{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SageMaker/DeepAR demo on electricity dataset\n",
    "\n",
    "This notebook complements the [DeepAR introduction notebook](https://github.com/awslabs/amazon-sagemaker-examples/blob/master/introduction_to_amazon_algorithms/deepar_synthetic/deepar_synthetic.ipynb). \n",
    "\n",
    "Here, we will consider a real use case and show how to use DeepAR on SageMaker for predicting energy consumption of 370 customers over time, based on a [dataset](https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014) that was used in the academic papers [[1](https://media.nips.cc/nipsbooks/nipspapers/paper_files/nips29/reviews/526.html)] and [[2](https://arxiv.org/abs/1704.04110)].  \n",
    "\n",
    "In particular, we will see how to:\n",
    "* Prepare the dataset\n",
    "* Use the SageMaker Python SDK to train a DeepAR model and deploy it\n",
    "* Make requests to the deployed model to obtain forecasts interactively\n",
    "* Illustrate advanced features of DeepAR: missing values, additional time features, non-regular frequencies and category information\n",
    "\n",
    "Running this notebook takes around 40 min on a ml.c4.2xlarge for the training, and inference is done on a ml.m4.xlarge (the usage time will depend on how long you leave your served model running).\n",
    "\n",
    "For more information see the DeepAR [documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html) or [paper](https://arxiv.org/abs/1704.04110), "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import sys\n",
    "from urllib.request import urlretrieve\n",
    "import zipfile\n",
    "from dateutil.parser import parse\n",
    "import json\n",
    "from random import shuffle\n",
    "import random\n",
    "import datetime\n",
    "import os\n",
    "\n",
    "import boto3\n",
    "import s3fs\n",
    "import sagemaker\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from __future__ import print_function\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import IntSlider, FloatSlider, Checkbox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set random seeds for reproducibility\n",
    "np.random.seed(42)\n",
    "random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sagemaker_session = sagemaker.Session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before starting, we can override the default values for the following:\n",
    "- The S3 bucket and prefix that you want to use for training and model data. This should be within the same region as the Notebook Instance, training, and hosting.\n",
    "- The IAM role arn used to give training and hosting access to your data. See the documentation for how to create these."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "s3_bucket = sagemaker.Session().default_bucket()  # replace with an existing bucket if needed\n",
    "s3_prefix = 'deepar-electricity-demo-notebook'    # prefix used for all data stored within the bucket\n",
    "\n",
    "role = sagemaker.get_execution_role()             # IAM role to use by SageMaker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "region = sagemaker_session.boto_region_name\n",
    "\n",
    "s3_data_path = \"s3://{}/{}/data\".format(s3_bucket, s3_prefix)\n",
    "s3_output_path = \"s3://{}/{}/output\".format(s3_bucket, s3_prefix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we configure the container image to be used for the region that we are running in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_name = sagemaker.amazon.amazon_estimator.get_image_uri(region, \"forecasting-deepar\", \"latest\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import electricity dataset and upload it to S3 to make it available for Sagemaker"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a first step, we need to download the original data set of from the UCI data set repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_HOST = \"https://archive.ics.uci.edu\"\n",
    "DATA_PATH = \"/ml/machine-learning-databases/00321/\"\n",
    "ARCHIVE_NAME = \"LD2011_2014.txt.zip\"\n",
    "FILE_NAME = ARCHIVE_NAME[:-4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading dataset (258MB), can take a few minutes depending on your connection\n",
      "258 MB downloaded\n",
      "extracting data archive\n"
     ]
    }
   ],
   "source": [
    "def progress_report_hook(count, block_size, total_size):\n",
    "    mb = int(count * block_size // 1e6)\n",
    "    if count % 500 == 0:\n",
    "        sys.stdout.write(\"\\r{} MB downloaded\".format(mb))\n",
    "        sys.stdout.flush()\n",
    "\n",
    "if not os.path.isfile(FILE_NAME):\n",
    "    print(\"downloading dataset (258MB), can take a few minutes depending on your connection\")\n",
    "    urlretrieve(DATA_HOST + DATA_PATH + ARCHIVE_NAME, ARCHIVE_NAME, reporthook=progress_report_hook)\n",
    "\n",
    "    print(\"\\nextracting data archive\")\n",
    "    zip_ref = zipfile.ZipFile(ARCHIVE_NAME, 'r')\n",
    "    zip_ref.extractall(\"./\")\n",
    "    zip_ref.close()\n",
    "else:\n",
    "    print(\"File found skipping download\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we load and parse the dataset and convert it to a collection of Pandas time series, which makes common time series operations such as indexing by time periods or resampling much easier. The data is originally recorded in 15min interval, which we could use directly. Here we want to forecast longer periods (one week) and resample the data to a granularity of 2 hours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(FILE_NAME, sep=\";\", index_col=0, parse_dates=True, decimal=',')\n",
    "num_timeseries = data.shape[1]\n",
    "data_kw = data.resample('2H').sum() / 8\n",
    "timeseries = []\n",
    "for i in range(num_timeseries):\n",
    "    timeseries.append(np.trim_zeros(data_kw.iloc[:,i], trim='f'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us plot the resulting time series for the first ten customers for the time period spanning the first two weeks of 2014."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(5, 2, figsize=(20, 20), sharex=True)\n",
    "axx = axs.ravel()\n",
    "for i in range(0, 10):\n",
    "    timeseries[i].loc[\"2014-01-01\":\"2014-01-14\"].plot(ax=axx[i])\n",
    "    axx[i].set_xlabel(\"date\")    \n",
    "    axx[i].set_ylabel(\"kW consumption\")   \n",
    "    axx[i].grid(which='minor', axis='x')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train and Test splits\n",
    "\n",
    "Often times one is interested in evaluating the model or tuning its hyperparameters by looking at error metrics on a hold-out test set. Here we split the available data into train and test sets for evaluating the trained model. For standard machine learning tasks such as classification and regression, one typically obtains this split by randomly separating examples into train and test sets. However, in forecasting it is important to do this train/test split based on time rather than by time series.\n",
    "\n",
    "In this example, we will reserve the last section of each of the time series for evalutation purpose and use only the first part as training data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we use 2 hour frequency for the time series\n",
    "freq = '2H'\n",
    "\n",
    "# we predict for 7 days\n",
    "prediction_length = 7 * 12\n",
    "\n",
    "# we also use 7 days as context length, this is the number of state updates accomplished before making predictions\n",
    "context_length = 7 * 12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We specify here the portion of the data that is used for training: the model sees data from 2014-01-01 to 2014-09-01 for training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_dataset = pd.Timestamp(\"2014-01-01 00:00:00\", freq=freq)\n",
    "end_training = pd.Timestamp(\"2014-09-01 00:00:00\", freq=freq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The DeepAR JSON input format represents each time series as a JSON object. In the simplest case each time series just consists of a start time stamp (``start``) and a list of values (``target``). For more complex cases, DeepAR also supports the fields ``dynamic_feat`` for time-series features and ``cat`` for categorical features, which we will use  later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "370\n"
     ]
    }
   ],
   "source": [
    "training_data = [\n",
    "    {\n",
    "        \"start\": str(start_dataset),\n",
    "        \"target\": ts[start_dataset:end_training - 1].tolist()  # We use -1, because pandas indexing includes the upper bound \n",
    "    }\n",
    "    for ts in timeseries\n",
    "]\n",
    "print(len(training_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As test data, we will consider time series extending beyond the training range: these will be used for computing test scores, by using the trained model to forecast their trailing 7 days, and comparing predictions with actual values.\n",
    "To evaluate our model performance on more than one week, we generate test data that extends to 1, 2, 3, 4 weeks beyond the training range. This way we perform *rolling evaluation* of our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1480\n"
     ]
    }
   ],
   "source": [
    "num_test_windows = 4\n",
    "\n",
    "test_data = [\n",
    "    {\n",
    "        \"start\": str(start_dataset),\n",
    "        \"target\": ts[start_dataset:end_training + k * prediction_length].tolist()\n",
    "    }\n",
    "    for k in range(1, num_test_windows + 1) \n",
    "    for ts in timeseries\n",
    "]\n",
    "print(len(test_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now write the dictionary to the `jsonlines` file format that DeepAR understands (it also supports gzipped jsonlines and parquet)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def write_dicts_to_file(path, data):\n",
    "    with open(path, 'wb') as fp:\n",
    "        for d in data:\n",
    "            fp.write(json.dumps(d).encode(\"utf-8\"))\n",
    "            fp.write(\"\\n\".encode('utf-8'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.86 s, sys: 82.4 ms, total: 2.94 s\n",
      "Wall time: 2.94 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "write_dicts_to_file(\"train.json\", training_data)\n",
    "write_dicts_to_file(\"test.json\", test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the data files locally, let us copy them to S3 where DeepAR can access them. Depending on your connection, this may take a couple of minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "s3 = boto3.resource('s3')\n",
    "def copy_to_s3(local_file, s3_path, override=False):\n",
    "    assert s3_path.startswith('s3://')\n",
    "    split = s3_path.split('/')\n",
    "    bucket = split[2]\n",
    "    path = '/'.join(split[3:])\n",
    "    buk = s3.Bucket(bucket)\n",
    "    \n",
    "    if len(list(buk.objects.filter(Prefix=path))) > 0:\n",
    "        if not override:\n",
    "            print('File s3://{}/{} already exists.\\nSet override to upload anyway.\\n'.format(s3_bucket, s3_path))\n",
    "            return\n",
    "        else:\n",
    "            print('Overwriting existing file')\n",
    "    with open(local_file, 'rb') as data:\n",
    "        print('Uploading file to {}'.format(s3_path))\n",
    "        buk.put_object(Key=path, Body=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File s3://sagemaker-ap-northeast-2-082256166551/s3://sagemaker-ap-northeast-2-082256166551/deepar-electricity-demo-notebook/data/train/train.json already exists.\n",
      "Set override to upload anyway.\n",
      "\n",
      "File s3://sagemaker-ap-northeast-2-082256166551/s3://sagemaker-ap-northeast-2-082256166551/deepar-electricity-demo-notebook/data/test/test.json already exists.\n",
      "Set override to upload anyway.\n",
      "\n",
      "CPU times: user 19 ms, sys: 0 ns, total: 19 ms\n",
      "Wall time: 95 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "copy_to_s3(\"train.json\", s3_data_path + \"/train/train.json\")\n",
    "copy_to_s3(\"test.json\", s3_data_path + \"/test/test.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look to what we just wrote to S3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"start\": \"2014-01-01 00:00:00\", \"target\": [2.6967005076142154, 2.8553299492385804, 2.53807106598985...\n"
     ]
    }
   ],
   "source": [
    "s3filesystem = s3fs.S3FileSystem()\n",
    "with s3filesystem.open(s3_data_path + \"/train/train.json\", 'rb') as fp:\n",
    "    print(fp.readline().decode(\"utf-8\")[:100] + \"...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are all set with our dataset processing, we can now call DeepAR to train a model and generate predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train a model\n",
    "\n",
    "Here we define the estimator that will launch the training job."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimator = sagemaker.estimator.Estimator(\n",
    "    sagemaker_session=sagemaker_session,\n",
    "    image_name=image_name,\n",
    "    role=role,\n",
    "    train_instance_count=1,\n",
    "    train_instance_type='ml.c4.2xlarge',\n",
    "    base_job_name='deepar-electricity-demo',\n",
    "    output_path=s3_output_path\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we need to set the hyperparameters for the training job. For example frequency of the time series used, number of data points the model will look at in the past, number of predicted data points. The other hyperparameters concern the model to train (number of layers, number of cells per layer, likelihood function) and the training options (number of epochs, batch size, learning rate...). We use default parameters for every optional parameter in this case (you can always use [Sagemaker Automated Model Tuning](https://aws.amazon.com/blogs/aws/sagemaker-automatic-model-tuning/) to tune them)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "hyperparameters = {\n",
    "    \"time_freq\": freq,\n",
    "    \"epochs\": \"400\",\n",
    "    \"early_stopping_patience\": \"40\",\n",
    "    \"mini_batch_size\": \"64\",\n",
    "    \"learning_rate\": \"5E-4\",\n",
    "    \"context_length\": str(context_length),\n",
    "    \"prediction_length\": str(prediction_length)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimator.set_hyperparameters(**hyperparameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are ready to launch the training job. SageMaker will start an EC2 instance, download the data from S3, start training the model and save the trained model.\n",
    "\n",
    "If you provide the `test` data channel as we do in this example, DeepAR will also calculate accuracy metrics for the trained model on this test. This is done by predicting the last `prediction_length` points of each time-series in the test set and comparing this to the actual value of the time-series. \n",
    "\n",
    "**Note:** the next cell may take a few minutes to complete, depending on data size, model complexity, training options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating training-job with name: deepar-electricity-demo-2019-02-19-01-41-19-423\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-02-19 01:41:19 Starting - Starting the training job...\n",
      "2019-02-19 01:41:20 Starting - Launching requested ML instances......\n",
      "2019-02-19 01:42:22 Starting - Preparing the instances for training...\n",
      "2019-02-19 01:43:14 Downloading - Downloading input data...\n",
      "2019-02-19 01:43:30 Training - Downloading the training image..\n",
      "\u001b[31mArguments: train\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:55 INFO 140560773478208] Reading default configuration from /opt/amazon/lib/python2.7/site-packages/algorithm/default-input.json: {u'num_dynamic_feat': u'auto', u'dropout_rate': u'0.10', u'mini_batch_size': u'128', u'test_quantiles': u'[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]', u'_tuning_objective_metric': u'', u'_num_gpus': u'auto', u'num_eval_samples': u'100', u'learning_rate': u'0.001', u'num_cells': u'40', u'num_layers': u'2', u'embedding_dimension': u'10', u'_kvstore': u'auto', u'_num_kv_servers': u'auto', u'cardinality': u'auto', u'likelihood': u'student-t', u'early_stopping_patience': u''}\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:55 INFO 140560773478208] Reading provided configuration from /opt/ml/input/config/hyperparameters.json: {u'learning_rate': u'5E-4', u'prediction_length': u'84', u'epochs': u'400', u'time_freq': u'2H', u'context_length': u'84', u'mini_batch_size': u'64', u'early_stopping_patience': u'40'}\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:55 INFO 140560773478208] Final configuration: {u'dropout_rate': u'0.10', u'test_quantiles': u'[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]', u'_tuning_objective_metric': u'', u'num_eval_samples': u'100', u'learning_rate': u'5E-4', u'num_layers': u'2', u'epochs': u'400', u'embedding_dimension': u'10', u'num_cells': u'40', u'_num_kv_servers': u'auto', u'mini_batch_size': u'64', u'likelihood': u'student-t', u'num_dynamic_feat': u'auto', u'cardinality': u'auto', u'_num_gpus': u'auto', u'prediction_length': u'84', u'time_freq': u'2H', u'context_length': u'84', u'_kvstore': u'auto', u'early_stopping_patience': u'40'}\u001b[0m\n",
      "\u001b[31mProcess 1 is a worker.\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:55 INFO 140560773478208] Detected entry point for worker worker\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:55 INFO 140560773478208] Using early stopping with patience 40\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:55 INFO 140560773478208] [cardinality=auto] `cat` field was NOT found in the file `/opt/ml/input/data/train/train.json` and will NOT be used for training.\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:55 INFO 140560773478208] [num_dynamic_feat=auto] `dynamic_feat` field was NOT found in the file `/opt/ml/input/data/train/train.json` and will NOT be used for training.\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:55 INFO 140560773478208] Training set statistics:\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:55 INFO 140560773478208] Real time series\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:55 INFO 140560773478208] number of time series: 370\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:55 INFO 140560773478208] number of observations: 1070956\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:55 INFO 140560773478208] mean target length: 2894\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:55 INFO 140560773478208] min/mean/max target: 0.0/611.175193457/163325.0\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:55 INFO 140560773478208] mean abs(target): 611.175193457\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:55 INFO 140560773478208] contains missing values: no\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:55 INFO 140560773478208] Small number of time series. Doing 1 number of passes over dataset per epoch.\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:56 INFO 140560773478208] Test set statistics:\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:56 INFO 140560773478208] Real time series\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:56 INFO 140560773478208] number of time series: 1480\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:56 INFO 140560773478208] number of observations: 4596104\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:56 INFO 140560773478208] mean target length: 3105\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:56 INFO 140560773478208] min/mean/max target: 0.0/618.973334448/163325.0\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:56 INFO 140560773478208] mean abs(target): 618.973334448\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:56 INFO 140560773478208] contains missing values: no\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:56 INFO 140560773478208] nvidia-smi took: 0.0251829624176 secs to identify 0 gpus\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:56 INFO 140560773478208] Number of GPUs being used: 0\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:56 INFO 140560773478208] Create Store: local\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"get_graph.time\": {\"count\": 1, \"max\": 664.4101142883301, \"sum\": 664.4101142883301, \"min\": 664.4101142883301}}, \"EndTime\": 1550540637.224266, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540636.558852}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:57 INFO 140560773478208] Number of GPUs being used: 0\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"initialize.time\": {\"count\": 1, \"max\": 1440.1211738586426, \"sum\": 1440.1211738586426, \"min\": 1440.1211738586426}}, \"EndTime\": 1550540637.999094, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540637.224348}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:58 INFO 140560773478208] Epoch[0] Batch[0] avg_epoch_loss=5.876866\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:59 INFO 140560773478208] Epoch[0] Batch[5] avg_epoch_loss=5.816988\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:59 INFO 140560773478208] Epoch[0] Batch [5]#011Speed: 323.33 samples/sec#011loss=5.816988\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:59 INFO 140560773478208] processed a total of 387 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"epochs\": {\"count\": 1, \"max\": 400, \"sum\": 400.0, \"min\": 400}, \"update.time\": {\"count\": 1, \"max\": 1790.6930446624756, \"sum\": 1790.6930446624756, \"min\": 1790.6930446624756}}, \"EndTime\": 1550540639.789987, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540637.999186}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:59 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=216.099344192 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:59 INFO 140560773478208] #progress_metric: host=algo-1, completed 0 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:59 INFO 140560773478208] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:43:59 INFO 140560773478208] Saved checkpoint to \"/opt/ml/model/state_19569327-04c7-4c34-9e1e-8cb759d1e482-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 68.67003440856934, \"sum\": 68.67003440856934, \"min\": 68.67003440856934}}, \"EndTime\": 1550540639.859256, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540639.790094}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:00 INFO 140560773478208] Epoch[1] Batch[0] avg_epoch_loss=5.616020\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:01 INFO 140560773478208] Epoch[1] Batch[5] avg_epoch_loss=5.532034\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:01 INFO 140560773478208] Epoch[1] Batch [5]#011Speed: 299.94 samples/sec#011loss=5.532034\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:01 INFO 140560773478208] processed a total of 382 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1493.8139915466309, \"sum\": 1493.8139915466309, \"min\": 1493.8139915466309}}, \"EndTime\": 1550540641.353224, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540639.859334}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:01 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=255.699836978 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:01 INFO 140560773478208] #progress_metric: host=algo-1, completed 0 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:01 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:01 INFO 140560773478208] Epoch[2] Batch[0] avg_epoch_loss=5.249897\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:02 INFO 140560773478208] Epoch[2] Batch[5] avg_epoch_loss=5.256293\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:02 INFO 140560773478208] Epoch[2] Batch [5]#011Speed: 310.75 samples/sec#011loss=5.256293\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:02 INFO 140560773478208] processed a total of 358 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1469.2821502685547, \"sum\": 1469.2821502685547, \"min\": 1469.2821502685547}}, \"EndTime\": 1550540642.823003, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540641.353305}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:02 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=243.636040188 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:02 INFO 140560773478208] #progress_metric: host=algo-1, completed 0 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:02 INFO 140560773478208] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:02 INFO 140560773478208] Saved checkpoint to \"/opt/ml/model/state_f27c904f-ce2b-4601-9734-8db37e09c4da-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 69.65088844299316, \"sum\": 69.65088844299316, \"min\": 69.65088844299316}}, \"EndTime\": 1550540642.893123, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540642.823086}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:03 INFO 140560773478208] Epoch[3] Batch[0] avg_epoch_loss=5.192884\u001b[0m\n",
      "\n",
      "2019-02-19 01:43:52 Training - Training image download completed. Training in progress.\u001b[31m[02/19/2019 01:44:04 INFO 140560773478208] Epoch[3] Batch[5] avg_epoch_loss=5.143271\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:04 INFO 140560773478208] Epoch[3] Batch [5]#011Speed: 254.19 samples/sec#011loss=5.143271\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:04 INFO 140560773478208] processed a total of 362 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1693.6941146850586, \"sum\": 1693.6941146850586, \"min\": 1693.6941146850586}}, \"EndTime\": 1550540644.586965, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540642.893199}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:04 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=213.71596884 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:04 INFO 140560773478208] #progress_metric: host=algo-1, completed 1 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:04 INFO 140560773478208] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:04 INFO 140560773478208] Saved checkpoint to \"/opt/ml/model/state_02e131bb-d21d-410c-835e-f3db44f79169-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 71.73395156860352, \"sum\": 71.73395156860352, \"min\": 71.73395156860352}}, \"EndTime\": 1550540644.659217, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540644.587055}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:05 INFO 140560773478208] Epoch[4] Batch[0] avg_epoch_loss=5.284318\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:06 INFO 140560773478208] Epoch[4] Batch[5] avg_epoch_loss=5.368214\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:06 INFO 140560773478208] Epoch[4] Batch [5]#011Speed: 320.19 samples/sec#011loss=5.368214\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:06 INFO 140560773478208] processed a total of 359 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1437.3760223388672, \"sum\": 1437.3760223388672, \"min\": 1437.3760223388672}}, \"EndTime\": 1550540646.096728, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540644.659286}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:06 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=249.739337539 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:06 INFO 140560773478208] #progress_metric: host=algo-1, completed 1 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:06 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:06 INFO 140560773478208] Epoch[5] Batch[0] avg_epoch_loss=5.004211\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:07 INFO 140560773478208] Epoch[5] Batch[5] avg_epoch_loss=5.032523\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:07 INFO 140560773478208] Epoch[5] Batch [5]#011Speed: 315.13 samples/sec#011loss=5.032523\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:07 INFO 140560773478208] processed a total of 392 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1644.7200775146484, \"sum\": 1644.7200775146484, \"min\": 1644.7200775146484}}, \"EndTime\": 1550540647.741911, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540646.096807}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:07 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=238.319186817 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:07 INFO 140560773478208] #progress_metric: host=algo-1, completed 1 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:07 INFO 140560773478208] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:07 INFO 140560773478208] Saved checkpoint to \"/opt/ml/model/state_2e19fa6b-d909-471c-b134-4163e169bb8c-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 66.86210632324219, \"sum\": 66.86210632324219, \"min\": 66.86210632324219}}, \"EndTime\": 1550540647.809736, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540647.742001}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:08 INFO 140560773478208] Epoch[6] Batch[0] avg_epoch_loss=5.052845\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:09 INFO 140560773478208] Epoch[6] Batch[5] avg_epoch_loss=4.899042\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:09 INFO 140560773478208] Epoch[6] Batch [5]#011Speed: 297.66 samples/sec#011loss=4.899042\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:09 INFO 140560773478208] processed a total of 353 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1496.7269897460938, \"sum\": 1496.7269897460938, \"min\": 1496.7269897460938}}, \"EndTime\": 1550540649.306605, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540647.809806}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:09 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=235.830599584 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:09 INFO 140560773478208] #progress_metric: host=algo-1, completed 1 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:09 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:09 INFO 140560773478208] Epoch[7] Batch[0] avg_epoch_loss=5.028772\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:10 INFO 140560773478208] Epoch[7] Batch[5] avg_epoch_loss=4.892409\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:10 INFO 140560773478208] Epoch[7] Batch [5]#011Speed: 320.07 samples/sec#011loss=4.892409\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:10 INFO 140560773478208] processed a total of 374 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1424.6389865875244, \"sum\": 1424.6389865875244, \"min\": 1424.6389865875244}}, \"EndTime\": 1550540650.731688, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540649.306672}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:10 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=262.499233756 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:10 INFO 140560773478208] #progress_metric: host=algo-1, completed 2 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:10 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:11 INFO 140560773478208] Epoch[8] Batch[0] avg_epoch_loss=4.311175\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:12 INFO 140560773478208] Epoch[8] Batch[5] avg_epoch_loss=4.652281\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:12 INFO 140560773478208] Epoch[8] Batch [5]#011Speed: 320.97 samples/sec#011loss=4.652281\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:12 INFO 140560773478208] processed a total of 375 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1434.7259998321533, \"sum\": 1434.7259998321533, \"min\": 1434.7259998321533}}, \"EndTime\": 1550540652.166843, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540650.73177}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:12 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=261.35370994 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:12 INFO 140560773478208] #progress_metric: host=algo-1, completed 2 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:12 INFO 140560773478208] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:12 INFO 140560773478208] Saved checkpoint to \"/opt/ml/model/state_4dfaa14b-0a82-4c3d-b50b-cdda9d064ab9-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 64.82410430908203, \"sum\": 64.82410430908203, \"min\": 64.82410430908203}}, \"EndTime\": 1550540652.232154, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540652.166911}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:12 INFO 140560773478208] Epoch[9] Batch[0] avg_epoch_loss=4.875826\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:13 INFO 140560773478208] Epoch[9] Batch[5] avg_epoch_loss=4.252999\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:13 INFO 140560773478208] Epoch[9] Batch [5]#011Speed: 303.69 samples/sec#011loss=4.252999\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:13 INFO 140560773478208] processed a total of 335 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1468.059778213501, \"sum\": 1468.059778213501, \"min\": 1468.059778213501}}, \"EndTime\": 1550540653.700366, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540652.232235}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:13 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=228.173814536 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:13 INFO 140560773478208] #progress_metric: host=algo-1, completed 2 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:13 INFO 140560773478208] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:13 INFO 140560773478208] Saved checkpoint to \"/opt/ml/model/state_aa6978ec-4654-4731-a72d-6905f110a81a-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 65.04702568054199, \"sum\": 65.04702568054199, \"min\": 65.04702568054199}}, \"EndTime\": 1550540653.765917, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540653.700442}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:14 INFO 140560773478208] Epoch[10] Batch[0] avg_epoch_loss=4.663337\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:15 INFO 140560773478208] Epoch[10] Batch[5] avg_epoch_loss=4.742623\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:15 INFO 140560773478208] Epoch[10] Batch [5]#011Speed: 318.21 samples/sec#011loss=4.742623\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:15 INFO 140560773478208] processed a total of 377 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1446.7120170593262, \"sum\": 1446.7120170593262, \"min\": 1446.7120170593262}}, \"EndTime\": 1550540655.21278, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540653.765998}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:15 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=260.568193117 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:15 INFO 140560773478208] #progress_metric: host=algo-1, completed 2 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:15 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:15 INFO 140560773478208] Epoch[11] Batch[0] avg_epoch_loss=4.619688\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:16 INFO 140560773478208] Epoch[11] Batch[5] avg_epoch_loss=4.493424\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:16 INFO 140560773478208] Epoch[11] Batch [5]#011Speed: 320.33 samples/sec#011loss=4.493424\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:16 INFO 140560773478208] processed a total of 349 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1421.89621925354, \"sum\": 1421.89621925354, \"min\": 1421.89621925354}}, \"EndTime\": 1550540656.635151, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540655.212865}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:16 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=245.426557322 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:16 INFO 140560773478208] #progress_metric: host=algo-1, completed 3 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:16 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:17 INFO 140560773478208] Epoch[12] Batch[0] avg_epoch_loss=4.311730\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:18 INFO 140560773478208] Epoch[12] Batch[5] avg_epoch_loss=4.606930\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:18 INFO 140560773478208] Epoch[12] Batch [5]#011Speed: 305.47 samples/sec#011loss=4.606930\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:18 INFO 140560773478208] processed a total of 366 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1468.6260223388672, \"sum\": 1468.6260223388672, \"min\": 1468.6260223388672}}, \"EndTime\": 1550540658.104212, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540656.63523}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:18 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=249.190114047 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:18 INFO 140560773478208] #progress_metric: host=algo-1, completed 3 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:18 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:18 INFO 140560773478208] Epoch[13] Batch[0] avg_epoch_loss=4.606467\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:19 INFO 140560773478208] Epoch[13] Batch[5] avg_epoch_loss=4.712512\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:19 INFO 140560773478208] Epoch[13] Batch [5]#011Speed: 307.68 samples/sec#011loss=4.712512\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:19 INFO 140560773478208] processed a total of 365 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1459.082841873169, \"sum\": 1459.082841873169, \"min\": 1459.082841873169}}, \"EndTime\": 1550540659.563742, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540658.104302}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:19 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=250.136015008 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:19 INFO 140560773478208] #progress_metric: host=algo-1, completed 3 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:19 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:20 INFO 140560773478208] Epoch[14] Batch[0] avg_epoch_loss=4.425260\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:20 INFO 140560773478208] Epoch[14] Batch[5] avg_epoch_loss=4.531450\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:20 INFO 140560773478208] Epoch[14] Batch [5]#011Speed: 324.09 samples/sec#011loss=4.531450\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:21 INFO 140560773478208] processed a total of 387 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1634.8609924316406, \"sum\": 1634.8609924316406, \"min\": 1634.8609924316406}}, \"EndTime\": 1550540661.199019, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540659.563825}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:21 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=236.698388108 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:21 INFO 140560773478208] #progress_metric: host=algo-1, completed 3 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:21 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:21 INFO 140560773478208] Epoch[15] Batch[0] avg_epoch_loss=4.384561\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:22 INFO 140560773478208] Epoch[15] Batch[5] avg_epoch_loss=4.296370\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:22 INFO 140560773478208] Epoch[15] Batch [5]#011Speed: 317.87 samples/sec#011loss=4.296370\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:22 INFO 140560773478208] processed a total of 360 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1459.6679210662842, \"sum\": 1459.6679210662842, \"min\": 1459.6679210662842}}, \"EndTime\": 1550540662.659126, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540661.199106}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:22 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=246.611983392 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:22 INFO 140560773478208] #progress_metric: host=algo-1, completed 4 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:22 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:23 INFO 140560773478208] Epoch[16] Batch[0] avg_epoch_loss=4.494394\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:24 INFO 140560773478208] Epoch[16] Batch[5] avg_epoch_loss=4.449899\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:24 INFO 140560773478208] Epoch[16] Batch [5]#011Speed: 295.03 samples/sec#011loss=4.449899\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:24 INFO 140560773478208] processed a total of 371 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1504.3411254882812, \"sum\": 1504.3411254882812, \"min\": 1504.3411254882812}}, \"EndTime\": 1550540664.163928, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540662.659204}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:24 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=246.599624258 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:24 INFO 140560773478208] #progress_metric: host=algo-1, completed 4 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:24 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:24 INFO 140560773478208] Epoch[17] Batch[0] avg_epoch_loss=4.281968\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:25 INFO 140560773478208] Epoch[17] Batch[5] avg_epoch_loss=4.520233\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:25 INFO 140560773478208] Epoch[17] Batch [5]#011Speed: 323.62 samples/sec#011loss=4.520233\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:25 INFO 140560773478208] processed a total of 355 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1440.5958652496338, \"sum\": 