches Seen\": {\"sum\": 84.0, \"count\": 1, \"min\": 84, \"max\": 84}, \"Max Records Seen Between Resets\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Max Batches Seen Between Resets\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}, \"Reset Count\": {\"sum\": 6.0, \"count\": 1, \"min\": 6, \"max\": 6}, \"Number of Records Since Last Reset\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Number of Batches Since Last Reset\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:52 INFO 139712331487040] #throughput_metric: host=algo-1, train throughput=31144.75560221653 records/second\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.2014008, \"EndTime\": 1679433473.201455, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 0}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.08165083795435288, \"count\": 1, \"min\": 0.08165083795435288, \"max\": 0.08165083795435288}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.2015336, \"EndTime\": 1679433473.2015514, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 1}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.1569258566463695, \"count\": 1, \"min\": 0.1569258566463695, \"max\": 0.1569258566463695}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.2016163, \"EndTime\": 1679433473.2016697, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 2}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.19545443007525276, \"count\": 1, \"min\": 0.19545443007525276, \"max\": 0.19545443007525276}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.2017252, \"EndTime\": 1679433473.2017424, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 3}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.13550136790556067, \"count\": 1, \"min\": 0.13550136790556067, \"max\": 0.13550136790556067}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.201842, \"EndTime\": 1679433473.201865, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 4}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.09719191876579733, \"count\": 1, \"min\": 0.09719191876579733, \"max\": 0.09719191876579733}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.2019155, \"EndTime\": 1679433473.201932, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 5}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.07937701707727769, \"count\": 1, \"min\": 0.07937701707727769, \"max\": 0.07937701707727769}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.2020214, \"EndTime\": 1679433473.202038, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 6}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.08349213207469267, \"count\": 1, \"min\": 0.08349213207469267, \"max\": 0.08349213207469267}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.202091, \"EndTime\": 1679433473.2021072, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 7}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.09419441503636977, \"count\": 1, \"min\": 0.09419441503636977, \"max\": 0.09419441503636977}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.202168, \"EndTime\": 1679433473.202185, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 8}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.19235536373362822, \"count\": 1, \"min\": 0.19235536373362822, \"max\": 0.19235536373362822}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.2022395, \"EndTime\": 1679433473.2022548, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 9}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.2106998488482307, \"count\": 1, \"min\": 0.2106998488482307, \"max\": 0.2106998488482307}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.2023048, \"EndTime\": 1679433473.2023203, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 10}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.13061453650979435, \"count\": 1, \"min\": 0.13061453650979435, \"max\": 0.13061453650979435}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.2023723, \"EndTime\": 1679433473.2023895, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 11}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.1628792132209329, \"count\": 1, \"min\": 0.1628792132209329, \"max\": 0.1628792132209329}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.2024455, \"EndTime\": 1679433473.2024627, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 12}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.07346436960556928, \"count\": 1, \"min\": 0.07346436960556928, \"max\": 0.07346436960556928}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.2025175, \"EndTime\": 1679433473.2025337, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 13}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.08573283487207749, \"count\": 1, \"min\": 0.08573283487207749, \"max\": 0.08573283487207749}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.2025871, \"EndTime\": 1679433473.2026036, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 14}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05730149650573731, \"count\": 1, \"min\": 0.05730149650573731, \"max\": 0.05730149650573731}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.2026496, \"EndTime\": 1679433473.2026646, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 15}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.10268480974085191, \"count\": 1, \"min\": 0.10268480974085191, \"max\": 0.10268480974085191}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.2027154, \"EndTime\": 1679433473.202731, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 16}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.2258400448069853, \"count\": 1, \"min\": 0.2258400448069853, \"max\": 0.2258400448069853}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.2027872, \"EndTime\": 1679433473.2028036, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 17}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.19514022378360524, \"count\": 1, \"min\": 0.19514022378360524, \"max\": 0.19514022378360524}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.2028604, \"EndTime\": 1679433473.202876, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 18}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.17932502567066866, \"count\": 1, \"min\": 0.17932502567066866, \"max\": 0.17932502567066866}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.2029276, \"EndTime\": 1679433473.2029436, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 19}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.18066771024816178, \"count\": 1, \"min\": 0.18066771024816178, \"max\": 0.18066771024816178}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.202995, \"EndTime\": 1679433473.203011, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 20}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12399680014217601, \"count\": 1, \"min\": 0.12399680014217601, \"max\": 0.12399680014217601}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.2030616, \"EndTime\": 1679433473.2030773, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 21}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12545709677303538, \"count\": 1, \"min\": 0.12545709677303538, \"max\": 0.12545709677303538}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.203135, \"EndTime\": 1679433473.2031522, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 22}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12395742797851562, \"count\": 1, \"min\": 0.12395742797851562, \"max\": 0.12395742797851562}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.2031953, \"EndTime\": 1679433473.2032108, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 23}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.1232993689144359, \"count\": 1, \"min\": 0.1232993689144359, \"max\": 0.1232993689144359}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.2032592, \"EndTime\": 1679433473.203275, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 24}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.552034262264476, \"count\": 1, \"min\": 0.552034262264476, \"max\": 0.552034262264476}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.2033312, \"EndTime\": 1679433473.2033486, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 25}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5521206880457261, \"count\": 1, \"min\": 0.5521206880457261, \"max\": 0.5521206880457261}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.2033925, \"EndTime\": 1679433473.203407, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 26}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5523453692267922, \"count\": 1, \"min\": 0.5523453692267922, \"max\": 0.5523453692267922}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.203457, \"EndTime\": 1679433473.2034733, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 27}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5516542681525736, \"count\": 1, \"min\": 0.5516542681525736, \"max\": 0.5516542681525736}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.2035322, \"EndTime\": 1679433473.2035482, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 28}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5535713393267463, \"count\": 1, \"min\": 0.5535713393267463, \"max\": 0.5535713393267463}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.2036035, \"EndTime\": 1679433473.2036195, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 29}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5546415692497703, \"count\": 1, \"min\": 0.5546415692497703, \"max\": 0.5546415692497703}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.2036717, \"EndTime\": 1679433473.203688, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 30}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5549532829733456, \"count\": 1, \"min\": 0.5549532829733456, \"max\": 0.5549532829733456}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.2037473, \"EndTime\": 1679433473.2037635, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 31}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5552084601907169, \"count\": 1, \"min\": 0.5552084601907169, \"max\": 0.5552084601907169}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:53 INFO 139712331487040] #quality_metric: host=algo-1, epoch=4, train absolute_loss_objective =0.08165083795435288\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.3153923, \"EndTime\": 1679433473.3154478, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 0}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.6907721801757812, \"count\": 1, \"min\": 0.6907721801757812, \"max\": 0.6907721801757812}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.3155339, \"EndTime\": 1679433473.3155522, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 1}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.3323885498046875, \"count\": 1, \"min\": 1.3323885498046875, \"max\": 1.3323885498046875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.3156166, \"EndTime\": 1679433473.315636, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 2}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.7195559814453125, \"count\": 1, \"min\": 1.7195559814453125, \"max\": 1.7195559814453125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.3156834, \"EndTime\": 1679433473.3157, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 3}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.119547314453125, \"count\": 1, \"min\": 1.119547314453125, \"max\": 1.119547314453125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.3157473, \"EndTime\": 1679433473.3157616, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 4}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.8385714721679688, \"count\": 1, \"min\": 0.8385714721679688, \"max\": 0.8385714721679688}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.3158062, \"EndTime\": 1679433473.3158202, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 5}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.47400499877929686, \"count\": 1, \"min\": 0.47400499877929686, \"max\": 0.47400499877929686}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.3158793, \"EndTime\": 1679433473.3158965, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 6}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.7893654296875, \"count\": 1, \"min\": 0.7893654296875, \"max\": 0.7893654296875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.3159568, \"EndTime\": 1679433473.3159733, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 7}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.7766783203125, \"count\": 1, \"min\": 0.7766783203125, \"max\": 0.7766783203125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.3160279, \"EndTime\": 1679433473.3160446, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 8}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.6779784912109375, \"count\": 1, \"min\": 1.6779784912109375, \"max\": 1.6779784912109375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.3160965, \"EndTime\": 1679433473.316112, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 9}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.849003076171875, \"count\": 1, \"min\": 1.849003076171875, \"max\": 1.849003076171875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.316165, \"EndTime\": 1679433473.3161807, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 10}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.082353955078125, \"count\": 1, \"min\": 1.082353955078125, \"max\": 1.082353955078125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.3162246, \"EndTime\": 1679433473.3162405, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 11}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.3639900634765625, \"count\": 1, \"min\": 1.3639900634765625, \"max\": 1.3639900634765625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.3162873, \"EndTime\": 1679433473.3163023, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 12}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.917236962890625, \"count\": 1, \"min\": 0.917236962890625, \"max\": 0.917236962890625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.316363, \"EndTime\": 1679433473.3163805, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 13}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.801346142578125, \"count\": 1, \"min\": 0.801346142578125, \"max\": 0.801346142578125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.3164234, \"EndTime\": 1679433473.3164396, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 14}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.889224560546875, \"count\": 1, \"min\": 0.889224560546875, \"max\": 0.889224560546875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.3164968, \"EndTime\": 1679433473.3165133, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 15}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.9698652587890625, \"count\": 1, \"min\": 0.9698652587890625, \"max\": 0.9698652587890625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.316564, \"EndTime\": 1679433473.3165796, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 16}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 2.0713642333984374, \"count\": 1, \"min\": 2.0713642333984374, \"max\": 2.0713642333984374}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.3166327, \"EndTime\": 1679433473.3166485, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 17}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.7677975341796874, \"count\": 1, \"min\": 1.7677975341796874, \"max\": 1.7677975341796874}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.3167055, \"EndTime\": 1679433473.3167214, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 18}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.64818349609375, \"count\": 1, \"min\": 1.64818349609375, \"max\": 1.64818349609375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.316774, \"EndTime\": 1679433473.3167894, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 19}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.642926416015625, \"count\": 1, \"min\": 1.642926416015625, \"max\": 1.642926416015625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.316841, \"EndTime\": 1679433473.3168566, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 20}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.394012939453125, \"count\": 1, \"min\": 1.394012939453125, \"max\": 1.394012939453125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.31691, \"EndTime\": 1679433473.3169262, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 21}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.35967509765625, \"count\": 1, \"min\": 1.35967509765625, \"max\": 1.35967509765625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.316981, \"EndTime\": 1679433473.3169963, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 22}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.2975130126953125, \"count\": 1, \"min\": 1.2975130126953125, \"max\": 1.2975130126953125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.3170462, \"EndTime\": 1679433473.3170626, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 23}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.3358265380859375, \"count\": 1, \"min\": 1.3358265380859375, \"max\": 1.3358265380859375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.3171117, \"EndTime\": 1679433473.317128, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 24}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.56346533203125, \"count\": 1, \"min\": 5.56346533203125, \"max\": 5.56346533203125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.3171787, \"EndTime\": 1679433473.3171942, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 25}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.56500625, \"count\": 1, \"min\": 5.56500625, \"max\": 5.56500625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.3172426, \"EndTime\": 1679433473.317259, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 26}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.562094140625, \"count\": 1, \"min\": 5.562094140625, \"max\": 5.562094140625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.3173082, \"EndTime\": 1679433473.317325, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 27}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.56554814453125, \"count\": 1, \"min\": 5.56554814453125, \"max\": 5.56554814453125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.3173819, \"EndTime\": 1679433473.3173976, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 28}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.56739541015625, \"count\": 1, \"min\": 5.56739541015625, \"max\": 5.56739541015625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.3174496, \"EndTime\": 1679433473.317465, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 29}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.537086328125, \"count\": 1, \"min\": 5.537086328125, \"max\": 5.537086328125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.3175144, \"EndTime\": 1679433473.3175313, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 30}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.53378681640625, \"count\": 1, \"min\": 5.53378681640625, \"max\": 5.53378681640625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.3175836, \"EndTime\": 1679433473.3176, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 31}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5382279296875, \"count\": 1, \"min\": 5.5382279296875, \"max\": 5.5382279296875}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:53 INFO 139712331487040] #quality_metric: host=algo-1, epoch=4, validation absolute_loss_objective =0.6907721801757812\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:53 INFO 139712331487040] #early_stopping_criteria_metric: host=algo-1, epoch=4, criteria=absolute_loss_objective, value=0.47400499877929686\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:53 INFO 139712331487040] Saving model for epoch: 4\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:53 INFO 139712331487040] Saved checkpoint to \"/tmp/tmpqk30840x/mx-mod-0000.params\"\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:53 INFO 139712331487040] #progress_metric: host=algo-1, completed 31.25 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433472.7648058, \"EndTime\": 1679433473.3242428, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 99490.0, \"count\": 1, \"min\": 99490, \"max\": 99490}, \"Total Batches Seen\": {\"sum\": 102.0, \"count\": 1, \"min\": 102, \"max\": 102}, \"Max Records Seen Between Resets\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Max Batches Seen Between Resets\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}, \"Reset Count\": {\"sum\": 7.0, \"count\": 1, \"min\": 7, \"max\": 7}, \"Number of Records Since Last Reset\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Number of Batches Since Last Reset\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:53 INFO 139712331487040] #throughput_metric: host=algo-1, train throughput=31269.423106042694 records/second\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.7902625, \"EndTime\": 1679433473.7903192, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 0}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11676553546681123, \"count\": 1, \"min\": 0.11676553546681123, \"max\": 0.11676553546681123}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.790396, \"EndTime\": 1679433473.7904088, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 1}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.10439095800063189, \"count\": 1, \"min\": 0.10439095800063189, \"max\": 0.10439095800063189}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.7904725, \"EndTime\": 1679433473.7904894, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 2}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.14188329404943129, \"count\": 1, \"min\": 0.14188329404943129, \"max\": 0.14188329404943129}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.790546, \"EndTime\": 1679433473.790557, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 3}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.08572696035048541, \"count\": 1, \"min\": 0.08572696035048541, \"max\": 0.08572696035048541}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.7906072, \"EndTime\": 1679433473.790624, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 4}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05311079159904929, \"count\": 1, \"min\": 0.05311079159904929, \"max\": 0.05311079159904929}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.7906728, \"EndTime\": 1679433473.7906895, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 5}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.07170492104923024, \"count\": 1, \"min\": 0.07170492104923024, \"max\": 0.07170492104923024}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.790739, \"EndTime\": 1679433473.7907538, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 6}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.07167567017499138, \"count\": 1, \"min\": 0.07167567017499138, \"max\": 0.07167567017499138}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.7908096, \"EndTime\": 1679433473.7908201, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 7}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.07098426089567296, \"count\": 1, \"min\": 0.07098426089567296, \"max\": 0.07098426089567296}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.7908697, \"EndTime\": 1679433473.7908866, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 8}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.1376894858865177, \"count\": 1, \"min\": 0.1376894858865177, \"max\": 0.1376894858865177}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.7909443, \"EndTime\": 1679433473.7909598, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 9}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.15251802511776194, \"count\": 1, \"min\": 0.15251802511776194, \"max\": 0.15251802511776194}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.7910125, \"EndTime\": 1679433473.7910283, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 10}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.08395123807121725, \"count\": 1, \"min\": 0.08395123807121725, \"max\": 0.08395123807121725}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.791083, \"EndTime\": 1679433473.7910995, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 11}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.10602381672578699, \"count\": 1, \"min\": 0.10602381672578699, \"max\": 0.10602381672578699}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.7911482, \"EndTime\": 1679433473.7911637, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 12}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.06449550550124225, \"count\": 1, \"min\": 0.06449550550124225, \"max\": 0.06449550550124225}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.7912166, \"EndTime\": 1679433473.7912323, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 13}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.06106287271836225, \"count\": 1, \"min\": 0.06106287271836225, \"max\": 0.06106287271836225}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.7912865, \"EndTime\": 1679433473.791303, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 14}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.07686278803208295, \"count\": 1, \"min\": 0.07686278803208295, \"max\": 0.07686278803208295}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.791355, \"EndTime\": 1679433473.7913702, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 15}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.07113794035070083, \"count\": 1, \"min\": 0.07113794035070083, \"max\": 0.07113794035070083}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.7914212, \"EndTime\": 1679433473.7914362, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 16}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.18774504538143383, \"count\": 1, \"min\": 0.18774504538143383, \"max\": 0.18774504538143383}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.7914927, \"EndTime\": 1679433473.7915092, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 17}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.15948033770392922, \"count\": 1, \"min\": 0.15948033770392922, \"max\": 0.15948033770392922}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.7915604, \"EndTime\": 1679433473.7915766, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 18}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.14975494654038374, \"count\": 1, \"min\": 0.14975494654038374, \"max\": 0.14975494654038374}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.7916267, \"EndTime\": 1679433473.7916431, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 19}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.1486492228788488, \"count\": 1, \"min\": 0.1486492228788488, \"max\": 0.1486492228788488}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.7916913, \"EndTime\": 1679433473.7917082, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 20}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12192356244255514, \"count\": 1, \"min\": 0.12192356244255514, \"max\": 