1440.5958652496338, \"min\": 1440.5958652496338}}, \"EndTime\": 1550540665.604951, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540664.164011}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:25 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=246.405091675 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:25 INFO 140560773478208] #progress_metric: host=algo-1, completed 4 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:25 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:26 INFO 140560773478208] Epoch[18] Batch[0] avg_epoch_loss=4.369987\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:27 INFO 140560773478208] Epoch[18] Batch[5] avg_epoch_loss=4.383295\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:27 INFO 140560773478208] Epoch[18] Batch [5]#011Speed: 320.02 samples/sec#011loss=4.383295\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:27 INFO 140560773478208] processed a total of 376 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1449.1209983825684, \"sum\": 1449.1209983825684, \"min\": 1449.1209983825684}}, \"EndTime\": 1550540667.054507, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540665.605034}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:27 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=259.443988492 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:27 INFO 140560773478208] #progress_metric: host=algo-1, completed 4 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:27 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:27 INFO 140560773478208] Epoch[19] Batch[0] avg_epoch_loss=4.433831\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:28 INFO 140560773478208] Epoch[19] Batch[5] avg_epoch_loss=4.317859\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:28 INFO 140560773478208] Epoch[19] Batch [5]#011Speed: 314.66 samples/sec#011loss=4.317859\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:28 INFO 140560773478208] processed a total of 380 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1437.3338222503662, \"sum\": 1437.3338222503662, \"min\": 1437.3338222503662}}, \"EndTime\": 1550540668.492325, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540667.054597}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:28 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=264.355367432 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:28 INFO 140560773478208] #progress_metric: host=algo-1, completed 5 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:28 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:28 INFO 140560773478208] Epoch[20] Batch[0] avg_epoch_loss=4.631585\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:29 INFO 140560773478208] Epoch[20] Batch[5] avg_epoch_loss=4.280704\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:29 INFO 140560773478208] Epoch[20] Batch [5]#011Speed: 316.35 samples/sec#011loss=4.280704\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:29 INFO 140560773478208] processed a total of 345 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1461.9390964508057, \"sum\": 1461.9390964508057, \"min\": 1461.9390964508057}}, \"EndTime\": 1550540669.95469, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540668.49241}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:29 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=235.968432196 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:29 INFO 140560773478208] #progress_metric: host=algo-1, completed 5 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:29 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:30 INFO 140560773478208] Epoch[21] Batch[0] avg_epoch_loss=4.004133\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:31 INFO 140560773478208] Epoch[21] Batch[5] avg_epoch_loss=4.185661\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:31 INFO 140560773478208] Epoch[21] Batch [5]#011Speed: 322.64 samples/sec#011loss=4.185661\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:31 INFO 140560773478208] processed a total of 355 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1464.5249843597412, \"sum\": 1464.5249843597412, \"min\": 1464.5249843597412}}, \"EndTime\": 1550540671.419634, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540669.954773}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:31 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=242.37574071 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:31 INFO 140560773478208] #progress_metric: host=algo-1, completed 5 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:31 INFO 140560773478208] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:31 INFO 140560773478208] Saved checkpoint to \"/opt/ml/model/state_5ab99705-b60e-4326-b3b4-39e06adcf161-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 65.39297103881836, \"sum\": 65.39297103881836, \"min\": 65.39297103881836}}, \"EndTime\": 1550540671.485529, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540671.419737}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:31 INFO 140560773478208] Epoch[22] Batch[0] avg_epoch_loss=4.257868\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:32 INFO 140560773478208] Epoch[22] Batch[5] avg_epoch_loss=4.306051\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:32 INFO 140560773478208] Epoch[22] Batch [5]#011Speed: 305.11 samples/sec#011loss=4.306051\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:32 INFO 140560773478208] processed a total of 378 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1484.079122543335, \"sum\": 1484.079122543335, \"min\": 1484.079122543335}}, \"EndTime\": 1550540672.969754, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540671.485605}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:32 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=254.682493546 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:32 INFO 140560773478208] #progress_metric: host=algo-1, completed 5 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:32 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:33 INFO 140560773478208] Epoch[23] Batch[0] avg_epoch_loss=4.168518\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:34 INFO 140560773478208] Epoch[23] Batch[5] avg_epoch_loss=4.268086\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:34 INFO 140560773478208] Epoch[23] Batch [5]#011Speed: 310.97 samples/sec#011loss=4.268086\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:34 INFO 140560773478208] processed a total of 378 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1490.6189441680908, \"sum\": 1490.6189441680908, \"min\": 1490.6189441680908}}, \"EndTime\": 1550540674.460793, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540672.969836}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:34 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=253.564643637 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:34 INFO 140560773478208] #progress_metric: host=algo-1, completed 6 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:34 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:34 INFO 140560773478208] Epoch[24] Batch[0] avg_epoch_loss=4.155177\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:35 INFO 140560773478208] Epoch[24] Batch[5] avg_epoch_loss=4.245621\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:35 INFO 140560773478208] Epoch[24] Batch [5]#011Speed: 322.05 samples/sec#011loss=4.245621\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:35 INFO 140560773478208] processed a total of 367 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1462.8541469573975, \"sum\": 1462.8541469573975, \"min\": 1462.8541469573975}}, \"EndTime\": 1550540675.924072, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540674.460878}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:35 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=250.858527621 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:35 INFO 140560773478208] #progress_metric: host=algo-1, completed 6 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:35 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:36 INFO 140560773478208] Epoch[25] Batch[0] avg_epoch_loss=4.152906\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:37 INFO 140560773478208] Epoch[25] Batch[5] avg_epoch_loss=4.418363\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:37 INFO 140560773478208] Epoch[25] Batch [5]#011Speed: 319.90 samples/sec#011loss=4.418363\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:37 INFO 140560773478208] processed a total of 351 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1451.2441158294678, \"sum\": 1451.2441158294678, \"min\": 1451.2441158294678}}, \"EndTime\": 1550540677.375732, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540675.924155}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:37 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=241.840785652 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:37 INFO 140560773478208] #progress_metric: host=algo-1, completed 6 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:37 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:37 INFO 140560773478208] Epoch[26] Batch[0] avg_epoch_loss=4.185227\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:38 INFO 140560773478208] Epoch[26] Batch[5] avg_epoch_loss=4.216115\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:38 INFO 140560773478208] Epoch[26] Batch [5]#011Speed: 318.12 samples/sec#011loss=4.216115\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:39 INFO 140560773478208] processed a total of 392 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1665.2240753173828, \"sum\": 1665.2240753173828, \"min\": 1665.2240753173828}}, \"EndTime\": 1550540679.041381, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540677.375815}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:39 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=235.385252838 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:39 INFO 140560773478208] #progress_metric: host=algo-1, completed 6 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:39 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:39 INFO 140560773478208] Epoch[27] Batch[0] avg_epoch_loss=4.360985\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:40 INFO 140560773478208] Epoch[27] Batch[5] avg_epoch_loss=4.191194\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:40 INFO 140560773478208] Epoch[27] Batch [5]#011Speed: 311.27 samples/sec#011loss=4.191194\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:40 INFO 140560773478208] processed a total of 366 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1455.7020664215088, \"sum\": 1455.7020664215088, \"min\": 1455.7020664215088}}, \"EndTime\": 1550540680.497599, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540679.041471}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:40 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=251.40403343 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:40 INFO 140560773478208] #progress_metric: host=algo-1, completed 7 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:40 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:40 INFO 140560773478208] Epoch[28] Batch[0] avg_epoch_loss=4.252416\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:41 INFO 140560773478208] processed a total of 313 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1222.8038311004639, \"sum\": 1222.8038311004639, \"min\": 1222.8038311004639}}, \"EndTime\": 1550540681.720829, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540680.497681}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:41 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=255.944999838 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:41 INFO 140560773478208] #progress_metric: host=algo-1, completed 7 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:41 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:42 INFO 140560773478208] Epoch[29] Batch[0] avg_epoch_loss=4.273024\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:43 INFO 140560773478208] Epoch[29] Batch[5] avg_epoch_loss=4.373638\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:43 INFO 140560773478208] Epoch[29] Batch [5]#011Speed: 312.03 samples/sec#011loss=4.373638\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:43 INFO 140560773478208] processed a total of 367 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1447.6330280303955, \"sum\": 1447.6330280303955, \"min\": 1447.6330280303955}}, \"EndTime\": 1550540683.168911, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540681.720905}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:43 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=253.497499048 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:43 INFO 140560773478208] #progress_metric: host=algo-1, completed 7 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:43 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:43 INFO 140560773478208] Epoch[30] Batch[0] avg_epoch_loss=4.124432\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:44 INFO 140560773478208] Epoch[30] Batch[5] avg_epoch_loss=4.148859\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:44 INFO 140560773478208] Epoch[30] Batch [5]#011Speed: 311.66 samples/sec#011loss=4.148859\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:44 INFO 140560773478208] processed a total of 358 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1444.2880153656006, \"sum\": 1444.2880153656006, \"min\": 1444.2880153656006}}, \"EndTime\": 1550540684.613613, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540683.168979}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:44 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=247.851558529 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:44 INFO 140560773478208] #progress_metric: host=algo-1, completed 7 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:44 INFO 140560773478208] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:44 INFO 140560773478208] Saved checkpoint to \"/opt/ml/model/state_cd106a68-f7be-488a-b42c-e21c7ba79f8d-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 99.10798072814941, \"sum\": 99.10798072814941, \"min\": 99.10798072814941}}, \"EndTime\": 1550540684.713201, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540684.613698}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:45 INFO 140560773478208] Epoch[31] Batch[0] avg_epoch_loss=4.328337\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:46 INFO 140560773478208] Epoch[31] Batch[5] avg_epoch_loss=4.283358\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:46 INFO 140560773478208] Epoch[31] Batch [5]#011Speed: 319.11 samples/sec#011loss=4.283358\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:46 INFO 140560773478208] processed a total of 333 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1451.4338970184326, \"sum\": 1451.4338970184326, \"min\": 1451.4338970184326}}, \"EndTime\": 1550540686.164802, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540684.713283}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:46 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=229.409450163 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:46 INFO 140560773478208] #progress_metric: host=algo-1, completed 8 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:46 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:46 INFO 140560773478208] Epoch[32] Batch[0] avg_epoch_loss=4.129793\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:47 INFO 140560773478208] Epoch[32] Batch[5] avg_epoch_loss=4.080110\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:47 INFO 140560773478208] Epoch[32] Batch [5]#011Speed: 321.05 samples/sec#011loss=4.080110\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:47 INFO 140560773478208] processed a total of 408 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1636.6989612579346, \"sum\": 1636.6989612579346, \"min\": 1636.6989612579346}}, \"EndTime\": 1550540687.801916, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540686.16488}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:47 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=249.262896874 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:47 INFO 140560773478208] #progress_metric: host=algo-1, completed 8 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:47 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:48 INFO 140560773478208] Epoch[33] Batch[0] avg_epoch_loss=4.022820\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:49 INFO 140560773478208] Epoch[33] Batch[5] avg_epoch_loss=4.025846\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:49 INFO 140560773478208] Epoch[33] Batch [5]#011Speed: 303.73 samples/sec#011loss=4.025846\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:49 INFO 140560773478208] processed a total of 365 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1481.1820983886719, \"sum\": 1481.1820983886719, \"min\": 1481.1820983886719}}, \"EndTime\": 1550540689.283528, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540687.802003}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:49 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=246.403856653 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:49 INFO 140560773478208] #progress_metric: host=algo-1, completed 8 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:49 INFO 140560773478208] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:49 INFO 140560773478208] Saved checkpoint to \"/opt/ml/model/state_ec809969-c945-4189-b8e6-b4d591a607ac-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 66.8790340423584, \"sum\": 66.8790340423584, \"min\": 66.8790340423584}}, \"EndTime\": 1550540689.350921, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540689.283612}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:49 INFO 140560773478208] Epoch[34] Batch[0] avg_epoch_loss=4.262436\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:50 INFO 140560773478208] Epoch[34] Batch[5] avg_epoch_loss=4.176692\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:50 INFO 140560773478208] Epoch[34] Batch [5]#011Speed: 322.69 samples/sec#011loss=4.176692\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:50 INFO 140560773478208] processed a total of 392 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1629.1790008544922, \"sum\": 1629.1790008544922, \"min\": 1629.1790008544922}}, \"EndTime\": 1550540690.980248, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540689.351004}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:50 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=240.596778555 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:50 INFO 140560773478208] #progress_metric: host=algo-1, completed 8 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:50 INFO 140560773478208] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:51 INFO 140560773478208] Saved checkpoint to \"/opt/ml/model/state_f1a1d9c1-94f5-41cf-a25c-ebc6d0a31107-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 65.66286087036133, \"sum\": 65.66286087036133, \"min\": 65.66286087036133}}, \"EndTime\": 1550540691.046396, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540690.980313}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:51 INFO 140560773478208] Epoch[35] Batch[0] avg_epoch_loss=4.146910\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:52 INFO 140560773478208] Epoch[35] Batch[5] avg_epoch_loss=4.235148\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:52 INFO 140560773478208] Epoch[35] Batch [5]#011Speed: 322.93 samples/sec#011loss=4.235148\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:52 INFO 140560773478208] processed a total of 407 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1612.2980117797852, \"sum\": 1612.2980117797852, \"min\": 1612.2980117797852}}, \"EndTime\": 1550540692.658846, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540691.04648}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:52 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=252.414528504 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:52 INFO 140560773478208] #progress_metric: host=algo-1, completed 9 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:52 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:53 INFO 140560773478208] Epoch[36] Batch[0] avg_epoch_loss=4.286760\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:54 INFO 140560773478208] Epoch[36] Batch[5] avg_epoch_loss=4.086237\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:54 INFO 140560773478208] Epoch[36] Batch [5]#011Speed: 321.01 samples/sec#011loss=4.086237\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:54 INFO 140560773478208] processed a total of 362 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1432.823896408081, \"sum\": 1432.823896408081, \"min\": 1432.823896408081}}, \"EndTime\": 1550540694.092097, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540692.658933}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:54 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=252.625908889 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:54 INFO 140560773478208] #progress_metric: host=algo-1, completed 9 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:54 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:54 INFO 140560773478208] Epoch[37] Batch[0] avg_epoch_loss=4.323045\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:55 INFO 140560773478208] Epoch[37] Batch[5] avg_epoch_loss=4.117376\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:55 INFO 140560773478208] Epoch[37] Batch [5]#011Speed: 317.25 samples/sec#011loss=4.117376\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:55 INFO 140560773478208] processed a total of 376 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1457.489013671875, \"sum\": 1457.489013671875, \"min\": 1457.489013671875}}, \"EndTime\": 1550540695.550073, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540694.092182}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:55 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=257.956157253 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:55 INFO 140560773478208] #progress_metric: host=algo-1, completed 9 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:55 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:55 INFO 140560773478208] Epoch[38] Batch[0] avg_epoch_loss=4.163548\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:56 INFO 140560773478208] Epoch[38] Batch[5] avg_epoch_loss=4.155320\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:56 INFO 140560773478208] Epoch[38] Batch [5]#011Speed: 323.31 samples/sec#011loss=4.155320\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:56 INFO 140560773478208] processed a total of 338 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1410.348892211914, \"sum\": 1410.348892211914, \"min\": 1410.348892211914}}, \"EndTime\": 1550540696.96084, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540695.550157}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:56 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=239.636229208 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:56 INFO 140560773478208] #progress_metric: host=algo-1, completed 9 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:56 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:57 INFO 140560773478208] Epoch[39] Batch[0] avg_epoch_loss=4.210711\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:58 INFO 140560773478208] Epoch[39] Batch[5] avg_epoch_loss=4.020217\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:58 INFO 140560773478208] Epoch[39] Batch [5]#011Speed: 322.52 samples/sec#011loss=4.020217\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:58 INFO 140560773478208] processed a total of 369 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1428.0188083648682, \"sum\": 1428.0188083648682, \"min\": 1428.0188083648682}}, \"EndTime\": 1550540698.389274, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540696.960923}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:58 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=258.373471556 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:58 INFO 140560773478208] #progress_metric: host=algo-1, completed 10 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:58 INFO 140560773478208] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:58 INFO 140560773478208] Saved checkpoint to \"/opt/ml/model/state_b84fcbfc-0323-4d40-b6a6-948559284971-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 72.18599319458008, \"sum\": 72.18599319458008, \"min\": 72.18599319458008}}, \"EndTime\": 1550540698.461953, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540698.389381}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:58 INFO 140560773478208] Epoch[40] Batch[0] avg_epoch_loss=4.145634\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:59 INFO 140560773478208] Epoch[40] Batch[5] avg_epoch_loss=4.078436\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:59 INFO 140560773478208] Epoch[40] Batch [5]#011Speed: 311.74 samples/sec#011loss=4.078436\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:59 INFO 140560773478208] processed a total of 374 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1445.8990097045898, \"sum\": 1445.8990097045898, \"min\": 1445.8990097045898}}, \"EndTime\": 1550540699.907993, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540698.462033}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:59 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=258.640766147 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:59 INFO 140560773478208] #progress_metric: host=algo-1, completed 10 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:44:59 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:00 INFO 140560773478208] Epoch[41] Batch[0] avg_epoch_loss=4.175732\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:01 INFO 140560773478208] Epoch[41] Batch[5] avg_epoch_loss=4.059382\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:01 INFO 140560773478208] Epoch[41] Batch [5]#011Speed: 315.22 samples/sec#011loss=4.059382\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:01 INFO 140560773478208] processed a total of 402 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1689.8391246795654, \"sum\": 1689.8391246795654, \"min\": 1689.8391246795654}}, \"EndTime\": 1550540701.598249, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540699.908076}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:01 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=237.874881758 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:01 INFO 140560773478208] #progress_metric: host=algo-1, completed 10 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:01 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:02 INFO 140560773478208] Epoch[42] Batch[0] avg_epoch_loss=4.212173\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:03 INFO 140560773478208] Epoch[42] Batch[5] avg_epoch_loss=4.062952\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:03 INFO 140560773478208] Epoch[42] Batch [5]#011Speed: 320.12 samples/sec#011loss=4.062952\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:03 INFO 140560773478208] processed a total of 357 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1437.4852180480957, \"sum\": 1437.4852180480957, \"min\": 1437.4852180480957}}, \"EndTime\": 1550540703.036168, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540701.598336}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:03 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=248.329168384 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:03 INFO 140560773478208] #progress_metric: host=algo-1, completed 10 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:03 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:03 INFO 140560773478208] Epoch[43] Batch[0] avg_epoch_loss=4.044727\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:04 INFO 140560773478208] Epoch[43] Batch[5] avg_epoch_loss=3.927994\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:04 INFO 140560773478208] Epoch[43] Batch [5]#011Speed: 313.40 samples/sec#011loss=3.927994\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:04 INFO 140560773478208] processed a total of 376 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1462.6319408416748, \"sum\": 1462.6319408416748, \"min\": 1462.6319408416748}}, \"EndTime\": 1550540704.499216, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540703.036253}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:04 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=257.049162543 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:04 INFO 140560773478208] #progress_metric: host=algo-1, completed 11 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:04 INFO 140560773478208] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:04 INFO 140560773478208] Saved checkpoint to \"/opt/ml/model/state_7eac2b62-43b5-4ea5-885a-5e4984f68e85-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 81.2978744506836, \"sum\": 81.2978744506836, \"min\": 81.2978744506836}}, \"EndTime\": 1550540704.580978, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540704.499302}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:05 INFO 140560773478208] Epoch[44] Batch[0] avg_epoch_loss=4.062463\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:06 INFO 140560773478208] Epoch[44] Batch[5] avg_epoch_loss=4.017578\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:06 INFO 140560773478208] Epoch[44] Batch [5]#011Speed: 314.31 samples/sec#011loss=4.017578\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:06 INFO 140560773478208] processed a total of 366 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1447.5278854370117, \"sum\": 1447.5278854370117, \"min\": 1447.5278854370117}}, \"EndTime\": 1550540706.028663, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540704.581062}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:06 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=252.822341822 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:06 INFO 140560773478208] #progress_metric: host=algo-1, completed 11 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:06 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:06 INFO 140560773478208] Epoch[45] Batch[0] avg_epoch_loss=4.144195\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:07 INFO 140560773478208] Epoch[45] Batch[5] avg_epoch_loss=4.083149\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:07 INFO 140560773478208] Epoch[45] Batch [5]#011Speed: 315.92 samples/sec#011loss=4.083149\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:07 INFO 140560773478208] processed a total of 345 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1448.3458995819092, \"sum\": 1448.3458995819092, \"min\": 1448.3458995819092}}, \"EndTime\": 1550540707.477435, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540706.02875}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:07 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=238.182378122 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:07 INFO 140560773478208] #progress_metric: host=algo-1, completed 11 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:07 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:07 INFO 140560773478208] Epoch[46] Batch[0] avg_epoch_loss=3.924318\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:08 INFO 140560773478208] Epoch[46] Batch[5] avg_epoch_loss=4.095946\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:08 INFO 140560773478208] Epoch[46] Batch [5]#011Speed: 321.69 samples/sec#011loss=4.095946\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:08 INFO 140560773478208] processed a total of 383 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1468.628168106079, \"sum\": 1468.628168106079, \"min\": 1468.628168106079}}, \"EndTime\": 1550540708.94649, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540707.47752}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:08 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=260.765788129 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:08 INFO 140560773478208] #progress_metric: host=algo-1, completed 11 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:08 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:09 INFO 140560773478208] Epoch[47] Batch[0] avg_epoch_loss=4.187752\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:10 INFO 140560773478208] Epoch[47] Batch[5] avg_epoch_loss=3.968885\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:10 INFO 140560773478208] Epoch[47] Batch [5]#011Speed: 317.32 samples/sec#011loss=3.968885\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:10 INFO 140560773478208] processed a total of 374 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1470.221996307373, \"sum\": 1470.221996307373, \"min\": 1470.221996307373}}, \"EndTime\": 1550540710.417136, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540708.946573}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:10 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=254.362439183 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:10 INFO 140560773478208] #progress_metric: host=algo-1, completed 12 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:10 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:10 INFO 140560773478208] Epoch[48] Batch[0] avg_epoch_loss=4.147904\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:11 INFO 140560773478208] Epoch[48] Batch[5] avg_epoch_loss=4.375812\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:11 INFO 140560773478208] Epoch[48] Batch [5]#011Speed: 319.30 samples/sec#011loss=4.375812\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:11 INFO 140560773478208] processed a total of 346 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1444.0219402313232, \"sum\": 1444.0219402313232, \"min\": 1444.0219402313232}}, \"EndTime\": 1550540711.861621, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540710.417218}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:11 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=239.587147082 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:11 INFO 140560773478208] #progress_metric: host=algo-1, completed 12 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:11 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:12 INFO 140560773478208] Epoch[49] Batch[0] avg_epoch_loss=3.811592\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:13 INFO 140560773478208] Epoch[49] Batch[5] avg_epoch_loss=4.146024\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:13 INFO 140560773478208] Epoch[49] Batch [5]#011Speed: 317.74 samples/sec#011loss=4.146024\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:13 INFO 140560773478208] processed a total of 364 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1470.000982284546, \"sum\": 1470.000982284546, \"min\": 1470.000982284546}}, \"EndTime\": 1550540713.332061, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540711.861711}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:13 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=247.597959959 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:13 INFO 140560773478208] #progress_metric: host=algo-1, completed 12 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:13 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:13 INFO 140560773478208] Epoch[50] Batch[0] avg_epoch_loss=3.837841\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:14 INFO 140560773478208] Epoch[50] Batch[5] avg_epoch_loss=4.016796\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:14 INFO 140560773478208] Epoch[50] Batch [5]#011Speed: 312.19 samples/sec#011loss=4.016796\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:14 INFO 140560773478208] processed a total of 404 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1667.2799587249756, \"sum\": 1667.2799587249756, \"min\": 1667.2799587249756}}, \"EndTime\": 1550540714.999769, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540713.332146}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:14 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=242.292403166 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:15 INFO 140560773478208] #progress_metric: host=algo-1, completed 12 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:15 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:15 INFO 140560773478208] Epoch[51] Batch[0] avg_epoch_loss=4.339432\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:16 INFO 140560773478208] Epoch[51] Batch[5] avg_epoch_loss=4.168315\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:16 INFO 140560773478208] Epoch[51] Batch [5]#011Speed: 306.67 samples/sec#011loss=4.168315\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:16 INFO 140560773478208] processed a total of 376 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1494.3299293518066, \"sum\": 1494.3299293518066, \"min\": 1494.3299293518066}}, \"EndTime\": 1550540716.494536, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540714.999856}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:16 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=251.598244525 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:16 INFO 140560773478208] #progress_metric: host=algo-1, completed 13 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:16 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:16 INFO 140560773478208] Epoch[52] Batch[0] avg_epoch_loss=4.226516\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:17 INFO 140560773478208] Epoch[52] Batch[5] avg_epoch_loss=3.995680\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:17 INFO 140560773478208] Epoch[52] Batch [5]#011Speed: 316.19 samples/sec#011loss=3.995680\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:17 INFO 140560773478208] processed a total of 364 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1426.1870384216309, \"sum\": 1426.1870384216309, \"min\": 1426.1870384216309}}, \"EndTime\": 1550540717.921133, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540716.494612}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:17 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=255.203806109 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:17 INFO 140560773478208] #progress_metric: host=algo-1, completed 13 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:17 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:18 INFO 140560773478208] Epoch[53] Batch[0] avg_epoch_loss=3.784023\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:19 INFO 140560773478208] Epoch[53] Batch[5] avg_epoch_loss=4.148746\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:19 INFO 140560773478208] Epoch[53] Batch [5]#011Speed: 318.12 samples/sec#011loss=4.148746\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:19 INFO 140560773478208] processed a total of 363 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1443.908929824829, \"sum\": 1443.908929824829, \"min\": 1443.908929824829}}, \"EndTime\": 1550540719.365485, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540717.921216}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:19 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=251.379644873 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:19 INFO 140560773478208] #progress_metric: host=algo-1, completed 13 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:19 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:19 INFO 140560773478208] Epoch[54] Batch[0] avg_epoch_loss=3.897927\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:20 INFO 140560773478208] Epoch[54] Batch[5] avg_epoch_loss=3.941292\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:20 INFO 140560773478208] Epoch[54] Batch [5]#011Speed: 315.57 samples/sec#011loss=3.941292\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:20 INFO 140560773478208] processed a total of 375 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1471.501111984253, \"sum\": 1471.501111984253, \"min\": 1471.501111984253}}, \"EndTime\": 1550540720.8374, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540719.365568}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:20 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=254.820501264 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:20 INFO 140560773478208] #progress_metric: host=algo-1, completed 13 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:20 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:21 INFO 140560773478208] Epoch[55] Batch[0] avg_epoch_loss=4.077930\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:22 INFO 140560773478208] Epoch[55] Batch[5] avg_epoch_loss=4.054700\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:22 INFO 140560773478208] Epoch[55] Batch [5]#011Speed: 323.85 samples/sec#011loss=4.054700\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:22 INFO 140560773478208] processed a total of 389 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1637.645959854126, \"sum\": 1637.645959854126, \"min\": 1637.645959854126}}, \"EndTime\": 1550540722.475465, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540720.837483}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:22 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=237.518063106 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:22 INFO 140560773478208] #progress_metric: host=algo-1, completed 14 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:22 INFO 140560773478208] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:22 INFO 140560773478208] Saved checkpoint to \"/opt/ml/model/state_0d1ce516-8c94-4f11-aad3-de2ed956051a-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 70.82295417785645, \"sum\": 70.82295417785645, \"min\": 70.82295417785645}}, \"EndTime\": 1550540722.546759, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540722.47555}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:22 INFO 140560773478208] Epoch[56] Batch[0] avg_epoch_loss=3.783471\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:23 INFO 140560773478208] Epoch[56] Batch[5] avg_epoch_loss=3.933873\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:23 INFO 140560773478208] Epoch[56] Batch [5]#011Speed: 319.92 samples/sec#011loss=3.933873\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:23 INFO 140560773478208] processed a total of 364 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1425.562858581543, \"sum\": 1425.562858581543, \"min\": 1425.562858581543}}, \"EndTime\": 1550540723.972459, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540722.546835}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:23 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=255.315494171 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:23 INFO 140560773478208] #progress_metric: host=algo-1, completed 14 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:23 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:24 INFO 140560773478208] Epoch[57] Batch[0] avg_epoch_loss=3.800761\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:25 INFO 140560773478208] Epoch[57] Batch[5] avg_epoch_loss=3.896337\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:25 INFO 140560773478208] Epoch[57] Batch [5]#011Speed: 317.52 samples/sec#011loss=3.896337\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:25 INFO 140560773478208] processed a total of 360 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1469.2928791046143, \"sum\": 1469.2928791046143, \"min\": 1469.2928791046143}}, \"EndTime\": 1550540725.442167, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540723.972542}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:25 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=244.996618609 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:25 INFO 140560773478208] #progress_metric: host=algo-1, completed 14 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:25 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:25 INFO 140560773478208] Epoch[58] Batch[0] avg_epoch_loss=4.318675\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:26 INFO 140560773478208] Epoch[58] Batch[5] avg_epoch_loss=3.906154\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:26 INFO 140560773478208] Epoch[58] Batch [5]#011Speed: 304.31 samples/sec#011loss=3.906154\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:26 INFO 140560773478208] processed a total of 360 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1472.45192527771, \"sum\": 1472.45192527771, \"min\": 1472.45192527771}}, \"EndTime\": 1550540726.915078, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540725.442242}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:26 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=244.471906809 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:26 INFO 140560773478208] #progress_metric: host=algo-1, completed 14 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:26 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:27 INFO 140560773478208] Epoch[59] Batch[0] avg_epoch_loss=3.898317\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:28 INFO 140560773478208] Epoch[59] Batch[5] avg_epoch_loss=3.910357\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:28 INFO 140560773478208] Epoch[59] Batch [5]#011Speed: 321.00 samples/sec#011loss=3.910357\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:28 INFO 140560773478208] processed a total of 358 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1414.067029953003, \"sum\": 1414.067029953003, \"min\": 1414.067029953003}}, \"EndTime\": 1550540728.329584, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540726.915149}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:28 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=253.146088457 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:28 INFO 140560773478208] #progress_metric: host=algo-1, completed 15 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:28 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:28 INFO 140560773478208] Epoch[60] Batch[0] avg_epoch_loss=3.870024\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:29 INFO 140560773478208] Epoch[60] Batch[5] avg_epoch_loss=4.337466\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:29 INFO 140560773478208] Epoch[60] Batch [5]#011Speed: 321.47 samples/sec#011loss=4.337466\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:29 INFO 140560773478208] processed a total of 357 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1450.3769874572754, \"sum\": 1450.3769874572754, \"min\": 1450.3769874572754}}, \"EndTime\": 1550540729.780403, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540728.32968}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:29 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=246.121863125 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:29 INFO 140560773478208] #progress_metric: host=algo-1, completed 15 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:29 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:30 INFO 140560773478208] Epoch[61] Batch[0] avg_epoch_loss=4.432902\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:31 INFO 140560773478208] Epoch[61] Batch[5] avg_epoch_loss=4.078879\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:31 INFO 140560773478208] Epoch[61] Batch [5]#011Speed: 308.83 samples/sec#011loss=4.078879\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:31 INFO 140560773478208] processed a total of 362 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1456.7739963531494, \"sum\": 1456.7739963531494, \"min\": 1456.7739963531494}}, \"EndTime\": 1550540731.237602, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540729.780487}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:31 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=248.471747704 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:31 INFO 140560773478208] #progress_metric: host=algo-1, completed 15 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:31 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:31 INFO 140560773478208] Epoch[62] Batch[0] avg_epoch_loss=4.104004\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:32 INFO 140560773478208] Epoch[62] Batch[5] avg_epoch_loss=3.876118\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:32 INFO 140560773478208] Epoch[62] Batch [5]#011Speed: 318.71 samples/sec#011loss=3.876118\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:32 INFO 140560773478208] processed a total of 400 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1642.1000957489014, \"sum\": 1642.1000957489014, \"min\": 1642.1000957489014}}, \"EndTime\": 1550540732.880121, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540731.237692}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:32 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=243.571837668 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:32 INFO 140560773478208] #progress_metric: host=algo-1, completed 15 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:32 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:33 INFO 140560773478208] Epoch[63] Batch[0] avg_epoch_loss=3.983330\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:34 INFO 140560773478208] Epoch[63] Batch[5] avg_epoch_loss=3.898115\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:34 INFO 140560773478208] Epoch[63] Batch [5]#011Speed: 326.75 samples/sec#011loss=3.898115\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:34 INFO 140560773478208] processed a total of 366 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1405.3778648376465, \"sum\": 1405.3778648376465, \"min\": 1405.3778648376465}}, \"EndTime\": 1550540734.285933, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540732.880207}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:34 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=260.405517468 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:34 INFO 140560773478208] #progress_metric: host=algo-1, completed 16 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:34 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:34 INFO 140560773478208] Epoch[64] Batch[0] avg_epoch_loss=3.236527\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:35 INFO 140560773478208] Epoch[64] Batch[5] avg_epoch_loss=3.786039\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:35 INFO 140560773478208] Epoch[64] Batch [5]#011Speed: 319.42 samples/sec#011loss=3.786039\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:35 INFO 140560773478208] processed a total of 346 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1444.441795349121, \"sum\": 1444.441795349121, \"min\": 1444.441795349121}}, \"EndTime\": 1550540735.730786, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540734.286018}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:35 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=239.5181056 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:35 INFO 140560773478208] #progress_metric: host=algo-1, completed 16 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:35 INFO 140560773478208] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:35 INFO 140560773478208] Saved checkpoint to \"/opt/ml/model/state_afc143fc-aa24-4d90-a81d-dfd97a2e452c-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 83.01496505737305, \"sum\": 83.01496505737305, \"min\": 83.01496505737305}}, \"EndTime\": 1550540735.814283, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540735.730872}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:36 INFO 140560773478208] Epoch[65] Batch[0] avg_epoch_loss=4.117001\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:37 INFO 140560773478208] Epoch[65] Batch[5] avg_epoch_loss=4.089301\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:37 INFO 140560773478208] Epoch[65] Batch [5]#011Speed: 320.73 samples/sec#011loss=4.089301\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:37 INFO 140560773478208] processed a total of 379 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1422.6000308990479, \"sum\": 1422.6000308990479, \"min\": 1422.6000308990479}}, \"EndTime\": 1550540737.23703, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540735.814361}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:37 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=266.390164331 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:37 INFO 140560773478208] #progress_metric: host=algo-1, completed 16 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:37 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:37 INFO 140560773478208] Epoch[66] Batch[0] avg_epoch_loss=3.809310\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:38 INFO 140560773478208] Epoch[66] Batch[5] avg_epoch_loss=3.910618\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:38 INFO 140560773478208] Epoch[66] Batch [5]#011Speed: 326.03 samples/sec#011loss=3.910618\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:38 INFO 140560773478208] processed a total of 374 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1418.8120365142822, \"sum\": 1418.8120365142822, \"min\": 1418.8120365142822}}, \"EndTime\": 1550540738.656301, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540737.237115}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:38 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=263.570336599 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:38 INFO 140560773478208] #progress_metric: host=algo-1, completed 16 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:38 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:39 INFO 140560773478208] Epoch[67] Batch[0] avg_epoch_loss=3.950366\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:40 INFO 140560773478208] Epoch[67] Batch[5] avg_epoch_loss=3.908702\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:40 INFO 140560773478208] Epoch[67] Batch [5]#011Speed: 320.32 samples/sec#011loss=3.908702\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:40 INFO 140560773478208] processed a total of 387 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1654.904842376709, \"sum\": 1654.904842376709, \"min\": 1654.904842376709}}, \"EndTime\": 1550540740.311679, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540738.656425}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:40 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=233.832314468 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:40 INFO 140560773478208] #progress_metric: host=algo-1, completed 17 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:40 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:40 INFO 140560773478208] Epoch[68] Batch[0] avg_epoch_loss=3.888217\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:41 INFO 140560773478208] Epoch[68] Batch[5] avg_epoch_loss=3.804896\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:41 INFO 140560773478208] Epoch[68] Batch [5]#011Speed: 317.10 samples/sec#011loss=3.804896\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:41 INFO 140560773478208] processed a total of 356 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1449.6550559997559, \"sum\": 1449.6550559997559, \"min\": 1449.6550559997559}}, \"EndTime\": 1550540741.761772, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540740.311766}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:41 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=245.556680707 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:41 INFO 140560773478208] #progress_metric: host=algo-1, completed 17 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:41 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:42 INFO 140560773478208] Epoch[69] Batch[0] avg_epoch_loss=3.726978\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:43 INFO 140560773478208] Epoch[69] Batch[5] avg_epoch_loss=4.106652\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:43 INFO 140560773478208] Epoch[69] Batch [5]#011Speed: 322.73 samples/sec#011loss=4.106652\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:43 INFO 140560773478208] processed a total of 353 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1404.7129154205322, \"sum\": 1404.7129154205322, \"min\": 1404.7129154205322}}, \"EndTime\": 1550540743.166919, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540741.761849}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:43 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=251.274850101 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:43 INFO 140560773478208] #progress_metric: host=algo-1, completed 17 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:43 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:43 INFO 140560773478208] Epoch[70] Batch[0] avg_epoch_loss=3.807394\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:44 INFO 140560773478208] Epoch[70] Batch[5] avg_epoch_loss=3.919592\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:44 INFO 140560773478208] Epoch[70] Batch [5]#011Speed: 321.19 samples/sec#011loss=3.919592\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:44 INFO 140560773478208] processed a total of 348 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1442.471981048584, \"sum\": 1442.471981048584, \"min\": 1442.471981048584}}, \"EndTime\": 1550540744.609815, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540743.167003}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:44 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=241.23178628 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:44 INFO 140560773478208] #progress_metric: host=algo-1, completed 17 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:44 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:45 INFO 140560773478208] Epoch[71] Batch[0] avg_epoch_loss=3.887175\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:46 INFO 140560773478208] Epoch[71] Batch[5] avg_epoch_loss=3.965408\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:46 INFO 140560773478208] Epoch[71] Batch [5]#011Speed: 319.99 samples/sec#011loss=3.965408\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:46 INFO 140560773478208] processed a total of 368 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1444.2229270935059, \"sum\": 1444.2229270935059, \"min\": 1444.2229270935059}}, \"EndTime\": 1550540746.054458, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540744.609901}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:46 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=254.787319068 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:46 INFO 140560773478208] #progress_metric: host=algo-1, completed 18 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:46 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:46 INFO 140560773478208] Epoch[72] Batch[0] avg_epoch_loss=4.244971\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:47 INFO 140560773478208] Epoch[72] Batch[5] avg_epoch_loss=3.968126\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:47 INFO 140560773478208] Epoch[72] Batch [5]#011Speed: 313.27 samples/sec#011loss=3.968126\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:47 INFO 140560773478208] processed a total of 406 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1663.6128425598145, \"sum\": 1663.6128425598145, \"min\": 1663.6128425598145}}, \"EndTime\": 1550540747.718485, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540746.054537}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:47 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=244.028351004 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:47 INFO 140560773478208] #progress_metric: host=algo-1, completed 18 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:47 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:48 INFO 140560773478208] Epoch[73] Batch[0] avg_epoch_loss=3.839542\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:49 INFO 140560773478208] Epoch[73] Batch[5] avg_epoch_loss=4.061093\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:49 INFO 140560773478208] Epoch[73] Batch [5]#011Speed: 323.40 samples/sec#011loss=4.061093\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:49 INFO 140560773478208] processed a total of 357 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1419.5671081542969, \"sum\": 1419.5671081542969, \"min\": 1419.5671081542969}}, \"EndTime\": 1550540749.13851, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540747.718573}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:49 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=251.463704297 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:49 INFO 140560773478208] #progress_metric: host=algo-1, completed 18 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:49 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:49 INFO 140560773478208] Epoch[74] Batch[0] avg_epoch_loss=4.066290\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:50 INFO 140560773478208] Epoch[74] Batch[5] avg_epoch_loss=4.045217\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:50 INFO 140560773478208] Epoch[74] Batch [5]#011Speed: 305.71 samples/sec#011loss=4.045217\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:50 INFO 140560773478208] processed a total of 375 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1491.8639659881592, \"sum\": 1491.8639659881592, \"min\": 1491.8639659881592}}, \"EndTime\": 1550540750.630823, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540749.138593}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:50 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=251.342352931 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:50 INFO 140560773478208] #progress_metric: host=algo-1, completed 18 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:50 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:51 INFO 140560773478208] Epoch[75] Batch[0] avg_epoch_loss=4.142125\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:52 INFO 140560773478208] Epoch[75] Batch[5] avg_epoch_loss=3.963163\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:52 INFO 140560773478208] Epoch[75] Batch [5]#011Speed: 308.59 samples/sec#011loss=3.963163\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:52 INFO 140560773478208] processed a total of 407 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1672.0540523529053, \"sum\": 1672.0540523529053, \"min\": 1672.0540523529053}}, \"EndTime\": 1550540752.303303, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540750.630908}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:52 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=243.394720142 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:52 INFO 140560773478208] #progress_metric: host=algo-1, completed 19 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:52 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:52 INFO 140560773478208] Epoch[76] Batch[0] avg_epoch_loss=3.897001\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:53 INFO 140560773478208] Epoch[76] Batch[5] avg_epoch_loss=3.982264\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:53 INFO 140560773478208] Epoch[76] Batch [5]#011Speed: 324.50 samples/sec#011loss=3.982264\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:53 INFO 140560773478208] processed a total of 375 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1443.5720443725586, \"sum\": 1443.5720443725586, \"min\": 1443.5720443725586}}, \"EndTime\": 1550540753.747303, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540752.30339}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:53 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=259.7499711 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:53 INFO 140560773478208] #progress_metric: host=algo-1, completed 19 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:53 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:54 INFO 140560773478208] Epoch[77] Batch[0] avg_epoch_loss=3.682838\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:55 INFO 140560773478208] Epoch[77] Batch[5] avg_epoch_loss=3.966000\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:55 INFO 140560773478208] Epoch[77] Batch [5]#011Speed: 306.02 samples/sec#011loss=3.966000\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:55 INFO 140560773478208] processed a total of 391 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1757.5900554656982, \"sum\": 1757.5900554656982, \"min\": 1757.5900554656982}}, \"EndTime\": 1550540755.505362, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540753.747389}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:55 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=222.44699048 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:55 INFO 140560773478208] #progress_metric: host=algo-1, completed 19 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:55 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:55 INFO 140560773478208] Epoch[78] Batch[0] avg_epoch_loss=3.817797\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:56 INFO 140560773478208] Epoch[78] Batch[5] avg_epoch_loss=4.107727\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:56 INFO 140560773478208] Epoch[78] Batch [5]#011Speed: 322.31 samples/sec#011loss=4.107727\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:56 INFO 140560773478208] processed a total of 361 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1445.2769756317139, \"sum\": 1445.2769756317139, \"min\": 1445.2769756317139}}, \"EndTime\": 1550540756.951079, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540755.505451}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:56 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=249.758017354 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:56 INFO 140560773478208] #progress_metric: host=algo-1, completed 19 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:56 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:57 INFO 140560773478208] Epoch[79] Batch[0] avg_epoch_loss=3.823550\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:58 INFO 140560773478208] Epoch[79] Batch[5] avg_epoch_loss=3.939202\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:58 INFO 140560773478208] Epoch[79] Batch [5]#011Speed: 316.27 samples/sec#011loss=3.939202\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:58 INFO 140560773478208] processed a total of 343 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1433.2060813903809, \"sum\": 1433.2060813903809, \"min\": 1433.2060813903809}}, \"EndTime\": 1550540758.384701, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540756.951163}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:58 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=239.30323049 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:58 INFO 140560773478208] #progress_metric: host=algo-1, completed 20 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:58 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:58 INFO 140560773478208] Epoch[80] Batch[0] avg_epoch_loss=3.908634\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:59 INFO 140560773478208] Epoch[80] Batch[5] avg_epoch_loss=3.991020\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:45:59 INFO 140560773478208] Epoch[80] Batch [5]#011Speed: 321.18 samples/sec#011loss=3.991020\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:00 INFO 140560773478208] processed a total of 402 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1638.0269527435303, \"sum\": 1638.0269527435303, \"min\": 1638.0269527435303}}, \"EndTime\": 1550540760.023148, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540758.384785}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:00 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=245.397562165 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:00 INFO 140560773478208] #progress_metric: host=algo-1, completed 20 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:00 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:00 INFO 140560773478208] Epoch[81] Batch[0] avg_epoch_loss=3.781516\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:01 INFO 140560773478208] Epoch[81] Batch[5] avg_epoch_loss=4.013987\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:01 INFO 140560773478208] Epoch[81] Batch [5]#011Speed: 324.42 samples/sec#011loss=4.013987\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:01 INFO 140560773478208] processed a total of 355 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1486.2220287322998, \"sum\": 1486.2220287322998, \"min\": 1486.2220287322998}}, \"EndTime\": 1550540761.50981, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540760.023238}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:01 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=238.840753413 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:01 INFO 140560773478208] #progress_metric: host=algo-1, completed 20 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:01 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:01 INFO 140560773478208] Epoch[82] Batch[0] avg_epoch_loss=3.805916\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:03 INFO 140560773478208] Epoch[82] Batch[5] avg_epoch_loss=3.670543\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:03 INFO 140560773478208] Epoch[82] Batch [5]#011Speed: 306.59 samples/sec#011loss=3.670543\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:03 INFO 140560773478208] processed a total of 358 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1497.5130558013916, \"sum\": 1497.5130558013916, \"min\": 1497.5130558013916}}, \"EndTime\": 1550540763.007741, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540761.509895}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:03 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=239.043386328 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:03 INFO 140560773478208] #progress_metric: host=algo-1, completed 20 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:03 INFO 140560773478208] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:03 INFO 140560773478208] Saved checkpoint to \"/opt/ml/model/state_c3ba8f16-da3a-45d6-b9ab-00009f9983da-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 65.06109237670898, \"sum\": 65.06109237670898, \"min\": 65.06109237670898}}, \"EndTime\": 1550540763.073277, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540763.007825}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:03 INFO 140560773478208] Epoch[83] Batch[0] avg_epoch_loss=4.271233\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:04 INFO 140560773478208] Epoch[83] Batch[5] avg_epoch_loss=3.953235\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:04 INFO 140560773478208] Epoch[83] Batch [5]#011Speed: 320.96 samples/sec#011loss=3.953235\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:04 INFO 140560773478208] processed a total of 419 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1627.8879642486572, \"sum\": 1627.8879642486572, \"min\": 1627.8879642486572}}, \"EndTime\": 1550540764.701359, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540763.073378}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:04 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=257.364219734 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:04 INFO 140560773478208] #progress_metric: host=algo-1, completed 21 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:04 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:05 INFO 140560773478208] Epoch[84] Batch[0] avg_epoch_loss=3.969399\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:06 INFO 140560773478208] Epoch[84] Batch[5] avg_epoch_loss=4.093167\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:06 INFO 140560773478208] Epoch[84] Batch [5]#011Speed: 322.48 samples/sec#011loss=4.093167\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:06 INFO 140560773478208] processed a total of 343 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1473.2749462127686, \"sum\": 1473.2749462127686, \"min\": 1473.2749462127686}}, \"EndTime\": 1550540766.175079, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540764.701448}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:06 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=232.795066185 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:06 INFO 140560773478208] #progress_metric: host=algo-1, completed 21 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:06 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:06 INFO 140560773478208] Epoch[85] Batch[0] avg_epoch_loss=4.127760\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:07 INFO 140560773478208] Epoch[85] Batch[5] avg_epoch_loss=3.941506\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:07 INFO 140560773478208] Epoch[85] Batch [5]#011Speed: 325.31 samples/sec#011loss=3.941506\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:07 INFO 140560773478208] processed a total of 362 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1428.9488792419434, \"sum\": 1428.9488792419434, \"min\": 1428.9488792419434}}, \"EndTime\": 1550540767.604447, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540766.175163}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:07 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=253.311087851 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:07 INFO 140560773478208] #progress_metric: host=algo-1, completed 21 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:07 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:08 INFO 140560773478208] Epoch[86] Batch[0] avg_epoch_loss=4.008886\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:09 INFO 140560773478208] Epoch[86] Batch[5] avg_epoch_loss=3.874503\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:09 INFO 140560773478208] Epoch[86] Batch [5]#011Speed: 317.28 samples/sec#011loss=3.874503\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:09 INFO 140560773478208] processed a total of 351 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1451.7078399658203, \"sum\": 1451.7078399658203, \"min\": 1451.7078399658203}}, \"EndTime\": 1550540769.056579, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540767.604533}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:09 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=241.763182878 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:09 INFO 140560773478208] #progress_metric: host=algo-1, completed 21 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:09 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:09 INFO 140560773478208] Epoch[87] Batch[0] avg_epoch_loss=3.659703\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:10 INFO 140560773478208] Epoch[87] Batch[5] avg_epoch_loss=3.840495\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:10 INFO 140560773478208] Epoch[87] Batch [5]#011Speed: 320.38 samples/sec#011loss=3.840495\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:10 INFO 140560773478208] processed a total of 371 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1428.2610416412354, \"sum\": 1428.2610416412354, \"min\": 1428.2610416412354}}, \"EndTime\": 1550540770.485278, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540769.056664}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:10 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=259.729166326 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:10 INFO 140560773478208] #progress_metric: host=algo-1, completed 22 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:10 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:10 INFO 140560773478208] Epoch[88] Batch[0] avg_epoch_loss=3.994537\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:11 INFO 140560773478208] Epoch[88] Batch[5] avg_epoch_loss=3.839277\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:11 INFO 140560773478208] Epoch[88] Batch [5]#011Speed: 325.68 samples/sec#011loss=3.839277\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:11 INFO 140560773478208] processed a total of 362 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1453.0589580535889, \"sum\": 1453.0589580535889, \"min\": 1453.0589580535889}}, \"EndTime\": 1550540771.938792, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540770.485388}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:11 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=249.108511166 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:11 INFO 140560773478208] #progress_metric: host=algo-1, completed 22 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:11 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:12 INFO 140560773478208] Epoch[89] Batch[0] avg_epoch_loss=3.765518\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:13 INFO 140560773478208] Epoch[89] Batch[5] avg_epoch_loss=3.914785\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:13 INFO 140560773478208] Epoch[89] Batch [5]#011Speed: 319.86 samples/sec#011loss=3.914785\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:13 INFO 140560773478208] processed a total of 371 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1422.692060470581, \"sum\": 1422.692060470581, \"min\": 1422.692060470581}}, \"EndTime\": 1550540773.361902, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540771.938876}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:13 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=260.750548367 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:13 INFO 140560773478208] #progress_metric: host=algo-1, completed 22 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:13 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:13 INFO 140560773478208] Epoch[90] Batch[0] avg_epoch_loss=4.062066\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:14 INFO 140560773478208] Epoch[90] Batch[5] avg_epoch_loss=3.908280\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:14 INFO 140560773478208] Epoch[90] Batch [5]#011Speed: 320.37 samples/sec#011loss=3.908280\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:15 INFO 140560773478208] processed a total of 391 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1666.111946105957, \"sum\": 1666.111946105957, \"min\": 1666.111946105957}}, \"EndTime\": 1550540775.02844, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540773.361985}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:15 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=234.660344851 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:15 INFO 140560773478208] #progress_metric: host=algo-1, completed 22 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:15 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:15 INFO 140560773478208] Epoch[91] Batch[0] avg_epoch_loss=3.886729\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:16 INFO 140560773478208] Epoch[91] Batch[5] avg_epoch_loss=3.813390\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:16 INFO 140560773478208] Epoch[91] Batch [5]#011Speed: 323.15 samples/sec#011loss=3.813390\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:16 INFO 140560773478208] processed a total of 362 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1429.9829006195068, \"sum\": 1429.9829006195068, \"min\": 1429.9829006195068}}, \"EndTime\": 1550540776.458845, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540775.028526}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:16 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=253.128820552 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:16 INFO 140560773478208] #progress_metric: host=algo-1, completed 23 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:16 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:16 INFO 140560773478208] Epoch[92] Batch[0] avg_epoch_loss=3.909321\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:17 INFO 140560773478208] Epoch[92] Batch[5] avg_epoch_loss=3.771090\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:17 INFO 140560773478208] Epoch[92] Batch [5]#011Speed: 325.08 samples/sec#011loss=3.771090\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:17 INFO 140560773478208] processed a total of 380 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1437.4189376831055, \"sum\": 1437.4189376831055, \"min\": 1437.4189376831055}}, \"EndTime\": 1550540777.896672, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540776.458926}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:17 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=264.340285172 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:17 INFO 140560773478208] #progress_metric: host=algo-1, completed 23 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:17 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:18 INFO 140560773478208] Epoch[93] Batch[0] avg_epoch_loss=3.801498\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:19 INFO 140560773478208] Epoch[93] Batch[5] avg_epoch_loss=3.833373\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:19 INFO 140560773478208] Epoch[93] Batch [5]#011Speed: 313.26 samples/sec#011loss=3.833373\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:19 INFO 140560773478208] processed a total of 379 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1453.908920288086, \"sum\": 1453.908920288086, \"min\": 1453.908920288086}}, \"EndTime\": 1550540779.351001, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540777.896757}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:19 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=260.65486295 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:19 INFO 140560773478208] #progress_metric: host=algo-1, completed 23 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:19 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:19 INFO 140560773478208] Epoch[94] Batch[0] avg_epoch_loss=3.909964\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:20 INFO 140560773478208] Epoch[94] Batch[5] avg_epoch_loss=3.838426\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:20 INFO 140560773478208] Epoch[94] Batch [5]#011Speed: 322.31 samples/sec#011loss=3.838426\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:20 INFO 140560773478208] processed a total of 358 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1418.1890487670898, \"sum\": 1418.1890487670898, \"min\": 1418.1890487670898}}, \"EndTime\": 1550540780.769605, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540779.351084}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:20 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=252.412544572 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:20 INFO 140560773478208] #progress_metric: host=algo-1, completed 23 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:20 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:21 INFO 140560773478208] Epoch[95] Batch[0] avg_epoch_loss=3.545268\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:22 INFO 140560773478208] Epoch[95] Batch[5] avg_epoch_loss=3.814905\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:22 INFO 140560773478208] Epoch[95] Batch [5]#011Speed: 322.47 