0.12192356244255514}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.7917504, \"EndTime\": 1679433473.7917647, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 21}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12500636201746324, \"count\": 1, \"min\": 0.12500636201746324, \"max\": 0.12500636201746324}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.791814, \"EndTime\": 1679433473.79183, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 22}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.1276991949642406, \"count\": 1, \"min\": 0.1276991949642406, \"max\": 0.1276991949642406}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.791883, \"EndTime\": 1679433473.7919002, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 23}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11926201225729549, \"count\": 1, \"min\": 0.11926201225729549, \"max\": 0.11926201225729549}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.7919602, \"EndTime\": 1679433473.7919772, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 24}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5523263513901654, \"count\": 1, \"min\": 0.5523263513901654, \"max\": 0.5523263513901654}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.792033, \"EndTime\": 1679433473.79205, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 25}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5519935733570772, \"count\": 1, \"min\": 0.5519935733570772, \"max\": 0.5519935733570772}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.7920995, \"EndTime\": 1679433473.792116, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 26}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5520575489717371, \"count\": 1, \"min\": 0.5520575489717371, \"max\": 0.5520575489717371}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.792167, \"EndTime\": 1679433473.7921822, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 27}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5522722311580882, \"count\": 1, \"min\": 0.5522722311580882, \"max\": 0.5522722311580882}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.792239, \"EndTime\": 1679433473.7922554, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 28}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.554604388068704, \"count\": 1, \"min\": 0.554604388068704, \"max\": 0.554604388068704}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.792312, \"EndTime\": 1679433473.7923276, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 29}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5529656982421876, \"count\": 1, \"min\": 0.5529656982421876, \"max\": 0.5529656982421876}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.792379, \"EndTime\": 1679433473.792395, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 30}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5534513872931985, \"count\": 1, \"min\": 0.5534513872931985, \"max\": 0.5534513872931985}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.7924492, \"EndTime\": 1679433473.7924662, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 31}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5538611091164981, \"count\": 1, \"min\": 0.5538611091164981, \"max\": 0.5538611091164981}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:53 INFO 139712331487040] #quality_metric: host=algo-1, epoch=5, train absolute_loss_objective =0.11676553546681123\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.9055758, \"EndTime\": 1679433473.9056304, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 0}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.647266259765625, \"count\": 1, \"min\": 0.647266259765625, \"max\": 0.647266259765625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.9057155, \"EndTime\": 1679433473.9057343, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 1}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.8482201171875, \"count\": 1, \"min\": 0.8482201171875, \"max\": 0.8482201171875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.9057975, \"EndTime\": 1679433473.9058163, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 2}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.1995578125, \"count\": 1, \"min\": 1.1995578125, \"max\": 1.1995578125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.9058645, \"EndTime\": 1679433473.9058812, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 3}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.6909904296875, \"count\": 1, \"min\": 0.6909904296875, \"max\": 0.6909904296875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.9059289, \"EndTime\": 1679433473.9059436, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 4}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.41093876953125, \"count\": 1, \"min\": 0.41093876953125, \"max\": 0.41093876953125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.9059894, \"EndTime\": 1679433473.906004, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 5}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.1468203369140626, \"count\": 1, \"min\": 1.1468203369140626, \"max\": 1.1468203369140626}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.9060786, \"EndTime\": 1679433473.9060953, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 6}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.107518505859375, \"count\": 1, \"min\": 1.107518505859375, \"max\": 1.107518505859375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.9061496, \"EndTime\": 1679433473.9061668, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 7}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.475750341796875, \"count\": 1, \"min\": 0.475750341796875, \"max\": 0.475750341796875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.9062166, \"EndTime\": 1679433473.9062338, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 8}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.155607861328125, \"count\": 1, \"min\": 1.155607861328125, \"max\": 1.155607861328125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.906291, \"EndTime\": 1679433473.906307, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 9}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.27982451171875, \"count\": 1, \"min\": 1.27982451171875, \"max\": 1.27982451171875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.9063485, \"EndTime\": 1679433473.9063582, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 10}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.6974135864257812, \"count\": 1, \"min\": 0.6974135864257812, \"max\": 0.6974135864257812}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.9064064, \"EndTime\": 1679433473.9064217, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 11}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.844905322265625, \"count\": 1, \"min\": 0.844905322265625, \"max\": 0.844905322265625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.906478, \"EndTime\": 1679433473.906495, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 12}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.0768949462890625, \"count\": 1, \"min\": 1.0768949462890625, \"max\": 1.0768949462890625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.9065425, \"EndTime\": 1679433473.9065597, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 13}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.7125823486328124, \"count\": 1, \"min\": 0.7125823486328124, \"max\": 0.7125823486328124}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.9066057, \"EndTime\": 1679433473.9066222, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 14}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.9187700073242188, \"count\": 1, \"min\": 0.9187700073242188, \"max\": 0.9187700073242188}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.9066775, \"EndTime\": 1679433473.906695, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 15}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.7623110961914062, \"count\": 1, \"min\": 0.7623110961914062, \"max\": 0.7623110961914062}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.9067447, \"EndTime\": 1679433473.9067605, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 16}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.7399674560546874, \"count\": 1, \"min\": 1.7399674560546874, \"max\": 1.7399674560546874}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.906822, \"EndTime\": 1679433473.90684, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 17}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.4794268798828125, \"count\": 1, \"min\": 1.4794268798828125, \"max\": 1.4794268798828125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.9068983, \"EndTime\": 1679433473.9069154, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 18}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.40841494140625, \"count\": 1, \"min\": 1.40841494140625, \"max\": 1.40841494140625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.906976, \"EndTime\": 1679433473.9069939, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 19}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.3906089599609375, \"count\": 1, \"min\": 1.3906089599609375, \"max\": 1.3906089599609375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.9070582, \"EndTime\": 1679433473.9070766, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 20}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.3505925537109376, \"count\": 1, \"min\": 1.3505925537109376, \"max\": 1.3505925537109376}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.90714, \"EndTime\": 1679433473.9071572, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 21}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.3401701171875, \"count\": 1, \"min\": 1.3401701171875, \"max\": 1.3401701171875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.9072144, \"EndTime\": 1679433473.9072306, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 22}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.417482080078125, \"count\": 1, \"min\": 1.417482080078125, \"max\": 1.417482080078125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.9072924, \"EndTime\": 1679433473.90731, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 23}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.304938427734375, \"count\": 1, \"min\": 1.304938427734375, \"max\": 1.304938427734375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.9073687, \"EndTime\": 1679433473.907386, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 24}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.55546748046875, \"count\": 1, \"min\": 5.55546748046875, \"max\": 5.55546748046875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.907451, \"EndTime\": 1679433473.9074693, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 25}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.56794423828125, \"count\": 1, \"min\": 5.56794423828125, \"max\": 5.56794423828125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.907528, \"EndTime\": 1679433473.907544, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 26}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.56281474609375, \"count\": 1, \"min\": 5.56281474609375, \"max\": 5.56281474609375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.9075947, \"EndTime\": 1679433473.9076104, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 27}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.56903134765625, \"count\": 1, \"min\": 5.56903134765625, \"max\": 5.56903134765625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.9076726, \"EndTime\": 1679433473.9076893, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 28}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5556125, \"count\": 1, \"min\": 5.5556125, \"max\": 5.5556125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.9077542, \"EndTime\": 1679433473.9077728, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 29}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5632615234375, \"count\": 1, \"min\": 5.5632615234375, \"max\": 5.5632615234375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.907838, \"EndTime\": 1679433473.9078555, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 30}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5397091796875, \"count\": 1, \"min\": 5.5397091796875, \"max\": 5.5397091796875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.907909, \"EndTime\": 1679433473.9079256, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 31}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.54866650390625, \"count\": 1, \"min\": 5.54866650390625, \"max\": 5.54866650390625}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:53 INFO 139712331487040] #quality_metric: host=algo-1, epoch=5, validation absolute_loss_objective =0.647266259765625\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:53 INFO 139712331487040] #early_stopping_criteria_metric: host=algo-1, epoch=5, criteria=absolute_loss_objective, value=0.41093876953125\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:53 INFO 139712331487040] Saving model for epoch: 5\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:53 INFO 139712331487040] Saved checkpoint to \"/tmp/tmps_u4ymoe/mx-mod-0000.params\"\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:53 INFO 139712331487040] #progress_metric: host=algo-1, completed 37.5 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.3245633, \"EndTime\": 1679433473.9143496, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 116988.0, \"count\": 1, \"min\": 116988, \"max\": 116988}, \"Total Batches Seen\": {\"sum\": 120.0, \"count\": 1, \"min\": 120, \"max\": 120}, \"Max Records Seen Between Resets\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Max Batches Seen Between Resets\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}, \"Reset Count\": {\"sum\": 8.0, \"count\": 1, \"min\": 8, \"max\": 8}, \"Number of Records Since Last Reset\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Number of Batches Since Last Reset\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:53 INFO 139712331487040] #throughput_metric: host=algo-1, train throughput=29660.352193571645 records/second\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.3835547, \"EndTime\": 1679433474.3836114, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 0}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.08191161974738626, \"count\": 1, \"min\": 0.08191161974738626, \"max\": 0.08191161974738626}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.3836925, \"EndTime\": 1679433474.3837104, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 1}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.06787190785127528, \"count\": 1, \"min\": 0.06787190785127528, \"max\": 0.06787190785127528}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.3838027, \"EndTime\": 1679433474.3838227, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 2}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.09393694440056297, \"count\": 1, \"min\": 0.09393694440056297, \"max\": 0.09393694440056297}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.3839004, \"EndTime\": 1679433474.3839183, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 3}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05977784078261431, \"count\": 1, \"min\": 0.05977784078261431, \"max\": 0.05977784078261431}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.3839781, \"EndTime\": 1679433474.3839936, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 4}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05606754190781537, \"count\": 1, \"min\": 0.05606754190781537, \"max\": 0.05606754190781537}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.3840392, \"EndTime\": 1679433474.3840537, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 5}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.08236034438189338, \"count\": 1, \"min\": 0.08236034438189338, \"max\": 0.08236034438189338}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.384106, \"EndTime\": 1679433474.384122, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 6}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.08155837272195256, \"count\": 1, \"min\": 0.08155837272195256, \"max\": 0.08155837272195256}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.3841817, \"EndTime\": 1679433474.3841991, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 7}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.07545879431331859, \"count\": 1, \"min\": 0.07545879431331859, \"max\": 0.07545879431331859}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.384254, \"EndTime\": 1679433474.3842702, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 8}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.09042664785946117, \"count\": 1, \"min\": 0.09042664785946117, \"max\": 0.09042664785946117}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.3843243, \"EndTime\": 1679433474.3843405, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 9}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.09949457774442785, \"count\": 1, \"min\": 0.09949457774442785, \"max\": 0.09949457774442785}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.384386, \"EndTime\": 1679433474.384401, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 10}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.06133890264174517, \"count\": 1, \"min\": 0.06133890264174517, \"max\": 0.06133890264174517}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.3844512, \"EndTime\": 1679433474.384468, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 11}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.06771968639598173, \"count\": 1, \"min\": 0.06771968639598173, \"max\": 0.06771968639598173}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.384522, \"EndTime\": 1679433474.3845387, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 12}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.07860717638801126, \"count\": 1, \"min\": 0.07860717638801126, \"max\": 0.07860717638801126}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.3845932, \"EndTime\": 1679433474.3846097, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 13}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.07779757712869083, \"count\": 1, \"min\": 0.07779757712869083, \"max\": 0.07779757712869083}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.3846579, \"EndTime\": 1679433474.384673, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 14}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.06803289121740004, \"count\": 1, \"min\": 0.06803289121740004, \"max\": 0.06803289121740004}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.3847234, \"EndTime\": 1679433474.384739, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 15}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.09580689531214097, \"count\": 1, \"min\": 0.09580689531214097, \"max\": 0.09580689531214097}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.3847895, \"EndTime\": 1679433474.3848064, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 16}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.1595437604118796, \"count\": 1, \"min\": 0.1595437604118796, \"max\": 0.1595437604118796}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.3848522, \"EndTime\": 1679433474.3848686, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 17}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.13536294690300438, \"count\": 1, \"min\": 0.13536294690300438, \"max\": 0.13536294690300438}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.3849144, \"EndTime\": 1679433474.3849308, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 18}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.13012640021829044, \"count\": 1, \"min\": 0.13012640021829044, \"max\": 0.13012640021829044}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.3849854, \"EndTime\": 1679433474.3850012, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 19}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12799010961196003, \"count\": 1, \"min\": 0.12799010961196003, \"max\": 0.12799010961196003}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.385052, \"EndTime\": 1679433474.3850672, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 20}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11967009376077091, \"count\": 1, \"min\": 0.11967009376077091, \"max\": 0.11967009376077091}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.3851156, \"EndTime\": 1679433474.385132, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 21}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12100808895335478, \"count\": 1, \"min\": 0.12100808895335478, \"max\": 0.12100808895335478}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.3851764, \"EndTime\": 1679433474.3851922, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 22}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.1257586871876436, \"count\": 1, \"min\": 0.1257586871876436, \"max\": 0.1257586871876436}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.3852487, \"EndTime\": 1679433474.3852654, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 23}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12200142983829274, \"count\": 1, \"min\": 0.12200142983829274, \"max\": 0.12200142983829274}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.385323, \"EndTime\": 1679433474.3853385, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 24}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.552011381261489, \"count\": 1, \"min\": 0.552011381261489, \"max\": 0.552011381261489}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.3853881, \"EndTime\": 1679433474.3854043, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 25}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5524342148724725, \"count\": 1, \"min\": 0.5524342148724725, \"max\": 0.5524342148724725}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.385444, \"EndTime\": 1679433474.385458, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 26}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5520965755687041, \"count\": 1, \"min\": 0.5520965755687041, \"max\": 0.5520965755687041}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.3855076, \"EndTime\": 1679433474.385524, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 27}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5524516816980699, \"count\": 1, \"min\": 0.5524516816980699, \"max\": 0.5524516816980699}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.3855686, \"EndTime\": 1679433474.3855848, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 28}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5557412647920497, \"count\": 1, \"min\": 0.5557412647920497, \"max\": 0.5557412647920497}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.3856318, \"EndTime\": 1679433474.385648, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 29}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.554779935948989, \"count\": 1, \"min\": 0.554779935948989, \"max\": 0.554779935948989}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.3856947, \"EndTime\": 1679433474.3857112, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 30}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5534771943933824, \"count\": 1, \"min\": 0.5534771943933824, \"max\": 0.5534771943933824}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.385754, \"EndTime\": 1679433474.3857682, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 31}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5531965224322151, \"count\": 1, \"min\": 0.5531965224322151, \"max\": 0.5531965224322151}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:54 INFO 139712331487040] #quality_metric: host=algo-1, epoch=6, train absolute_loss_objective =0.08191161974738626\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5280302, \"EndTime\": 1679433474.5280988, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 0}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5506097534179687, \"count\": 1, \"min\": 0.5506097534179687, \"max\": 0.5506097534179687}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5281975, \"EndTime\": 1679433474.528219, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 1}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.6380568603515625, \"count\": 1, \"min\": 0.6380568603515625, \"max\": 0.6380568603515625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5282772, \"EndTime\": 1679433474.5282948, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 2}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.76551591796875, \"count\": 1, \"min\": 0.76551591796875, \"max\": 0.76551591796875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5283482, \"EndTime\": 1679433474.5283654, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 3}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5975060241699218, \"count\": 1, \"min\": 0.5975060241699218, \"max\": 0.5975060241699218}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5284169, \"EndTime\": 1679433474.5284333, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 4}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5258678405761719, \"count\": 1, \"min\": 0.5258678405761719, \"max\": 0.5258678405761719}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5284786, \"EndTime\": 1679433474.5284922, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 5}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.9424122192382812, \"count\": 1, \"min\": 0.9424122192382812, \"max\": 0.9424122192382812}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.528542, \"EndTime\": 1679433474.5285585, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 6}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.2987256103515625, \"count\": 1, \"min\": 1.2987256103515625, \"max\": 1.2987256103515625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5286093, \"EndTime\": 1679433474.528625, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 7}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.6686074096679687, \"count\": 1, \"min\": 0.6686074096679687, \"max\": 0.6686074096679687}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5286715, \"EndTime\": 1679433474.5286856, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 8}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.7485883056640625, \"count\": 1, \"min\": 0.7485883056640625, \"max\": 0.7485883056640625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5287302, \"EndTime\": 1679433474.5287447, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 9}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.791834375, \"count\": 1, \"min\": 0.791834375, \"max\": 0.791834375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5287929, \"EndTime\": 1679433474.5288095, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 10}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.6143529418945313, \"count\": 1, \"min\": 0.6143529418945313, \"max\": 0.6143529418945313}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5288606, \"EndTime\": 1679433474.5288775, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 11}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.6419713623046875, \"count\": 