samples/sec#011loss=3.814905\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:22 INFO 140560773478208] processed a total of 375 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1450.265884399414, \"sum\": 1450.265884399414, \"min\": 1450.265884399414}}, \"EndTime\": 1550540782.220297, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540780.76969}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:22 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=258.551512912 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:22 INFO 140560773478208] #progress_metric: host=algo-1, completed 24 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:22 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:22 INFO 140560773478208] Epoch[96] Batch[0] avg_epoch_loss=3.683420\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:23 INFO 140560773478208] Epoch[96] Batch[5] avg_epoch_loss=3.488875\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:23 INFO 140560773478208] Epoch[96] Batch [5]#011Speed: 311.61 samples/sec#011loss=3.488875\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:23 INFO 140560773478208] processed a total of 345 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1460.3328704833984, \"sum\": 1460.3328704833984, \"min\": 1460.3328704833984}}, \"EndTime\": 1550540783.681051, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540782.220379}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:23 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=236.22887888 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:23 INFO 140560773478208] #progress_metric: host=algo-1, completed 24 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:23 INFO 140560773478208] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:23 INFO 140560773478208] Saved checkpoint to \"/opt/ml/model/state_ae0acda9-6d69-478e-a6f5-8f69f37b437d-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 96.28105163574219, \"sum\": 96.28105163574219, \"min\": 96.28105163574219}}, \"EndTime\": 1550540783.777828, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540783.681129}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:24 INFO 140560773478208] Epoch[97] Batch[0] avg_epoch_loss=4.111654\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:25 INFO 140560773478208] Epoch[97] Batch[5] avg_epoch_loss=3.821948\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:25 INFO 140560773478208] Epoch[97] Batch [5]#011Speed: 315.58 samples/sec#011loss=3.821948\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:25 INFO 140560773478208] processed a total of 371 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1452.604055404663, \"sum\": 1452.604055404663, \"min\": 1452.604055404663}}, \"EndTime\": 1550540785.230583, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540783.777911}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:25 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=255.38197071 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:25 INFO 140560773478208] #progress_metric: host=algo-1, completed 24 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:25 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:25 INFO 140560773478208] Epoch[98] Batch[0] avg_epoch_loss=3.925447\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:26 INFO 140560773478208] Epoch[98] Batch[5] avg_epoch_loss=3.685529\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:26 INFO 140560773478208] Epoch[98] Batch [5]#011Speed: 322.86 samples/sec#011loss=3.685529\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:26 INFO 140560773478208] processed a total of 345 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1431.5240383148193, \"sum\": 1431.5240383148193, \"min\": 1431.5240383148193}}, \"EndTime\": 1550540786.662527, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540785.230665}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:26 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=240.981221237 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:26 INFO 140560773478208] #progress_metric: host=algo-1, completed 24 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:26 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:27 INFO 140560773478208] Epoch[99] Batch[0] avg_epoch_loss=3.724425\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:28 INFO 140560773478208] Epoch[99] Batch[5] avg_epoch_loss=3.914912\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:28 INFO 140560773478208] Epoch[99] Batch [5]#011Speed: 316.33 samples/sec#011loss=3.914912\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:28 INFO 140560773478208] processed a total of 347 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1454.719066619873, \"sum\": 1454.719066619873, \"min\": 1454.719066619873}}, \"EndTime\": 1550540788.117661, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540786.662611}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:28 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=238.514366268 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:28 INFO 140560773478208] #progress_metric: host=algo-1, completed 25 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:28 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:28 INFO 140560773478208] Epoch[100] Batch[0] avg_epoch_loss=3.692513\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:29 INFO 140560773478208] Epoch[100] Batch[5] avg_epoch_loss=3.859445\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:29 INFO 140560773478208] Epoch[100] Batch [5]#011Speed: 315.57 samples/sec#011loss=3.859445\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:29 INFO 140560773478208] processed a total of 377 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1442.7108764648438, \"sum\": 1442.7108764648438, \"min\": 1442.7108764648438}}, \"EndTime\": 1550540789.56079, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540788.117744}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:29 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=261.291510457 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:29 INFO 140560773478208] #progress_metric: host=algo-1, completed 25 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:29 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:30 INFO 140560773478208] Epoch[101] Batch[0] avg_epoch_loss=3.630078\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:31 INFO 140560773478208] Epoch[101] Batch[5] avg_epoch_loss=3.825923\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:31 INFO 140560773478208] Epoch[101] Batch [5]#011Speed: 309.50 samples/sec#011loss=3.825923\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:31 INFO 140560773478208] processed a total of 374 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1496.1609840393066, \"sum\": 1496.1609840393066, \"min\": 1496.1609840393066}}, \"EndTime\": 1550540791.05737, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540789.560872}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:31 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=249.95071572 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:31 INFO 140560773478208] #progress_metric: host=algo-1, completed 25 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:31 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:31 INFO 140560773478208] Epoch[102] Batch[0] avg_epoch_loss=4.098979\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:32 INFO 140560773478208] Epoch[102] Batch[5] avg_epoch_loss=3.668361\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:32 INFO 140560773478208] Epoch[102] Batch [5]#011Speed: 320.72 samples/sec#011loss=3.668361\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:32 INFO 140560773478208] processed a total of 349 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1452.4481296539307, \"sum\": 1452.4481296539307, \"min\": 1452.4481296539307}}, \"EndTime\": 1550540792.510247, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540791.057455}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:32 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=240.263617466 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:32 INFO 140560773478208] #progress_metric: host=algo-1, completed 25 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:32 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:32 INFO 140560773478208] Epoch[103] Batch[0] avg_epoch_loss=3.720620\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:33 INFO 140560773478208] Epoch[103] Batch[5] avg_epoch_loss=3.826656\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:33 INFO 140560773478208] Epoch[103] Batch [5]#011Speed: 316.16 samples/sec#011loss=3.826656\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:33 INFO 140560773478208] processed a total of 347 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1446.746826171875, \"sum\": 1446.746826171875, \"min\": 1446.746826171875}}, \"EndTime\": 1550540793.957422, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540792.51033}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:33 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=239.827909362 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:33 INFO 140560773478208] #progress_metric: host=algo-1, completed 26 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:33 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:34 INFO 140560773478208] Epoch[104] Batch[0] avg_epoch_loss=3.856289\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:35 INFO 140560773478208] Epoch[104] Batch[5] avg_epoch_loss=3.846865\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:35 INFO 140560773478208] Epoch[104] Batch [5]#011Speed: 302.92 samples/sec#011loss=3.846865\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:35 INFO 140560773478208] processed a total of 367 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1484.1270446777344, \"sum\": 1484.1270446777344, \"min\": 1484.1270446777344}}, \"EndTime\": 1550540795.441973, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540793.957506}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:35 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=247.262757092 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:35 INFO 140560773478208] #progress_metric: host=algo-1, completed 26 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:35 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:35 INFO 140560773478208] Epoch[105] Batch[0] avg_epoch_loss=3.738755\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:36 INFO 140560773478208] Epoch[105] Batch[5] avg_epoch_loss=3.711624\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:36 INFO 140560773478208] Epoch[105] Batch [5]#011Speed: 320.99 samples/sec#011loss=3.711624\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:36 INFO 140560773478208] processed a total of 331 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1437.5629425048828, \"sum\": 1437.5629425048828, \"min\": 1437.5629425048828}}, \"EndTime\": 1550540796.880007, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540795.442057}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:36 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=230.231237753 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:36 INFO 140560773478208] #progress_metric: host=algo-1, completed 26 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:36 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:37 INFO 140560773478208] Epoch[106] Batch[0] avg_epoch_loss=4.024120\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:38 INFO 140560773478208] Epoch[106] Batch[5] avg_epoch_loss=3.975285\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:38 INFO 140560773478208] Epoch[106] Batch [5]#011Speed: 319.75 samples/sec#011loss=3.975285\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:38 INFO 140560773478208] processed a total of 367 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1443.692922592163, \"sum\": 1443.692922592163, \"min\": 1443.692922592163}}, \"EndTime\": 1550540798.324113, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540796.880091}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:38 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=254.189286532 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:38 INFO 140560773478208] #progress_metric: host=algo-1, completed 26 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:38 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:38 INFO 140560773478208] Epoch[107] Batch[0] avg_epoch_loss=3.807858\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:39 INFO 140560773478208] Epoch[107] Batch[5] avg_epoch_loss=3.873092\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:39 INFO 140560773478208] Epoch[107] Batch [5]#011Speed: 322.01 samples/sec#011loss=3.873092\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:39 INFO 140560773478208] processed a total of 394 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1633.5980892181396, \"sum\": 1633.5980892181396, \"min\": 1633.5980892181396}}, \"EndTime\": 1550540799.958179, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540798.324186}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:39 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=241.166528879 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:39 INFO 140560773478208] #progress_metric: host=algo-1, completed 27 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:39 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:40 INFO 140560773478208] Epoch[108] Batch[0] avg_epoch_loss=3.511425\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:41 INFO 140560773478208] Epoch[108] Batch[5] avg_epoch_loss=3.840559\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:41 INFO 140560773478208] Epoch[108] Batch [5]#011Speed: 316.52 samples/sec#011loss=3.840559\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:41 INFO 140560773478208] processed a total of 380 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1442.0349597930908, \"sum\": 1442.0349597930908, \"min\": 1442.0349597930908}}, \"EndTime\": 1550540801.400674, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540799.958266}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:41 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=263.493842552 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:41 INFO 140560773478208] #progress_metric: host=algo-1, completed 27 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:41 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:41 INFO 140560773478208] Epoch[109] Batch[0] avg_epoch_loss=3.872323\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:42 INFO 140560773478208] Epoch[109] Batch[5] avg_epoch_loss=3.872704\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:42 INFO 140560773478208] Epoch[109] Batch [5]#011Speed: 321.82 samples/sec#011loss=3.872704\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:43 INFO 140560773478208] processed a total of 412 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1639.0459537506104, \"sum\": 1639.0459537506104, \"min\": 1639.0459537506104}}, \"EndTime\": 1550540803.040137, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540801.400758}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:43 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=251.346399646 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:43 INFO 140560773478208] #progress_metric: host=algo-1, completed 27 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:43 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:43 INFO 140560773478208] Epoch[110] Batch[0] avg_epoch_loss=3.901220\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:44 INFO 140560773478208] Epoch[110] Batch[5] avg_epoch_loss=3.824173\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:44 INFO 140560773478208] Epoch[110] Batch [5]#011Speed: 318.55 samples/sec#011loss=3.824173\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:44 INFO 140560773478208] processed a total of 385 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1659.4271659851074, \"sum\": 1659.4271659851074, \"min\": 1659.4271659851074}}, \"EndTime\": 1550540804.700005, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540803.040222}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:44 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=231.99017336 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:44 INFO 140560773478208] #progress_metric: host=algo-1, completed 27 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:44 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:45 INFO 140560773478208] Epoch[111] Batch[0] avg_epoch_loss=3.909992\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:46 INFO 140560773478208] Epoch[111] Batch[5] avg_epoch_loss=3.938812\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:46 INFO 140560773478208] Epoch[111] Batch [5]#011Speed: 313.28 samples/sec#011loss=3.938812\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:46 INFO 140560773478208] processed a total of 357 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1468.1000709533691, \"sum\": 1468.1000709533691, \"min\": 1468.1000709533691}}, \"EndTime\": 1550540806.168535, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540804.700092}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:46 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=243.152243273 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:46 INFO 140560773478208] #progress_metric: host=algo-1, completed 28 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:46 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:46 INFO 140560773478208] Epoch[112] Batch[0] avg_epoch_loss=3.795234\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:47 INFO 140560773478208] Epoch[112] Batch[5] avg_epoch_loss=3.840944\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:47 INFO 140560773478208] Epoch[112] Batch [5]#011Speed: 322.78 samples/sec#011loss=3.840944\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:47 INFO 140560773478208] processed a total of 351 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1454.0810585021973, \"sum\": 1454.0810585021973, \"min\": 1454.0810585021973}}, \"EndTime\": 1550540807.623001, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540806.168611}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:47 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=241.369503875 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:47 INFO 140560773478208] #progress_metric: host=algo-1, completed 28 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:47 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:48 INFO 140560773478208] Epoch[113] Batch[0] avg_epoch_loss=3.628777\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:49 INFO 140560773478208] Epoch[113] Batch[5] avg_epoch_loss=3.760556\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:49 INFO 140560773478208] Epoch[113] Batch [5]#011Speed: 322.84 samples/sec#011loss=3.760556\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:49 INFO 140560773478208] processed a total of 384 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1422.5058555603027, \"sum\": 1422.5058555603027, \"min\": 1422.5058555603027}}, \"EndTime\": 1550540809.045971, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540807.623084}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:49 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=269.922820716 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:49 INFO 140560773478208] #progress_metric: host=algo-1, completed 28 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:49 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:49 INFO 140560773478208] Epoch[114] Batch[0] avg_epoch_loss=4.190056\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:50 INFO 140560773478208] Epoch[114] Batch[5] avg_epoch_loss=3.783161\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:50 INFO 140560773478208] Epoch[114] Batch [5]#011Speed: 304.73 samples/sec#011loss=3.783161\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:50 INFO 140560773478208] processed a total of 383 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1489.2370700836182, \"sum\": 1489.2370700836182, \"min\": 1489.2370700836182}}, \"EndTime\": 1550540810.535634, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540809.046056}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:50 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=257.157791004 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:50 INFO 140560773478208] #progress_metric: host=algo-1, completed 28 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:50 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:50 INFO 140560773478208] Epoch[115] Batch[0] avg_epoch_loss=3.772967\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:51 INFO 140560773478208] Epoch[115] Batch[5] avg_epoch_loss=3.769972\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:51 INFO 140560773478208] Epoch[115] Batch [5]#011Speed: 325.84 samples/sec#011loss=3.769972\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:51 INFO 140560773478208] processed a total of 369 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1422.1830368041992, \"sum\": 1422.1830368041992, \"min\": 1422.1830368041992}}, \"EndTime\": 1550540811.958241, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540810.535717}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:51 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=259.438179314 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:51 INFO 140560773478208] #progress_metric: host=algo-1, completed 29 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:51 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:52 INFO 140560773478208] Epoch[116] Batch[0] avg_epoch_loss=3.917510\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:53 INFO 140560773478208] Epoch[116] Batch[5] avg_epoch_loss=3.821948\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:53 INFO 140560773478208] Epoch[116] Batch [5]#011Speed: 324.21 samples/sec#011loss=3.821948\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:53 INFO 140560773478208] processed a total of 405 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1633.6748600006104, \"sum\": 1633.6748600006104, \"min\": 1633.6748600006104}}, \"EndTime\": 1550540813.592327, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540811.958325}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:53 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=247.888064521 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:53 INFO 140560773478208] #progress_metric: host=algo-1, completed 29 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:53 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:54 INFO 140560773478208] Epoch[117] Batch[0] avg_epoch_loss=3.878085\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:55 INFO 140560773478208] Epoch[117] Batch[5] avg_epoch_loss=3.794171\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:55 INFO 140560773478208] Epoch[117] Batch [5]#011Speed: 316.03 samples/sec#011loss=3.794171\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:55 INFO 140560773478208] processed a total of 358 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1446.7380046844482, \"sum\": 1446.7380046844482, \"min\": 1446.7380046844482}}, \"EndTime\": 1550540815.0395, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540813.592414}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:55 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=247.432888011 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:55 INFO 140560773478208] #progress_metric: host=algo-1, completed 29 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:55 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:55 INFO 140560773478208] Epoch[118] Batch[0] avg_epoch_loss=3.828809\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:56 INFO 140560773478208] Epoch[118] Batch[5] avg_epoch_loss=3.944014\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:56 INFO 140560773478208] Epoch[118] Batch [5]#011Speed: 321.17 samples/sec#011loss=3.944014\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:56 INFO 140560773478208] processed a total of 376 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1433.0799579620361, \"sum\": 1433.0799579620361, \"min\": 1433.0799579620361}}, \"EndTime\": 1550540816.473021, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540815.039576}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:56 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=262.350749485 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:56 INFO 140560773478208] #progress_metric: host=algo-1, completed 29 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:56 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:56 INFO 140560773478208] Epoch[119] Batch[0] avg_epoch_loss=3.966453\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:57 INFO 140560773478208] Epoch[119] Batch[5] avg_epoch_loss=3.893536\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:57 INFO 140560773478208] Epoch[119] Batch [5]#011Speed: 323.11 samples/sec#011loss=3.893536\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:57 INFO 140560773478208] processed a total of 377 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1413.9020442962646, \"sum\": 1413.9020442962646, \"min\": 1413.9020442962646}}, \"EndTime\": 1550540817.887398, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540816.4731}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:57 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=266.614837334 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:57 INFO 140560773478208] #progress_metric: host=algo-1, completed 30 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:57 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:58 INFO 140560773478208] Epoch[120] Batch[0] avg_epoch_loss=3.975962\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:59 INFO 140560773478208] Epoch[120] Batch[5] avg_epoch_loss=3.837524\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:59 INFO 140560773478208] Epoch[120] Batch [5]#011Speed: 315.35 samples/sec#011loss=3.837524\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:59 INFO 140560773478208] processed a total of 377 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1457.4098587036133, \"sum\": 1457.4098587036133, \"min\": 1457.4098587036133}}, \"EndTime\": 1550540819.345229, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540817.887482}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:59 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=258.653210779 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:59 INFO 140560773478208] #progress_metric: host=algo-1, completed 30 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:59 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:46:59 INFO 140560773478208] Epoch[121] Batch[0] avg_epoch_loss=3.961862\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:00 INFO 140560773478208] Epoch[121] Batch[5] avg_epoch_loss=3.858808\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:00 INFO 140560773478208] Epoch[121] Batch [5]#011Speed: 312.35 samples/sec#011loss=3.858808\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:01 INFO 140560773478208] processed a total of 413 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1658.4670543670654, \"sum\": 1658.4670543670654, \"min\": 1658.4670543670654}}, \"EndTime\": 1550540821.004148, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540819.345308}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:01 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=249.008356975 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:01 INFO 140560773478208] #progress_metric: host=algo-1, completed 30 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:01 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:01 INFO 140560773478208] Epoch[122] Batch[0] avg_epoch_loss=3.902910\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:02 INFO 140560773478208] Epoch[122] Batch[5] avg_epoch_loss=3.849241\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:02 INFO 140560773478208] Epoch[122] Batch [5]#011Speed: 322.19 samples/sec#011loss=3.849241\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:02 INFO 140560773478208] processed a total of 387 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1612.562894821167, \"sum\": 1612.562894821167, \"min\": 1612.562894821167}}, \"EndTime\": 1550540822.617155, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540821.004227}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:02 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=239.97172844 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:02 INFO 140560773478208] #progress_metric: host=algo-1, completed 30 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:02 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:03 INFO 140560773478208] Epoch[123] Batch[0] avg_epoch_loss=3.880599\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:04 INFO 140560773478208] Epoch[123] Batch[5] avg_epoch_loss=4.084962\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:04 INFO 140560773478208] Epoch[123] Batch [5]#011Speed: 322.76 samples/sec#011loss=4.084962\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:04 INFO 140560773478208] processed a total of 372 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1436.9840621948242, \"sum\": 1436.9840621948242, \"min\": 1436.9840621948242}}, \"EndTime\": 1550540824.054612, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540822.617242}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:04 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=258.853361435 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:04 INFO 140560773478208] #progress_metric: host=algo-1, completed 31 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:04 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:04 INFO 140560773478208] Epoch[124] Batch[0] avg_epoch_loss=3.716366\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:05 INFO 140560773478208] Epoch[124] Batch[5] avg_epoch_loss=3.734958\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:05 INFO 140560773478208] Epoch[124] Batch [5]#011Speed: 302.62 samples/sec#011loss=3.734958\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:05 INFO 140560773478208] processed a total of 381 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1526.0889530181885, \"sum\": 1526.0889530181885, \"min\": 1526.0889530181885}}, \"EndTime\": 1550540825.581112, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540824.054695}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:05 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=249.637668778 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:05 INFO 140560773478208] #progress_metric: host=algo-1, completed 31 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:05 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:06 INFO 140560773478208] Epoch[125] Batch[0] avg_epoch_loss=3.955291\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:07 INFO 140560773478208] Epoch[125] Batch[5] avg_epoch_loss=3.830411\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:07 INFO 140560773478208] Epoch[125] Batch [5]#011Speed: 316.65 samples/sec#011loss=3.830411\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:07 INFO 140560773478208] processed a total of 399 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1691.6770935058594, \"sum\": 1691.6770935058594, \"min\": 1691.6770935058594}}, \"EndTime\": 1550540827.273246, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540825.581195}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:07 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=235.843364229 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:07 INFO 140560773478208] #progress_metric: host=algo-1, completed 31 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:07 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:07 INFO 140560773478208] Epoch[126] Batch[0] avg_epoch_loss=3.783231\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:08 INFO 140560773478208] Epoch[126] Batch[5] avg_epoch_loss=3.865015\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:08 INFO 140560773478208] Epoch[126] Batch [5]#011Speed: 318.54 samples/sec#011loss=3.865015\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:08 INFO 140560773478208] processed a total of 359 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1423.6629009246826, \"sum\": 1423.6629009246826, \"min\": 1423.6629009246826}}, \"EndTime\": 1550540828.69737, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540827.273331}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:08 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=252.14354961 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:08 INFO 140560773478208] #progress_metric: host=algo-1, completed 31 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:08 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:09 INFO 140560773478208] Epoch[127] Batch[0] avg_epoch_loss=3.793158\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:10 INFO 140560773478208] Epoch[127] Batch[5] avg_epoch_loss=3.761202\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:10 INFO 140560773478208] Epoch[127] Batch [5]#011Speed: 311.00 samples/sec#011loss=3.761202\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:10 INFO 140560773478208] processed a total of 382 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1478.1548976898193, \"sum\": 1478.1548976898193, \"min\": 1478.1548976898193}}, \"EndTime\": 1550540830.17595, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540828.697456}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:10 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=258.410867737 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:10 INFO 140560773478208] #progress_metric: host=algo-1, completed 32 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:10 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:10 INFO 140560773478208] Epoch[128] Batch[0] avg_epoch_loss=3.651374\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:11 INFO 140560773478208] Epoch[128] Batch[5] avg_epoch_loss=3.684101\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:11 INFO 140560773478208] Epoch[128] Batch [5]#011Speed: 309.92 samples/sec#011loss=3.684101\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:11 INFO 140560773478208] processed a total of 389 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1666.3098335266113, \"sum\": 1666.3098335266113, \"min\": 1666.3098335266113}}, \"EndTime\": 1550540831.842703, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540830.176019}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:11 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=233.433281589 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:11 INFO 140560773478208] #progress_metric: host=algo-1, completed 32 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:11 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:12 INFO 140560773478208] Epoch[129] Batch[0] avg_epoch_loss=3.815140\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:13 INFO 140560773478208] Epoch[129] Batch[5] avg_epoch_loss=3.856284\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:13 INFO 140560773478208] Epoch[129] Batch [5]#011Speed: 311.56 samples/sec#011loss=3.856284\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:13 INFO 140560773478208] processed a total of 356 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1448.0700492858887, \"sum\": 1448.0700492858887, \"min\": 1448.0700492858887}}, \"EndTime\": 1550540833.291225, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540831.842781}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:13 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=245.824628227 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:13 INFO 140560773478208] #progress_metric: host=algo-1, completed 32 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:13 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:13 INFO 140560773478208] Epoch[130] Batch[0] avg_epoch_loss=3.680664\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:14 INFO 140560773478208] Epoch[130] Batch[5] avg_epoch_loss=3.779802\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:14 INFO 140560773478208] Epoch[130] Batch [5]#011Speed: 320.52 samples/sec#011loss=3.779802\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:14 INFO 140560773478208] processed a total of 397 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1640.2099132537842, \"sum\": 1640.2099132537842, \"min\": 1640.2099132537842}}, \"EndTime\": 1550540834.931883, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540833.291299}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:14 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=242.022243262 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:14 INFO 140560773478208] #progress_metric: host=algo-1, completed 32 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:14 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:15 INFO 140560773478208] Epoch[131] Batch[0] avg_epoch_loss=3.886952\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:16 INFO 140560773478208] Epoch[131] Batch[5] avg_epoch_loss=3.900540\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:16 INFO 140560773478208] Epoch[131] Batch [5]#011Speed: 299.41 samples/sec#011loss=3.900540\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:16 INFO 140560773478208] processed a total of 377 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1493.933916091919, \"sum\": 1493.933916091919, \"min\": 1493.933916091919}}, \"EndTime\": 1550540836.426285, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540834.931974}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:16 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=252.331919373 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:16 INFO 140560773478208] #progress_metric: host=algo-1, completed 33 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:16 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:16 INFO 140560773478208] Epoch[132] Batch[0] avg_epoch_loss=3.610595\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:17 INFO 140560773478208] Epoch[132] Batch[5] avg_epoch_loss=3.988628\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:17 INFO 140560773478208] Epoch[132] Batch [5]#011Speed: 307.04 samples/sec#011loss=3.988628\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:17 INFO 140560773478208] processed a total of 343 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1468.3890342712402, \"sum\": 1468.3890342712402, \"min\": 1468.3890342712402}}, \"EndTime\": 1550540837.89513, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540836.426373}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:17 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=233.568995452 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:17 INFO 140560773478208] #progress_metric: host=algo-1, completed 33 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:17 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:18 INFO 140560773478208] Epoch[133] Batch[0] avg_epoch_loss=3.929572\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:19 INFO 140560773478208] Epoch[133] Batch[5] avg_epoch_loss=3.893823\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:19 INFO 140560773478208] Epoch[133] Batch [5]#011Speed: 323.49 samples/sec#011loss=3.893823\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:19 INFO 140560773478208] processed a total of 375 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1431.548833847046, \"sum\": 1431.548833847046, \"min\": 1431.548833847046}}, \"EndTime\": 1550540839.327109, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540837.895216}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:19 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=261.931486254 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:19 INFO 140560773478208] #progress_metric: host=algo-1, completed 33 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:19 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:19 INFO 140560773478208] Epoch[134] Batch[0] avg_epoch_loss=4.073855\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:20 INFO 140560773478208] Epoch[134] Batch[5] avg_epoch_loss=3.800034\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:20 INFO 140560773478208] Epoch[134] Batch [5]#011Speed: 309.00 samples/sec#011loss=3.800034\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:20 INFO 140560773478208] processed a total of 382 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1482.450008392334, \"sum\": 1482.450008392334, \"min\": 1482.450008392334}}, \"EndTime\": 1550540840.809986, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540839.327191}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:20 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=257.658416533 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:20 INFO 140560773478208] #progress_metric: host=algo-1, completed 33 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:20 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:21 INFO 140560773478208] Epoch[135] Batch[0] avg_epoch_loss=4.194668\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:22 INFO 140560773478208] Epoch[135] Batch[5] avg_epoch_loss=3.881843\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:22 INFO 140560773478208] Epoch[135] Batch [5]#011Speed: 313.01 samples/sec#011loss=3.881843\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:22 INFO 140560773478208] processed a total of 361 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1444.7548389434814, \"sum\": 1444.7548389434814, \"min\": 1444.7548389434814}}, \"EndTime\": 1550540842.255242, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540840.810078}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:22 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=249.848231444 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:22 INFO 140560773478208] #progress_metric: host=algo-1, completed 34 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:22 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:22 INFO 140560773478208] Epoch[136] Batch[0] avg_epoch_loss=3.571914\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:23 INFO 140560773478208] Epoch[136] Batch[5] avg_epoch_loss=3.728600\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:23 INFO 140560773478208] Epoch[136] Batch [5]#011Speed: 322.51 samples/sec#011loss=3.728600\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:23 INFO 140560773478208] processed a total of 375 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1429.0249347686768, \"sum\": 1429.0249347686768, \"min\": 1429.0249347686768}}, \"EndTime\": 1550540843.684681, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540842.255323}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:23 INFO 140560773478208] #throughput_metric: host=algo-1, train throughput=262.393930219 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:23 INFO 140560773478208] #progress_metric: host=algo-1, completed 34 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:23 INFO 140560773478208] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:23 INFO 140560773478208] Loading parameters from best epoch (96)\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.deserialize.time\": {\"count\": 1, \"max\": 61.00606918334961, \"sum\": 61.00606918334961, \"min\": 61.00606918334961}}, \"EndTime\": 1550540843.746174, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540843.684765}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:23 INFO 140560773478208] stopping training now\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:23 INFO 140560773478208] #progress_metric: host=algo-1, completed 100 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:23 INFO 140560773478208] Final loss: 3.48887477318 (occurred at epoch 96)\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:23 INFO 140560773478208] #quality_metric: host=algo-1, train final_loss <loss>=3.48887477318\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:23 INFO 140560773478208] Worker algo-1 finished training.