1, \"min\": 0.6419713623046875, \"max\": 0.6419713623046875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5289314, \"EndTime\": 1679433474.5289466, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 12}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.03478369140625, \"count\": 1, \"min\": 1.03478369140625, \"max\": 1.03478369140625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5289986, \"EndTime\": 1679433474.5290155, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 13}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.1707942626953125, \"count\": 1, \"min\": 1.1707942626953125, \"max\": 1.1707942626953125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5290668, \"EndTime\": 1679433474.5290835, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 14}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5524767578125, \"count\": 1, \"min\": 0.5524767578125, \"max\": 0.5524767578125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5291357, \"EndTime\": 1679433474.5291533, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 15}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.8833426025390625, \"count\": 1, \"min\": 0.8833426025390625, \"max\": 0.8833426025390625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5292063, \"EndTime\": 1679433474.5292232, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 16}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.5053503662109375, \"count\": 1, \"min\": 1.5053503662109375, \"max\": 1.5053503662109375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5292754, \"EndTime\": 1679433474.529292, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 17}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.294985205078125, \"count\": 1, \"min\": 1.294985205078125, \"max\": 1.294985205078125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5293458, \"EndTime\": 1679433474.529362, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 18}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.2578305908203125, \"count\": 1, \"min\": 1.2578305908203125, \"max\": 1.2578305908203125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5294178, \"EndTime\": 1679433474.5294342, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 19}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.2395828125, \"count\": 1, \"min\": 1.2395828125, \"max\": 1.2395828125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5294862, \"EndTime\": 1679433474.5295033, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 20}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.3258509765625, \"count\": 1, \"min\": 1.3258509765625, \"max\": 1.3258509765625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5295582, \"EndTime\": 1679433474.5295768, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 21}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.3311484375, \"count\": 1, \"min\": 1.3311484375, \"max\": 1.3311484375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5296278, \"EndTime\": 1679433474.5296433, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 22}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.3312428955078126, \"count\": 1, \"min\": 1.3312428955078126, \"max\": 1.3312428955078126}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5296965, \"EndTime\": 1679433474.5297108, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 23}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.4156914306640624, \"count\": 1, \"min\": 1.4156914306640624, \"max\": 1.4156914306640624}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.529758, \"EndTime\": 1679433474.529772, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 24}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.55816298828125, \"count\": 1, \"min\": 5.55816298828125, \"max\": 5.55816298828125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5298193, \"EndTime\": 1679433474.5298352, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 25}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.55921640625, \"count\": 1, \"min\": 5.55921640625, \"max\": 5.55921640625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5298855, \"EndTime\": 1679433474.5299017, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 26}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.56738330078125, \"count\": 1, \"min\": 5.56738330078125, \"max\": 5.56738330078125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5299532, \"EndTime\": 1679433474.5299716, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 27}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.56044912109375, \"count\": 1, \"min\": 5.56044912109375, \"max\": 5.56044912109375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5300505, \"EndTime\": 1679433474.5300684, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 28}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.55325927734375, \"count\": 1, \"min\": 5.55325927734375, \"max\": 5.55325927734375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5301216, \"EndTime\": 1679433474.5301378, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 29}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5547966796875, \"count\": 1, \"min\": 5.5547966796875, \"max\": 5.5547966796875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5301926, \"EndTime\": 1679433474.5302086, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 30}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.55355458984375, \"count\": 1, \"min\": 5.55355458984375, \"max\": 5.55355458984375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5302591, \"EndTime\": 1679433474.530275, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 31}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.55644765625, \"count\": 1, \"min\": 5.55644765625, \"max\": 5.55644765625}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:54 INFO 139712331487040] #quality_metric: host=algo-1, epoch=6, validation absolute_loss_objective =0.5506097534179687\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:54 INFO 139712331487040] #early_stopping_criteria_metric: host=algo-1, epoch=6, criteria=absolute_loss_objective, value=0.5258678405761719\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:54 INFO 139712331487040] Saving model for epoch: 6\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:54 INFO 139712331487040] Saved checkpoint to \"/tmp/tmpqixm72gk/mx-mod-0000.params\"\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:54 INFO 139712331487040] #progress_metric: host=algo-1, completed 43.75 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433473.9146457, \"EndTime\": 1679433474.5388696, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 134486.0, \"count\": 1, \"min\": 134486, \"max\": 134486}, \"Total Batches Seen\": {\"sum\": 138.0, \"count\": 1, \"min\": 138, \"max\": 138}, \"Max Records Seen Between Resets\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Max Batches Seen Between Resets\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}, \"Reset Count\": {\"sum\": 9.0, \"count\": 1, \"min\": 9, \"max\": 9}, \"Number of Records Since Last Reset\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Number of Batches Since Last Reset\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:54 INFO 139712331487040] #throughput_metric: host=algo-1, train throughput=28024.573988045096 records/second\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.1204152, \"EndTime\": 1679433475.120478, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 0}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05244462720085593, \"count\": 1, \"min\": 0.05244462720085593, \"max\": 0.05244462720085593}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.1205654, \"EndTime\": 1679433475.12058, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 1}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.059698827182545385, \"count\": 1, \"min\": 0.059698827182545385, \"max\": 0.059698827182545385}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.1206293, \"EndTime\": 1679433475.1206448, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 2}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.06310953140258789, \"count\": 1, \"min\": 0.06310953140258789, \"max\": 0.06310953140258789}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.1207056, \"EndTime\": 1679433475.1207213, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 3}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05651955817727482, \"count\": 1, \"min\": 0.05651955817727482, \"max\": 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\"training\", \"epoch\": 7, \"model\": 6}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.09475901727115407, \"count\": 1, \"min\": 0.09475901727115407, \"max\": 0.09475901727115407}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.1212487, \"EndTime\": 1679433475.12127, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 7}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.0572099542056813, \"count\": 1, \"min\": 0.0572099542056813, \"max\": 0.0572099542056813}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.1213312, \"EndTime\": 1679433475.1213484, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 8}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.06386054095099954, \"count\": 1, \"min\": 0.06386054095099954, \"max\": 0.06386054095099954}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.1214044, \"EndTime\": 1679433475.1214223, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 9}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.06394559501199161, \"count\": 1, \"min\": 0.06394559501199161, \"max\": 0.06394559501199161}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.1214733, \"EndTime\": 1679433475.1214905, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 10}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.058128202774945424, \"count\": 1, \"min\": 0.058128202774945424, \"max\": 0.058128202774945424}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.1215472, \"EndTime\": 1679433475.1215646, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 11}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.06019746825274299, \"count\": 1, \"min\": 0.06019746825274299, \"max\": 0.06019746825274299}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.1216192, \"EndTime\": 1679433475.1216366, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 12}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.07969561206593233, \"count\": 1, \"min\": 0.07969561206593233, \"max\": 0.07969561206593233}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.121689, \"EndTime\": 1679433475.1217055, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 13}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12319754230274874, \"count\": 1, \"min\": 0.12319754230274874, \"max\": 0.12319754230274874}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.1217606, \"EndTime\": 1679433475.121778, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 14}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.06506012770708869, \"count\": 1, \"min\": 0.06506012770708869, \"max\": 0.06506012770708869}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.1218412, \"EndTime\": 1679433475.121859, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 15}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.078546308854047, \"count\": 1, \"min\": 0.078546308854047, \"max\": 0.078546308854047}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.1219108, \"EndTime\": 1679433475.1219282, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 16}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.13925160531436695, \"count\": 1, \"min\": 0.13925160531436695, \"max\": 0.13925160531436695}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.1219742, \"EndTime\": 1679433475.1219919, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 17}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12095526751349954, \"count\": 1, \"min\": 0.12095526751349954, \"max\": 0.12095526751349954}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.1220818, \"EndTime\": 1679433475.1221006, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 18}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11858651598762063, \"count\": 1, \"min\": 0.11858651598762063, \"max\": 0.11858651598762063}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.1221569, \"EndTime\": 1679433475.1221745, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 19}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11671993704403148, \"count\": 1, \"min\": 0.11671993704403148, \"max\": 0.11671993704403148}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.1222308, \"EndTime\": 1679433475.122248, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 20}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11906366280948415, \"count\": 1, \"min\": 0.11906366280948415, \"max\": 0.11906366280948415}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.1223044, \"EndTime\": 1679433475.1223218, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 21}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12870559871897977, \"count\": 1, \"min\": 0.12870559871897977, \"max\": 0.12870559871897977}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.1223817, \"EndTime\": 1679433475.1223984, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 22}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11896240593405331, \"count\": 1, \"min\": 0.11896240593405331, \"max\": 0.11896240593405331}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.1224532, \"EndTime\": 1679433475.12247, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 23}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11852739895091337, \"count\": 1, \"min\": 0.11852739895091337, \"max\": 0.11852739895091337}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.122525, \"EndTime\": 1679433475.122542, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 24}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.552030610926011, \"count\": 1, \"min\": 0.552030610926011, \"max\": 0.552030610926011}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.122596, \"EndTime\": 1679433475.1226127, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 25}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5520127204446231, \"count\": 1, \"min\": 0.5520127204446231, \"max\": 0.5520127204446231}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.1226711, \"EndTime\": 1679433475.1226878, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 26}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5523892427332261, \"count\": 1, \"min\": 0.5523892427332261, \"max\": 0.5523892427332261}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.122741, \"EndTime\": 1679433475.1227572, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 27}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5520884291704963, \"count\": 1, \"min\": 0.5520884291704963, \"max\": 0.5520884291704963}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.1228108, \"EndTime\": 1679433475.122828, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 28}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5557189115636489, \"count\": 1, \"min\": 0.5557189115636489, \"max\": 0.5557189115636489}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.122891, \"EndTime\": 1679433475.122909, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 29}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5536672399184283, \"count\": 1, \"min\": 0.5536672399184283, \"max\": 0.5536672399184283}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.1229713, \"EndTime\": 1679433475.1229897, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 30}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.553019276338465, \"count\": 1, \"min\": 0.553019276338465, \"max\": 0.553019276338465}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.1230485, \"EndTime\": 1679433475.1230662, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 31}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.554829159007353, \"count\": 1, \"min\": 0.554829159007353, \"max\": 0.554829159007353}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:55 INFO 139712331487040] #quality_metric: host=algo-1, epoch=7, train absolute_loss_objective =0.05244462720085593\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.2595668, \"EndTime\": 1679433475.259664, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 0}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.1742788818359375, \"count\": 1, \"min\": 1.1742788818359375, \"max\": 1.1742788818359375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.2597923, \"EndTime\": 1679433475.259816, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 1}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.6019083740234376, \"count\": 1, \"min\": 0.6019083740234376, \"max\": 0.6019083740234376}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.259886, \"EndTime\": 1679433475.2599068, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 2}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.6089069396972656, \"count\": 1, \"min\": 0.6089069396972656, \"max\": 0.6089069396972656}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.259978, \"EndTime\": 1679433475.2599995, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 3}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5719580749511719, \"count\": 1, \"min\": 0.5719580749511719, \"max\": 0.5719580749511719}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.2600555, \"EndTime\": 1679433475.2600722, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 4}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.22611181640625, \"count\": 1, \"min\": 1.22611181640625, \"max\": 1.22611181640625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.2601202, \"EndTime\": 1679433475.2601361, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 5}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.319104296875, \"count\": 1, \"min\": 1.319104296875, \"max\": 1.319104296875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.2601862, \"EndTime\": 1679433475.2602026, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 6}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.9334408813476562, \"count\": 1, \"min\": 0.9334408813476562, \"max\": 0.9334408813476562}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.2602572, \"EndTime\": 1679433475.2602742, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 7}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.27518994140625, \"count\": 1, \"min\": 0.27518994140625, \"max\": 0.27518994140625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.260326, \"EndTime\": 1679433475.2603428, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 8}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.6310225463867187, \"count\": 1, \"min\": 0.6310225463867187, \"max\": 0.6310225463867187}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.260396, \"EndTime\": 1679433475.2604132, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 9}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.6083741149902344, \"count\": 1, \"min\": 0.6083741149902344, \"max\": 0.6083741149902344}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.2604704, \"EndTime\": 1679433475.2604866, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 10}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5875474182128906, \"count\": 1, \"min\": 0.5875474182128906, \"max\": 0.5875474182128906}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.260541, \"EndTime\": 1679433475.2605581, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 11}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.6031041809082032, \"count\": 1, \"min\": 0.6031041809082032, \"max\": 0.6031041809082032}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.2606118, \"EndTime\": 1679433475.2606294, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 12}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.7187999877929687, \"count\": 1, \"min\": 0.7187999877929687, \"max\": 0.7187999877929687}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.2606897, \"EndTime\": 1679433475.2607076, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 13}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.544545458984375, \"count\": 1, \"min\": 1.544545458984375, \"max\": 1.544545458984375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.2607737, \"EndTime\": 1679433475.260793, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 14}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.8504294067382813, \"count\": 1, \"min\": 0.8504294067382813, \"max\": 0.8504294067382813}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.2608614, \"EndTime\": 1679433475.2608805, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 15}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.4814784362792969, \"count\": 1, \"min\": 0.4814784362792969, \"max\": 0.4814784362792969}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.260949, \"EndTime\": 1679433475.2609673, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 16}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.3407555908203126, \"count\": 1, \"min\": 1.3407555908203126, \"max\": 1.3407555908203126}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.2610343, \"EndTime\": 1679433475.2610533, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 17}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.1939520751953125, \"count\": 1, \"min\": 1.1939520751953125, \"max\": 1.1939520751953125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.261119, \"EndTime\": 1679433475.2611392, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 18}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.1820397216796874, \"count\": 1, \"min\": 1.1820397216796874, \"max\": 1.1820397216796874}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.2612066, \"EndTime\": 1679433475.2612264, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 19}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.1658316162109374, \"count\": 1, \"min\": 1.1658316162109374, \"max\": 1.1658316162109374}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.2612944, \"EndTime\": 1679433475.2613134, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 20}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.3500169677734375, \"count\": 1, \"min\": 1.3500169677734375, \"max\": 1.3500169677734375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.2613811, \"EndTime\": 1679433475.261401, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 21}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.406856640625, \"count\": 1, \"min\": 1.406856640625, \"max\": 1.406856640625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.2614675, \"EndTime\": 1679433475.261487, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 22}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.4081262451171874, \"count\": 1, \"min\": 1.4081262451171874, \"max\": 1.4081262451171874}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.2615595, \"EndTime\": 1679433475.2615783, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 23}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.3111925537109375, \"count\": 1, \"min\": 1.3111925537109375, \"max\": 1.3111925537109375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.2616427, \"EndTime\": 1679433475.2616596, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 24}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.56329140625, \"count\": 1, \"min\": 5.56329140625, \"max\": 5.56329140625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.261725, \"EndTime\": 1679433475.2617433, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 25}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.56184697265625, \"count\": 1, \"min\": 5.56184697265625, \"max\": 5.56184697265625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.2618082, \"EndTime\": 1679433475.261826, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 26}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5586873046875, \"count\": 1, \"min\": 5.5586873046875, \"max\": 5.5586873046875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.261891, \"EndTime\": 1679433475.26191, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 27}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.56232978515625, \"count\": 1, \"min\": 5.56232978515625, \"max\": 5.56232978515625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.2620692, \"EndTime\": 1679433475.2620933, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 28}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.556776171875, \"count\": 1, \"min\": 5.556776171875, \"max\": 5.556776171875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.2621944, \"EndTime\": 1679433475.262215, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 29}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.54275107421875, \"count\": 1, \"min\": 5.54275107421875, \"max\": 5.54275107421875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.2622833, \"EndTime\": 1679433475.2623024, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 30}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5689416015625, \"count\": 1, \"min\": 5.5689416015625, \"max\": 5.5689416015625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.2623672, \"EndTime\": 1679433475.2623868, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 31}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5522265625, \"count\": 1, \"min\": 5.5522265625, \"max\": 5.5522265625}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:55 INFO 139712331487040] #quality_metric: host=algo-1, epoch=7, validation absolute_loss_objective =1.1742788818359375\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:55 INFO 139712331487040] #early_stopping_criteria_metric: host=algo-1, epoch=7, criteria=absolute_loss_objective, value=0.27518994140625\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:55 INFO 139712331487040] Epoch 7: Loss improved. Updating best model\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:55 INFO 139712331487040] Saving model for epoch: 7\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:55 INFO 139712331487040] Saved checkpoint to \"/tmp/tmpuquh56w8/mx-mod-0000.params\"\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:55 INFO 139712331487040] #progress_metric: host=algo-1, completed 50.0 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433474.5392299, \"EndTime\": 1679433475.2800608, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 151984.0, \"count\": 1, \"min\": 151984, \"max\": 151984}, \"Total Batches Seen\": {\"sum\": 156.0, \"count\": 1, \"min\": 156, \"max\": 156}, \"Max Records Seen Between Resets\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Max Batches Seen Between Resets\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}, \"Reset Count\": {\"sum\": 10.0, \"count\": 1, \"min\": 10, \"max\": 10}, \"Number of Records Since Last Reset\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Number of Batches Since Last Reset\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:55 INFO 139712331487040] #throughput_metric: host=algo-1, train throughput=23614.120670483095 records/second\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.830546, \"EndTime\": 1679433475.8306358, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 0}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.0778980868844425, \"count\": 1, \"min\": 0.0778980868844425, \"max\": 0.0778980868844425}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.8314078, \"EndTime\": 1679433475.831437, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 1}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05773958228616154, \"count\": 1, \"min\": 0.05773958228616154, \"max\": 0.05773958228616154}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.831551, \"EndTime\": 1679433475.8315759, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 2}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.056983874377082376, \"count\": 1, \"min\": 0.056983874377082376, \"max\": 0.056983874377082376}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.83167, \"EndTime\": 1679433475.831692, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 3}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.054912474688361676, \"count\": 1, \"min\": 0.054912474688361676, \"max\": 0.054912474688361676}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.831875, \"EndTime\": 1679433475.8319001, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 4}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.06933329907585593, \"count\": 1, \"min\": 0.06933329907585593, \"max\": 0.06933329907585593}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.8320305, \"EndTime\": 1679433475.832052, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 5}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.06719229900135713, \"count\": 1, \"min\": 0.06719229900135713, \"max\": 0.06719229900135713}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.8321507, \"EndTime\": 1679433475.8321779, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 6}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.0784676545087029, \"count\": 1, \"min\": 0.0784676545087029, \"max\": 0.0784676545087029}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.832308, \"EndTime\": 1679433475.8323293, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 7}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.1006455317104564, \"count\": 1, \"min\": 0.1006455317104564, \"max\": 0.1006455317104564}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.8324184, \"EndTime\": 1679433475.832439, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 8}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.059590779921587775, \"count\": 1, \"min\": 0.059590779921587775, \"max\": 0.059590779921587775}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.8325, \"EndTime\": 1679433475.8325176, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 9}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05733843612670898, \"count\": 1, \"min\": 0.05733843612670898, \"max\": 0.05733843612670898}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.8326256, \"EndTime\": 1679433475.8326457, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 10}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05662922511381262, \"count\": 1, \"min\": 0.05662922511381262, \"max\": 0.05662922511381262}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.8327584, \"EndTime\": 1679433475.8327806, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 11}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05812517525168026, \"count\": 1, \"min\": 0.05812517525168026, \"max\": 0.05812517525168026}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.8328502, \"EndTime\": 1679433475.8328688, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 12}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.09147249670589672, \"count\": 1, \"min\": 0.09147249670589672, \"max\": 0.09147249670589672}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.832957, \"EndTime\": 1679433475.8329759, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 13}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12112009878719554, \"count\": 1, \"min\": 0.12112009878719554, \"max\": 0.12112009878719554}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 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{\"train_absolute_loss_objective\": {\"sum\": 0.12591959470861097, \"count\": 1, \"min\": 0.12591959470861097, \"max\": 0.12591959470861097}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.8332849, \"EndTime\": 1679433475.8333013, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 17}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11367076559627758, \"count\": 1, \"min\": 0.11367076559627758, \"max\": 0.11367076559627758}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.8333597, \"EndTime\": 1679433475.8333771, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 18}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.1133488441916073, \"count\": 1, \"min\": 0.1133488441916073, \"max\": 0.1133488441916073}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.8334339, \"EndTime\": 1679433475.83345, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 19}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11167841698141659, \"count\": 1, \"min\": 0.11167841698141659, \"max\": 0.11167841698141659}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.8335092, \"EndTime\": 1679433475.8335264, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 20}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12173151666977826, \"count\": 1, \"min\": 0.12173151666977826, \"max\": 0.12173151666977826}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.833579, \"EndTime\": 1679433475.833595, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 21}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.13473281905230353, \"count\": 1, \"min\": 0.13473281905230353, \"max\": 0.13473281905230353}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.8336453, \"EndTime\": 1679433475.8336616, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 22}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12033016653621897, \"count\": 1, \"min\": 0.12033016653621897, \"max\": 0.12033016653621897}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.833713, \"EndTime\": 1679433475.8337233, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 23}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12031897062413832, \"count\": 1, \"min\": 0.12031897062413832, \"max\": 0.12031897062413832}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.8337524, \"EndTime\": 1679433475.83376, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 24}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5522938304227941, \"count\": 1, \"min\": 0.5522938304227941, \"max\": 0.5522938304227941}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.8338094, \"EndTime\": 1679433475.8338253, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 25}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5521291647518383, \"count\": 1, \"min\": 0.5521291647518383, \"max\": 0.5521291647518383}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.8338852, \"EndTime\": 1679433475.8339028, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 26}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5521245871151195, \"count\": 1, \"min\": 0.5521245871151195, \"max\": 0.5521245871151195}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.833956, \"EndTime\": 1679433475.8339725, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 27}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5522310898724724, \"count\": 1, \"min\": 0.5522310898724724, \"max\": 0.5522310898724724}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.8340516, \"EndTime\": 1679433475.834071, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 28}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5581186774758732, \"count\": 1, \"min\": 0.5581186774758732, \"max\": 0.5581186774758732}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.8341317, \"EndTime\": 1679433475.8341486, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 29}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5546454324161305, \"count\": 1, \"min\": 0.5546454324161305, \"max\": 0.5546454324161305}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.8342004, \"EndTime\": 1679433475.8342159, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 30}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5541976713292739, \"count\": 1, \"min\": 0.5541976713292739, \"max\": 0.5541976713292739}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.8342662, \"EndTime\": 1679433475.8342788, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 31}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.558590181238511, \"count\": 1, \"min\": 0.558590181238511, \"max\": 0.558590181238511}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:55 INFO 139712331487040] #quality_metric: host=algo-1, epoch=8, train absolute_loss_objective =0.0778980868844425\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.9680603, \"EndTime\": 1679433475.9681525, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 0}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.22794169921875, \"count\": 1, \"min\": 1.22794169921875, \"max\": 1.22794169921875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.968276, \"EndTime\": 1679433475.968299, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 1}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5841018127441406, \"count\": 1, \"min\": 0.5841018127441406, \"max\": 0.5841018127441406}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.9683702, \"EndTime\": 1679433475.96839, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 2}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5744908325195313, \"count\": 1, \"min\": 0.5744908325195313, \"max\": 0.5744908325195313}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.9684522, \"EndTime\": 1679433475.9684703, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 3}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5547177612304688, \"count\": 1, \"min\": 0.5547177612304688, \"max\": 0.5547177612304688}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.968526, \"EndTime\": 1679433475.9685419, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 4}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.53761962890625, \"count\": 1, \"min\": 0.53761962890625, \"max\": 0.53761962890625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.9685981, \"EndTime\": 1679433475.9686148, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 5}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.34931126098632814, \"count\": 1, \"min\": 0.34931126098632814, \"max\": 0.34931126098632814}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.968669, \"EndTime\": 1679433475.9686852, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 6}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.2893835144042969, \"count\": 1, \"min\": 0.2893835144042969, \"max\": 0.2893835144042969}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.9687378, \"EndTime\": 1679433475.9687545, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 7}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.65775869140625, \"count\": 1, \"min\": 0.65775869140625, \"max\": 0.65775869140625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.9688075, \"EndTime\": 1679433475.9688246, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 8}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.6035365356445312, \"count\": 1, \"min\": 0.6035365356445312, \"max\": 0.6035365356445312}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.9688792, \"EndTime\": 1679433475.9688969, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 9}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5759310852050781, \"count\": 1, \"min\": 0.5759310852050781, \"max\": 0.5759310852050781}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.9689548, \"EndTime\": 1679433475.9689717, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 10}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5764394653320313, \"count\": 1, \"min\": 0.5764394653320313, \"max\": 0.5764394653320313}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.9690473, \"EndTime\": 1679433475.9690661, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 11}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5867299072265625, \"count\": 1, \"min\": 0.5867299072265625, \"max\": 0.5867299072265625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.969122, \"EndTime\": 1679433475.969139, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 12}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.35021318359375, \"count\": 1, \"min\": 1.35021318359375, \"max\": 1.35021318359375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.9692068, \"EndTime\": 1679433475.9692185, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 13}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.658154443359375, \"count\": 1, \"min\": 0.658154443359375, \"max\": 0.658154443359375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.9692547, \"EndTime\": 1679433475.9692633, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 14}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5742504760742188, \"count\": 1, \"min\": 0.5742504760742188, \"max\": 0.5742504760742188}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.9692926, \"EndTime\": 1679433475.9693003, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 15}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.367119873046875, \"count\": 1, \"min\": 0.367119873046875, \"max\": 0.367119873046875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.969343, \"EndTime\": 1679433475.9693553, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 16}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.2453509033203125, \"count\": 1, \"min\": 1.2453509033203125, \"max\": 1.2453509033203125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.9693866, \"EndTime\": 1679433475.9693944, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 17}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.152056005859375, \"count\": 1, \"min\": 1.152056005859375, \"max\": 1.152056005859375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.969422, \"EndTime\": 1679433475.9694297, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 18}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.151147216796875, \"count\": 1, \"min\": 1.151147216796875, \"max\": 1.151147216796875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.969454, \"EndTime\": 1679433475.9694612, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 19}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.1461684814453126, \"count\": 1, \"min\": 1.1461684814453126, \"max\": 1.1461684814453126}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.9694877, \"EndTime\": 1679433475.969501, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 20}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.380105712890625, \"count\": 1, \"min\": 1.380105712890625, \"max\": 1.380105712890625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.969542, \"EndTime\": 1679433475.9695516, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 21}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.3593185791015625, \"count\": 1, \"min\": 1.3593185791015625, \"max\": 1.3593185791015625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.9695766, \"EndTime\": 1679433475.9695842, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 22}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.35295791015625, \"count\": 1, \"min\": 1.35295791015625, \"max\": 1.35295791015625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.9696078, \"EndTime\": 1679433475.9696152, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 23}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.31653125, \"count\": 1, \"min\": 1.31653125, \"max\": 1.31653125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.9696422, \"EndTime\": 1679433475.9696496, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 24}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.55647294921875, \"count\": 1, \"min\": 5.55647294921875, \"max\": 5.55647294921875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.9696822, \"EndTime\": 1679433475.969696, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 25}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.56944697265625, \"count\": 1, \"min\": 5.56944697265625, \"max\": 5.56944697265625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.9697301, \"EndTime\": 1679433475.9697385, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 26}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.56787900390625, \"count\": 1, \"min\": 5.56787900390625, \"max\": 5.56787900390625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.9697626, \"EndTime\": 1679433475.9697697, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 27}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.56829287109375, \"count\": 1, \"min\": 5.56829287109375, \"max\": 5.56829287109375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.9697933, \"EndTime\": 1679433475.9698002, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 28}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.55694521484375, \"count\": 1, \"min\": 5.55694521484375, \"max\": 5.55694521484375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.969824, \"EndTime\": 1679433475.969831, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 29}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5616708984375, \"count\": 1, \"min\": 5.5616708984375, \"max\": 5.5616708984375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.969866, \"EndTime\": 1679433475.969881, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 30}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5637212890625, \"count\": 1, \"min\": 5.5637212890625, \"max\": 5.5637212890625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.969915, \"EndTime\": 1679433475.9699235, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 31}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5558103515625, \"count\": 1, \"min\": 5.5558103515625, \"max\": 5.5558103515625}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:55 INFO 139712331487040] #quality_metric: host=algo-1, epoch=8, validation absolute_loss_objective =1.22794169921875\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:55 INFO 139712331487040] #early_stopping_criteria_metric: host=algo-1, epoch=8, criteria=absolute_loss_objective, value=0.2893835144042969\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:55 INFO 139712331487040] Saving model for epoch: 8\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:55 INFO 139712331487040] Saved checkpoint to \"/tmp/tmpvrdbqaj3/mx-mod-0000.params\"\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:55 INFO 139712331487040] #progress_metric: host=algo-1, completed 56.25 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.2804103, \"EndTime\": 1679433475.9781876, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 169482.0, \"count\": 1, \"min\": 169482, \"max\": 169482}, \"Total Batches Seen\": {\"sum\": 174.0, \"count\": 1, \"min\": 174, \"max\": 174}, \"Max Records Seen Between Resets\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Max Batches Seen Between Resets\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}, \"Reset Count\": {\"sum\": 11.0, \"count\": 1, \"min\": 11, \"max\": 11}, \"Number of Records Since Last Reset\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Number of Batches Since Last Reset\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:55 INFO 139712331487040] #throughput_metric: host=algo-1, train throughput=25071.175940776327 records/second\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.5639079, \"EndTime\": 1679433476.564012, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 0}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.09703755098230699, \"count\": 1, \"min\": 0.09703755098230699, \"max\": 0.09703755098230699}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.5641382, \"EndTime\": 1679433476.56416, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 1}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05620091718785903, \"count\": 1, \"min\": 0.05620091718785903, \"max\": 0.05620091718785903}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.5642242, \"EndTime\": 1679433476.5642416, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 2}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05514826471665326, \"count\": 1, \"min\": 0.05514826471665326, \"max\": 0.05514826471665326}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.5642927, \"EndTime\": 1679433476.5643077, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 3}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.053623405680936924, \"count\": 1, \"min\": 0.053623405680936924, \"max\": 0.053623405680936924}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.5643597, \"EndTime\": 1679433476.5643759, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 4}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.06637732718972598, \"count\": 1, \"min\": 0.06637732718972598, \"max\": 0.06637732718972598}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.5644217, \"EndTime\": 1679433476.5644364, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 5}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.06340519523620605, \"count\": 1, \"min\": 0.06340519523620605, \"max\": 0.06340519523620605}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.564517, \"EndTime\": 1679433476.5645363, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 6}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.039853305928847366, \"count\": 1, \"min\": 0.039853305928847366, \"max\": 0.039853305928847366}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.5645936, \"EndTime\": 1679433476.564609, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 7}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.056144248850205365, \"count\": 1, \"min\": 0.056144248850205365, \"max\": 0.056144248850205365}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.5646636, \"EndTime\": 1679433476.56468, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 8}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05771536703670726, \"count\": 1, \"min\": 0.05771536703670726, \"max\": 0.05771536703670726}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.5647347, \"EndTime\": 1679433476.5647528, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 9}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.055258449330049406, \"count\": 1, \"min\": 0.055258449330049406, \"max\": 0.055258449330049406}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.5648081, \"EndTime\": 1679433476.5648246, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 10}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05528055594949161, \"count\": 1, \"min\": 0.05528055594949161, \"max\": 0.05528055594949161}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.564884, \"EndTime\": 1679433476.564901, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 11}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05654295169605928, \"count\": 1, \"min\": 0.05654295169605928, \"max\": 0.05654295169605928}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.5649529, \"EndTime\": 1679433476.5649698, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 12}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.08272802330465878, \"count\": 1, \"min\": 0.08272802330465878, \"max\": 0.08272802330465878}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.5650218, \"EndTime\": 1679433476.565039, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 13}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.06754204121757956, \"count\": 1, \"min\": 0.06754204121757956, \"max\": 0.06754204121757956}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.5651238, \"EndTime\": 1679433476.5651417, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 14}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.10189239389756147, \"count\": 1, \"min\": 0.10189239389756147, \"max\": 0.10189239389756147}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.565197, \"EndTime\": 1679433476.5652142, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 15}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.09292489635243135, \"count\": 1, \"min\": 0.09292489635243135, \"max\": 0.09292489635243135}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.5652697, \"EndTime\": 1679433476.5652866, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 16}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11798659560259651, \"count\": 1, \"min\": 0.11798659560259651, \"max\": 0.11798659560259651}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.5653453, \"EndTime\": 1679433476.565363, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 17}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11109268996294806, \"count\": 1, \"min\": 0.11109268996294806, \"max\": 0.11109268996294806}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.5654202, \"EndTime\": 1679433476.5654366, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 18}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.1111948174869313, \"count\": 1, \"min\": 0.1111948174869313, \"max\": 0.1111948174869313}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.5654907, \"EndTime\": 1679433476.5655077, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 19}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11067461888930377, \"count\": 1, \"min\": 0.11067461888930377, \"max\": 0.11067461888930377}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.5655663, \"EndTime\": 1679433476.5655837, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 20}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12029664297664867, \"count\": 1, \"min\": 0.12029664297664867, \"max\": 0.12029664297664867}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.5656416, \"EndTime\": 1679433476.5656576, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 21}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12690076760684743, \"count\": 1, \"min\": 0.12690076760684743, \"max\": 0.12690076760684743}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.565712, \"EndTime\": 1679433476.5657282, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 22}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11980983464858111, \"count\": 1, \"min\": 0.11980983464858111, \"max\": 0.11980983464858111}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.5657825, \"EndTime\": 1679433476.5658, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 23}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.1242515936458812, \"count\": 1, \"min\": 0.1242515936458812, \"max\": 0.1242515936458812}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.5658567, \"EndTime\": 1679433476.5658734, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 24}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5521133602366728, \"count\": 1, \"min\": 0.5521133602366728, \"max\": 