\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:23 WARNING 140560773478208] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:23 INFO 140560773478208] All workers finished. Serializing model for prediction.\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"get_graph.time\": {\"count\": 1, \"max\": 836.6739749908447, \"sum\": 836.6739749908447, \"min\": 836.6739749908447}}, \"EndTime\": 1550540844.583762, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540843.746251}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:24 INFO 140560773478208] Number of GPUs being used: 0\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"finalize.time\": {\"count\": 1, \"max\": 1114.9578094482422, \"sum\": 1114.9578094482422, \"min\": 1114.9578094482422}}, \"EndTime\": 1550540844.862005, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540844.583844}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:24 INFO 140560773478208] Serializing to /opt/ml/model/model_algo-1\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:24 INFO 140560773478208] Saved checkpoint to \"/opt/ml/model/model_algo-1-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"model.serialize.time\": {\"count\": 1, \"max\": 47.7299690246582, \"sum\": 47.7299690246582, \"min\": 47.7299690246582}}, \"EndTime\": 1550540844.909885, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540844.862104}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:24 INFO 140560773478208] Successfully serialized the model for prediction.\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:47:24 INFO 140560773478208] Evaluating model accuracy on testset using 100 samples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"model.bind.time\": {\"count\": 1, \"max\": 0.03409385681152344, \"sum\": 0.03409385681152344, \"min\": 0.03409385681152344}}, \"EndTime\": 1550540844.910637, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540844.909947}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:48:00 INFO 140560773478208] Number of test batches scored: 10\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:48:35 INFO 140560773478208] Number of test batches scored: 20\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"model.score.time\": {\"count\": 1, \"max\": 84888.76986503601, \"sum\": 84888.76986503601, \"min\": 84888.76986503601}}, \"EndTime\": 1550540929.799388, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540844.910681}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:48:49 INFO 140560773478208] #test_score (algo-1, RMSE): 536.771628581\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:48:49 INFO 140560773478208] #test_score (algo-1, mean_wQuantileLoss): 0.0704429\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:48:49 INFO 140560773478208] #test_score (algo-1, wQuantileLoss[0.1]): 0.0554997\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:48:49 INFO 140560773478208] #test_score (algo-1, wQuantileLoss[0.2]): 0.074501\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:48:49 INFO 140560773478208] #test_score (algo-1, wQuantileLoss[0.3]): 0.0838566\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:48:49 INFO 140560773478208] #test_score (algo-1, wQuantileLoss[0.4]): 0.087148\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:48:49 INFO 140560773478208] #test_score (algo-1, wQuantileLoss[0.5]): 0.0858723\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:48:49 INFO 140560773478208] #test_score (algo-1, wQuantileLoss[0.6]): 0.0806377\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:48:49 INFO 140560773478208] #test_score (algo-1, wQuantileLoss[0.7]): 0.0714343\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:48:49 INFO 140560773478208] #test_score (algo-1, wQuantileLoss[0.8]): 0.0574403\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:48:49 INFO 140560773478208] #test_score (algo-1, wQuantileLoss[0.9]): 0.037596\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:48:49 INFO 140560773478208] #quality_metric: host=algo-1, test RMSE <loss>=536.771628581\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:48:49 INFO 140560773478208] #quality_metric: host=algo-1, test mean_wQuantileLoss <loss>=0.0704428702593\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"totaltime\": {\"count\": 1, \"max\": 294341.53389930725, \"sum\": 294341.53389930725, \"min\": 294341.53389930725}, \"setuptime\": {\"count\": 1, \"max\": 9.662866592407227, \"sum\": 9.662866592407227, \"min\": 9.662866592407227}}, \"EndTime\": 1550540929.886142, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550540929.799477}\n",
      "\u001b[0m\n",
      "\n",
      "2019-02-19 01:49:03 Uploading - Uploading generated training model\n",
      "2019-02-19 01:49:03 Completed - Training job completed\n",
      "Billable seconds: 349\n",
      "CPU times: user 792 ms, sys: 56.4 ms, total: 848 ms\n",
      "Wall time: 8min 14s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "data_channels = {\n",
    "    \"train\": \"{}/train/\".format(s3_data_path),\n",
    "    \"test\": \"{}/test/\".format(s3_data_path)\n",
    "}\n",
    "\n",
    "estimator.fit(inputs=data_channels, wait=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since you pass a test set in this example, accuracy metrics for the forecast are computed and logged (see bottom of the log).\n",
    "You can find the definition of these metrics from [our documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html). You can use these to optimize the parameters and tune your model or use SageMaker's [Automated Model Tuning service](https://aws.amazon.com/blogs/aws/sagemaker-automatic-model-tuning/) to tune the model for you."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create endpoint and predictor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have a trained model, we can use it to perform predictions by deploying it to an endpoint.\n",
    "\n",
    "**Note: Remember to delete the endpoint after running this experiment. A cell at the very bottom of this notebook will do that: make sure you run it at the end.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To query the endpoint and perform predictions, we can define the following utility class: this allows making requests using `pandas.Series` objects rather than raw JSON strings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DeepARPredictor(sagemaker.predictor.RealTimePredictor):\n",
    "    \n",
    "    def __init__(self, *args, **kwargs):\n",
    "        super().__init__(*args, content_type=sagemaker.content_types.CONTENT_TYPE_JSON, **kwargs)\n",
    "        \n",
    "    def predict(self, ts, cat=None, dynamic_feat=None, \n",
    "                num_samples=100, return_samples=False, quantiles=[\"0.1\", \"0.5\", \"0.9\"]):\n",
    "        \"\"\"Requests the prediction of for the time series listed in `ts`, each with the (optional)\n",
    "        corresponding category listed in `cat`.\n",
    "        \n",
    "        ts -- `pandas.Series` object, the time series to predict\n",
    "        cat -- integer, the group associated to the time series (default: None)\n",
    "        num_samples -- integer, number of samples to compute at prediction time (default: 100)\n",
    "        return_samples -- boolean indicating whether to include samples in the response (default: False)\n",
    "        quantiles -- list of strings specifying the quantiles to compute (default: [\"0.1\", \"0.5\", \"0.9\"])\n",
    "        \n",
    "        Return value: list of `pandas.DataFrame` objects, each containing the predictions\n",
    "        \"\"\"\n",
    "        prediction_time = ts.index[-1] + 1\n",
    "        quantiles = [str(q) for q in quantiles]\n",
    "        req = self.__encode_request(ts, cat, dynamic_feat, num_samples, return_samples, quantiles)\n",
    "        res = super(DeepARPredictor, self).predict(req)\n",
    "        return self.__decode_response(res, ts.index.freq, prediction_time, return_samples)\n",
    "    \n",
    "    def __encode_request(self, ts, cat, dynamic_feat, num_samples, return_samples, quantiles):\n",
    "        instance = series_to_dict(ts, cat if cat is not None else None, dynamic_feat if dynamic_feat else None)\n",
    "\n",
    "        configuration = {\n",
    "            \"num_samples\": num_samples,\n",
    "            \"output_types\": [\"quantiles\", \"samples\"] if return_samples else [\"quantiles\"],\n",
    "            \"quantiles\": quantiles\n",
    "        }\n",
    "        \n",
    "        http_request_data = {\n",
    "            \"instances\": [instance],\n",
    "            \"configuration\": configuration\n",
    "        }\n",
    "        \n",
    "        return json.dumps(http_request_data).encode('utf-8')\n",
    "    \n",
    "    def __decode_response(self, response, freq, prediction_time, return_samples):\n",
    "        # we only sent one time series so we only receive one in return\n",
    "        # however, if possible one will pass multiple time series as predictions will then be faster\n",
    "        predictions = json.loads(response.decode('utf-8'))['predictions'][0]\n",
    "        prediction_length = len(next(iter(predictions['quantiles'].values())))\n",
    "        prediction_index = pd.DatetimeIndex(start=prediction_time, freq=freq, periods=prediction_length)        \n",
    "        if return_samples:\n",
    "            dict_of_samples = {'sample_' + str(i): s for i, s in enumerate(predictions['samples'])}\n",
    "        else:\n",
    "            dict_of_samples = {}\n",
    "        return pd.DataFrame(data={**predictions['quantiles'], **dict_of_samples}, index=prediction_index)\n",
    "\n",
    "    def set_frequency(self, freq):\n",
    "        self.freq = freq\n",
    "        \n",
    "def encode_target(ts):\n",
    "    return [x if np.isfinite(x) else \"NaN\" for x in ts]        \n",
    "\n",
    "def series_to_dict(ts, cat=None, dynamic_feat=None):\n",
    "    \"\"\"Given a pandas.Series object, returns a dictionary encoding the time series.\n",
    "\n",
    "    ts -- a pands.Series object with the target time series\n",
    "    cat -- an integer indicating the time series category\n",
    "\n",
    "    Return value: a dictionary\n",
    "    \"\"\"\n",
    "    obj = {\"start\": str(ts.index[0]), \"target\": encode_target(ts)}\n",
    "    if cat is not None:\n",
    "        obj[\"cat\"] = cat\n",
    "    if dynamic_feat is not None:\n",
    "        obj[\"dynamic_feat\"] = dynamic_feat        \n",
    "    return obj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can deploy the model and create and endpoint that can be queried using our custom DeepARPredictor class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating model with name: forecasting-deepar-2019-02-19-01-49-34-171\n",
      "INFO:sagemaker:Creating endpoint with name deepar-electricity-demo-2019-02-19-01-41-19-423\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------------------------!"
     ]
    }
   ],
   "source": [
    "predictor = estimator.deploy(\n",
    "    initial_instance_count=1,\n",
    "    instance_type='ml.m4.xlarge',\n",
    "    predictor_cls=DeepARPredictor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make predictions and plot results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use the `predictor` object to generate predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0.1</th>\n",
       "      <th>0.5</th>\n",
       "      <th>0.9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01 02:00:00</th>\n",
       "      <td>172.032852</td>\n",
       "      <td>191.525665</td>\n",
       "      <td>208.794357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 04:00:00</th>\n",
       "      <td>165.706039</td>\n",
       "      <td>189.372910</td>\n",
       "      <td>204.664810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 06:00:00</th>\n",
       "      <td>197.379837</td>\n",
       "      <td>218.226089</td>\n",
       "      <td>244.045868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 08:00:00</th>\n",
       "      <td>309.392456</td>\n",
       "      <td>332.628510</td>\n",
       "      <td>356.055328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 10:00:00</th>\n",
       "      <td>315.750671</td>\n",
       "      <td>341.675171</td>\n",
       "      <td>369.738495</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            0.1         0.5         0.9\n",
       "2015-01-01 02:00:00  172.032852  191.525665  208.794357\n",
       "2015-01-01 04:00:00  165.706039  189.372910  204.664810\n",
       "2015-01-01 06:00:00  197.379837  218.226089  244.045868\n",
       "2015-01-01 08:00:00  309.392456  332.628510  356.055328\n",
       "2015-01-01 10:00:00  315.750671  341.675171  369.738495"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictor.predict(ts=timeseries[120], quantiles=[0.10, 0.5, 0.90]).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we define a plotting function that queries the model and displays the forecast."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(\n",
    "    predictor, \n",
    "    target_ts, \n",
    "    cat=None, \n",
    "    dynamic_feat=None, \n",
    "    forecast_date=end_training, \n",
    "    show_samples=False, \n",
    "    plot_history=7 * 12,\n",
    "    confidence=80\n",
    "):\n",
    "    print(\"calling served model to generate predictions starting from {}\".format(str(forecast_date)))\n",
    "    assert(confidence > 50 and confidence < 100)\n",
    "    low_quantile = 0.5 - confidence * 0.005\n",
    "    up_quantile = confidence * 0.005 + 0.5\n",
    "        \n",
    "    # we first construct the argument to call our model\n",
    "    args = {\n",
    "        \"ts\": target_ts[:forecast_date],\n",
    "        \"return_samples\": show_samples,\n",
    "        \"quantiles\": [low_quantile, 0.5, up_quantile],\n",
    "        \"num_samples\": 100\n",
    "    }\n",
    "\n",
    "\n",
    "    if dynamic_feat is not None:\n",
    "        args[\"dynamic_feat\"] = dynamic_feat\n",
    "        fig = plt.figure(figsize=(20, 6))\n",
    "        ax = plt.subplot(2, 1, 1)\n",
    "    else:\n",
    "        fig = plt.figure(figsize=(20, 3))\n",
    "        ax = plt.subplot(1,1,1)\n",
    "    \n",
    "    if cat is not None:\n",
    "        args[\"cat\"] = cat\n",
    "        ax.text(0.9, 0.9, 'cat = {}'.format(cat), transform=ax.transAxes)\n",
    "\n",
    "    # call the end point to get the prediction\n",
    "    prediction = predictor.predict(**args)\n",
    "\n",
    "    # plot the samples\n",
    "    if show_samples: \n",
    "        for key in prediction.keys():\n",
    "            if \"sample\" in key:\n",
    "                prediction[key].plot(color='lightskyblue', alpha=0.2, label='_nolegend_')\n",
    "                \n",
    "                \n",
    "    # plot the target\n",
    "    target_section = target_ts[forecast_date-plot_history:forecast_date+prediction_length]\n",
    "    target_section.plot(color=\"black\", label='target')\n",
    "    \n",
    "    # plot the confidence interval and the median predicted\n",
    "    ax.fill_between(\n",
    "        prediction[str(low_quantile)].index, \n",
    "        prediction[str(low_quantile)].values, \n",
    "        prediction[str(up_quantile)].values, \n",
    "        color=\"b\", alpha=0.3, label='{}% confidence interval'.format(confidence)\n",
    "    )\n",
    "    prediction[\"0.5\"].plot(color=\"b\", label='P50')\n",
    "    ax.legend(loc=2)    \n",
    "    \n",
    "    # fix the scale as the samples may change it\n",
    "    ax.set_ylim(target_section.min() * 0.5, target_section.max() * 1.5)\n",
    "    \n",
    "    if dynamic_feat is not None:\n",
    "        for i, f in enumerate(dynamic_feat, start=1):\n",
    "            ax = plt.subplot(len(dynamic_feat) * 2, 1, len(dynamic_feat) + i, sharex=ax)\n",
    "            feat_ts = pd.Series(\n",
    "                index=pd.DatetimeIndex(start=target_ts.index[0], freq=target_ts.index.freq, periods=len(f)),\n",
    "                data=f\n",
    "            )\n",
    "            feat_ts[forecast_date-plot_history:forecast_date+prediction_length].plot(ax=ax, color='g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can interact with the function previously defined, to look at the forecast of any customer at any point in (future) time. \n",
    "\n",
    "For each request, the predictions are obtained by calling our served model on the fly.\n",
    "\n",
    "Here we forecast the consumption of an office after week-end (note the lower week-end consumption). \n",
    "You can select any time series and any forecast date, just click on `Run Interact` to generate the predictions from our served endpoint and see the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "style = {'description_width': 'initial'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dff350453c8f4807812f16670b9e225e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=91, description='customer_id', max=369, style=SliderStyle(description_wi…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact_manual(\n",
    "    customer_id=IntSlider(min=0, max=369, value=91, style=style), \n",
    "    forecast_day=IntSlider(min=0, max=100, value=51, style=style),\n",
    "    confidence=IntSlider(min=60, max=95, value=80, step=5, style=style),\n",
    "    history_weeks_plot=IntSlider(min=1, max=20, value=1, style=style),\n",
    "    show_samples=Checkbox(value=False),\n",
    "    continuous_update=False\n",
    ")\n",
    "def plot_interact(customer_id, forecast_day, confidence, history_weeks_plot, show_samples):\n",
    "    plot(\n",
    "        predictor,\n",
    "        target_ts=timeseries[customer_id],\n",
    "        forecast_date=end_training + datetime.timedelta(days=forecast_day),\n",
    "        show_samples=show_samples,\n",
    "        plot_history=history_weeks_plot * 12 * 7,\n",
    "        confidence=confidence\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Additional features\n",
    "\n",
    "We have seen how to prepare a dataset and run DeepAR for a simple example.\n",
    "\n",
    "In addition DeepAR supports the following features:\n",
    "\n",
    "* missing values: DeepAR can handle missing values in the time series during training as well as for inference.\n",
    "* Additional time features: DeepAR provides a set default time series features such as hour of day etc. However, you can provide additional feature time series via the `dynamic_feat` field. \n",
    "* generalize frequencies: any integer multiple of the previously supported base frequencies (minutes `min`, hours `H`, days `D`, weeks `W`, month `M`) are now allowed; e.g., `15min`. We already demonstrated this above by using `2H` frequency.\n",
    "* categories: If your time series belong to different groups (e.g. types of product, regions, etc), this information can be encoded as one or more categorical features using the `cat` field.\n",
    "\n",
    "We will now demonstrate the missing values and time features support. For this part we will reuse the electricity dataset but will do some artificial changes to demonstrate the new features: \n",
    "* We will randomly mask parts of the time series to demonstrate the missing values support.\n",
    "* We will include a \"special-day\" that occurs at different days for different time series during this day we introduce a strong up-lift\n",
    "* We train the model on this dataset giving \"special-day\" as a custom time series feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As discussed above we will create a \"special-day\" feature and create an up-lift for the time series during this day. This simulates real world application where you may have things like promotions of a product for a certain time or a special event that influences your time series. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_special_day_feature(ts, fraction=0.05):\n",
    "    # First select random day indices (plus the forecast day)\n",
    "    num_days = (ts.index[-1] - ts.index[0]).days\n",
    "    rand_indices = list(np.random.randint(0, num_days, int(num_days * 0.1))) + [num_days]\n",
    "    \n",
    "    feature_value = np.zeros_like(ts)\n",
    "    for i in rand_indices:\n",
    "        feature_value[i * 12: (i + 1) * 12] = 1.0\n",
    "    feature = pd.Series(index=ts.index, data=feature_value)\n",
    "    return feature\n",
    "\n",
    "def drop_at_random(ts, drop_probability=0.1):\n",
    "    assert(0 <= drop_probability < 1)\n",
    "    random_mask = np.random.random(len(ts)) < drop_probability\n",
    "    return ts.mask(random_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "special_day_features = [create_special_day_feature(ts) for ts in timeseries]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now create the up-lifted time series and randomly remove time points.\n",
    "\n",
    "The figures below show some example time series and the `special_day` feature value in green. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "timeseries_uplift = [ts * (1.0 + feat) for ts, feat in zip(timeseries, special_day_features)]\n",
    "time_series_processed = [drop_at_random(ts) for ts in timeseries_uplift]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(5, 2, figsize=(20, 20), sharex=True)\n",
    "axx = axs.ravel()\n",
    "for i in range(0, 10):\n",
    "    ax = axx[i]\n",
    "    ts = time_series_processed[i][:400]\n",
    "    ts.plot(ax=ax)\n",
    "    ax.set_ylim(-0.1 * ts.max(), ts.max())\n",
    "    ax2 = ax.twinx()\n",
    "    special_day_features[i][:400].plot(ax=ax2, color='g')\n",
    "    ax2.set_ylim(-0.2, 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "370\n",
      "CPU times: user 8.34 s, sys: 309 ms, total: 8.64 s\n",
      "Wall time: 8.64 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "training_data_new_features = [\n",
    "    {\n",
    "        \"start\": str(start_dataset),\n",
    "        \"target\": encode_target(ts[start_dataset:end_training]),\n",
    "        \"dynamic_feat\": [special_day_features[i][start_dataset:end_training].tolist()]\n",
    "    }\n",
    "    for i, ts in enumerate(time_series_processed)\n",
    "]\n",
    "print(len(training_data_new_features))\n",
    "\n",
    "# as in our previous example, we do a rolling evaluation over the next 7 days\n",
    "num_test_windows = 7\n",
    "\n",
    "test_data_new_features = [\n",
    "    {\n",
    "        \"start\": str(start_dataset),\n",
    "        \"target\": encode_target(ts[start_dataset:end_training + 2*k*prediction_length]),\n",
    "        \"dynamic_feat\": [special_day_features[i][start_dataset:end_training + 2*k*prediction_length].tolist()]\n",
    "    }\n",
    "    for k in range(1, num_test_windows + 1) \n",
    "    for i, ts in enumerate(timeseries_uplift)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_dataset_consistency(train_dataset, test_dataset=None):\n",
    "    d = train_dataset[0]\n",
    "    has_dynamic_feat = 'dynamic_feat' in d\n",
    "    if has_dynamic_feat:\n",
    "        num_dynamic_feat = len(d['dynamic_feat'])\n",
    "    has_cat = 'cat' in d\n",
    "    if has_cat:\n",
    "        num_cat = len(d['cat'])\n",
    "    \n",
    "    def check_ds(ds):\n",
    "        for i, d in enumerate(ds):\n",
    "            if has_dynamic_feat:\n",
    "                assert 'dynamic_feat' in d\n",
    "                assert num_dynamic_feat == len(d['dynamic_feat'])\n",
    "                for f in d['dynamic_feat']:\n",
    "                    assert len(d['target']) == len(f)\n",
    "            if has_cat:\n",
    "                assert 'cat' in d\n",
    "                assert len(d['cat']) == num_cat\n",
    "    check_ds(train_dataset)\n",
    "    if test_dataset is not None:\n",
    "        check_ds(test_dataset)\n",
    "        \n",
    "check_dataset_consistency(training_data_new_features, test_data_new_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6.23 s, sys: 321 ms, total: 6.55 s\n",
      "Wall time: 6.55 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "write_dicts_to_file(\"train_new_features.json\", training_data_new_features)\n",
    "write_dicts_to_file(\"test_new_features.json\", test_data_new_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploading to S3 this may take a few minutes depending on your connection.\n",
      "Overwriting existing file\n",
      "Uploading file to s3://sagemaker-ap-northeast-2-082256166551/deepar-electricity-demo-notebook-new-features/data/train/train_new_features.json\n",
      "Overwriting existing file\n",
      "Uploading file to s3://sagemaker-ap-northeast-2-082256166551/deepar-electricity-demo-notebook-new-features/data/test/test_new_features.json\n",
      "CPU times: user 627 ms, sys: 314 ms, total: 941 ms\n",
      "Wall time: 3.25 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "s3_data_path_new_features = \"s3://{}/{}-new-features/data\".format(s3_bucket, s3_prefix)\n",
    "s3_output_path_new_features = \"s3://{}/{}-new-features/output\".format(s3_bucket, s3_prefix)\n",
    "\n",
    "print('Uploading to S3 this may take a few minutes depending on your connection.')\n",
    "copy_to_s3(\"train_new_features.json\", s3_data_path_new_features + \"/train/train_new_features.json\", override=True)\n",
    "copy_to_s3(\"test_new_features.json\", s3_data_path_new_features + \"/test/test_new_features.json\", override=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating training-job with name: deepar-electricity-demo-new-features-2019-02-19-01-56-15-381\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-02-19 01:56:15 Starting - Starting the training job...\n",
      "2019-02-19 01:56:20 Starting - Launching requested ML instances......\n",
      "2019-02-19 01:57:20 Starting - Preparing the instances for training...\n",
      "2019-02-19 01:58:11 Downloading - Downloading input data...\n",
      "2019-02-19 01:58:32 Training - Downloading the training image..\n",
      "\u001b[31mArguments: train\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:50 INFO 140350967232320] Reading default configuration from /opt/amazon/lib/python2.7/site-packages/algorithm/default-input.json: {u'num_dynamic_feat': u'auto', u'dropout_rate': u'0.10', u'mini_batch_size': u'128', u'test_quantiles': u'[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]', u'_tuning_objective_metric': u'', u'_num_gpus': u'auto', u'num_eval_samples': u'100', u'learning_rate': u'0.001', u'num_cells': u'40', u'num_layers': u'2', u'embedding_dimension': u'10', u'_kvstore': u'auto', u'_num_kv_servers': u'auto', u'cardinality': u'auto', u'likelihood': u'student-t', u'early_stopping_patience': u''}\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:50 INFO 140350967232320] Reading provided configuration from /opt/ml/input/config/hyperparameters.json: {u'num_dynamic_feat': u'auto', u'learning_rate': u'5E-4', u'prediction_length': u'84', u'epochs': u'400', u'time_freq': u'2H', u'context_length': u'84', u'mini_batch_size': u'64', u'early_stopping_patience': u'40'}\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:50 INFO 140350967232320] Final configuration: {u'dropout_rate': u'0.10', u'test_quantiles': u'[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]', u'_tuning_objective_metric': u'', u'num_eval_samples': u'100', u'learning_rate': u'5E-4', u'num_layers': u'2', u'epochs': u'400', u'embedding_dimension': u'10', u'num_cells': u'40', u'_num_kv_servers': u'auto', u'mini_batch_size': u'64', u'likelihood': u'student-t', u'num_dynamic_feat': u'auto', u'cardinality': u'auto', u'_num_gpus': u'auto', u'prediction_length': u'84', u'time_freq': u'2H', u'context_length': u'84', u'_kvstore': u'auto', u'early_stopping_patience': u'40'}\u001b[0m\n",
      "\u001b[31mProcess 1 is a worker.\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:50 INFO 140350967232320] Detected entry point for worker worker\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:50 INFO 140350967232320] Using early stopping with patience 40\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:50 INFO 140350967232320] [cardinality=auto] `cat` field was NOT found in the file `/opt/ml/input/data/train/train_new_features.json` and will NOT be used for training.\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:50 INFO 140350967232320] [num_dynamic_feat=auto] `dynamic_feat` field was found in the file `/opt/ml/input/data/train/train_new_features.json` and will be used for training.\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:50 INFO 140350967232320] [num_dynamic_feat=auto] Inferred value of num_dynamic_feat=1 from dataset.\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:50 INFO 140350967232320] Training set statistics:\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:50 INFO 140350967232320] Real time series\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:50 INFO 140350967232320] number of time series: 370\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:50 INFO 140350967232320] number of observations: 1071326\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:50 INFO 140350967232320] mean target length: 2895\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:50 INFO 140350967232320] min/mean/max target: 0.0/605.395632481/287350.0\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:50 INFO 140350967232320] mean abs(target): 605.395632481\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:50 INFO 140350967232320] contains missing values: yes (10.0%)\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:50 INFO 140350967232320] Small number of time series. Doing 1 number of passes over dataset per epoch.\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:52 INFO 140350967232320] Test set statistics:\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:52 INFO 140350967232320] Real time series\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:52 INFO 140350967232320] number of time series: 2590\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:52 INFO 140350967232320] number of observations: 9239762\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:52 INFO 140350967232320] mean target length: 3567\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:52 INFO 140350967232320] min/mean/max target: 0.0/679.931757037/287350.0\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:52 INFO 140350967232320] mean abs(target): 679.931757037\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:52 INFO 140350967232320] contains missing values: no\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:52 INFO 140350967232320] nvidia-smi took: 0.0252039432526 secs to identify 0 gpus\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:52 INFO 140350967232320] Number of GPUs being used: 0\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:52 INFO 140350967232320] Create Store: local\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"get_graph.time\": {\"count\": 1, \"max\": 663.8379096984863, \"sum\": 663.8379096984863, \"min\": 663.8379096984863}}, \"EndTime\": 1550541533.384136, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541532.71933}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:53 INFO 140350967232320] Number of GPUs being used: 0\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"initialize.time\": {\"count\": 1, \"max\": 1382.7719688415527, \"sum\": 1382.7719688415527, \"min\": 1382.7719688415527}}, \"EndTime\": 1550541534.102218, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541533.384213}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:55 INFO 140350967232320] Epoch[0] Batch[0] avg_epoch_loss=6.120714\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:56 INFO 140350967232320] Epoch[0] Batch[5] avg_epoch_loss=5.621360\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:56 INFO 140350967232320] Epoch[0] Batch [5]#011Speed: 330.35 samples/sec#011loss=5.621360\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:56 INFO 140350967232320] processed a total of 356 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"epochs\": {\"count\": 1, \"max\": 400, \"sum\": 400.0, \"min\": 400}, \"update.time\": {\"count\": 1, \"max\": 1990.1878833770752, \"sum\": 1990.1878833770752, \"min\": 1990.1878833770752}}, \"EndTime\": 1550541536.092572, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541534.1023}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:56 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=178.867148542 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:56 INFO 140350967232320] #progress_metric: host=algo-1, completed 0 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:56 INFO 140350967232320] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:56 INFO 140350967232320] Saved checkpoint to \"/opt/ml/model/state_198cd08b-b1ac-4de4-bf06-56b75fdf5531-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 59.71503257751465, \"sum\": 59.71503257751465, \"min\": 59.71503257751465}}, \"EndTime\": 1550541536.152769, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541536.092652}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:57 INFO 140350967232320] Epoch[1] Batch[0] avg_epoch_loss=5.322126\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:58 INFO 140350967232320] Epoch[1] Batch[5] avg_epoch_loss=5.416230\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:58 INFO 140350967232320] Epoch[1] Batch [5]#011Speed: 322.02 samples/sec#011loss=5.416230\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:58 INFO 140350967232320] processed a total of 398 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2074.4199752807617, \"sum\": 2074.4199752807617, \"min\": 2074.4199752807617}}, \"EndTime\": 1550541538.22731, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541536.152838}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:58 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=191.850782966 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:58 INFO 140350967232320] #progress_metric: host=algo-1, completed 0 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:58 INFO 140350967232320] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:58 INFO 140350967232320] Saved checkpoint to \"/opt/ml/model/state_1e618ebf-2e0f-41a5-b3a1-ff027b8a5774-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 63.139915466308594, \"sum\": 63.139915466308594, \"min\": 63.139915466308594}}, \"EndTime\": 1550541538.290884, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541538.227386}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:58:59 INFO 140350967232320] Epoch[2] Batch[0] avg_epoch_loss=5.121240\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:00 INFO 140350967232320] Epoch[2] Batch[5] avg_epoch_loss=5.127140\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:00 INFO 140350967232320] Epoch[2] Batch [5]#011Speed: 326.81 samples/sec#011loss=5.127140\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:00 INFO 140350967232320] processed a total of 385 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2050.2068996429443, \"sum\": 2050.2068996429443, \"min\": 2050.2068996429443}}, \"EndTime\": 1550541540.341226, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541538.290962}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:00 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=187.775574979 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:00 INFO 140350967232320] #progress_metric: host=algo-1, completed 0 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:00 INFO 140350967232320] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:00 INFO 140350967232320] Saved checkpoint to \"/opt/ml/model/state_ca6a2568-e151-4a4d-8155-115af789ee80-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 58.90488624572754, \"sum\": 58.90488624572754, \"min\": 58.90488624572754}}, \"EndTime\": 1550541540.40056, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541540.341305}\n",
      "\u001b[0m\n",
      "\n",