0.5521133602366728}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.5659266, \"EndTime\": 1679433476.5659437, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 25}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5524917782054228, \"count\": 1, \"min\": 0.5524917782054228, \"max\": 0.5524917782054228}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.5659974, \"EndTime\": 1679433476.566051, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 26}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5523767592486213, \"count\": 1, \"min\": 0.5523767592486213, \"max\": 0.5523767592486213}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.5661154, \"EndTime\": 1679433476.5661337, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 27}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5523973460477941, \"count\": 1, \"min\": 0.5523973460477941, \"max\": 0.5523973460477941}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.566188, \"EndTime\": 1679433476.5662043, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 28}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5586984719669118, \"count\": 1, \"min\": 0.5586984719669118, \"max\": 0.5586984719669118}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.5662584, \"EndTime\": 1679433476.5662751, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 29}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.558432164809283, \"count\": 1, \"min\": 0.558432164809283, \"max\": 0.558432164809283}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.5663872, \"EndTime\": 1679433476.5664077, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 30}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.555741501752068, \"count\": 1, \"min\": 0.555741501752068, \"max\": 0.555741501752068}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.5664644, \"EndTime\": 1679433476.566483, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 31}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5563543593462775, \"count\": 1, \"min\": 0.5563543593462775, \"max\": 0.5563543593462775}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:56 INFO 139712331487040] #quality_metric: host=algo-1, epoch=9, train absolute_loss_objective =0.09703755098230699\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.7183623, \"EndTime\": 1679433476.718444, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 0}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.3209183837890626, \"count\": 1, \"min\": 1.3209183837890626, \"max\": 1.3209183837890626}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.718533, \"EndTime\": 1679433476.718554, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 1}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5723020812988281, \"count\": 1, \"min\": 0.5723020812988281, \"max\": 0.5723020812988281}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.7186682, \"EndTime\": 1679433476.7186897, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 2}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5596476318359375, \"count\": 1, \"min\": 0.5596476318359375, \"max\": 0.5596476318359375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.7188034, \"EndTime\": 1679433476.7188244, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 3}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5421558349609376, \"count\": 1, \"min\": 0.5421558349609376, \"max\": 0.5421558349609376}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.7188988, \"EndTime\": 1679433476.7189176, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 4}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.84078876953125, \"count\": 1, \"min\": 0.84078876953125, \"max\": 0.84078876953125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.7190387, \"EndTime\": 1679433476.719062, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 5}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.7200741333007813, \"count\": 1, \"min\": 0.7200741333007813, \"max\": 0.7200741333007813}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.7191293, \"EndTime\": 1679433476.719149, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 6}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.6351157592773438, \"count\": 1, \"min\": 0.6351157592773438, \"max\": 0.6351157592773438}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.7191994, \"EndTime\": 1679433476.719217, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 7}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.6015498168945312, \"count\": 1, \"min\": 0.6015498168945312, \"max\": 0.6015498168945312}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.7192657, \"EndTime\": 1679433476.7192824, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 8}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5872384094238281, \"count\": 1, \"min\": 0.5872384094238281, \"max\": 0.5872384094238281}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.7193372, \"EndTime\": 1679433476.7193534, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 9}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5586853637695313, \"count\": 1, \"min\": 0.5586853637695313, \"max\": 0.5586853637695313}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.719413, \"EndTime\": 1679433476.719431, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 10}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5634891662597656, \"count\": 1, \"min\": 0.5634891662597656, \"max\": 0.5634891662597656}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.719485, \"EndTime\": 1679433476.7195032, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 11}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5744001953125, \"count\": 1, \"min\": 0.5744001953125, \"max\": 0.5744001953125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.7196167, \"EndTime\": 1679433476.719635, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 12}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.04571845703125, \"count\": 1, \"min\": 1.04571845703125, \"max\": 1.04571845703125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.719744, \"EndTime\": 1679433476.719767, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 13}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.34399578857421875, \"count\": 1, \"min\": 0.34399578857421875, \"max\": 0.34399578857421875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.7198255, \"EndTime\": 1679433476.7198431, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 14}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5351367492675781, \"count\": 1, \"min\": 0.5351367492675781, \"max\": 0.5351367492675781}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.719894, \"EndTime\": 1679433476.7199109, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 15}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.7194220947265626, \"count\": 1, \"min\": 1.7194220947265626, \"max\": 1.7194220947265626}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.720029, \"EndTime\": 1679433476.720046, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 16}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.184464111328125, \"count\": 1, \"min\": 1.184464111328125, \"max\": 1.184464111328125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.720104, \"EndTime\": 1679433476.7201219, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 17}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.1411445068359376, \"count\": 1, \"min\": 1.1411445068359376, \"max\": 1.1411445068359376}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.7202342, \"EndTime\": 1679433476.720255, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 18}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.14507353515625, \"count\": 1, \"min\": 1.14507353515625, \"max\": 1.14507353515625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.7203095, \"EndTime\": 1679433476.7203274, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 19}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.1451085205078124, \"count\": 1, \"min\": 1.1451085205078124, \"max\": 1.1451085205078124}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.7204092, \"EndTime\": 1679433476.720428, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 20}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.3465355712890625, \"count\": 1, \"min\": 1.3465355712890625, \"max\": 1.3465355712890625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.720476, \"EndTime\": 1679433476.7204928, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 21}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.332797705078125, \"count\": 1, \"min\": 1.332797705078125, \"max\": 1.332797705078125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.7205453, \"EndTime\": 1679433476.7205625, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 22}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.3311227294921875, \"count\": 1, \"min\": 1.3311227294921875, \"max\": 1.3311227294921875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.7206147, \"EndTime\": 1679433476.7206318, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 23}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.228161083984375, \"count\": 1, \"min\": 1.228161083984375, \"max\": 1.228161083984375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.7206774, \"EndTime\": 1679433476.720694, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 24}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.56203642578125, \"count\": 1, \"min\": 5.56203642578125, \"max\": 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\"epoch\": 9, \"model\": 27}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.56252626953125, \"count\": 1, \"min\": 5.56252626953125, \"max\": 5.56252626953125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.7209513, \"EndTime\": 1679433476.7209685, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 28}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.53883779296875, \"count\": 1, \"min\": 5.53883779296875, \"max\": 5.53883779296875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.721013, \"EndTime\": 1679433476.7210295, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 29}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5700103515625, \"count\": 1, \"min\": 5.5700103515625, \"max\": 5.5700103515625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.7210739, \"EndTime\": 1679433476.7211807, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 30}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.53215849609375, \"count\": 1, \"min\": 5.53215849609375, \"max\": 5.53215849609375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.7212896, \"EndTime\": 1679433476.7213094, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 31}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.54479228515625, \"count\": 1, \"min\": 5.54479228515625, \"max\": 5.54479228515625}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:56 INFO 139712331487040] #quality_metric: host=algo-1, epoch=9, validation absolute_loss_objective =1.3209183837890626\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:56 INFO 139712331487040] #early_stopping_criteria_metric: host=algo-1, epoch=9, criteria=absolute_loss_objective, value=0.34399578857421875\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:56 INFO 139712331487040] Saving model for epoch: 9\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:56 INFO 139712331487040] Saved checkpoint to \"/tmp/tmp7_dmh8w7/mx-mod-0000.params\"\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:56 INFO 139712331487040] #progress_metric: host=algo-1, completed 62.5 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433475.9784963, \"EndTime\": 1679433476.7304668, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 186980.0, \"count\": 1, \"min\": 186980, \"max\": 186980}, \"Total Batches Seen\": {\"sum\": 192.0, \"count\": 1, \"min\": 192, \"max\": 192}, \"Max Records Seen Between Resets\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Max Batches Seen Between Resets\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}, \"Reset Count\": {\"sum\": 12.0, \"count\": 1, \"min\": 12, \"max\": 12}, \"Number of Records Since Last Reset\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Number of Batches Since Last Reset\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:56 INFO 139712331487040] #throughput_metric: host=algo-1, train throughput=23264.6237379773 records/second\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.2449646, \"EndTime\": 1679433477.2450378, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 0}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11476628696217256, \"count\": 1, \"min\": 0.11476628696217256, \"max\": 0.11476628696217256}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.245116, \"EndTime\": 1679433477.2451296, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 1}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05493302782844095, \"count\": 1, \"min\": 0.05493302782844095, \"max\": 0.05493302782844095}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.2451673, \"EndTime\": 1679433477.2451818, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 2}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.053848250893985525, \"count\": 1, \"min\": 0.053848250893985525, \"max\": 0.053848250893985525}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.2452395, \"EndTime\": 1679433477.245256, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 3}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05207538200827206, \"count\": 1, 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\"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 6}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.07540523989060346, \"count\": 1, \"min\": 0.07540523989060346, \"max\": 0.07540523989060346}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.245517, \"EndTime\": 1679433477.245533, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 7}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.06732481552572811, \"count\": 1, \"min\": 0.06732481552572811, \"max\": 0.06732481552572811}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.2455888, \"EndTime\": 1679433477.2456055, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 8}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05643168729894302, \"count\": 1, \"min\": 0.05643168729894302, \"max\": 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\"training\", \"epoch\": 10, \"model\": 11}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05520055142570945, \"count\": 1, \"min\": 0.05520055142570945, \"max\": 0.05520055142570945}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.245823, \"EndTime\": 1679433477.245838, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 12}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.08346623409495635, \"count\": 1, \"min\": 0.08346623409495635, \"max\": 0.08346623409495635}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.2458956, \"EndTime\": 1679433477.2459116, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 13}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.08403359222412109, \"count\": 1, \"min\": 0.08403359222412109, \"max\": 0.08403359222412109}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.2459676, \"EndTime\": 1679433477.2459838, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 14}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.10143624563778147, \"count\": 1, \"min\": 0.10143624563778147, \"max\": 0.10143624563778147}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.2460725, \"EndTime\": 1679433477.2460892, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 15}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12156768664191751, \"count\": 1, \"min\": 0.12156768664191751, \"max\": 0.12156768664191751}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.2461462, \"EndTime\": 1679433477.2461622, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 16}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11369152248606962, \"count\": 1, \"min\": 0.11369152248606962, \"max\": 0.11369152248606962}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.2462811, \"EndTime\": 1679433477.2463012, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 17}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11051678870705997, \"count\": 1, \"min\": 0.11051678870705997, \"max\": 0.11051678870705997}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.2463677, \"EndTime\": 1679433477.2463841, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 18}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11109020278033088, \"count\": 1, \"min\": 0.11109020278033088, \"max\": 0.11109020278033088}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.246441, \"EndTime\": 1679433477.246457, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 19}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.1111934253468233, \"count\": 1, \"min\": 0.1111934253468233, \"max\": 0.1111934253468233}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.2465103, \"EndTime\": 1679433477.2465255, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 20}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12278490492876838, \"count\": 1, \"min\": 0.12278490492876838, \"max\": 0.12278490492876838}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.246568, \"EndTime\": 1679433477.2465832, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 21}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.13081373237161076, \"count\": 1, \"min\": 0.13081373237161076, \"max\": 0.13081373237161076}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.246677, \"EndTime\": 1679433477.2466967, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 22}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12402565316592946, \"count\": 1, \"min\": 0.12402565316592946, \"max\": 0.12402565316592946}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.2467613, \"EndTime\": 1679433477.2467794, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 23}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12542329855526194, \"count\": 1, \"min\": 0.12542329855526194, \"max\": 0.12542329855526194}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.2468283, \"EndTime\": 1679433477.246845, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 24}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5522541719324449, \"count\": 1, \"min\": 0.5522541719324449, \"max\": 0.5522541719324449}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.2468882, \"EndTime\": 1679433477.2469032, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 25}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.552035784553079, \"count\": 1, \"min\": 0.552035784553079, \"max\": 0.552035784553079}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.2469614, \"EndTime\": 1679433477.2469718, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 26}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5521138772403492, \"count\": 1, \"min\": 0.5521138772403492, \"max\": 0.5521138772403492}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.2470164, \"EndTime\": 1679433477.2470312, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 27}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5523199965533089, \"count\": 1, \"min\": 0.5523199965533089, \"max\": 0.5523199965533089}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.2470865, \"EndTime\": 1679433477.2471032, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 28}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5552543514476103, \"count\": 1, \"min\": 0.5552543514476103, \"max\": 0.5552543514476103}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.2471967, \"EndTime\": 1679433477.2472105, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 29}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5600661118451287, \"count\": 1, \"min\": 0.5600661118451287, \"max\": 0.5600661118451287}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.2472603, \"EndTime\": 1679433477.2472763, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 30}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5529642369887408, \"count\": 1, \"min\": 0.5529642369887408, \"max\": 0.5529642369887408}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.2473319, \"EndTime\": 1679433477.2473476, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 31}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5557004322725184, \"count\": 1, \"min\": 0.5557004322725184, \"max\": 0.5557004322725184}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:57 INFO 139712331487040] #quality_metric: host=algo-1, epoch=10, train absolute_loss_objective =0.11476628696217256\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.3659327, \"EndTime\": 1679433477.365989, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 0}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.038270068359375, \"count\": 1, \"min\": 1.038270068359375, \"max\": 1.038270068359375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.3661084, \"EndTime\": 1679433477.3661232, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 1}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.554715625, \"count\": 1, \"min\": 0.554715625, \"max\": 0.554715625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.3661666, \"EndTime\": 1679433477.366181, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 2}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.548865478515625, \"count\": 1, \"min\": 0.548865478515625, \"max\": 0.548865478515625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.3662376, \"EndTime\": 1679433477.3662527, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 3}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5248456787109375, \"count\": 1, \"min\": 0.5248456787109375, \"max\": 0.5248456787109375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.3663068, \"EndTime\": 1679433477.3663208, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 4}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5936552856445313, \"count\": 1, \"min\": 0.5936552856445313, \"max\": 0.5936552856445313}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.366375, \"EndTime\": 1679433477.3663895, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 5}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.7601745483398438, \"count\": 1, \"min\": 0.7601745483398438, \"max\": 0.7601745483398438}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.3664467, \"EndTime\": 1679433477.366463, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 6}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.855465380859375, \"count\": 1, \"min\": 1.855465380859375, \"max\": 1.855465380859375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.3665125, \"EndTime\": 1679433477.3665287, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 7}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.0173250122070312, \"count\": 1, \"min\": 1.0173250122070312, \"max\": 1.0173250122070312}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.3665805, \"EndTime\": 1679433477.366596, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 8}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.576002587890625, \"count\": 1, \"min\": 0.576002587890625, \"max\": 0.576002587890625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.366662, \"EndTime\": 1679433477.3666792, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 9}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.54796025390625, \"count\": 1, \"min\": 0.54796025390625, \"max\": 0.54796025390625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.3667338, \"EndTime\": 1679433477.36675, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 10}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5500864501953125, \"count\": 1, \"min\": 0.5500864501953125, \"max\": 0.5500864501953125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.366805, \"EndTime\": 1679433477.366821, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 11}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5587049865722656, \"count\": 1, \"min\": 0.5587049865722656, \"max\": 0.5587049865722656}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.3668766, \"EndTime\": 1679433477.3668923, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 12}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.6713232299804688, \"count\": 1, \"min\": 0.6713232299804688, \"max\": 0.6713232299804688}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.3669508, \"EndTime\": 1679433477.3669667, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 13}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.9102767578125, \"count\": 1, \"min\": 0.9102767578125, \"max\": 0.9102767578125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.3670206, \"EndTime\": 1679433477.3670368, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 14}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.7230345581054688, \"count\": 1, \"min\": 0.7230345581054688, \"max\": 0.7230345581054688}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.3670943, \"EndTime\": 1679433477.3671112, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 15}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.2975125, \"count\": 1, \"min\": 1.2975125, \"max\": 1.2975125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.3671632, \"EndTime\": 1679433477.3671768, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 16}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.156157275390625, \"count\": 1, \"min\": 1.156157275390625, \"max\": 1.156157275390625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.3672338, \"EndTime\": 1679433477.367251, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 17}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.1465614624023437, \"count\": 1, \"min\": 1.1465614624023437, \"max\": 1.1465614624023437}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.3673096, \"EndTime\": 1679433477.3673258, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 18}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.1379953979492188, \"count\": 1, \"min\": 1.1379953979492188, \"max\": 1.1379953979492188}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.3673832, \"EndTime\": 1679433477.367399, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 19}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.1457227416992188, \"count\": 1, \"min\": 1.1457227416992188, \"max\": 1.1457227416992188}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.3674557, \"EndTime\": 1679433477.3674695, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 20}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.3701525634765626, \"count\": 1, \"min\": 1.3701525634765626, \"max\": 1.3701525634765626}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.3675294, \"EndTime\": 1679433477.3675458, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 21}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.404151611328125, \"count\": 1, \"min\": 1.404151611328125, \"max\": 1.404151611328125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.3676028, \"EndTime\": 1679433477.367619, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 22}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.400950390625, \"count\": 1, \"min\": 1.400950390625, \"max\": 1.400950390625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.3676684, \"EndTime\": 1679433477.3676846, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 23}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.294437109375, \"count\": 1, \"min\": 1.294437109375, \"max\": 1.294437109375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.3677254, \"EndTime\": 1679433477.3677347, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 24}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5579048828125, \"count\": 1, \"min\": 5.5579048828125, \"max\": 5.5579048828125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.367782, \"EndTime\": 1679433477.367796, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 25}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.563497265625, \"count\": 1, \"min\": 5.563497265625, \"max\": 5.563497265625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.3678484, \"EndTime\": 1679433477.3678646, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 26}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5671791015625, \"count\": 1, \"min\": 5.5671791015625, \"max\": 5.5671791015625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.3679144, \"EndTime\": 1679433477.3679304, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 27}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5672771484375, \"count\": 1, \"min\": 5.5672771484375, \"max\": 5.5672771484375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.3679833, \"EndTime\": 1679433477.367999, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 28}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.534478125, \"count\": 1, \"min\": 5.534478125, \"max\": 5.534478125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.3680494, \"EndTime\": 1679433477.368065, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 29}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.58655908203125, \"count\": 1, \"min\": 5.58655908203125, \"max\": 5.58655908203125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.3681233, \"EndTime\": 1679433477.36814, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 30}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.594275390625, \"count\": 1, \"min\": 5.594275390625, \"max\": 5.594275390625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.3681939, \"EndTime\": 1679433477.36821, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 31}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.55516396484375, \"count\": 1, \"min\": 5.55516396484375, \"max\": 5.55516396484375}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:57 INFO 139712331487040] #quality_metric: host=algo-1, epoch=10, validation absolute_loss_objective =1.038270068359375\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:57 INFO 139712331487040] #early_stopping_criteria_metric: host=algo-1, epoch=10, criteria=absolute_loss_objective, value=0.5248456787109375\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:57 INFO 139712331487040] Saving model for epoch: 10\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:57 INFO 139712331487040] Saved checkpoint to \"/tmp/tmpdfpw9xeu/mx-mod-0000.params\"\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:57 INFO 139712331487040] #progress_metric: host=algo-1, completed 68.75 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433476.7307947, \"EndTime\": 1679433477.3750231, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 204478.0, \"count\": 1, \"min\": 204478, \"max\": 204478}, \"Total Batches Seen\": {\"sum\": 210.0, \"count\": 1, \"min\": 210, \"max\": 210}, \"Max Records Seen Between Resets\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Max Batches Seen Between Resets\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}, \"Reset Count\": {\"sum\": 13.0, \"count\": 1, \"min\": 13, \"max\": 13}, \"Number of Records Since Last Reset\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Number of Batches Since Last Reset\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:57 INFO 139712331487040] #throughput_metric: host=algo-1, train throughput=27154.690116493108 records/second\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.8408298, \"EndTime\": 1679433477.8408868, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 0}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11263784027099609, \"count\": 1, \"min\": 0.11263784027099609, \"max\": 0.11263784027099609}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.8409636, \"EndTime\": 1679433477.8409767, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 1}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05354221119600184, \"count\": 1, \"min\": 0.05354221119600184, \"max\": 0.05354221119600184}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.8410373, \"EndTime\": 1679433477.8410535, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 2}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05282091656853171, \"count\": 1, \"min\": 0.05282091656853171, \"max\": 0.05282091656853171}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.841098, \"EndTime\": 1679433477.8411076, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 3}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.050498660368077895, \"count\": 1, \"min\": 0.050498660368077895, \"max\": 0.050498660368077895}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.8411622, \"EndTime\": 1679433477.8411787, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 4}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.061047126770019534, \"count\": 1, \"min\": 0.061047126770019534, \"max\": 0.061047126770019534}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.8412318, \"EndTime\": 1679433477.8412426, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 5}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.09777294652602252, \"count\": 1, \"min\": 0.09777294652602252, \"max\": 0.09777294652602252}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.8412879, \"EndTime\": 1679433477.8413017, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": 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{\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 27}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.56056884765625, \"count\": 1, \"min\": 5.56056884765625, \"max\": 5.56056884765625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.9573147, \"EndTime\": 1679433477.957329, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 28}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.54207529296875, \"count\": 1, \"min\": 5.54207529296875, \"max\": 5.54207529296875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.9573755, \"EndTime\": 1679433477.9573855, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 29}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.58370283203125, \"count\": 1, \"min\": 5.58370283203125, \"max\": 5.58370283203125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.9574273, \"EndTime\": 1679433477.9574401, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 30}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5691916015625, \"count\": 1, \"min\": 5.5691916015625, \"max\": 5.5691916015625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.9574814, \"EndTime\": 1679433477.9574957, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 31}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5643982421875, \"count\": 1, \"min\": 5.5643982421875, \"max\": 5.5643982421875}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:57 INFO 139712331487040] #quality_metric: host=algo-1, epoch=11, validation absolute_loss_objective =1.4280133544921876\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:57 INFO 139712331487040] #early_stopping_criteria_metric: host=algo-1, epoch=11, criteria=absolute_loss_objective, value=0.22450526428222656\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:57 INFO 139712331487040] Epoch 11: Loss improved. Updating best model\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:57 INFO 139712331487040] Saving model for epoch: 11\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:57 INFO 139712331487040] Saved checkpoint to \"/tmp/tmprthfnvlz/mx-mod-0000.params\"\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:57 INFO 139712331487040] #progress_metric: host=algo-1, completed 75.0 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.375314, \"EndTime\": 1679433477.9643366, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 221976.0, \"count\": 1, \"min\": 221976, \"max\": 221976}, \"Total Batches Seen\": {\"sum\": 228.0, \"count\": 1, \"min\": 228, \"max\": 228}, \"Max Records Seen Between Resets\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Max Batches Seen Between Resets\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}, \"Reset Count\": {\"sum\": 14.0, \"count\": 1, \"min\": 14, \"max\": 14}, \"Number of Records Since Last Reset\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Number of Batches Since Last Reset\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:57 INFO 139712331487040] #throughput_metric: host=algo-1, train throughput=29699.865401923016 records/second\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.3817856, \"EndTime\": 1679433478.3818436, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 0}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.10743226645974552, \"count\": 1, \"min\": 0.10743226645974552, \"max\": 0.10743226645974552}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.3819253, \"EndTime\": 1679433478.381939, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 1}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.051919904596665324, \"count\": 1, \"min\": 0.051919904596665324, \"max\": 0.051919904596665324}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.3819883, \"EndTime\": 1679433478.3820038, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 2}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.051609665590174054, \"count\": 1, \"min\": 0.051609665590174054, \"max\": 0.051609665590174054}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.38209, \"EndTime\": 1679433478.3821096, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 3}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.04904333137063419, \"count\": 1, \"min\": 0.04904333137063419, \"max\": 0.04904333137063419}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.3821704, \"EndTime\": 1679433478.3821876, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 4}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05884064001195571, \"count\": 1, \"min\": 0.05884064001195571, \"max\": 0.05884064001195571}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.3822396, \"EndTime\": 1679433478.3822546, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 5}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.07826367883121266, \"count\": 1, \"min\": 0.07826367883121266, \"max\": 0.07826367883121266}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.382299, \"EndTime\": 1679433478.3823137, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 6}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.1975588019875919, \"count\": 1, \"min\": 0.1975588019875919, \"max\": 0.1975588019875919}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.3823767, \"EndTime\": 1679433478.3823936, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 7}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.0652495330361759, \"count\": 1, \"min\": 0.0652495330361759, \"max\": 0.0652495330361759}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.3824563, \"EndTime\": 1679433478.3824735, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 8}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05405651496438419, \"count\": 1, \"min\": 0.05405651496438419, \"max\": 0.05405651496438419}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.3825397, \"EndTime\": 1679433478.3825593, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 9}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05126492556403665, \"count\": 1, \"min\": 0.05126492556403665, \"max\": 0.05126492556403665}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.3826265, \"EndTime\": 1679433478.3826447, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 10}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05142103778614717, \"count\": 1, \"min\": 0.05142103778614717, \"max\": 0.05142103778614717}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.3826993, \"EndTime\": 1679433478.3827162, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 11}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05211726267197553, \"count\": 1, \"min\": 0.05211726267197553, \"max\": 0.05211726267197553}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.3827739, \"EndTime\": 1679433478.3827899, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 12}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.03909667878992417, \"count\": 1, \"min\": 0.03909667878992417, \"max\": 0.03909667878992417}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.3828561, \"EndTime\": 1679433478.382876, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 13}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.07605733781702378, \"count\": 1, \"min\": 0.07605733781702378, \"max\": 0.07605733781702378}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.3829405, \"EndTime\": 1679433478.3829575, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 14}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.07172605335011202, \"count\": 1, \"min\": 0.07172605335011202, \"max\": 0.07172605335011202}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.3830235, \"EndTime\": 1679433478.383042, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 15}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.07560267571841969, \"count\": 1, \"min\": 0.07560267571841969, \"max\": 0.07560267571841969}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.3831072, \"EndTime\": 1679433478.3831258, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 16}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.1108078676111558, \"count\": 1, \"min\": 0.1108078676111558, \"max\": 0.1108078676111558}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.3831909, \"EndTime\": 1679433478.3832095, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 17}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.1112231970394359, \"count\": 1, \"min\": 0.1112231970394359, \"max\": 0.1112231970394359}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.3832676, \"EndTime\": 1679433478.3832855, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 18}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11116336104449104, \"count\": 1, \"min\": 0.11116336104449104, \"max\": 0.11116336104449104}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.383349, \"EndTime\": 1679433478.383368, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 19}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11146796506993911, \"count\": 1, \"min\": 0.11146796506993911, \"max\": 0.11146796506993911}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.383434, \"EndTime\": 1679433478.3834515, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 20}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11872847882439108, \"count\": 1, \"min\": 0.11872847882439108, \"max\": 0.11872847882439108}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.3835073, \"EndTime\": 1679433478.3835227, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 21}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.13074565752814798, \"count\": 1, \"min\": 0.13074565752814798, \"max\": 0.13074565752814798}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.3835819, \"EndTime\": 1679433478.3835983, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 22}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12343952134076287, \"count\": 1, \"min\": 0.12343952134076287, \"max\": 0.12343952134076287}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.383664, \"EndTime\": 1679433478.383682, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 23}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.1332016170726103, \"count\": 1, \"min\": 0.1332016170726103, \"max\": 0.1332016170726103}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.383738, \"EndTime\": 1679433478.3837547, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 24}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5524533404181985, \"count\": 1, \"min\": 0.5524533404181985, \"max\": 0.5524533404181985}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.3838093, \"EndTime\": 1679433478.383826, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 25}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5524326207778033, \"count\": 1, \"min\": 0.5524326207778033, \"max\": 0.5524326207778033}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.3838766, \"EndTime\": 1679433478.3838875, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 26}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5521253267176011, \"count\": 1, \"min\": 0.5521253267176011, \"max\": 0.5521253267176011}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.383951, \"EndTime\": 1679433478.3839693, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 27}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5522119822782628, \"count\": 1, \"min\": 0.5522119822782628, \"max\": 0.5522119822782628}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.384026, \"EndTime\": 1679433478.3840437, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 28}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5539852402630975, \"count\": 1, \"min\": 0.5539852402630975, \"max\": 0.5539852402630975}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.3841107, \"EndTime\": 1679433478.384129, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 29}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5596195786420036, \"count\": 1, \"min\": 0.5596195786420036, \"max\": 0.5596195786420036}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.3841927, \"EndTime\": 1679433478.3842106, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 30}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5578086727366728, \"count\": 1, \"min\": 0.5578086727366728, \"max\": 0.5578086727366728}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.3842757, \"EndTime\": 1679433478.3842945, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 31}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5596417703067555, \"count\": 1, \"min\": 0.5596417703067555, \"max\": 0.5596417703067555}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:58 INFO 139712331487040] #quality_metric: host=algo-1, epoch=12, train absolute_loss_objective =0.10743226645974552\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.4899201, \"EndTime\": 1679433478.4899743, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 0}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.81489833984375, \"count\": 1, \"min\": 1.81489833984375, \"max\": 1.81489833984375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.4900882, \"EndTime\": 1679433478.4901037, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 1}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5248093200683593, \"count\": 1, \"min\": 0.5248093200683593, \"max\": 0.5248093200683593}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.4901607, \"EndTime\": 1679433478.490177, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 2}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5254496337890625, \"count\": 1, \"min\": 0.5254496337890625, \"max\": 0.5254496337890625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.4902382, \"EndTime\": 1679433478.4902542, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 3}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.4923114562988281, \"count\": 1, \"min\": 0.4923114562988281, \"max\": 0.4923114562988281}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.4903107, \"EndTime\": 1679433478.4903276, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 4}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.6997939819335938, \"count\": 1, \"min\": 0.6997939819335938, \"max\": 0.6997939819335938}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.4903812, \"EndTime\": 1679433478.4903975, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 5}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.422708056640625, \"count\": 1, \"min\": 0.422708056640625, \"max\": 0.422708056640625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.4904492, \"EndTime\": 1679433478.4904647, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 6}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.0630600341796874, \"count\": 1, \"min\": 1.0630600341796874, \"max\": 1.0630600341796874}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.4905157, \"EndTime\": 1679433478.490532, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 7}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.66822685546875, \"count\": 1, \"min\": 0.66822685546875, \"max\": 0.66822685546875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.49059, \"EndTime\": 1679433478.4906058, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 8}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5502632263183593, \"count\": 1, \"min\": 0.5502632263183593, \"max\": 0.5502632263183593}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.4906538, \"EndTime\": 1679433478.4906638, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 9}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5174059265136719, \"count\": 1, \"min\": 0.5174059265136719, \"max\": 0.5174059265136719}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.490719, \"EndTime\": 1679433478.4907362, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 10}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5220412414550781, \"count\": 1, \"min\": 0.5220412414550781, \"max\": 0.5220412414550781}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.4908001, \"EndTime\": 1679433478.4908187, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 11}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5263141723632813, \"count\": 1, \"min\": 0.5263141723632813, \"max\": 0.5263141723632813}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.4908867, \"EndTime\": 1679433478.490899, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 12}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.38885081787109377, \"count\": 1, \"min\": 0.38885081787109377, \"max\": 0.38885081787109377}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.4909441, \"EndTime\": 1679433478.4909601, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 13}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5327310729980469, \"count\": 1, \"min\": 0.5327310729980469, \"max\": 0.5327310729980469}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.4910202, \"EndTime\": 1679433478.4910316, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 14}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.1077838134765625, \"count\": 1, \"min\": 1.1077838134765625, \"max\": 1.1077838134765625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.4910889, \"EndTime\": 1679433478.4911065, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 15}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.7787463256835937, \"count\": 1, \"min\": 0.7787463256835937, \"max\": 0.7787463256835937}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.4911618, \"EndTime\": 1679433478.4911778, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 16}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.1373549560546874, \"count\": 1, \"min\": 1.1373549560546874, \"max\": 1.1373549560546874}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.4912353, \"EndTime\": 1679433478.491253, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 17}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.151603955078125, \"count\": 1, \"min\": 1.151603955078125, \"max\": 1.151603955078125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.491304, \"EndTime\": 1679433478.4913206, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 18}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.1380606201171874, \"count\": 1, \"min\": 1.1380606201171874, \"max\": 1.1380606201171874}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.4913805, \"EndTime\": 1679433478.4913986, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 19}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.1433278442382813, \"count\": 1, 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\"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 22}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.36312724609375, \"count\": 1, \"min\": 1.36312724609375, \"max\": 1.36312724609375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.4916763, \"EndTime\": 1679433478.4916935, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 23}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.308734375, \"count\": 1, \"min\": 1.308734375, \"max\": 1.308734375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.4917557, \"EndTime\": 1679433478.4917743, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 24}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.55496728515625, \"count\": 1, \"min\": 5.55496728515625, \"max\": 5.55496728515625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.4918387, \"EndTime\": 1679433478.4918554, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 25}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5618513671875, \"count\": 1, \"min\": 5.5618513671875, \"max\": 5.5618513671875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.4919097, \"EndTime\": 1679433478.4919264, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 26}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.56723623046875, \"count\": 1, \"min\": 5.56723623046875, \"max\": 5.56723623046875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.491988, \"EndTime\": 1679433478.492005, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 27}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.56991796875, \"count\": 1, \"min\": 5.56991796875, \"max\": 5.56991796875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.492058, \"EndTime\": 1679433478.4920685, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 28}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.55924931640625, \"count\": 1, \"min\": 5.55924931640625, \"max\": 5.55924931640625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.492113, \"EndTime\": 1679433478.492129, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 29}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5795904296875, \"count\": 1, \"min\": 5.5795904296875, \"max\": 5.5795904296875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.4921908, \"EndTime\": 1679433478.4922082, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 30}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.57410078125, \"count\": 1, \"min\": 5.57410078125, \"max\": 5.57410078125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.4922688, \"EndTime\": 