      "2019-02-19 01:58:47 Training - Training image download completed. Training in progress.\u001b[31m[02/19/2019 01:59:01 INFO 140350967232320] Epoch[3] Batch[0] avg_epoch_loss=5.026726\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:02 INFO 140350967232320] Epoch[3] Batch[5] avg_epoch_loss=4.979253\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:02 INFO 140350967232320] Epoch[3] Batch [5]#011Speed: 315.79 samples/sec#011loss=4.979253\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:02 INFO 140350967232320] processed a total of 360 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2114.561080932617, \"sum\": 2114.561080932617, \"min\": 2114.561080932617}}, \"EndTime\": 1550541542.515239, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541540.400614}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:02 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=170.238921979 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:02 INFO 140350967232320] #progress_metric: host=algo-1, completed 1 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:02 INFO 140350967232320] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:02 INFO 140350967232320] Saved checkpoint to \"/opt/ml/model/state_8b0b2ca4-bdce-4050-b66d-4191ec97228d-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 60.39619445800781, \"sum\": 60.39619445800781, \"min\": 60.39619445800781}}, \"EndTime\": 1550541542.576108, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541542.515315}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:03 INFO 140350967232320] Epoch[4] Batch[0] avg_epoch_loss=4.995583\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:04 INFO 140350967232320] Epoch[4] Batch[5] avg_epoch_loss=5.040868\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:04 INFO 140350967232320] Epoch[4] Batch [5]#011Speed: 321.07 samples/sec#011loss=5.040868\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:04 INFO 140350967232320] processed a total of 414 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2105.268955230713, \"sum\": 2105.268955230713, \"min\": 2105.268955230713}}, \"EndTime\": 1550541544.681513, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541542.576184}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:04 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=196.638813872 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:04 INFO 140350967232320] #progress_metric: host=algo-1, completed 1 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:04 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:05 INFO 140350967232320] Epoch[5] Batch[0] avg_epoch_loss=4.710999\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:06 INFO 140350967232320] Epoch[5] Batch[5] avg_epoch_loss=4.900249\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:06 INFO 140350967232320] Epoch[5] Batch [5]#011Speed: 324.63 samples/sec#011loss=4.900249\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:06 INFO 140350967232320] processed a total of 365 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1860.2240085601807, \"sum\": 1860.2240085601807, \"min\": 1860.2240085601807}}, \"EndTime\": 1550541546.542148, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541544.681592}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:06 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=196.196684066 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:06 INFO 140350967232320] #progress_metric: host=algo-1, completed 1 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:06 INFO 140350967232320] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:06 INFO 140350967232320] Saved checkpoint to \"/opt/ml/model/state_2d986334-c29a-48d5-8776-a0a5a903cf2f-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 62.51096725463867, \"sum\": 62.51096725463867, \"min\": 62.51096725463867}}, \"EndTime\": 1550541546.605114, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541546.542245}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:07 INFO 140350967232320] Epoch[6] Batch[0] avg_epoch_loss=5.007076\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:08 INFO 140350967232320] Epoch[6] Batch[5] avg_epoch_loss=4.850597\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:08 INFO 140350967232320] Epoch[6] Batch [5]#011Speed: 317.02 samples/sec#011loss=4.850597\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:08 INFO 140350967232320] processed a total of 384 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1921.8270778656006, \"sum\": 1921.8270778656006, \"min\": 1921.8270778656006}}, \"EndTime\": 1550541548.52707, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541546.605187}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:08 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=199.798755889 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:08 INFO 140350967232320] #progress_metric: host=algo-1, completed 1 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:08 INFO 140350967232320] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:08 INFO 140350967232320] Saved checkpoint to \"/opt/ml/model/state_270d6dad-4476-4078-8ee8-1d7f814197d5-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 88.68598937988281, \"sum\": 88.68598937988281, \"min\": 88.68598937988281}}, \"EndTime\": 1550541548.616177, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541548.527144}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:09 INFO 140350967232320] Epoch[7] Batch[0] avg_epoch_loss=4.992370\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:10 INFO 140350967232320] Epoch[7] Batch[5] avg_epoch_loss=4.914445\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:10 INFO 140350967232320] Epoch[7] Batch [5]#011Speed: 327.58 samples/sec#011loss=4.914445\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:10 INFO 140350967232320] processed a total of 428 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2042.7720546722412, \"sum\": 2042.7720546722412, \"min\": 2042.7720546722412}}, \"EndTime\": 1550541550.659083, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541548.61625}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:10 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=209.50752811 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:10 INFO 140350967232320] #progress_metric: host=algo-1, completed 2 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:10 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:11 INFO 140350967232320] Epoch[8] Batch[0] avg_epoch_loss=5.050777\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:12 INFO 140350967232320] Epoch[8] Batch[5] avg_epoch_loss=4.714517\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:12 INFO 140350967232320] Epoch[8] Batch [5]#011Speed: 323.16 samples/sec#011loss=4.714517\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:12 INFO 140350967232320] processed a total of 381 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1894.1681385040283, \"sum\": 1894.1681385040283, \"min\": 1894.1681385040283}}, \"EndTime\": 1550541552.55367, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541550.65916}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:12 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=201.130440725 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:12 INFO 140350967232320] #progress_metric: host=algo-1, completed 2 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:12 INFO 140350967232320] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:12 INFO 140350967232320] Saved checkpoint to \"/opt/ml/model/state_92fc8c91-886f-4eea-99fc-a02c160df841-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 91.34817123413086, \"sum\": 91.34817123413086, \"min\": 91.34817123413086}}, \"EndTime\": 1550541552.645463, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541552.55376}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:13 INFO 140350967232320] Epoch[9] Batch[0] avg_epoch_loss=4.976442\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:14 INFO 140350967232320] Epoch[9] Batch[5] avg_epoch_loss=4.842269\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:14 INFO 140350967232320] Epoch[9] Batch [5]#011Speed: 315.61 samples/sec#011loss=4.842269\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:14 INFO 140350967232320] processed a total of 337 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1907.8669548034668, \"sum\": 1907.8669548034668, \"min\": 1907.8669548034668}}, \"EndTime\": 1550541554.553456, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541552.645529}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:14 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=176.626967806 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:14 INFO 140350967232320] #progress_metric: host=algo-1, completed 2 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:14 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:15 INFO 140350967232320] Epoch[10] Batch[0] avg_epoch_loss=4.759174\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:16 INFO 140350967232320] Epoch[10] Batch[5] avg_epoch_loss=4.417637\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:16 INFO 140350967232320] Epoch[10] Batch [5]#011Speed: 322.68 samples/sec#011loss=4.417637\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:16 INFO 140350967232320] processed a total of 349 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1891.2241458892822, \"sum\": 1891.2241458892822, \"min\": 1891.2241458892822}}, \"EndTime\": 1550541556.445065, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541554.55353}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:16 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=184.525255535 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:16 INFO 140350967232320] #progress_metric: host=algo-1, completed 2 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:16 INFO 140350967232320] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:16 INFO 140350967232320] Saved checkpoint to \"/opt/ml/model/state_f0628ecd-bf43-4293-bddd-1329391e6be0-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 60.02688407897949, \"sum\": 60.02688407897949, \"min\": 60.02688407897949}}, \"EndTime\": 1550541556.505537, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541556.445145}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:17 INFO 140350967232320] Epoch[11] Batch[0] avg_epoch_loss=4.404834\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:18 INFO 140350967232320] Epoch[11] Batch[5] avg_epoch_loss=4.847208\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:18 INFO 140350967232320] Epoch[11] Batch [5]#011Speed: 316.86 samples/sec#011loss=4.847208\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:18 INFO 140350967232320] processed a total of 342 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1864.0201091766357, \"sum\": 1864.0201091766357, \"min\": 1864.0201091766357}}, \"EndTime\": 1550541558.369694, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541556.505615}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:18 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=183.459866858 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:18 INFO 140350967232320] #progress_metric: host=algo-1, completed 3 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:18 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:19 INFO 140350967232320] Epoch[12] Batch[0] avg_epoch_loss=4.598197\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:20 INFO 140350967232320] Epoch[12] Batch[5] avg_epoch_loss=4.613337\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:20 INFO 140350967232320] Epoch[12] Batch [5]#011Speed: 326.71 samples/sec#011loss=4.613337\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:20 INFO 140350967232320] processed a total of 368 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1879.1790008544922, \"sum\": 1879.1790008544922, \"min\": 1879.1790008544922}}, \"EndTime\": 1550541560.249406, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541558.369807}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:20 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=195.817082565 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:20 INFO 140350967232320] #progress_metric: host=algo-1, completed 3 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:20 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:21 INFO 140350967232320] Epoch[13] Batch[0] avg_epoch_loss=4.516537\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:22 INFO 140350967232320] Epoch[13] Batch[5] avg_epoch_loss=4.543565\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:22 INFO 140350967232320] Epoch[13] Batch [5]#011Speed: 320.82 samples/sec#011loss=4.543565\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:22 INFO 140350967232320] processed a total of 402 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2142.040967941284, \"sum\": 2142.040967941284, \"min\": 2142.040967941284}}, \"EndTime\": 1550541562.391804, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541560.24948}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:22 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=187.663940115 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:22 INFO 140350967232320] #progress_metric: host=algo-1, completed 3 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:22 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:23 INFO 140350967232320] Epoch[14] Batch[0] avg_epoch_loss=4.512862\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:24 INFO 140350967232320] Epoch[14] Batch[5] avg_epoch_loss=4.490025\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:24 INFO 140350967232320] Epoch[14] Batch [5]#011Speed: 327.73 samples/sec#011loss=4.490025\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:24 INFO 140350967232320] processed a total of 364 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1863.2569313049316, \"sum\": 1863.2569313049316, \"min\": 1863.2569313049316}}, \"EndTime\": 1550541564.255485, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541562.391861}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:24 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=195.34532425 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:24 INFO 140350967232320] #progress_metric: host=algo-1, completed 3 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:24 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:25 INFO 140350967232320] Epoch[15] Batch[0] avg_epoch_loss=4.548387\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:26 INFO 140350967232320] Epoch[15] Batch[5] avg_epoch_loss=4.704649\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:26 INFO 140350967232320] Epoch[15] Batch [5]#011Speed: 329.17 samples/sec#011loss=4.704649\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:26 INFO 140350967232320] processed a total of 347 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1860.1000308990479, \"sum\": 1860.1000308990479, \"min\": 1860.1000308990479}}, \"EndTime\": 1550541566.115966, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541564.25556}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:26 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=186.538467395 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:26 INFO 140350967232320] #progress_metric: host=algo-1, completed 4 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:26 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:26 INFO 140350967232320] Epoch[16] Batch[0] avg_epoch_loss=4.726884\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:27 INFO 140350967232320] Epoch[16] Batch[5] avg_epoch_loss=4.577055\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:27 INFO 140350967232320] Epoch[16] Batch [5]#011Speed: 331.47 samples/sec#011loss=4.577055\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:27 INFO 140350967232320] processed a total of 365 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1835.77299118042, \"sum\": 1835.77299118042, \"min\": 1835.77299118042}}, \"EndTime\": 1550541567.95212, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541566.116039}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:27 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=198.81289974 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:27 INFO 140350967232320] #progress_metric: host=algo-1, completed 4 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:27 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:28 INFO 140350967232320] Epoch[17] Batch[0] avg_epoch_loss=4.488051\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:29 INFO 140350967232320] Epoch[17] Batch[5] avg_epoch_loss=4.640161\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:29 INFO 140350967232320] Epoch[17] Batch [5]#011Speed: 329.31 samples/sec#011loss=4.640161\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:29 INFO 140350967232320] processed a total of 354 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1861.9470596313477, \"sum\": 1861.9470596313477, \"min\": 1861.9470596313477}}, \"EndTime\": 1550541569.814464, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541567.95221}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:29 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=190.112311419 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:29 INFO 140350967232320] #progress_metric: host=algo-1, completed 4 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:29 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:30 INFO 140350967232320] Epoch[18] Batch[0] avg_epoch_loss=4.377072\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:31 INFO 140350967232320] Epoch[18] Batch[5] avg_epoch_loss=4.434021\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:31 INFO 140350967232320] Epoch[18] Batch [5]#011Speed: 330.41 samples/sec#011loss=4.434021\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:31 INFO 140350967232320] processed a total of 384 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1852.5810241699219, \"sum\": 1852.5810241699219, \"min\": 1852.5810241699219}}, \"EndTime\": 1550541571.667467, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541569.81454}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:31 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=207.266193396 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:31 INFO 140350967232320] #progress_metric: host=algo-1, completed 4 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:31 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:32 INFO 140350967232320] Epoch[19] Batch[0] avg_epoch_loss=4.533819\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:33 INFO 140350967232320] Epoch[19] Batch[5] avg_epoch_loss=4.523539\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:33 INFO 140350967232320] Epoch[19] Batch [5]#011Speed: 323.52 samples/sec#011loss=4.523539\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:33 INFO 140350967232320] processed a total of 402 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2103.6031246185303, \"sum\": 2103.6031246185303, \"min\": 2103.6031246185303}}, \"EndTime\": 1550541573.771451, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541571.667541}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:33 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=191.090615004 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:33 INFO 140350967232320] #progress_metric: host=algo-1, completed 5 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:33 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:34 INFO 140350967232320] Epoch[20] Batch[0] avg_epoch_loss=4.520261\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:35 INFO 140350967232320] Epoch[20] Batch[5] avg_epoch_loss=4.490268\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:35 INFO 140350967232320] Epoch[20] Batch [5]#011Speed: 326.40 samples/sec#011loss=4.490268\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:35 INFO 140350967232320] processed a total of 385 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2084.6879482269287, \"sum\": 2084.6879482269287, \"min\": 2084.6879482269287}}, \"EndTime\": 1550541575.856527, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541573.771527}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:35 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=184.669991886 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:35 INFO 140350967232320] #progress_metric: host=algo-1, completed 5 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:35 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:36 INFO 140350967232320] Epoch[21] Batch[0] avg_epoch_loss=4.288675\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:37 INFO 140350967232320] Epoch[21] Batch[5] avg_epoch_loss=4.325328\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:37 INFO 140350967232320] Epoch[21] Batch [5]#011Speed: 319.91 samples/sec#011loss=4.325328\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:37 INFO 140350967232320] processed a total of 370 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1925.4350662231445, \"sum\": 1925.4350662231445, \"min\": 1925.4350662231445}}, \"EndTime\": 1550541577.782377, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541575.856605}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:37 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=192.153178486 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:37 INFO 140350967232320] #progress_metric: host=algo-1, completed 5 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:37 INFO 140350967232320] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:37 INFO 140350967232320] Saved checkpoint to \"/opt/ml/model/state_82bb5338-236b-44bb-8abc-fd89ce6b38e8-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 60.57596206665039, \"sum\": 60.57596206665039, \"min\": 60.57596206665039}}, \"EndTime\": 1550541577.843417, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541577.782454}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:38 INFO 140350967232320] Epoch[22] Batch[0] avg_epoch_loss=4.122755\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:39 INFO 140350967232320] Epoch[22] Batch[5] avg_epoch_loss=4.374393\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:39 INFO 140350967232320] Epoch[22] Batch [5]#011Speed: 320.61 samples/sec#011loss=4.374393\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:39 INFO 140350967232320] processed a total of 348 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1834.4638347625732, \"sum\": 1834.4638347625732, \"min\": 1834.4638347625732}}, \"EndTime\": 1550541579.678018, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541577.843492}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:39 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=189.68816353 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:39 INFO 140350967232320] #progress_metric: host=algo-1, completed 5 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:39 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:40 INFO 140350967232320] Epoch[23] Batch[0] avg_epoch_loss=4.460719\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:41 INFO 140350967232320] Epoch[23] Batch[5] avg_epoch_loss=4.512016\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:41 INFO 140350967232320] Epoch[23] Batch [5]#011Speed: 324.84 samples/sec#011loss=4.512016\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:41 INFO 140350967232320] processed a total of 389 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2089.1809463500977, \"sum\": 2089.1809463500977, \"min\": 2089.1809463500977}}, \"EndTime\": 1550541581.767629, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541579.678108}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:41 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=186.187384645 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:41 INFO 140350967232320] #progress_metric: host=algo-1, completed 6 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:41 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:42 INFO 140350967232320] Epoch[24] Batch[0] avg_epoch_loss=4.260568\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:43 INFO 140350967232320] Epoch[24] Batch[5] avg_epoch_loss=4.408205\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:43 INFO 140350967232320] Epoch[24] Batch [5]#011Speed: 320.84 samples/sec#011loss=4.408205\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:43 INFO 140350967232320] processed a total of 392 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2100.342035293579, \"sum\": 2100.342035293579, \"min\": 2100.342035293579}}, \"EndTime\": 1550541583.868384, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541581.767707}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:43 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=186.626226923 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:43 INFO 140350967232320] #progress_metric: host=algo-1, completed 6 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:43 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:44 INFO 140350967232320] Epoch[25] Batch[0] avg_epoch_loss=4.385610\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:45 INFO 140350967232320] Epoch[25] Batch[5] avg_epoch_loss=4.438964\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:45 INFO 140350967232320] Epoch[25] Batch [5]#011Speed: 316.86 samples/sec#011loss=4.438964\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:45 INFO 140350967232320] processed a total of 384 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1904.783010482788, \"sum\": 1904.783010482788, \"min\": 1904.783010482788}}, \"EndTime\": 1550541585.773556, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541583.868463}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:45 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=201.587698466 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:45 INFO 140350967232320] #progress_metric: host=algo-1, completed 6 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:45 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:46 INFO 140350967232320] Epoch[26] Batch[0] avg_epoch_loss=4.366714\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:47 INFO 140350967232320] Epoch[26] Batch[5] avg_epoch_loss=4.353046\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:47 INFO 140350967232320] Epoch[26] Batch [5]#011Speed: 321.00 samples/sec#011loss=4.353046\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:47 INFO 140350967232320] processed a total of 382 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1895.5440521240234, \"sum\": 1895.5440521240234, \"min\": 1895.5440521240234}}, \"EndTime\": 1550541587.6695, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541585.773615}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:47 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=201.51293766 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:47 INFO 140350967232320] #progress_metric: host=algo-1, completed 6 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:47 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:48 INFO 140350967232320] Epoch[27] Batch[0] avg_epoch_loss=4.661837\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:49 INFO 140350967232320] Epoch[27] Batch[5] avg_epoch_loss=4.445977\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:49 INFO 140350967232320] Epoch[27] Batch [5]#011Speed: 324.61 samples/sec#011loss=4.445977\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:49 INFO 140350967232320] processed a total of 393 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2082.472085952759, \"sum\": 2082.472085952759, \"min\": 2082.472085952759}}, \"EndTime\": 1550541589.752397, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541587.66958}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:49 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=188.709762661 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:49 INFO 140350967232320] #progress_metric: host=algo-1, completed 7 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:49 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:50 INFO 140350967232320] Epoch[28] Batch[0] avg_epoch_loss=4.248563\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:51 INFO 140350967232320] Epoch[28] Batch[5] avg_epoch_loss=4.272463\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:51 INFO 140350967232320] Epoch[28] Batch [5]#011Speed: 319.14 samples/sec#011loss=4.272463\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:51 INFO 140350967232320] processed a total of 355 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1884.307861328125, \"sum\": 1884.307861328125, \"min\": 1884.307861328125}}, \"EndTime\": 1550541591.637112, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541589.752458}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:51 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=188.386862017 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:51 INFO 140350967232320] #progress_metric: host=algo-1, completed 7 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:51 INFO 140350967232320] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:51 INFO 140350967232320] Saved checkpoint to \"/opt/ml/model/state_82b8215a-3fed-45e4-adb4-6c902766c838-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 58.08401107788086, \"sum\": 58.08401107788086, \"min\": 58.08401107788086}}, \"EndTime\": 1550541591.695678, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541591.637186}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:52 INFO 140350967232320] Epoch[29] Batch[0] avg_epoch_loss=4.010551\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:53 INFO 140350967232320] Epoch[29] Batch[5] avg_epoch_loss=4.254455\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:53 INFO 140350967232320] Epoch[29] Batch [5]#011Speed: 323.30 samples/sec#011loss=4.254455\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:53 INFO 140350967232320] processed a total of 364 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1846.8539714813232, \"sum\": 1846.8539714813232, \"min\": 1846.8539714813232}}, \"EndTime\": 1550541593.542655, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541591.69575}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:53 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=197.080499143 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:53 INFO 140350967232320] #progress_metric: host=algo-1, completed 7 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:53 INFO 140350967232320] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:53 INFO 140350967232320] Saved checkpoint to \"/opt/ml/model/state_c52b9a5f-5938-4da7-b3ad-f862f248b3e3-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 70.8920955657959, \"sum\": 70.8920955657959, \"min\": 70.8920955657959}}, \"EndTime\": 1550541593.613963, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541593.54273}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:54 INFO 140350967232320] Epoch[30] Batch[0] avg_epoch_loss=4.074955\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:55 INFO 140350967232320] Epoch[30] Batch[5] avg_epoch_loss=4.327385\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:55 INFO 140350967232320] Epoch[30] Batch [5]#011Speed: 332.69 samples/sec#011loss=4.327385\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:55 INFO 140350967232320] processed a total of 372 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1860.177993774414, \"sum\": 1860.177993774414, \"min\": 1860.177993774414}}, \"EndTime\": 1550541595.474281, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541593.614042}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:55 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=199.96925232 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:55 INFO 140350967232320] #progress_metric: host=algo-1, completed 7 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:55 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:56 INFO 140350967232320] Epoch[31] Batch[0] avg_epoch_loss=4.534651\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:57 INFO 140350967232320] Epoch[31] Batch[5] avg_epoch_loss=4.173448\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:57 INFO 140350967232320] Epoch[31] Batch [5]#011Speed: 325.99 samples/sec#011loss=4.173448\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:57 INFO 140350967232320] processed a total of 343 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1889.7478580474854, \"sum\": 1889.7478580474854, \"min\": 1889.7478580474854}}, \"EndTime\": 1550541597.364397, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541595.474354}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:57 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=181.496078582 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:57 INFO 140350967232320] #progress_metric: host=algo-1, completed 8 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:57 INFO 140350967232320] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:57 INFO 140350967232320] Saved checkpoint to \"/opt/ml/model/state_b66473f3-5045-44f9-8148-76c18be4d09f-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 57.9829216003418, \"sum\": 57.9829216003418, \"min\": 57.9829216003418}}, \"EndTime\": 1550541597.42284, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541597.364462}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:58 INFO 140350967232320] Epoch[32] Batch[0] avg_epoch_loss=4.200655\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:59 INFO 140350967232320] Epoch[32] Batch[5] avg_epoch_loss=4.352365\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:59 INFO 140350967232320] Epoch[32] Batch [5]#011Speed: 321.01 samples/sec#011loss=4.352365\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:59 INFO 140350967232320] processed a total of 368 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1873.0101585388184, \"sum\": 1873.0101585388184, \"min\": 1873.0101585388184}}, \"EndTime\": 1550541599.295959, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541597.42289}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:59 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=196.463972022 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:59 INFO 140350967232320] #progress_metric: host=algo-1, completed 8 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 01:59:59 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:00 INFO 140350967232320] Epoch[33] Batch[0] avg_epoch_loss=4.201747\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:01 INFO 140350967232320] Epoch[33] Batch[5] avg_epoch_loss=4.310334\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:01 INFO 140350967232320] Epoch[33] Batch [5]#011Speed: 329.64 samples/sec#011loss=4.310334\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:01 INFO 140350967232320] processed a total of 390 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2063.17400932312, \"sum\": 2063.17400932312, \"min\": 2063.17400932312}}, \"EndTime\": 1550541601.359517, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541599.296032}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:01 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=189.018869664 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:01 INFO 140350967232320] #progress_metric: host=algo-1, completed 8 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:01 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:02 INFO 140350967232320] Epoch[34] Batch[0] avg_epoch_loss=4.169922\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:03 INFO 140350967232320] Epoch[34] Batch[5] avg_epoch_loss=4.314147\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:03 INFO 140350967232320] Epoch[34] Batch [5]#011Speed: 328.15 samples/sec#011loss=4.314147\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:03 INFO 140350967232320] processed a total of 364 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1846.5828895568848, \"sum\": 1846.5828895568848, \"min\": 1846.5828895568848}}, \"EndTime\": 1550541603.206478, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541601.359595}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:03 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=197.109531055 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:03 INFO 140350967232320] #progress_metric: host=algo-1, completed 8 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:03 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:04 INFO 140350967232320] Epoch[35] Batch[0] avg_epoch_loss=4.224183\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:05 INFO 140350967232320] Epoch[35] Batch[5] avg_epoch_loss=4.449150\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:05 INFO 140350967232320] Epoch[35] Batch [5]#011Speed: 328.87 samples/sec#011loss=4.449150\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:05 INFO 140350967232320] processed a total of 351 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1932.6961040496826, \"sum\": 1932.6961040496826, \"min\": 1932.6961040496826}}, \"EndTime\": 1550541605.139551, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541603.206552}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:05 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=181.601435245 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:05 INFO 140350967232320] #progress_metric: host=algo-1, completed 9 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:05 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:06 INFO 140350967232320] Epoch[36] Batch[0] avg_epoch_loss=4.242451\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:07 INFO 140350967232320] Epoch[36] Batch[5] avg_epoch_loss=4.194803\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:07 INFO 140350967232320] Epoch[36] Batch [5]#011Speed: 325.16 samples/sec#011loss=4.194803\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:07 INFO 140350967232320] processed a total of 364 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1899.846076965332, \"sum\": 1899.846076965332, \"min\": 1899.846076965332}}, \"EndTime\": 1550541607.039781, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541605.139625}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:07 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=191.583769912 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:07 INFO 140350967232320] #progress_metric: host=algo-1, completed 9 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:07 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:07 INFO 140350967232320] Epoch[37] Batch[0] avg_epoch_loss=4.590774\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:08 INFO 140350967232320] Epoch[37] Batch[5] avg_epoch_loss=4.076248\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:08 INFO 140350967232320] Epoch[37] Batch [5]#011Speed: 313.58 samples/sec#011loss=4.076248\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:08 INFO 140350967232320] processed a total of 356 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1907.5119495391846, \"sum\": 1907.5119495391846, \"min\": 1907.5119495391846}}, \"EndTime\": 1550541608.947679, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541607.039853}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:08 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=186.622127157 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:08 INFO 140350967232320] #progress_metric: host=algo-1, completed 9 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:08 INFO 140350967232320] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:09 INFO 140350967232320] Saved checkpoint to \"/opt/ml/model/state_ea481741-dd88-42cf-b9fe-c83ad121eb8a-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 61.86509132385254, \"sum\": 61.86509132385254, \"min\": 61.86509132385254}}, \"EndTime\": 1550541609.009981, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541608.947734}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:09 INFO 140350967232320] Epoch[38] Batch[0] avg_epoch_loss=4.428471\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:10 INFO 140350967232320] Epoch[38] Batch[5] avg_epoch_loss=4.218300\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:10 INFO 140350967232320] Epoch[38] Batch [5]#011Speed: 323.66 samples/sec#011loss=4.218300\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:10 INFO 140350967232320] processed a total of 341 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1845.7081317901611, \"sum\": 1845.7081317901611, \"min\": 1845.7081317901611}}, \"EndTime\": 1550541610.85582, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541609.010055}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:10 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=184.742246906 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:10 INFO 140350967232320] #progress_metric: host=algo-1, completed 9 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:10 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:11 INFO 140350967232320] Epoch[39] Batch[0] avg_epoch_loss=4.268898\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:12 INFO 140350967232320] Epoch[39] Batch[5] avg_epoch_loss=4.102751\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:12 INFO 140350967232320] Epoch[39] Batch [5]#011Speed: 319.68 samples/sec#011loss=4.102751\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:12 INFO 140350967232320] processed a total of 354 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1905.0099849700928, \"sum\": 1905.0099849700928, \"min\": 1905.0099849700928}}, \"EndTime\": 1550541612.761211, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541610.855892}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:12 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=185.814867631 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:12 INFO 140350967232320] #progress_metric: host=algo-1, completed 10 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:12 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:13 INFO 140350967232320] Epoch[40] Batch[0] avg_epoch_loss=4.330387\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:14 INFO 140350967232320] Epoch[40] Batch[5] avg_epoch_loss=4.094913\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:14 INFO 140350967232320] Epoch[40] Batch [5]#011Speed: 321.48 samples/sec#011loss=4.094913\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:14 INFO 140350967232320] processed a total of 385 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2059.3271255493164, \"sum\": 2059.3271255493164, \"min\": 2059.3271255493164}}, \"EndTime\": 1550541614.820975, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541612.761288}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:14 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=186.944833686 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:14 INFO 140350967232320] #progress_metric: host=algo-1, completed 10 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:14 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:15 INFO 140350967232320] Epoch[41] Batch[0] avg_epoch_loss=4.165929\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:16 INFO 140350967232320] Epoch[41] Batch[5] avg_epoch_loss=4.160557\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:16 INFO 140350967232320] Epoch[41] Batch [5]#011Speed: 318.52 samples/sec#011loss=4.160557\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:16 INFO 140350967232320] processed a total of 352 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1877.211093902588, \"sum\": 1877.211093902588, \"min\": 1877.211093902588}}, \"EndTime\": 1550541616.698622, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541614.821043}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:16 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=187.501445635 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:16 INFO 140350967232320] #progress_metric: host=algo-1, completed 10 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:16 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:17 INFO 140350967232320] Epoch[42] Batch[0] avg_epoch_loss=4.193860\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:18 INFO 140350967232320] Epoch[42] Batch[5] avg_epoch_loss=3.882639\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:18 INFO 140350967232320] Epoch[42] Batch [5]#011Speed: 328.02 