1679433478.492286, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"model\": 31}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.594571484375, \"count\": 1, \"min\": 5.594571484375, \"max\": 5.594571484375}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:58 INFO 139712331487040] #quality_metric: host=algo-1, epoch=12, validation absolute_loss_objective =1.81489833984375\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:58 INFO 139712331487040] #early_stopping_criteria_metric: host=algo-1, epoch=12, criteria=absolute_loss_objective, value=0.38885081787109377\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:58 INFO 139712331487040] Saving model for epoch: 12\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:58 INFO 139712331487040] Saved checkpoint to \"/tmp/tmpl4d7dspe/mx-mod-0000.params\"\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:58 INFO 139712331487040] #progress_metric: host=algo-1, completed 81.25 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433477.9646516, \"EndTime\": 1679433478.4991708, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 12, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 239474.0, \"count\": 1, \"min\": 239474, \"max\": 239474}, \"Total Batches Seen\": {\"sum\": 246.0, \"count\": 1, \"min\": 246, \"max\": 246}, \"Max Records Seen Between Resets\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Max Batches Seen Between Resets\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}, \"Reset Count\": {\"sum\": 15.0, \"count\": 1, \"min\": 15, \"max\": 15}, \"Number of Records Since Last Reset\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Number of Batches Since Last Reset\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:58 INFO 139712331487040] #throughput_metric: host=algo-1, train throughput=32726.269237492197 records/second\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.9480789, \"EndTime\": 1679433478.9481375, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 0}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.1128998695822323, \"count\": 1, \"min\": 0.1128998695822323, \"max\": 0.1128998695822323}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.9482195, \"EndTime\": 1679433478.9482384, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": 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\"training\", \"epoch\": 13, \"model\": 6}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.16874183026482079, \"count\": 1, \"min\": 0.16874183026482079, \"max\": 0.16874183026482079}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.948666, \"EndTime\": 1679433478.9486825, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 7}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.10227099586935605, \"count\": 1, \"min\": 0.10227099586935605, \"max\": 0.10227099586935605}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.9487388, \"EndTime\": 1679433478.9487557, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 8}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05284815081428079, \"count\": 1, \"min\": 0.05284815081428079, \"max\": 0.05284815081428079}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.948811, \"EndTime\": 1679433478.948828, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 9}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.049953159556669346, \"count\": 1, \"min\": 0.049953159556669346, \"max\": 0.049953159556669346}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.9488816, \"EndTime\": 1679433478.9488976, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 10}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05002479620540843, \"count\": 1, \"min\": 0.05002479620540843, \"max\": 0.05002479620540843}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.9489577, \"EndTime\": 1679433478.9489741, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 11}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.05041655820958755, \"count\": 1, \"min\": 0.05041655820958755, \"max\": 0.05041655820958755}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.949029, \"EndTime\": 1679433478.949045, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 12}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.03526167140287512, \"count\": 1, \"min\": 0.03526167140287512, \"max\": 0.03526167140287512}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.9490993, \"EndTime\": 1679433478.9491167, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 13}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.08038676205803366, \"count\": 1, \"min\": 0.08038676205803366, \"max\": 0.08038676205803366}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.9491708, \"EndTime\": 1679433478.9491868, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 14}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.10284505496305578, \"count\": 1, \"min\": 0.10284505496305578, \"max\": 0.10284505496305578}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.949246, \"EndTime\": 1679433478.9492633, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 15}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.07333392333984375, \"count\": 1, \"min\": 0.07333392333984375, \"max\": 0.07333392333984375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.9493165, \"EndTime\": 1679433478.9493325, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 16}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11047798381132239, \"count\": 1, \"min\": 0.11047798381132239, \"max\": 0.11047798381132239}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.9493864, \"EndTime\": 1679433478.9494028, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 17}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11154531411563649, \"count\": 1, \"min\": 0.11154531411563649, \"max\": 0.11154531411563649}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.9494545, \"EndTime\": 1679433478.949471, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 18}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11067199303122127, \"count\": 1, \"min\": 0.11067199303122127, \"max\": 0.11067199303122127}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.9495308, \"EndTime\": 1679433478.9495473, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 19}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11124471597110525, \"count\": 1, \"min\": 0.11124471597110525, \"max\": 0.11124471597110525}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.9496038, \"EndTime\": 1679433478.9496171, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 20}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.1193282533533433, \"count\": 1, \"min\": 0.1193282533533433, \"max\": 0.1193282533533433}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.9496682, \"EndTime\": 1679433478.9496844, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 21}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12366345977783202, \"count\": 1, \"min\": 0.12366345977783202, \"max\": 0.12366345977783202}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.9497485, \"EndTime\": 1679433478.9497654, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 22}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.1270941821827608, \"count\": 1, \"min\": 0.1270941821827608, \"max\": 0.1270941821827608}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.9498146, \"EndTime\": 1679433478.9498248, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 23}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12803313176772174, \"count\": 1, \"min\": 0.12803313176772174, \"max\": 0.12803313176772174}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.949874, \"EndTime\": 1679433478.9498887, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 24}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5520424158432904, \"count\": 1, \"min\": 0.5520424158432904, \"max\": 0.5520424158432904}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.9499247, \"EndTime\": 1679433478.9499397, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 25}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5522152817670036, \"count\": 1, \"min\": 0.5522152817670036, \"max\": 0.5522152817670036}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.950003, \"EndTime\": 1679433478.9500542, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 26}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5523788272633272, \"count\": 1, \"min\": 0.5523788272633272, \"max\": 0.5523788272633272}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.9501197, \"EndTime\": 1679433478.9501371, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 27}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5524079266716452, \"count\": 1, \"min\": 0.5524079266716452, \"max\": 0.5524079266716452}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.9501908, \"EndTime\": 1679433478.950208, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 28}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5549387889188878, \"count\": 1, \"min\": 0.5549387889188878, \"max\": 0.5549387889188878}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.9502723, \"EndTime\": 1679433478.9502892, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 29}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5577348596909467, \"count\": 1, \"min\": 0.5577348596909467, \"max\": 0.5577348596909467}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.9503565, \"EndTime\": 1679433478.9503736, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 30}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5551042265050552, \"count\": 1, \"min\": 0.5551042265050552, \"max\": 0.5551042265050552}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.9504318, \"EndTime\": 1679433478.9504473, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 31}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.560306048224954, \"count\": 1, \"min\": 0.560306048224954, \"max\": 0.560306048224954}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:58 INFO 139712331487040] #quality_metric: host=algo-1, epoch=13, train absolute_loss_objective =0.1128998695822323\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.0647132, \"EndTime\": 1679433479.0647724, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 0}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.2334332763671876, \"count\": 1, \"min\": 1.2334332763671876, \"max\": 1.2334332763671876}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.0648582, \"EndTime\": 1679433479.064872, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 1}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5064493225097656, \"count\": 1, \"min\": 0.5064493225097656, \"max\": 0.5064493225097656}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.0649314, \"EndTime\": 1679433479.064943, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 2}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5121934509277344, \"count\": 1, \"min\": 0.5121934509277344, \"max\": 0.5121934509277344}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.0649958, \"EndTime\": 1679433479.0650077, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 3}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.4761453857421875, \"count\": 1, \"min\": 0.4761453857421875, \"max\": 0.4761453857421875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.0650682, \"EndTime\": 1679433479.0650856, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 4}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.25023623352050783, \"count\": 1, \"min\": 0.25023623352050783, \"max\": 0.25023623352050783}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.0651407, \"EndTime\": 1679433479.0651574, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 5}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.8894883911132813, \"count\": 1, \"min\": 0.8894883911132813, \"max\": 0.8894883911132813}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.0652122, \"EndTime\": 1679433479.06523, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 6}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 2.103273193359375, \"count\": 1, \"min\": 2.103273193359375, \"max\": 2.103273193359375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.0652816, \"EndTime\": 1679433479.0652993, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 7}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.8034966674804688, \"count\": 1, \"min\": 0.8034966674804688, \"max\": 0.8034966674804688}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.0653517, \"EndTime\": 1679433479.0653675, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 8}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5384437255859374, \"count\": 1, \"min\": 0.5384437255859374, \"max\": 0.5384437255859374}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.0654104, \"EndTime\": 1679433479.065425, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 9}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.502614990234375, \"count\": 1, \"min\": 0.502614990234375, \"max\": 0.502614990234375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.0654802, \"EndTime\": 1679433479.0654976, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 10}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5086995971679688, \"count\": 1, \"min\": 0.5086995971679688, \"max\": 0.5086995971679688}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.0655422, \"EndTime\": 1679433479.0655584, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 11}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5091309692382813, \"count\": 1, \"min\": 0.5091309692382813, \"max\": 0.5091309692382813}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.0656173, \"EndTime\": 1679433479.0656338, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 12}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.37011171264648435, \"count\": 1, \"min\": 0.37011171264648435, \"max\": 0.37011171264648435}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.0656917, \"EndTime\": 1679433479.0657089, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 13}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.6308933349609376, \"count\": 1, \"min\": 1.6308933349609376, \"max\": 1.6308933349609376}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.065755, \"EndTime\": 1679433479.0657697, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 14}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.3933172119140625, \"count\": 1, \"min\": 0.3933172119140625, \"max\": 0.3933172119140625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.06582, \"EndTime\": 1679433479.0658364, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 15}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.6341366088867187, \"count\": 1, \"min\": 0.6341366088867187, \"max\": 0.6341366088867187}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.0658877, \"EndTime\": 1679433479.0659034, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 16}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.1377966186523438, \"count\": 1, \"min\": 1.1377966186523438, \"max\": 1.1377966186523438}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.065959, \"EndTime\": 1679433479.0659752, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 17}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.145939306640625, \"count\": 1, \"min\": 1.145939306640625, \"max\": 1.145939306640625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.0660539, \"EndTime\": 1679433479.0660722, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 18}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.1406923217773437, \"count\": 1, \"min\": 1.1406923217773437, \"max\": 1.1406923217773437}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.0661259, \"EndTime\": 1679433479.0661426, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 19}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.1469538452148438, \"count\": 1, \"min\": 1.1469538452148438, \"max\": 1.1469538452148438}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.0662005, \"EndTime\": 1679433479.0662172, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 20}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.3613353271484374, \"count\": 1, \"min\": 1.3613353271484374, \"max\": 1.3613353271484374}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.066268, \"EndTime\": 1679433479.0662837, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 21}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.32441640625, \"count\": 1, \"min\": 1.32441640625, \"max\": 1.32441640625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.0663328, \"EndTime\": 1679433479.0663495, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 22}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.3872216796875, \"count\": 1, \"min\": 1.3872216796875, \"max\": 1.3872216796875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.0663998, \"EndTime\": 1679433479.0664158, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 23}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.2310135986328126, \"count\": 1, \"min\": 1.2310135986328126, \"max\": 1.2310135986328126}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.0664728, \"EndTime\": 1679433479.06649, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 24}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.55826298828125, \"count\": 1, \"min\": 5.55826298828125, \"max\": 5.55826298828125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.0665407, \"EndTime\": 1679433479.0665567, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 25}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.570084765625, \"count\": 1, \"min\": 5.570084765625, \"max\": 5.570084765625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.0666063, \"EndTime\": 1679433479.0666223, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 26}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.55846201171875, \"count\": 1, \"min\": 5.55846201171875, \"max\": 5.55846201171875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.0666747, \"EndTime\": 1679433479.06669, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 27}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.57060478515625, \"count\": 1, \"min\": 5.57060478515625, \"max\": 5.57060478515625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.0667388, \"EndTime\": 1679433479.0667558, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 28}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5786318359375, \"count\": 1, \"min\": 5.5786318359375, \"max\": 5.5786318359375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.06681, \"EndTime\": 1679433479.0668266, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 29}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.56665576171875, \"count\": 1, \"min\": 5.56665576171875, \"max\": 5.56665576171875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.0668776, \"EndTime\": 1679433479.0668933, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 30}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.51951259765625, \"count\": 1, \"min\": 5.51951259765625, \"max\": 5.51951259765625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.0669432, \"EndTime\": 1679433479.0669599, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"model\": 31}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.57436201171875, \"count\": 1, \"min\": 5.57436201171875, \"max\": 5.57436201171875}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:59 INFO 139712331487040] #quality_metric: host=algo-1, epoch=13, validation absolute_loss_objective =1.2334332763671876\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:59 INFO 139712331487040] #early_stopping_criteria_metric: host=algo-1, epoch=13, criteria=absolute_loss_objective, value=0.25023623352050783\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:59 INFO 139712331487040] Saving model for epoch: 13\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:59 INFO 139712331487040] Saved checkpoint to \"/tmp/tmp5gm0rgw5/mx-mod-0000.params\"\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:59 INFO 139712331487040] #progress_metric: host=algo-1, completed 87.5 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433478.4994771, \"EndTime\": 1679433479.0735292, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 13, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 256972.0, \"count\": 1, \"min\": 256972, \"max\": 256972}, \"Total Batches Seen\": {\"sum\": 264.0, \"count\": 1, \"min\": 264, \"max\": 264}, \"Max Records Seen Between Resets\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Max Batches Seen Between Resets\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}, \"Reset Count\": {\"sum\": 16.0, \"count\": 1, \"min\": 16, \"max\": 16}, \"Number of Records Since Last Reset\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Number of Batches Since Last Reset\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:59 INFO 139712331487040] #throughput_metric: host=algo-1, train throughput=30473.162977098604 records/second\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.505889, \"EndTime\": 1679433479.5059447, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 14, \"model\": 0}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11503261397866642, \"count\": 1, \"min\": 0.11503261397866642, \"max\": 0.11503261397866642}}}\u001b[0m\n", "\u001b[34m#metrics 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\"EndTime\": 1679433479.6266718, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 14, \"model\": 24}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5649640625, \"count\": 1, \"min\": 5.5649640625, \"max\": 5.5649640625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.6267366, \"EndTime\": 1679433479.6267538, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 14, \"model\": 25}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.56191005859375, \"count\": 1, \"min\": 5.56191005859375, \"max\": 5.56191005859375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.6268172, \"EndTime\": 1679433479.6268337, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 14, \"model\": 26}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.56716240234375, \"count\": 1, \"min\": 5.56716240234375, \"max\": 5.56716240234375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.6268969, \"EndTime\": 1679433479.6269157, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 14, \"model\": 27}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.560460546875, \"count\": 1, \"min\": 5.560460546875, \"max\": 5.560460546875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.6269774, \"EndTime\": 1679433479.626996, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 14, \"model\": 28}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.58732685546875, \"count\": 1, \"min\": 5.58732685546875, \"max\": 5.58732685546875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.6270607, \"EndTime\": 1679433479.627077, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 14, \"model\": 29}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5787265625, \"count\": 1, \"min\": 5.5787265625, \"max\": 5.5787265625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.6271403, \"EndTime\": 1679433479.6271598, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 14, \"model\": 30}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.5836080078125, \"count\": 1, \"min\": 5.5836080078125, \"max\": 5.5836080078125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.6272197, \"EndTime\": 1679433479.6272366, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 14, \"model\": 31}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.57168623046875, \"count\": 1, \"min\": 5.57168623046875, \"max\": 5.57168623046875}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:59 INFO 139712331487040] #quality_metric: host=algo-1, epoch=14, validation absolute_loss_objective =0.7763571655273438\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:59 INFO 139712331487040] #early_stopping_criteria_metric: host=algo-1, epoch=14, criteria=absolute_loss_objective, value=0.4588751953125\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:59 INFO 139712331487040] Saving model for epoch: 14\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:59 INFO 139712331487040] Saved checkpoint to \"/tmp/tmpc7d5cfbp/mx-mod-0000.params\"\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:59 INFO 139712331487040] #progress_metric: host=algo-1, completed 93.75 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.073829, \"EndTime\": 1679433479.6342518, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 14, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 274470.0, \"count\": 1, \"min\": 274470, \"max\": 274470}, \"Total Batches Seen\": {\"sum\": 282.0, \"count\": 1, \"min\": 282, \"max\": 282}, \"Max Records Seen Between Resets\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Max Batches Seen Between Resets\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}, \"Reset Count\": {\"sum\": 17.0, \"count\": 1, \"min\": 17, \"max\": 17}, \"Number of Records Since Last Reset\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Number of Batches Since Last Reset\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:17:59 INFO 139712331487040] #throughput_metric: host=algo-1, train throughput=31214.29797195338 records/second\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.1265075, \"EndTime\": 1679433480.1265645, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 0}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.10348980241663315, \"count\": 1, \"min\": 0.10348980241663315, \"max\": 0.10348980241663315}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.1266425, \"EndTime\": 1679433480.1266558, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 1}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.04683308679917279, \"count\": 1, \"min\": 0.04683308679917279, \"max\": 0.04683308679917279}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.1267133, \"EndTime\": 1679433480.1267252, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 2}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.047847556843477135, \"count\": 1, \"min\": 0.047847556843477135, \"max\": 0.047847556843477135}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.126779, \"EndTime\": 1679433480.1267898, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 3}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.04388302073759191, \"count\": 1, \"min\": 0.04388302073759191, \"max\": 0.04388302073759191}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.126844, \"EndTime\": 1679433480.1268609, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 4}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.06446943215762868, \"count\": 1, \"min\": 0.06446943215762868, \"max\": 0.06446943215762868}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.126911, \"EndTime\": 1679433480.1269252, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 5}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11696337239882525, \"count\": 1, \"min\": 0.11696337239882525, \"max\": 0.11696337239882525}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.126976, \"EndTime\": 1679433480.126992, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 6}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12922937886855182, \"count\": 1, \"min\": 0.12922937886855182, \"max\": 0.12922937886855182}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.1270528, \"EndTime\": 1679433480.1270664, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 7}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.07467839386883904, \"count\": 1, \"min\": 0.07467839386883904, \"max\": 0.07467839386883904}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.127122, \"EndTime\": 1679433480.1271372, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 8}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.050333419126622814, \"count\": 1, \"min\": 0.050333419126622814, \"max\": 0.050333419126622814}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.127192, \"EndTime\": 1679433480.1272085, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 9}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.046914120618034814, \"count\": 1, \"min\": 0.046914120618034814, \"max\": 0.046914120618034814}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.1272714, \"EndTime\": 1679433480.1272883, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 10}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.047243816824520335, \"count\": 1, \"min\": 0.047243816824520335, \"max\": 0.047243816824520335}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.1273506, \"EndTime\": 1679433480.1273685, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 11}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.04708488374597886, \"count\": 1, \"min\": 0.04708488374597886, \"max\": 0.04708488374597886}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.1274233, \"EndTime\": 1679433480.1274393, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 12}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.08601265985825482, \"count\": 1, \"min\": 0.08601265985825482, \"max\": 0.08601265985825482}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.1274896, \"EndTime\": 1679433480.1275043, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 13}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.08642462180642521, \"count\": 1, \"min\": 0.08642462180642521, \"max\": 0.08642462180642521}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.1275563, \"EndTime\": 1679433480.127572, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 14}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.0694113670797909, \"count\": 1, \"min\": 0.0694113670797909, \"max\": 0.0694113670797909}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.1276245, \"EndTime\": 1679433480.1276407, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 15}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12505926221959732, \"count\": 1, \"min\": 0.12505926221959732, \"max\": 0.12505926221959732}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.1276972, \"EndTime\": 1679433480.1277137, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 16}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11032499874339384, \"count\": 1, \"min\": 0.11032499874339384, \"max\": 0.11032499874339384}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.12777, \"EndTime\": 1679433480.127787, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 17}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11143479066736559, \"count\": 1, \"min\": 0.11143479066736559, \"max\": 0.11143479066736559}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.1278358, \"EndTime\": 1679433480.1278515, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 18}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11035958278880399, \"count\": 1, \"min\": 0.11035958278880399, \"max\": 0.11035958278880399}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.1279016, \"EndTime\": 1679433480.1279166, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 19}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11104128175623276, \"count\": 1, \"min\": 0.11104128175623276, \"max\": 0.11104128175623276}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.1279693, \"EndTime\": 1679433480.1279845, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 20}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12310286667767693, \"count\": 1, \"min\": 0.12310286667767693, \"max\": 0.12310286667767693}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.1280465, \"EndTime\": 1679433480.1280627, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 21}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.11918962007410386, \"count\": 1, \"min\": 0.11918962007410386, \"max\": 0.11918962007410386}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.1281252, \"EndTime\": 1679433480.1281416, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 22}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12306705654368681, \"count\": 1, \"min\": 0.12306705654368681, \"max\": 0.12306705654368681}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.1282032, \"EndTime\": 1679433480.1282194, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 23}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.12798497009277343, \"count\": 1, \"min\": 0.12798497009277343, \"max\": 0.12798497009277343}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.1282735, \"EndTime\": 1679433480.12829, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 24}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5524696188534007, \"count\": 1, \"min\": 0.5524696188534007, \"max\": 0.5524696188534007}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.1283443, \"EndTime\": 1679433480.1283605, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 25}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5521955315085019, \"count\": 1, \"min\": 0.5521955315085019, \"max\": 0.5521955315085019}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.1284125, \"EndTime\": 1679433480.1284275, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 26}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.552326904296875, \"count\": 1, \"min\": 0.552326904296875, \"max\": 0.552326904296875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.1284807, \"EndTime\": 1679433480.1284914, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 27}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5522552562040441, \"count\": 1, \"min\": 0.5522552562040441, \"max\": 0.5522552562040441}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.1285446, \"EndTime\": 1679433480.12856, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 28}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5574275548598345, \"count\": 1, \"min\": 0.5574275548598345, \"max\": 0.5574275548598345}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.128614, \"EndTime\": 1679433480.12863, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 29}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5572349135454964, \"count\": 1, \"min\": 0.5572349135454964, \"max\": 0.5572349135454964}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.1286774, \"EndTime\": 1679433480.1286924, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 30}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.5577777279124541, \"count\": 1, \"min\": 0.5577777279124541, \"max\": 0.5577777279124541}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.128744, \"EndTime\": 1679433480.1287596, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 31}, \"Metrics\": {\"train_absolute_loss_objective\": {\"sum\": 0.55867918844784, \"count\": 1, \"min\": 0.55867918844784, \"max\": 0.55867918844784}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:18:00 INFO 139712331487040] #quality_metric: host=algo-1, epoch=15, train absolute_loss_objective =0.10348980241663315\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.2436042, \"EndTime\": 1679433480.24366, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 0}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.6917234375, \"count\": 1, \"min\": 1.6917234375, \"max\": 1.6917234375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.243743, \"EndTime\": 1679433480.2437568, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 1}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.4712309143066406, \"count\": 1, \"min\": 0.4712309143066406, \"max\": 0.4712309143066406}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.2438018, \"EndTime\": 1679433480.2438166, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 2}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.4861897094726563, \"count\": 1, \"min\": 0.4861897094726563, \"max\": 0.4861897094726563}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.2438748, \"EndTime\": 1679433480.2438948, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 3}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.44143792724609376, \"count\": 1, \"min\": 0.44143792724609376, \"max\": 0.44143792724609376}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.2439508, \"EndTime\": 1679433480.243966, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 4}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.8905391845703124, \"count\": 1, \"min\": 0.8905391845703124, \"max\": 0.8905391845703124}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.244024, \"EndTime\": 1679433480.2440405, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 5}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.7182361572265625, \"count\": 1, \"min\": 1.7182361572265625, \"max\": 1.7182361572265625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.2440968, \"EndTime\": 1679433480.2441125, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 6}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.617585693359375, \"count\": 1, \"min\": 1.617585693359375, \"max\": 1.617585693359375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.244175, \"EndTime\": 1679433480.2441928, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 7}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5271304565429688, \"count\": 1, \"min\": 0.5271304565429688, \"max\": 0.5271304565429688}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.2442582, \"EndTime\": 1679433480.2442758, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 8}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.5119539306640625, \"count\": 1, \"min\": 0.5119539306640625, \"max\": 0.5119539306640625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.2443397, \"EndTime\": 1679433480.244357, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 9}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.4709744873046875, \"count\": 1, \"min\": 0.4709744873046875, \"max\": 0.4709744873046875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.2444234, \"EndTime\": 1679433480.244441, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 10}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.47868703002929686, \"count\": 1, \"min\": 0.47868703002929686, \"max\": 0.47868703002929686}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.2445078, \"EndTime\": 1679433480.244526, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 11}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.4756488525390625, \"count\": 1, \"min\": 0.4756488525390625, \"max\": 0.4756488525390625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.2445931, \"EndTime\": 1679433480.2446113, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 12}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.61713369140625, \"count\": 1, \"min\": 0.61713369140625, \"max\": 0.61713369140625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.244677, \"EndTime\": 1679433480.2446947, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 13}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.144653857421875, \"count\": 1, \"min\": 1.144653857421875, \"max\": 1.144653857421875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.2447608, \"EndTime\": 1679433480.2447786, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 14}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 0.7796881469726562, \"count\": 1, \"min\": 0.7796881469726562, \"max\": 0.7796881469726562}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.2448473, \"EndTime\": 1679433480.2448661, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 15}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.55809951171875, \"count\": 1, \"min\": 1.55809951171875, \"max\": 1.55809951171875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.2449305, \"EndTime\": 1679433480.2449496, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 16}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.1388075561523439, \"count\": 1, \"min\": 1.1388075561523439, \"max\": 1.1388075561523439}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.2450154, \"EndTime\": 1679433480.2450335, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 17}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.143972216796875, \"count\": 1, \"min\": 1.143972216796875, \"max\": 1.143972216796875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.2450976, \"EndTime\": 1679433480.2451162, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 18}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.140155712890625, \"count\": 1, \"min\": 1.140155712890625, \"max\": 1.140155712890625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.2451806, \"EndTime\": 1679433480.2451985, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 19}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.1450669677734375, \"count\": 1, \"min\": 1.1450669677734375, \"max\": 1.1450669677734375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.2452638, \"EndTime\": 1679433480.2452824, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 20}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.346161279296875, \"count\": 1, \"min\": 1.346161279296875, \"max\": 1.346161279296875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.2453465, \"EndTime\": 1679433480.245365, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 21}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.3780642333984374, \"count\": 1, \"min\": 1.3780642333984374, \"max\": 1.3780642333984374}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.245434, \"EndTime\": 1679433480.2454512, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 22}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.30037470703125, \"count\": 1, \"min\": 1.30037470703125, \"max\": 1.30037470703125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.2455173, \"EndTime\": 1679433480.2455342, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 23}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 1.3438822265625, \"count\": 1, \"min\": 1.3438822265625, \"max\": 1.3438822265625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.2456, \"EndTime\": 1679433480.2456174, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 24}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.55506435546875, \"count\": 1, \"min\": 5.55506435546875, \"max\": 5.55506435546875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.2456834, \"EndTime\": 1679433480.2457008, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 25}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.57101416015625, \"count\": 1, \"min\": 5.57101416015625, \"max\": 5.57101416015625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.2457592, \"EndTime\": 1679433480.2457757, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 26}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.55895791015625, \"count\": 1, \"min\": 5.55895791015625, \"max\": 5.55895791015625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.2458389, \"EndTime\": 1679433480.2458568, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 27}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.56984423828125, \"count\": 1, \"min\": 5.56984423828125, \"max\": 5.56984423828125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.2459207, \"EndTime\": 1679433480.2459393, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 28}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.58893271484375, \"count\": 1, \"min\": 5.58893271484375, \"max\": 5.58893271484375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.2460034, \"EndTime\": 1679433480.246043, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 29}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.57765263671875, \"count\": 1, \"min\": 5.57765263671875, \"max\": 5.57765263671875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.246114, \"EndTime\": 1679433480.2461317, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 30}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.58907724609375, \"count\": 1, \"min\": 5.58907724609375, \"max\": 5.58907724609375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433480.2461972, \"EndTime\": 1679433480.246215, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"model\": 31}, \"Metrics\": {\"validation_absolute_loss_objective\": {\"sum\": 5.61434052734375, \"count\": 1, \"min\": 5.61434052734375, \"max\": 5.61434052734375}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:18:00 INFO 139712331487040] #quality_metric: host=algo-1, epoch=15, validation absolute_loss_objective =1.6917234375\u001b[0m\n", "\u001b[34m[03/21/2023 21:18:00 INFO 139712331487040] #early_stopping_criteria_metric: host=algo-1, epoch=15, criteria=absolute_loss_objective, value=0.44143792724609376\u001b[0m\n", "\u001b[34m[03/21/2023 21:18:00 INFO 139712331487040] Saving model for epoch: 15\u001b[0m\n", "\u001b[34m[03/21/2023 21:18:00 INFO 139712331487040] Saved checkpoint to \"/tmp/tmpv_oh6qtm/mx-mod-0000.params\"\u001b[0m\n", "\u001b[34m[03/21/2023 21:18:00 INFO 139712331487040] Early stop condition met. Stopping training.\u001b[0m\n", "\u001b[34m[03/21/2023 21:18:00 INFO 139712331487040] #progress_metric: host=algo-1, completed 100 % epochs\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433479.6345665, \"EndTime\": 1679433480.2536018, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 15, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 291968.0, \"count\": 1, \"min\": 291968, \"max\": 291968}, \"Total Batches Seen\": {\"sum\": 300.0, \"count\": 1, \"min\": 300, \"max\": 300}, \"Max Records Seen Between Resets\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Max Batches Seen Between Resets\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}, \"Reset Count\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}, \"Number of Records Since Last Reset\": {\"sum\": 17498.0, \"count\": 1, \"min\": 17498, \"max\": 17498}, \"Number of Batches Since Last Reset\": {\"sum\": 18.0, \"count\": 1, \"min\": 18, \"max\": 18}}}\u001b[0m\n", "\u001b[34m[03/21/2023 21:18:00 INFO 139712331487040] #throughput_metric: host=algo-1, train throughput=28259.936593748786 records/second\u001b[0m\n", "\u001b[34m[03/21/2023 21:18:00 WARNING 139712331487040] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n", "\u001b[34m[03/21/2023 21:18:00 WARNING 139712331487040] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n", "\u001b[34m[03/21/2023 21:18:00 INFO 139712331487040] #early_stopping_criteria_metric: host=algo-1, epoch=15, criteria=absolute_loss_objective, value=0.44143792724609376\u001b[0m\n", "\u001b[34m[03/21/2023 21:18:00 INFO 139712331487040] #validation_score (algo-1) : ('absolute_loss_objective', 0.22450526428222656)\u001b[0m\n", "\u001b[34m[03/21/2023 21:18:00 INFO 139712331487040] #validation_score (algo-1) : ('mse', 0.1365243377685547)\u001b[0m\n", "\u001b[34m[03/21/2023 21:18:00 INFO 139712331487040] #validation_score (algo-1) : ('absolute_loss', 0.22450526428222656)\u001b[0m\n", "\u001b[34m[03/21/2023 21:18:00 INFO 139712331487040] #validation_score (algo-1) : ('rmse', 0.3694919996002007)\u001b[0m\n", "\u001b[34m[03/21/2023 21:18:00 INFO 139712331487040] #validation_score (algo-1) : ('r2', 0.9986198254223321)\u001b[0m\n", "\u001b[34m[03/21/2023 21:18:00 INFO 139712331487040] #validation_score (algo-1) : ('mae', 0.22450526247024535)\u001b[0m\n", "\u001b[34m[03/21/2023 21:18:00 INFO 139712331487040] #quality_metric: host=algo-1, validation absolute_loss_objective =0.22450526428222656\u001b[0m\n", "\u001b[34m[03/21/2023 21:18:00 INFO 139712331487040] #quality_metric: host=algo-1, validation mse =0.1365243377685547\u001b[0m\n", "\u001b[34m[03/21/2023 21:18:00 INFO 139712331487040] #quality_metric: host=algo-1, validation absolute_loss =0.22450526428222656\u001b[0m\n", "\u001b[34m[03/21/2023 21:18:00 INFO 139712331487040] #quality_metric: host=algo-1, validation rmse =0.3694919996002007\u001b[0m\n", "\u001b[34m[03/21/2023 21:18:00 INFO 139712331487040] #quality_metric: host=algo-1, validation r2 =0.9986198254223321\u001b[0m\n", "\u001b[34m[03/21/2023 21:18:00 INFO 139712331487040] #quality_metric: host=algo-1, validation mae =0.22450526247024535\u001b[0m\n", "\u001b[34m[03/21/2023 21:18:00 INFO 139712331487040] Best model found for hyperparameters: {\"optimizer\": \"adam\", \"learning_rate\": 0.1, \"wd\": 0.0001, \"l1\": 0.0, \"lr_scheduler_step\": 10, \"lr_scheduler_factor\": 0.99, \"lr_scheduler_minimum_lr\": 1e-05}\u001b[0m\n", "\u001b[34m[03/21/2023 21:18:00 INFO 139712331487040] Saved checkpoint to \"/tmp/tmp72d9auhf/mx-mod-0000.params\"\u001b[0m\n", "\u001b[34m[03/21/2023 21:18:00 INFO 139712331487040] Test data is not provided.\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1679433470.1409924, \"EndTime\": 1679433480.406506, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\"}, \"Metrics\": {\"initialize.time\": {\"sum\": 280.70855140686035, \"count\": 1, \"min\": 280.70855140686035, \"max\": 280.70855140686035}, \"epochs\": {\"sum\": 16.0, \"count\": 1, \"min\": 16, \"max\": 16}, \"check_early_stopping.time\": {\"sum\": 9.89985466003418, \"count\": 17, \"min\": 0.17309188842773438, \"max\": 1.5544891357421875}, \"update.time\": {\"sum\": 9748.092412948608, \"count\": 16, \"min\": 531.4757823944092, \"max\": 747.4837303161621}, \"finalize.time\": {\"sum\": 146.88777923583984, \"count\": 1, \"min\": 146.88777923583984, \"max\": 146.88777923583984}, \"setuptime\": {\"sum\": 2.60162353515625, \"count\": 1, \"min\": 2.60162353515625, \"max\": 2.60162353515625}, \"totaltime\": {\"sum\": 10397.409677505493, \"count\": 1, \"min\": 10397.409677505493, \"max\": 10397.409677505493}}}\u001b[0m\n", "\n", "2023-03-21 21:18:17 Completed - Training job completed\n", "Training seconds: 143\n", "Billable seconds: 143\n", "CPU times: user 788 ms, sys: 92.7 ms, total: 880 ms\n", "Wall time: 4min 55s\n" ] } ], "source": [ "%%time\n", "linear.fit(inputs={\"train\": train_data, \"validation\": validation_data}, job_name=job_name)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set up hosting for the model\n", "\n", "Once the training is done, we can deploy the trained model as an Amazon SageMaker real-time hosted endpoint. This will allow us to make predictions (or inference) from the model. Note that we don't have to host on the same insantance (or type of instance) that we used to train. Training is a prolonged and compute heavy job that require a different of compute and memory requirements that hosting typically do not. We can choose any type of instance we want to host the model. In our case we chose the ml.m4.xlarge instance to train, but we choose to host the model on the less expensive cpu instance, ml.c4.xlarge. The endpoint deployment can be accomplished as follows:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:sagemaker:Creating model with name: linear-learner-2023-03-21-21-19-20-259\n", "INFO:sagemaker:Creating endpoint-config with name linear-learner-2023-03-21-21-19-20-259\n", "INFO:sagemaker:Creating endpoint with name linear-learner-2023-03-21-21-19-20-259\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "---------!\n", "created endpoint: linear-learner-2023-03-21-21-19-20-259\n", "CPU times: user 159 ms, sys: 12.7 ms, total: 172 ms\n", "Wall time: 5min 3s\n" ] } ], "source": [ "%%time\n", "# creating the endpoint out of the trained model\n", "linear_predictor = linear.deploy(initial_instance_count=1, instance_type=\"ml.c4.xlarge\")\n", "print(f\"\\ncreated endpoint: {linear_predictor.endpoint_name}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Copy the endpoint name of the deployed model from above and save it for later" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inference\n", "\n", "Now that the trained model is deployed at an endpoint that is up-and-running, we can use this endpoint for inference. To do this, we are going to configure the [predictor object](https://sagemaker.readthedocs.io/en/v1.2.4/predictors.html) to parse contents of type text/csv and deserialize the reply received from the endpoint to json format.\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "tags": [] }, "outputs": [], "source": [ "# cell 18\n", "# configure the predictor to accept to serialize csv input and parse the reposne as json\n", "from sagemaker.serializers import CSVSerializer\n", "from sagemaker.deserializers import JSONDeserializer\n", "\n", "linear_predictor.serializer = CSVSerializer()\n", "linear_predictor.deserializer = JSONDeserializer()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "We then use the test file containing the records of the data that we kept to test the model prediction. By running below cell multiple times we are selecting random sample from the testing samples to perform inference with." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "payload: 6,0.56,-73.988713,40.769042,1,-73.988713,40.769042,0.5,0.5,0.0,0.0,5.5,180.0,0.0,1.0\n", " \n", "Result: {'predictions': [{'score': 4.768509864807129}]}\n", "Actual fare: 4.5\n", "Prediction: 4.77\n", "Accuracy: 94.0\n", "CPU times: user 261 ms, sys: 3.79 ms, total: 265 ms\n", "Wall time: 639 ms\n" ] } ], "source": [ "%%time\n", "import json\n", "from itertools import islice\n", "import math\n", "import struct\n", "import boto3\n", "import random\n", "\n", "# downloading the test file from data_bucket\n", "FILE_TEST = \"test.csv\"\n", "s3 = boto3.client(\"s3\")\n", "s3.download_file(data_bucket, f\"{data_prefix}/test/{FILE_TEST}\", FILE_TEST)\n", "\n", "# getting testing sample from our test file\n", "test_data = [l for l in open(FILE_TEST, \"r\")]\n", "sample = random.choice(test_data).split(\",\")\n", "actual_fare = sample[0]\n", "payload = sample[1:] # removing actual age from the sample\n", "payload = \",\".join(map(str, payload))\n", "print('payload: ', payload, type(payload))\n", "# Invoke the predicor and analyise the result\n", "result = linear_predictor.predict(payload)\n", "print('Result:', result)\n", "# extracting the prediction value\n", "result = round(float(result[\"predictions\"][0][\"score\"]), 2)\n", "\n", "\n", "accuracy = str(round(100 - ((abs(float(result) - float(actual_fare)) / float(actual_fare)) * 100), 2))\n", "print(f\"Actual fare: {actual_fare}\\nPrediction: {result}\\nAccuracy: {accuracy}\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# cell 20\n" ] } ], "metadata": { "availableInstances": [ { "_defaultOrder": 0, "_isFastLaunch": true, "category": "General purpose", "gpuNum": 0, "memoryGiB": 4, "name": 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