samples/sec#011loss=3.882639\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:18 INFO 140350967232320] processed a total of 336 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1866.6269779205322, \"sum\": 1866.6269779205322, \"min\": 1866.6269779205322}}, \"EndTime\": 1550541618.565627, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541616.698696}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:18 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=179.99407941 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:18 INFO 140350967232320] #progress_metric: host=algo-1, completed 10 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:18 INFO 140350967232320] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:18 INFO 140350967232320] Saved checkpoint to \"/opt/ml/model/state_35c2590f-8ce4-4ace-8acd-37583814c9ef-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 91.35103225708008, \"sum\": 91.35103225708008, \"min\": 91.35103225708008}}, \"EndTime\": 1550541618.657395, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541618.565695}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:19 INFO 140350967232320] Epoch[43] Batch[0] avg_epoch_loss=4.238378\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:20 INFO 140350967232320] Epoch[43] Batch[5] avg_epoch_loss=4.038905\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:20 INFO 140350967232320] Epoch[43] Batch [5]#011Speed: 324.06 samples/sec#011loss=4.038905\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:20 INFO 140350967232320] processed a total of 358 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1928.704023361206, \"sum\": 1928.704023361206, \"min\": 1928.704023361206}}, \"EndTime\": 1550541620.586225, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541618.657466}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:20 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=185.606566115 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:20 INFO 140350967232320] #progress_metric: host=algo-1, completed 11 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:20 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:21 INFO 140350967232320] Epoch[44] Batch[0] avg_epoch_loss=4.236056\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:22 INFO 140350967232320] Epoch[44] Batch[5] avg_epoch_loss=4.210166\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:22 INFO 140350967232320] Epoch[44] Batch [5]#011Speed: 324.16 samples/sec#011loss=4.210166\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:22 INFO 140350967232320] processed a total of 371 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1914.0360355377197, \"sum\": 1914.0360355377197, \"min\": 1914.0360355377197}}, \"EndTime\": 1550541622.500698, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541620.5863}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:22 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=193.821352765 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:22 INFO 140350967232320] #progress_metric: host=algo-1, completed 11 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:22 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:23 INFO 140350967232320] Epoch[45] Batch[0] avg_epoch_loss=4.012816\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:24 INFO 140350967232320] Epoch[45] Batch[5] avg_epoch_loss=4.013812\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:24 INFO 140350967232320] Epoch[45] Batch [5]#011Speed: 319.56 samples/sec#011loss=4.013812\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:24 INFO 140350967232320] processed a total of 416 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2093.6479568481445, \"sum\": 2093.6479568481445, \"min\": 2093.6479568481445}}, \"EndTime\": 1550541624.594781, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541622.500758}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:24 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=198.684870322 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:24 INFO 140350967232320] #progress_metric: host=algo-1, completed 11 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:24 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:25 INFO 140350967232320] Epoch[46] Batch[0] avg_epoch_loss=4.142137\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:26 INFO 140350967232320] Epoch[46] Batch[5] avg_epoch_loss=4.069204\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:26 INFO 140350967232320] Epoch[46] Batch [5]#011Speed: 319.14 samples/sec#011loss=4.069204\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:26 INFO 140350967232320] processed a total of 407 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2107.074975967407, \"sum\": 2107.074975967407, \"min\": 2107.074975967407}}, \"EndTime\": 1550541626.702327, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541624.594863}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:26 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=193.148667909 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:26 INFO 140350967232320] #progress_metric: host=algo-1, completed 11 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:26 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:27 INFO 140350967232320] Epoch[47] Batch[0] avg_epoch_loss=3.967339\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:28 INFO 140350967232320] Epoch[47] Batch[5] avg_epoch_loss=4.136959\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:28 INFO 140350967232320] Epoch[47] Batch [5]#011Speed: 324.81 samples/sec#011loss=4.136959\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:28 INFO 140350967232320] processed a total of 390 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2096.6739654541016, \"sum\": 2096.6739654541016, \"min\": 2096.6739654541016}}, \"EndTime\": 1550541628.799388, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541626.702402}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:28 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=185.997004997 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:28 INFO 140350967232320] #progress_metric: host=algo-1, completed 12 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:28 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:29 INFO 140350967232320] Epoch[48] Batch[0] avg_epoch_loss=4.049558\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:30 INFO 140350967232320] Epoch[48] Batch[5] avg_epoch_loss=4.058431\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:30 INFO 140350967232320] Epoch[48] Batch [5]#011Speed: 325.58 samples/sec#011loss=4.058431\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:30 INFO 140350967232320] processed a total of 354 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1834.1529369354248, \"sum\": 1834.1529369354248, \"min\": 1834.1529369354248}}, \"EndTime\": 1550541630.633974, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541628.799485}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:30 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=192.993135332 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:30 INFO 140350967232320] #progress_metric: host=algo-1, completed 12 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:30 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:31 INFO 140350967232320] Epoch[49] Batch[0] avg_epoch_loss=4.098729\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:32 INFO 140350967232320] processed a total of 317 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1665.8828258514404, \"sum\": 1665.8828258514404, \"min\": 1665.8828258514404}}, \"EndTime\": 1550541632.3003, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541630.634046}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:32 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=190.277348442 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:32 INFO 140350967232320] #progress_metric: host=algo-1, completed 12 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:32 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:33 INFO 140350967232320] Epoch[50] Batch[0] avg_epoch_loss=4.167030\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:34 INFO 140350967232320] Epoch[50] Batch[5] avg_epoch_loss=3.950365\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:34 INFO 140350967232320] Epoch[50] Batch [5]#011Speed: 325.62 samples/sec#011loss=3.950365\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:34 INFO 140350967232320] processed a total of 342 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1852.560043334961, \"sum\": 1852.560043334961, \"min\": 1852.560043334961}}, \"EndTime\": 1550541634.153301, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541632.300367}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:34 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=184.598543984 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:34 INFO 140350967232320] #progress_metric: host=algo-1, completed 12 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:34 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:35 INFO 140350967232320] Epoch[51] Batch[0] avg_epoch_loss=3.966711\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:36 INFO 140350967232320] Epoch[51] Batch[5] avg_epoch_loss=3.979943\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:36 INFO 140350967232320] Epoch[51] Batch [5]#011Speed: 318.78 samples/sec#011loss=3.979943\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:36 INFO 140350967232320] processed a total of 361 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1905.5230617523193, \"sum\": 1905.5230617523193, \"min\": 1905.5230617523193}}, \"EndTime\": 1550541636.0592, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541634.153376}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:36 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=189.439055147 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:36 INFO 140350967232320] #progress_metric: host=algo-1, completed 13 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:36 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:36 INFO 140350967232320] Epoch[52] Batch[0] avg_epoch_loss=4.359019\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:37 INFO 140350967232320] Epoch[52] Batch[5] avg_epoch_loss=3.969033\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:37 INFO 140350967232320] Epoch[52] Batch [5]#011Speed: 315.71 samples/sec#011loss=3.969033\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:38 INFO 140350967232320] processed a total of 388 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2090.670108795166, \"sum\": 2090.670108795166, \"min\": 2090.670108795166}}, \"EndTime\": 1550541638.150301, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541636.059272}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:38 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=185.57586883 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:38 INFO 140350967232320] #progress_metric: host=algo-1, completed 13 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:38 INFO 140350967232320] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:38 INFO 140350967232320] Saved checkpoint to \"/opt/ml/model/state_213642a3-2f6d-43b9-b9a6-b680b8df57bb-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 61.2030029296875, \"sum\": 61.2030029296875, \"min\": 61.2030029296875}}, \"EndTime\": 1550541638.211993, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541638.150381}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:39 INFO 140350967232320] Epoch[53] Batch[0] avg_epoch_loss=3.890142\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:40 INFO 140350967232320] Epoch[53] Batch[5] avg_epoch_loss=4.023356\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:40 INFO 140350967232320] Epoch[53] Batch [5]#011Speed: 324.25 samples/sec#011loss=4.023356\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:40 INFO 140350967232320] processed a total of 341 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1870.2809810638428, \"sum\": 1870.2809810638428, \"min\": 1870.2809810638428}}, \"EndTime\": 1550541640.082409, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541638.212069}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:40 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=182.31480809 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:40 INFO 140350967232320] #progress_metric: host=algo-1, completed 13 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:40 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:40 INFO 140350967232320] Epoch[54] Batch[0] avg_epoch_loss=3.990969\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:41 INFO 140350967232320] Epoch[54] Batch[5] avg_epoch_loss=4.037468\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:41 INFO 140350967232320] Epoch[54] Batch [5]#011Speed: 323.84 samples/sec#011loss=4.037468\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:41 INFO 140350967232320] processed a total of 362 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1860.4860305786133, \"sum\": 1860.4860305786133, \"min\": 1860.4860305786133}}, \"EndTime\": 1550541641.94328, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541640.082484}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:41 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=194.561642832 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:41 INFO 140350967232320] #progress_metric: host=algo-1, completed 13 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:41 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:42 INFO 140350967232320] Epoch[55] Batch[0] avg_epoch_loss=4.302196\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:43 INFO 140350967232320] Epoch[55] Batch[5] avg_epoch_loss=4.128165\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:43 INFO 140350967232320] Epoch[55] Batch [5]#011Speed: 320.06 samples/sec#011loss=4.128165\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:43 INFO 140350967232320] processed a total of 360 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1899.2199897766113, \"sum\": 1899.2199897766113, \"min\": 1899.2199897766113}}, \"EndTime\": 1550541643.842883, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541641.943353}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:43 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=189.54062726 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:43 INFO 140350967232320] #progress_metric: host=algo-1, completed 14 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:43 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:44 INFO 140350967232320] Epoch[56] Batch[0] avg_epoch_loss=4.127428\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:45 INFO 140350967232320] Epoch[56] Batch[5] avg_epoch_loss=4.065261\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:45 INFO 140350967232320] Epoch[56] Batch [5]#011Speed: 316.61 samples/sec#011loss=4.065261\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:45 INFO 140350967232320] processed a total of 373 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1929.128885269165, \"sum\": 1929.128885269165, \"min\": 1929.128885269165}}, \"EndTime\": 1550541645.772396, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541643.842957}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:45 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=193.340885672 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:45 INFO 140350967232320] #progress_metric: host=algo-1, completed 14 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:45 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:46 INFO 140350967232320] Epoch[57] Batch[0] avg_epoch_loss=4.005484\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:47 INFO 140350967232320] Epoch[57] Batch[5] avg_epoch_loss=3.787875\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:47 INFO 140350967232320] Epoch[57] Batch [5]#011Speed: 320.93 samples/sec#011loss=3.787875\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:47 INFO 140350967232320] processed a total of 345 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1855.9470176696777, \"sum\": 1855.9470176696777, \"min\": 1855.9470176696777}}, \"EndTime\": 1550541647.628722, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541645.772468}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:47 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=185.87741789 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:47 INFO 140350967232320] #progress_metric: host=algo-1, completed 14 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:47 INFO 140350967232320] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:47 INFO 140350967232320] Saved checkpoint to \"/opt/ml/model/state_4a0c025d-7fb3-44c0-ba91-24d65b91593c-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 59.4940185546875, \"sum\": 59.4940185546875, \"min\": 59.4940185546875}}, \"EndTime\": 1550541647.688646, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541647.628799}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:48 INFO 140350967232320] Epoch[58] Batch[0] avg_epoch_loss=4.256174\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:49 INFO 140350967232320] Epoch[58] Batch[5] avg_epoch_loss=3.999802\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:49 INFO 140350967232320] Epoch[58] Batch [5]#011Speed: 318.82 samples/sec#011loss=3.999802\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:49 INFO 140350967232320] processed a total of 378 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1886.9309425354004, \"sum\": 1886.9309425354004, \"min\": 1886.9309425354004}}, \"EndTime\": 1550541649.575699, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541647.688716}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:49 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=200.3146659 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:49 INFO 140350967232320] #progress_metric: host=algo-1, completed 14 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:49 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:50 INFO 140350967232320] Epoch[59] Batch[0] avg_epoch_loss=3.863948\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:51 INFO 140350967232320] Epoch[59] Batch[5] avg_epoch_loss=3.948027\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:51 INFO 140350967232320] Epoch[59] Batch [5]#011Speed: 320.28 samples/sec#011loss=3.948027\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:51 INFO 140350967232320] processed a total of 365 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1882.5769424438477, \"sum\": 1882.5769424438477, \"min\": 1882.5769424438477}}, \"EndTime\": 1550541651.458682, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541649.575765}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:51 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=193.871220567 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:51 INFO 140350967232320] #progress_metric: host=algo-1, completed 15 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:51 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:52 INFO 140350967232320] Epoch[60] Batch[0] avg_epoch_loss=3.935184\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:53 INFO 140350967232320] Epoch[60] Batch[5] avg_epoch_loss=3.989012\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:53 INFO 140350967232320] Epoch[60] Batch [5]#011Speed: 325.32 samples/sec#011loss=3.989012\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:53 INFO 140350967232320] processed a total of 385 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2084.599018096924, \"sum\": 2084.599018096924, \"min\": 2084.599018096924}}, \"EndTime\": 1550541653.543683, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541651.458758}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:53 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=184.678418726 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:53 INFO 140350967232320] #progress_metric: host=algo-1, completed 15 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:53 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:54 INFO 140350967232320] Epoch[61] Batch[0] avg_epoch_loss=4.013062\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:55 INFO 140350967232320] Epoch[61] Batch[5] avg_epoch_loss=3.868271\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:55 INFO 140350967232320] Epoch[61] Batch [5]#011Speed: 331.45 samples/sec#011loss=3.868271\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:55 INFO 140350967232320] processed a total of 362 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1876.9681453704834, \"sum\": 1876.9681453704834, \"min\": 1876.9681453704834}}, \"EndTime\": 1550541655.421048, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541653.543752}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:55 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=192.8529284 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:55 INFO 140350967232320] #progress_metric: host=algo-1, completed 15 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:55 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:56 INFO 140350967232320] Epoch[62] Batch[0] avg_epoch_loss=4.144532\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:57 INFO 140350967232320] Epoch[62] Batch[5] avg_epoch_loss=4.045391\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:57 INFO 140350967232320] Epoch[62] Batch [5]#011Speed: 327.20 samples/sec#011loss=4.045391\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:57 INFO 140350967232320] processed a total of 430 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2100.7089614868164, \"sum\": 2100.7089614868164, \"min\": 2100.7089614868164}}, \"EndTime\": 1550541657.522253, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541655.421123}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:57 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=204.676028577 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:57 INFO 140350967232320] #progress_metric: host=algo-1, completed 15 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:57 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:58 INFO 140350967232320] Epoch[63] Batch[0] avg_epoch_loss=3.844644\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:59 INFO 140350967232320] Epoch[63] Batch[5] avg_epoch_loss=4.006676\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:59 INFO 140350967232320] Epoch[63] Batch [5]#011Speed: 324.56 samples/sec#011loss=4.006676\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:59 INFO 140350967232320] processed a total of 381 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1847.580909729004, \"sum\": 1847.580909729004, \"min\": 1847.580909729004}}, \"EndTime\": 1550541659.370219, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541657.522319}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:59 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=206.202425324 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:59 INFO 140350967232320] #progress_metric: host=algo-1, completed 16 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:00:59 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:00 INFO 140350967232320] Epoch[64] Batch[0] avg_epoch_loss=3.991921\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:01 INFO 140350967232320] Epoch[64] Batch[5] avg_epoch_loss=4.048174\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:01 INFO 140350967232320] Epoch[64] Batch [5]#011Speed: 326.65 samples/sec#011loss=4.048174\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:01 INFO 140350967232320] processed a total of 375 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1872.279167175293, \"sum\": 1872.279167175293, \"min\": 1872.279167175293}}, \"EndTime\": 1550541661.242896, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541659.370299}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:01 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=200.279218209 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:01 INFO 140350967232320] #progress_metric: host=algo-1, completed 16 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:01 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:02 INFO 140350967232320] Epoch[65] Batch[0] avg_epoch_loss=3.965047\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:03 INFO 140350967232320] Epoch[65] Batch[5] avg_epoch_loss=3.954234\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:03 INFO 140350967232320] Epoch[65] Batch [5]#011Speed: 321.45 samples/sec#011loss=3.954234\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:03 INFO 140350967232320] processed a total of 352 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1892.3161029815674, \"sum\": 1892.3161029815674, \"min\": 1892.3161029815674}}, \"EndTime\": 1550541663.135615, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541661.242966}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:03 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=186.006387282 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:03 INFO 140350967232320] #progress_metric: host=algo-1, completed 16 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:03 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:04 INFO 140350967232320] Epoch[66] Batch[0] avg_epoch_loss=3.980526\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:05 INFO 140350967232320] Epoch[66] Batch[5] avg_epoch_loss=4.154326\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:05 INFO 140350967232320] Epoch[66] Batch [5]#011Speed: 315.63 samples/sec#011loss=4.154326\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:05 INFO 140350967232320] processed a total of 354 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1893.3589458465576, \"sum\": 1893.3589458465576, \"min\": 1893.3589458465576}}, \"EndTime\": 1550541665.029379, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541663.135676}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:05 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=186.958542795 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:05 INFO 140350967232320] #progress_metric: host=algo-1, completed 16 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:05 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:05 INFO 140350967232320] Epoch[67] Batch[0] avg_epoch_loss=4.114121\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:06 INFO 140350967232320] Epoch[67] Batch[5] avg_epoch_loss=3.905266\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:06 INFO 140350967232320] Epoch[67] Batch [5]#011Speed: 315.04 samples/sec#011loss=3.905266\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:06 INFO 140350967232320] processed a total of 322 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1902.7760028839111, \"sum\": 1902.7760028839111, \"min\": 1902.7760028839111}}, \"EndTime\": 1550541666.932548, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541665.029454}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:06 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=169.217551157 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:06 INFO 140350967232320] #progress_metric: host=algo-1, completed 17 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:06 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:07 INFO 140350967232320] Epoch[68] Batch[0] avg_epoch_loss=4.033893\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:08 INFO 140350967232320] Epoch[68] Batch[5] avg_epoch_loss=4.029891\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:08 INFO 140350967232320] Epoch[68] Batch [5]#011Speed: 314.57 samples/sec#011loss=4.029891\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:08 INFO 140350967232320] processed a total of 369 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1881.519079208374, \"sum\": 1881.519079208374, \"min\": 1881.519079208374}}, \"EndTime\": 1550541668.814423, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541666.932611}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:08 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=196.106871212 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:08 INFO 140350967232320] #progress_metric: host=algo-1, completed 17 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:08 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:09 INFO 140350967232320] Epoch[69] Batch[0] avg_epoch_loss=4.363622\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:10 INFO 140350967232320] Epoch[69] Batch[5] avg_epoch_loss=4.024773\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:10 INFO 140350967232320] Epoch[69] Batch [5]#011Speed: 328.92 samples/sec#011loss=4.024773\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:10 INFO 140350967232320] processed a total of 380 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1862.5600337982178, \"sum\": 1862.5600337982178, \"min\": 1862.5600337982178}}, \"EndTime\": 1550541670.677358, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541668.814496}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:10 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=204.009196977 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:10 INFO 140350967232320] #progress_metric: host=algo-1, completed 17 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:10 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:11 INFO 140350967232320] Epoch[70] Batch[0] avg_epoch_loss=4.200217\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:12 INFO 140350967232320] Epoch[70] Batch[5] avg_epoch_loss=3.936362\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:12 INFO 140350967232320] Epoch[70] Batch [5]#011Speed: 327.36 samples/sec#011loss=3.936362\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:12 INFO 140350967232320] processed a total of 361 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1900.7079601287842, \"sum\": 1900.7079601287842, \"min\": 1900.7079601287842}}, \"EndTime\": 1550541672.57843, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541670.677427}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:12 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=189.918533938 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:12 INFO 140350967232320] #progress_metric: host=algo-1, completed 17 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:12 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:13 INFO 140350967232320] Epoch[71] Batch[0] avg_epoch_loss=3.854553\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:14 INFO 140350967232320] Epoch[71] Batch[5] avg_epoch_loss=3.911850\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:14 INFO 140350967232320] Epoch[71] Batch [5]#011Speed: 320.07 samples/sec#011loss=3.911850\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:14 INFO 140350967232320] processed a total of 350 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1901.216983795166, \"sum\": 1901.216983795166, \"min\": 1901.216983795166}}, \"EndTime\": 1550541674.480053, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541672.5785}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:14 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=184.080307819 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:14 INFO 140350967232320] #progress_metric: host=algo-1, completed 18 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:14 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:15 INFO 140350967232320] Epoch[72] Batch[0] avg_epoch_loss=3.950644\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:16 INFO 140350967232320] Epoch[72] Batch[5] avg_epoch_loss=4.057667\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:16 INFO 140350967232320] Epoch[72] Batch [5]#011Speed: 327.23 samples/sec#011loss=4.057667\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:16 INFO 140350967232320] processed a total of 347 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1853.438138961792, \"sum\": 1853.438138961792, \"min\": 1853.438138961792}}, \"EndTime\": 1550541676.333923, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541674.480144}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:16 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=187.209730941 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:16 INFO 140350967232320] #progress_metric: host=algo-1, completed 18 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:16 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:17 INFO 140350967232320] Epoch[73] Batch[0] avg_epoch_loss=3.971816\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:18 INFO 140350967232320] Epoch[73] Batch[5] avg_epoch_loss=4.047871\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:18 INFO 140350967232320] Epoch[73] Batch [5]#011Speed: 325.23 samples/sec#011loss=4.047871\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:18 INFO 140350967232320] processed a total of 376 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1828.2480239868164, \"sum\": 1828.2480239868164, \"min\": 1828.2480239868164}}, \"EndTime\": 1550541678.162541, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541676.333987}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:18 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=205.648876328 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:18 INFO 140350967232320] #progress_metric: host=algo-1, completed 18 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:18 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:19 INFO 140350967232320] Epoch[74] Batch[0] avg_epoch_loss=3.793705\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:20 INFO 140350967232320] Epoch[74] Batch[5] avg_epoch_loss=3.902883\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:20 INFO 140350967232320] Epoch[74] Batch [5]#011Speed: 324.79 samples/sec#011loss=3.902883\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:20 INFO 140350967232320] processed a total of 387 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2076.559066772461, \"sum\": 2076.559066772461, \"min\": 2076.559066772461}}, \"EndTime\": 1550541680.239483, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541678.162615}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:20 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=186.356945919 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:20 INFO 140350967232320] #progress_metric: host=algo-1, completed 18 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:20 INFO 140350967232320] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:20 INFO 140350967232320] Saved checkpoint to \"/opt/ml/model/state_475b0d13-48c6-4dbc-8098-9eb76bd1ae2f-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 58.21394920349121, \"sum\": 58.21394920349121, \"min\": 58.21394920349121}}, \"EndTime\": 1550541680.298151, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541680.239554}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:21 INFO 140350967232320] Epoch[75] Batch[0] avg_epoch_loss=3.805108\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:22 INFO 140350967232320] Epoch[75] Batch[5] avg_epoch_loss=3.791514\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:22 INFO 140350967232320] Epoch[75] Batch [5]#011Speed: 325.49 samples/sec#011loss=3.791514\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:22 INFO 140350967232320] processed a total of 381 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1877.8431415557861, \"sum\": 1877.8431415557861, \"min\": 1877.8431415557861}}, \"EndTime\": 1550541682.176121, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541680.29822}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:22 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=202.881346843 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:22 INFO 140350967232320] #progress_metric: host=algo-1, completed 19 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:22 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:23 INFO 140350967232320] Epoch[76] Batch[0] avg_epoch_loss=4.116601\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:24 INFO 140350967232320] Epoch[76] Batch[5] avg_epoch_loss=3.946199\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:24 INFO 140350967232320] Epoch[76] Batch [5]#011Speed: 324.53 samples/sec#011loss=3.946199\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:24 INFO 140350967232320] processed a total of 380 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1865.3781414031982, \"sum\": 1865.3781414031982, \"min\": 1865.3781414031982}}, \"EndTime\": 1550541684.041917, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541682.176189}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:24 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=203.700930041 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:24 INFO 140350967232320] #progress_metric: host=algo-1, completed 19 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:24 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:24 INFO 140350967232320] Epoch[77] Batch[0] avg_epoch_loss=3.946274\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:25 INFO 140350967232320] Epoch[77] Batch[5] avg_epoch_loss=3.876604\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:25 INFO 140350967232320] Epoch[77] Batch [5]#011Speed: 316.79 samples/sec#011loss=3.876604\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:25 INFO 140350967232320] processed a total of 377 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1860.8288764953613, \"sum\": 1860.8288764953613, \"min\": 1860.8288764953613}}, \"EndTime\": 1550541685.903171, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541684.041987}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:25 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=202.586337254 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:25 INFO 140350967232320] #progress_metric: host=algo-1, completed 19 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:25 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:26 INFO 140350967232320] Epoch[78] Batch[0] avg_epoch_loss=3.937468\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:27 INFO 140350967232320] Epoch[78] Batch[5] avg_epoch_loss=4.079927\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:27 INFO 140350967232320] Epoch[78] Batch [5]#011Speed: 317.04 samples/sec#011loss=4.079927\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:27 INFO 140350967232320] processed a total of 343 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1906.9781303405762, \"sum\": 1906.9781303405762, \"min\": 1906.9781303405762}}, \"EndTime\": 1550541687.810524, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541685.903243}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:27 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=179.855649043 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:27 INFO 140350967232320] #progress_metric: host=algo-1, completed 19 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:27 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:28 INFO 140350967232320] Epoch[79] Batch[0] avg_epoch_loss=4.000609\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:29 INFO 140350967232320] Epoch[79] Batch[5] avg_epoch_loss=3.949798\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:29 INFO 140350967232320] Epoch[79] Batch [5]#011Speed: 315.35 samples/sec#011loss=3.949798\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:29 INFO 140350967232320] processed a total of 393 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2109.6789836883545, \"sum\": 2109.6789836883545, \"min\": 2109.6789836883545}}, \"EndTime\": 1550541689.920588, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541687.810597}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:29 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=186.274539877 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:29 INFO 140350967232320] #progress_metric: host=algo-1, completed 20 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:29 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:30 INFO 140350967232320] Epoch[80] Batch[0] avg_epoch_loss=3.740028\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:31 INFO 140350967232320] Epoch[80] Batch[5] avg_epoch_loss=3.935373\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:31 INFO 140350967232320] Epoch[80] Batch [5]#011Speed: 323.69 samples/sec#011loss=3.935373\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:31 INFO 140350967232320] processed a total of 362 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1913.377046585083, \"sum\": 1913.377046585083, \"min\": 1913.377046585083}}, \"EndTime\": 1550541691.834348, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541689.920665}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:31 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=189.183722907 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:31 INFO 140350967232320] #progress_metric: host=algo-1, completed 20 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:31 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:32 INFO 140350967232320] Epoch[81] Batch[0] avg_epoch_loss=3.966125\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:33 INFO 140350967232320] Epoch[81] Batch[5] avg_epoch_loss=4.035626\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:33 INFO 140350967232320] Epoch[81] Batch [5]#011Speed: 321.71 samples/sec#011loss=4.035626\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:33 INFO 140350967232320] processed a total of 343 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1865.8421039581299, \"sum\": 1865.8421039581299, \"min\": 1865.8421039581299}}, \"EndTime\": 1550541693.700562, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541691.834421}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:33 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=183.82068081 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:33 INFO 140350967232320] #progress_metric: host=algo-1, completed 20 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:33 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:34 INFO 140350967232320] Epoch[82] Batch[0] avg_epoch_loss=3.894536\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:35 INFO 140350967232320] Epoch[82] Batch[5] avg_epoch_loss=3.977559\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:35 INFO 140350967232320] Epoch[82] Batch [5]#011Speed: 331.48 samples/sec#011loss=3.977559\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:35 INFO 140350967232320] processed a total of 375 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1896.104097366333, \"sum\": 1896.104097366333, \"min\": 1896.104097366333}}, \"EndTime\": 1550541695.597025, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541693.700635}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:35 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=197.762686888 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:35 INFO 140350967232320] #progress_metric: host=algo-1, completed 20 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:35 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:36 INFO 140350967232320] Epoch[83] Batch[0] avg_epoch_loss=3.739482\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:37 INFO 140350967232320] Epoch[83] Batch[5] avg_epoch_loss=3.905859\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:37 INFO 140350967232320] Epoch[83] Batch [5]#011Speed: 330.35 samples/sec#011loss=3.905859\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:37 INFO 140350967232320] processed a total of 354 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1877.3980140686035, \"sum\": 1877.3980140686035, \"min\": 1877.3980140686035}}, \"EndTime\": 1550541697.4748, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541695.597099}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:37 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=188.548189083 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:37 INFO 140350967232320] #progress_metric: host=algo-1, completed 21 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:37 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:38 INFO 140350967232320] Epoch[84] Batch[0] avg_epoch_loss=3.594888\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:39 INFO 140350967232320] Epoch[84] Batch[5] avg_epoch_loss=3.870839\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:39 INFO 140350967232320] Epoch[84] Batch [5]#011Speed: 328.42 samples/sec#011loss=3.870839\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:39 INFO 140350967232320] processed a total of 400 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2090.6550884246826, \"sum\": 2090.6550884246826, \"min\": 2090.6550884246826}}, \"EndTime\": 1550541699.565831, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541697.474874}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:39 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=191.317444888 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:39 INFO 140350967232320] #progress_metric: host=algo-1, completed 21 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:39 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:40 INFO 140350967232320] Epoch[85] Batch[0] avg_epoch_loss=3.567646\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:41 INFO 140350967232320] Epoch[85] Batch[5] avg_epoch_loss=4.029806\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:41 INFO 140350967232320] Epoch[85] Batch [5]#011Speed: 328.33 samples/sec#011loss=4.029806\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:41 INFO 140350967232320] processed a total of 355 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1891.5390968322754, \"sum\": 1891.5390968322754, \"min\": 1891.5390968322754}}, \"EndTime\": 1550541701.457783, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541699.565908}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:41 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=187.667163003 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:41 INFO 140350967232320] #progress_metric: host=algo-1, completed 21 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:41 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:42 INFO 140350967232320] Epoch[86] Batch[0] avg_epoch_loss=3.915004\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:43 INFO 140350967232320] Epoch[86] Batch[5] avg_epoch_loss=4.044631\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:43 INFO 140350967232320] Epoch[86] Batch [5]#011Speed: 331.27 samples/sec#011loss=4.044631\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:43 INFO 140350967232320] processed a total of 358 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1855.2980422973633, \"sum\": 1855.2980422973633, \"min\": 1855.2980422973633}}, \"EndTime\": 1550541703.313483, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541701.457857}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:43 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=192.949703269 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:43 INFO 140350967232320] #progress_metric: host=algo-1, completed 21 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:43 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:44 INFO 140350967232320] Epoch[87] Batch[0] avg_epoch_loss=4.088072\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:45 INFO 140350967232320] Epoch[87] Batch[5] avg_epoch_loss=3.970157\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:45 INFO 140350967232320] Epoch[87] Batch [5]#011Speed: 317.49 samples/sec#011loss=3.970157\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:45 INFO 140350967232320] processed a total of 392 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2080.6949138641357, \"sum\": 2080.6949138641357, \"min\": 2080.6949138641357}}, \"EndTime\": 1550541705.394561, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541703.313557}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:45 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=188.388536371 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:45 INFO 140350967232320] #progress_metric: host=algo-1, completed 22 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:45 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:46 INFO 140350967232320] Epoch[88] Batch[0] avg_epoch_loss=3.684222\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:47 INFO 140350967232320] Epoch[88] Batch[5] avg_epoch_loss=3.824621\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:47 INFO 140350967232320] Epoch[88] Batch [5]#011Speed: 324.36 samples/sec#011loss=3.824621\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:47 INFO 140350967232320] processed a total of 370 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1903.3160209655762, \"sum\": 1903.3160209655762, \"min\": 1903.3160209655762}}, \"EndTime\": 1550541707.298256, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541705.394637}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:47 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=194.386753729 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:47 INFO 140350967232320] #progress_metric: host=algo-1, completed 22 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:47 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:48 INFO 140350967232320] Epoch[89] Batch[0] avg_epoch_loss=4.023229\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:49 INFO 140350967232320] Epoch[89] Batch[5] avg_epoch_loss=3.968786\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:49 INFO 140350967232320] Epoch[89] Batch [5]#011Speed: 330.85 samples/sec#011loss=3.968786\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:49 INFO 140350967232320] processed a total of 364 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1889.9900913238525, \"sum\": 1889.9900913238525, \"min\": 1889.9900913238525}}, \"EndTime\": 1550541709.188674, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541707.298329}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:49 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=192.582402814 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:49 INFO 140350967232320] #progress_metric: host=algo-1, completed 22 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:49 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:50 INFO 140350967232320] Epoch[90] Batch[0] avg_epoch_loss=4.037105\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:51 INFO 140350967232320] Epoch[90] Batch[5] avg_epoch_loss=3.876477\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:51 INFO 140350967232320] Epoch[90] Batch [5]#011Speed: 322.70 samples/sec#011loss=3.876477\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:51 INFO 140350967232320] processed a total of 375 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1931.7278861999512, \"sum\": 1931.7278861999512, \"min\": 1931.7278861999512}}, \"EndTime\": 1550541711.120794, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541709.188749}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:51 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=194.115963525 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:51 INFO 140350967232320] #progress_metric: host=algo-1, completed 22 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:51 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:52 INFO 140350967232320] Epoch[91] Batch[0] avg_epoch_loss=3.870495\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:53 INFO 140350967232320] Epoch[91] Batch[5] avg_epoch_loss=3.860214\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:53 INFO 140350967232320] Epoch[91] Batch [5]#011Speed: 325.79 samples/sec#011loss=3.860214\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:53 INFO 140350967232320] processed a total of 384 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1888.063907623291, \"sum\": 1888.063907623291, \"min\": 1888.063907623291}}, \"EndTime\": 1550541713.009252, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541711.120866}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:53 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=203.371415317 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:53 INFO 140350967232320] #progress_metric: host=algo-1, completed 23 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:53 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:53 INFO 140350967232320] Epoch[92] Batch[0] avg_epoch_loss=3.801593\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:54 INFO 140350967232320] Epoch[92] Batch[5] avg_epoch_loss=3.831136\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:54 INFO 140350967232320] Epoch[92] Batch [5]#011Speed: 321.31 samples/sec#011loss=3.831136\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:55 INFO 140350967232320] processed a total of 401 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2102.207899093628, \"sum\": 2102.207899093628, \"min\": 2102.207899093628}}, \"EndTime\": 1550541715.111841, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541713.009325}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:55 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=190.741574318 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:55 INFO 140350967232320] #progress_metric: host=algo-1, completed 23 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:55 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:56 INFO 140350967232320] Epoch[93] Batch[0] avg_epoch_loss=3.919593\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:57 INFO 140350967232320] Epoch[93] Batch[5] avg_epoch_loss=3.913598\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:57 INFO 140350967232320] Epoch[93] Batch [5]#011Speed: 321.17 samples/sec#011loss=3.913598\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:57 INFO 140350967232320] processed a total of 404 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2114.7000789642334, \"sum\": 2114.7000789642334, \"min\": 2114.7000789642334}}, \"EndTime\": 1550541717.226935, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541715.111919}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:57 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=191.033624647 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:57 INFO 140350967232320] #progress_metric: host=algo-1, completed 23 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:57 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:58 INFO 140350967232320] Epoch[94] Batch[0] avg_epoch_loss=4.039033\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:59 INFO 140350967232320] Epoch[94] Batch[5] avg_epoch_loss=3.884219\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:59 INFO 140350967232320] Epoch[94] Batch [5]#011Speed: 326.52 samples/sec#011loss=3.884219\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:59 INFO 140350967232320] processed a total of 398 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2037.4348163604736, \"sum\": 2037.4348163604736, \"min\": 2037.4348163604736}}, \"EndTime\": 1550541719.264755, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541717.227012}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:59 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=195.333021066 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:59 INFO 140350967232320] #progress_metric: host=algo-1, completed 23 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:01:59 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:00 INFO 140350967232320] Epoch[95] Batch[0] avg_epoch_loss=4.177203\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:01 INFO 140350967232320] Epoch[95] Batch[5] avg_epoch_loss=3.958863\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:01 INFO 140350967232320] Epoch[95] Batch [5]#011Speed: 315.62 samples/sec#011loss=3.958863\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:01 INFO 140350967232320] processed a total of 388 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2085.679054260254, \"sum\": 2085.679054260254, \"min\": 2085.679054260254}}, \"EndTime\": 1550541721.350874, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541719.264832}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:01 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=186.020460319 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:01 INFO 140350967232320] #progress_metric: host=algo-1, completed 24 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:01 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:02 INFO 140350967232320] Epoch[96] Batch[0] avg_epoch_loss=3.932956\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:03 INFO 140350967232320] Epoch[96] Batch[5] avg_epoch_loss=3.738482\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:03 INFO 140350967232320] Epoch[96] Batch [5]#011Speed: 322.87 samples/sec#011loss=3.738482\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:03 INFO 140350967232320] processed a total of 358 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1884.1750621795654, \"sum\": 1884.1750621795654, \"min\": 1884.1750621795654}}, \"EndTime\": 1550541723.235442, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541721.350952}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:03 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=189.992901905 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:03 INFO 140350967232320] #progress_metric: host=algo-1, completed 24 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:03 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:04 INFO 140350967232320] Epoch[97] Batch[0] avg_epoch_loss=3.786141\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:05 INFO 140350967232320] Epoch[97] Batch[5] avg_epoch_loss=3.720942\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:05 INFO 140350967232320] Epoch[97] Batch [5]#011Speed: 311.45 samples/sec#011loss=3.720942\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:05 INFO 140350967232320] processed a total of 354 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1936.7430210113525, \"sum\": 1936.7430210113525, \"min\": 1936.7430210113525}}, \"EndTime\": 1550541725.17257, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541723.235514}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:05 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=182.770784959 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:05 INFO 140350967232320] #progress_metric: host=algo-1, completed 24 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:05 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:06 INFO 140350967232320] Epoch[98] Batch[0] avg_epoch_loss=4.107701\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:07 INFO 140350967232320] Epoch[98] Batch[5] avg_epoch_loss=3.930870\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:07 INFO 140350967232320] Epoch[98] Batch [5]#011Speed: 308.71 samples/sec#011loss=3.930870\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:07 INFO 140350967232320] processed a total of 332 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1924.010992050171, \"sum\": 1924.010992050171, \"min\": 1924.010992050171}}, \"EndTime\": 1550541727.096955, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541725.172645}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:07 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=172.546671934 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:07 INFO 140350967232320] #progress_metric: host=algo-1, completed 24 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:07 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:08 INFO 140350967232320] Epoch[99] Batch[0] avg_epoch_loss=3.791827\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:08 INFO 140350967232320] Epoch[99] Batch[5] avg_epoch_loss=3.907497\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:08 INFO 140350967232320] Epoch[99] Batch [5]#011Speed: 331.69 samples/sec#011loss=3.907497\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:08 INFO 140350967232320] processed a total of 368 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1881.6139698028564, \"sum\": 1881.6139698028564, \"min\": 1881.6139698028564}}, \"EndTime\": 1550541728.978938, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541727.097028}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:08 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=195.567214072 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:08 INFO 140350967232320] #progress_metric: host=algo-1, completed 25 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:08 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:09 INFO 140350967232320] Epoch[100] Batch[0] avg_epoch_loss=3.827761\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:10 INFO 140350967232320] Epoch[100] Batch[5] avg_epoch_loss=3.919661\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:10 INFO 140350967232320] Epoch[100] Batch [5]#011Speed: 327.05 samples/sec#011loss=3.919661\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:10 INFO 140350967232320] processed a total of 373 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1889.0039920806885, \"sum\": 1889.0039920806885, \"min\": 1889.0039920806885}}, \"EndTime\": 1550541730.868336, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541728.978997}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:10 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=197.447366355 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:10 INFO 140350967232320] #progress_metric: host=algo-1, completed 25 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:10 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:11 INFO 140350967232320] Epoch[101] Batch[0] avg_epoch_loss=4.007084\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:12 INFO 140350967232320] Epoch[101] Batch[5] avg_epoch_loss=3.860999\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:12 INFO 140350967232320] Epoch[101] Batch [5]#011Speed: 323.36 samples/sec#011loss=3.860999\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:13 INFO 140350967232320] processed a total of 386 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2133.842945098877, \"sum\": 2133.842945098877, \"min\": 2133.842945098877}}, \"EndTime\": 1550541733.002559, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541730.868409}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:13 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=180.884970773 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:13 INFO 140350967232320] #progress_metric: host=algo-1, completed 25 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:13 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:13 INFO 140350967232320] Epoch[102] Batch[0] avg_epoch_loss=3.997960\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:14 INFO 140350967232320] Epoch[102] Batch[5] avg_epoch_loss=3.868162\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:14 INFO 140350967232320] Epoch[102] Batch [5]#011Speed: 321.18 samples/sec#011loss=3.868162\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:14 INFO 140350967232320] processed a total of 368 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1913.0070209503174, \"sum\": 1913.0070209503174, \"min\": 1913.0070209503174}}, \"EndTime\": 1550541734.91597, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541733.002636}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:14 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=192.356034861 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:14 INFO 140350967232320] #progress_metric: host=algo-1, completed 25 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:14 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:15 INFO 140350967232320] Epoch[103] Batch[0] avg_epoch_loss=3.616328\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:16 INFO 140350967232320] Epoch[103] Batch[5] avg_epoch_loss=3.883335\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:16 INFO 140350967232320] Epoch[103] Batch [5]#011Speed: 329.81 samples/sec#011loss=3.883335\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:16 INFO 140350967232320] processed a total of 378 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1862.7691268920898, \"sum\": 1862.7691268920898, \"min\": 1862.7691268920898}}, \"EndTime\": 1550541736.779135, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541734.916048}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:16 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=202.911933272 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:16 INFO 140350967232320] #progress_metric: host=algo-1, completed 26 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:16 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:17 INFO 140350967232320] Epoch[104] Batch[0] avg_epoch_loss=4.133736\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:18 INFO 140350967232320] Epoch[104] Batch[5] avg_epoch_loss=3.829209\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:18 INFO 140350967232320] Epoch[104] Batch [5]#011Speed: 332.47 samples/sec#011loss=3.829209\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:18 INFO 140350967232320] processed a total of 395 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2057.6391220092773, \"sum\": 2057.6391220092773, \"min\": 2057.6391220092773}}, \"EndTime\": 1550541738.83718, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541736.77921}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:18 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=191.956946958 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:18 INFO 140350967232320] #progress_metric: host=algo-1, completed 26 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:18 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:19 INFO 140350967232320] Epoch[105] Batch[0] avg_epoch_loss=4.009001\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:20 INFO 140350967232320] Epoch[105] Batch[5] avg_epoch_loss=3.698776\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:20 INFO 140350967232320] Epoch[105] Batch [5]#011Speed: 324.76 samples/sec#011loss=3.698776\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:20 INFO 140350967232320] processed a total of 359 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1906.7268371582031, \"sum\": 1906.7268371582031, \"min\": 1906.7268371582031}}, \"EndTime\": 1550541740.744302, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541738.837259}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:20 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=188.269777699 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:20 INFO 140350967232320] #progress_metric: host=algo-1, completed 26 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:20 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:21 INFO 140350967232320] Epoch[106] Batch[0] avg_epoch_loss=3.769443\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:22 INFO 140350967232320] Epoch[106] Batch[5] avg_epoch_loss=3.828584\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:22 INFO 140350967232320] Epoch[106] Batch [5]#011Speed: 331.56 samples/sec#011loss=3.828584\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:22 INFO 140350967232320] processed a total of 402 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2095.9439277648926, \"sum\": 2095.9439277648926, \"min\": 2095.9439277648926}}, \"EndTime\": 1550541742.840633, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541740.744377}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:22 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=191.788574084 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:22 INFO 140350967232320] #progress_metric: host=algo-1, completed 26 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:22 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:23 INFO 140350967232320] Epoch[107] Batch[0] avg_epoch_loss=3.867964\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:24 INFO 140350967232320] Epoch[107] Batch[5] avg_epoch_loss=3.961032\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:24 INFO 140350967232320] Epoch[107] Batch [5]#011Speed: 326.40 samples/sec#011loss=3.961032\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:24 INFO 140350967232320] processed a total of 330 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1844.3841934204102, \"sum\": 1844.3841934204102, \"min\": 1844.3841934204102}}, \"EndTime\": 1550541744.685411, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541742.840712}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:24 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=178.910660636 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:24 INFO 140350967232320] #progress_metric: host=algo-1, completed 27 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:24 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:25 INFO 140350967232320] Epoch[108] Batch[0] avg_epoch_loss=3.900292\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:26 INFO 140350967232320] Epoch[108] Batch[5] avg_epoch_loss=3.772553\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:26 INFO 140350967232320] Epoch[108] Batch [5]#011Speed: 322.21 samples/sec#011loss=3.772553\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:26 INFO 140350967232320] processed a total of 355 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1903.5170078277588, \"sum\": 1903.5170078277588, \"min\": 1903.5170078277588}}, \"EndTime\": 1550541746.589313, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541744.685488}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:26 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=186.48640132 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:26 INFO 140350967232320] #progress_metric: host=algo-1, completed 27 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:26 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:27 INFO 140350967232320] Epoch[109] Batch[0] avg_epoch_loss=3.657867\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:28 INFO 140350967232320] Epoch[109] Batch[5] avg_epoch_loss=3.879890\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:28 INFO 140350967232320] Epoch[109] Batch [5]#011Speed: 312.94 samples/sec#011loss=3.879890\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:28 INFO 140350967232320] processed a total of 351 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1924.241065979004, \"sum\": 1924.241065979004, \"min\": 1924.241065979004}}, \"EndTime\": 1550541748.513927, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541746.589386}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:28 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=182.398209966 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:28 INFO 140350967232320] #progress_metric: host=algo-1, completed 27 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:28 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:29 INFO 140350967232320] Epoch[110] Batch[0] avg_epoch_loss=3.875151\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:30 INFO 140350967232320] Epoch[110] Batch[5] avg_epoch_loss=3.744921\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:30 INFO 140350967232320] Epoch[110] Batch [5]#011Speed: 311.80 samples/sec#011loss=3.744921\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:30 INFO 140350967232320] processed a total of 341 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1892.0400142669678, \"sum\": 1892.0400142669678, \"min\": 1892.0400142669678}}, \"EndTime\": 1550541750.406421, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541748.51401}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:30 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=180.217982136 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:30 INFO 140350967232320] #progress_metric: host=algo-1, completed 27 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:30 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:31 INFO 140350967232320] Epoch[111] Batch[0] avg_epoch_loss=3.657532\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:32 INFO 140350967232320] Epoch[111] Batch[5] avg_epoch_loss=3.820678\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:32 INFO 140350967232320] Epoch[111] Batch [5]#011Speed: 317.87 samples/sec#011loss=3.820678\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:32 INFO 140350967232320] processed a total of 344 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1904.6409130096436, \"sum\": 1904.6409130096436, \"min\": 1904.6409130096436}}, \"EndTime\": 1550541752.311487, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541750.406493}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:32 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=180.602067903 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:32 INFO 140350967232320] #progress_metric: host=algo-1, completed 28 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:32 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:33 INFO 140350967232320] Epoch[112] Batch[0] avg_epoch_loss=3.831089\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:34 INFO 140350967232320] Epoch[112] Batch[5] avg_epoch_loss=3.632549\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:34 INFO 140350967232320] Epoch[112] Batch [5]#011Speed: 320.68 samples/sec#011loss=3.632549\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:34 INFO 140350967232320] processed a total of 384 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1857.928991317749, \"sum\": 1857.928991317749, \"min\": 1857.928991317749}}, \"EndTime\": 1550541754.169813, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541752.311549}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:34 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=206.67007256 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:34 INFO 140350967232320] #progress_metric: host=algo-1, completed 28 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:34 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:35 INFO 140350967232320] Epoch[113] Batch[0] avg_epoch_loss=3.945000\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:36 INFO 140350967232320] Epoch[113] Batch[5] avg_epoch_loss=3.884125\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:36 INFO 140350967232320] Epoch[113] Batch [5]#011Speed: 326.43 samples/sec#011loss=3.884125\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:36 INFO 140350967232320] processed a total of 391 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2080.0349712371826, \"sum\": 2080.0349712371826, \"min\": 2080.0349712371826}}, \"EndTime\": 1550541756.250284, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541754.16988}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:36 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=187.969098286 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:36 INFO 140350967232320] #progress_metric: host=algo-1, completed 28 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:36 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:37 INFO 140350967232320] Epoch[114] Batch[0] avg_epoch_loss=4.194147\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:38 INFO 140350967232320] Epoch[114] Batch[5] avg_epoch_loss=3.982943\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:38 INFO 140350967232320] Epoch[114] Batch [5]#011Speed: 323.07 samples/sec#011loss=3.982943\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:38 INFO 140350967232320] processed a total of 379 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1895.5700397491455, \"sum\": 1895.5700397491455, \"min\": 1895.5700397491455}}, \"EndTime\": 1550541758.14627, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541756.250343}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:38 INFO 140350967232320] #throughput_metric: host=algo-1, train throughput=199.92740821 records/second\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:38 INFO 140350967232320] #progress_metric: host=algo-1, completed 28 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:38 INFO 140350967232320] loss did not improve\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:38 INFO 140350967232320] Loading parameters from best epoch (74)\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.deserialize.time\": {\"count\": 1, \"max\": 23.49710464477539, \"sum\": 23.49710464477539, \"min\": 23.49710464477539}}, \"EndTime\": 1550541758.170278, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541758.146351}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:38 INFO 140350967232320] stopping training now\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:38 INFO 140350967232320] #progress_metric: host=algo-1, completed 100 % of epochs\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:38 INFO 140350967232320] Final loss: 3.60401683194 (occurred at epoch 74)\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:38 INFO 140350967232320] #quality_metric: host=algo-1, train final_loss <loss>=3.60401683194\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:38 INFO 140350967232320] Worker algo-1 finished training.\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:38 WARNING 140350967232320] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:38 INFO 140350967232320] All workers finished. Serializing model for prediction.\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"get_graph.time\": {\"count\": 1, \"max\": 850.5539894104004, \"sum\": 850.5539894104004, \"min\": 850.5539894104004}}, \"EndTime\": 1550541759.021693, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541758.170346}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:39 INFO 140350967232320] Number of GPUs being used: 0\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"finalize.time\": {\"count\": 1, \"max\": 1101.9067764282227, \"sum\": 1101.9067764282227, \"min\": 1101.9067764282227}}, \"EndTime\": 1550541759.273003, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541759.021781}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:39 INFO 140350967232320] Serializing to /opt/ml/model/model_algo-1\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:39 INFO 140350967232320] Saved checkpoint to \"/opt/ml/model/model_algo-1-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"model.serialize.time\": {\"count\": 1, \"max\": 41.786909103393555, \"sum\": 41.786909103393555, \"min\": 41.786909103393555}}, \"EndTime\": 1550541759.314889, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541759.273062}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:39 INFO 140350967232320] Successfully serialized the model for prediction.\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:02:39 INFO 140350967232320] Evaluating model accuracy on testset using 100 samples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"model.bind.time\": {\"count\": 1, \"max\": 0.030040740966796875, \"sum\": 0.030040740966796875, \"min\": 0.030040740966796875}}, \"EndTime\": 1550541759.315578, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1550541759.314944}\n",
      "\u001b[0m\n",
      "\u001b[31m[02/19/2019 02:03:13 INFO 140350967232320] Number of test batches scored: 10\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "estimator_new_features = sagemaker.estimator.Estimator(\n",
    "    sagemaker_session=sagemaker_session,\n",
    "    image_name=image_name,\n",
    "    role=role,\n",
    "    train_instance_count=1,\n",
    "    train_instance_type='ml.c4.2xlarge',\n",
    "    base_job_name='deepar-electricity-demo-new-features',\n",
    "    output_path=s3_output_path_new_features\n",
    ")\n",
    "\n",
    "hyperparameters = {\n",
    "    \"time_freq\": freq,\n",
    "    \"context_length\": str(context_length),\n",
    "    \"prediction_length\": str(prediction_length),\n",
    "    \"epochs\": \"400\",\n",
    "    \"learning_rate\": \"5E-4\",\n",
    "    \"mini_batch_size\": \"64\",\n",
    "    \"early_stopping_patience\": \"40\",\n",
    "    \"num_dynamic_feat\": \"auto\",  # this will use the `dynamic_feat` field if it's present in the data\n",
    "}\n",
    "estimator_new_features.set_hyperparameters(**hyperparameters)\n",
    "\n",
    "estimator_new_features.fit(\n",
    "    inputs={\n",
    "        \"train\": \"{}/train/\".format(s3_data_path_new_features),\n",
    "        \"test\": \"{}/test/\".format(s3_data_path_new_features)\n",
    "    }, \n",
    "    wait=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As before, we spawn an endpoint to visualize our forecasts on examples we send on the fly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "predictor_new_features = estimator_new_features.deploy(\n",
    "    initial_instance_count=1,\n",
    "    instance_type='ml.m4.xlarge',\n",
    "    predictor_cls=DeepARPredictor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_id = 120\n",
    "predictor_new_features.predict(\n",
    "    ts=time_series_processed[customer_id][:-prediction_length], \n",
    "    dynamic_feat=[special_day_features[customer_id].tolist()], \n",
    "    quantiles=[0.1, 0.5, 0.9]\n",
    ").head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As before, we can query the endpoint to see predictions for arbitrary time series and time points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "@interact_manual(\n",
    "    customer_id=IntSlider(min=0, max=369, value=13, style=style), \n",
    "    forecast_day=IntSlider(min=0, max=100, value=21, style=style),\n",
    "    confidence=IntSlider(min=60, max=95, value=80, step=5, style=style),\n",
    "    missing_ratio=FloatSlider(min=0.0, max=0.95, value=0.2, step=0.05, style=style),\n",
    "    show_samples=Checkbox(value=False),\n",
    "    continuous_update=False\n",
    ")\n",
    "def plot_interact(customer_id, forecast_day, confidence, missing_ratio, show_samples): \n",
    "    forecast_date = end_training + datetime.timedelta(days=forecast_day)\n",
    "    target = time_series_processed[customer_id][start_dataset:forecast_date + prediction_length]\n",
    "    target = drop_at_random(target, missing_ratio)\n",
    "    dynamic_feat = [special_day_features[customer_id][start_dataset:forecast_date + prediction_length].tolist()]\n",
    "    plot(\n",
    "        predictor_new_features,\n",
    "        target_ts=target, \n",
    "        dynamic_feat=dynamic_feat,\n",
    "        forecast_date=forecast_date,\n",
    "        show_samples=show_samples, \n",
    "        plot_history=7*12,\n",
    "        confidence=confidence\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Delete endpoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor.delete_endpoint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor_new_features.delete_endpoint()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "conda_mxnet_p36",
   "language": "python",
   "name": "conda_mxnet_p36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "notice": "Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.  Licensed under the Apache License, Version 2.0 (the \"License\"). You may not use this file except in compliance with the License. A copy of the License is located at http://aws.amazon.com/apache2.0/ or in the \"license\" file accompanying this file. This file is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
 },
 "nbformat": 4,
 "nbformat_minor": 2
}