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    "# Air Quality Predictions with Amazon SageMaker and Amazon EMR\n",
    "\n",
    "This notebook demonstrates the ability to use Apache Spark on Amazon EMR to do data prep with two different datasets in order to build an urban air quality predictor with Amazon SageMaker.\n",
    "\n",
    "To create the environment, use the `us-east-1` CloudFormation template from the [Create and Managed Amazon EMR Clusters from SageMaker Studio](https://aws.amazon.com/blogs/machine-learning/part-1-create-and-manage-amazon-emr-clusters-from-sagemaker-studio-to-run-interactive-spark-and-ml-workloads/) blog post. This notebook makes use of the approach demonstrated in the blog post about how to [Build a model to predict the impact of weather on urban air quality using Amazon SageMaker](https://aws.amazon.com/blogs/machine-learning/build-a-model-to-predict-the-impact-of-weather-on-urban-air-quality-using-amazon-sagemaker/) and combines data from these two open datasets:\n",
    "- [OpenAQ physical air quality data](https://registry.opendata.aws/openaq/)\n",
    "- [NOAA Global Surface Summary of Day](https://registry.opendata.aws/noaa-gsod/)\n"
   ]
  },
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   "metadata": {},
   "source": [
    "Next, we use the `sagemaker_studio_analytics_extension` to connect to our EMR cluster that we created using \"Clusters\" section under the \"SageMaker resources\" tab to the left."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Part 0: Connecting to the EMR Cluster"
   ]
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    "  "
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     "output_type": "stream",
     "text": [
      "Successfully read emr cluster(j-17PGAEZFHXT8W) details\n",
      "Initiating EMR connection..\n",
      "Starting Spark application\n"
     ]
    },
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     "text": [
      "SparkSession available as 'spark'.\n",
      "{\"namespace\": \"sagemaker-analytics\", \"cluster_id\": \"j-17PGAEZFHXT8W\", \"error_message\": null, \"success\": true, \"service\": \"emr\", \"operation\": \"connect\"}\n"
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   ],
   "source": [
    "%load_ext sagemaker_studio_analytics_extension.magics\n",
    "%sm_analytics emr connect --cluster-id j-17PGAEZFHXT8W --auth-type None   "
   ]
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       "Current session configs: <tt>{'name': 'sagemaker_studio_analytics_spark_session_a9926510be50494cb93bc02bd0ffaefe', 'kind': 'pyspark'}</tt><br>"
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       "<table>\n",
       "<tr><th>ID</th><th>YARN Application ID</th><th>Kind</th><th>State</th><th>Spark UI</th><th>Driver log</th><th>User</th><th>Current session?</th></tr><tr><td>12</td><td>application_1674172997216_0014</td><td>pyspark</td><td>idle</td><td><a target=\"_blank\" href=\"http://ip-10-0-20-142.us-west-2.compute.internal:20888/proxy/application_1674172997216_0014/\">Link</a></td><td><a target=\"_blank\" href=\"http://ip-10-0-20-112.us-west-2.compute.internal:8042/node/containerlogs/container_1674172997216_0014_01_000001/livy\">Link</a></td><td>None</td><td>✔</td></tr></table>"
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   "source": [
    "%%info"
   ]
  },
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   "metadata": {
    "tags": []
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   "source": [
    "## Part 1: Data Prep in Amazon EMR\n",
    "\n",
    "In the cells below, we're going to perform the following operations:\n",
    "\n",
    "- Use Spark on the EMR cluster to read our data from the OpenAQ S3 Bucket.\n",
    "- Filter the available data to Boston and NO2 readings (indicative of air quality).\n",
    "- Group the readings by day.\n",
    "- Export the aggregate dataset to a local Pandas dataframe in the notebook."
   ]
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       "<table><tr><th>attribution</th><th>averagingPeriod</th><th>city</th><th>coordinates</th><th>country</th><th>date</th><th>location</th><th>mobile</th><th>parameter</th><th>sourceName</th><th>sourceType</th><th>unit</th><th>value</th></tr><tr><td>[{Central Pollution Control Board, http://www.cpcb.gov.in/CAAQM}]</td><td>{hours, 0.25}</td><td>Jorapokhar</td><td>null</td><td>IN</td><td>{2017-05-25T17:15:00+05:30, 2017-05-25T11:45:00.000Z}</td><td>Tata Stadium - Jorapokhar - JSPCB</td><td>false</td><td>no2</td><td>CPCB</td><td>government</td><td>µg/m³</td><td>9.59</td></tr><tr><td>[{Central Pollution Control Board, http://www.cpcb.gov.in/CAAQM}]</td><td>{hours, 0.25}</td><td>Delhi</td><td>{28.6517, 77.1581}</td><td>IN</td><td>{2017-05-25T17:15:00+05:30, 2017-05-25T11:45:00.000Z}</td><td>Shadipur</td><td>false</td><td>no2</td><td>CPCB</td><td>government</td><td>µg/m³</td><td>13.44</td></tr></table><br /><pre>only showing top 2 rows</pre>"
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   "source": [
    "%%pretty\n",
    "\n",
    "df = spark.read.json(\"s3://openaq-fetches/realtime-gzipped/2022-01-05/1641409725.ndjson.gz\")\n",
    "df2 = spark.read.schema(df.schema).json(\"s3://openaq-fetches/realtime-gzipped/20*\")\n",
    "\n",
    "df2.show(2, truncate=False)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
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     "text": [
      "+--------------------+---------------+--------------------+--------------------+-------+--------------------+--------------------+------+---------+----------+----------+----+-----+----+\n",
      "|         attribution|averagingPeriod|                city|         coordinates|country|                date|            location|mobile|parameter|sourceName|sourceType|unit|value|year|\n",
      "+--------------------+---------------+--------------------+--------------------+-------+--------------------+--------------------+------+---------+----------+----------+----+-----+----+\n",
      "|[{US EPA AirNow, ...|   {hours, 1.0}|Boston-Cambridge-...|{42.474701, -70.9...|     US|{2017-05-25T09:00...|                LYNN| false|      no2|    AirNow|government| ppm|0.002|2017|\n",
      "|[{US EPA AirNow, ...|   {hours, 1.0}|Boston-Cambridge-...|{42.3489, -71.097...|     US|{2017-05-25T09:00...|      BOSTON-KENMORE| false|      no2|    AirNow|government| ppm|0.015|2017|\n",
      "|[{US EPA AirNow, ...|   {hours, 1.0}|Boston-Cambridge-...|{42.329399, -71.0...|     US|{2017-05-25T09:00...|    Boston - Roxbury| false|      no2|    AirNow|government| ppm|0.013|2017|\n",
      "|[{US EPA AirNow, ...|   {hours, 1.0}|Boston-Cambridge-...|{42.2117, -71.114...|     US|{2017-05-25T09:00...|E. Milton - Blue Hil| false|      no2|    AirNow|government| ppm|0.001|2017|\n",
      "|[{US EPA AirNow, ...|   {hours, 1.0}|Boston-Cambridge-...|{42.474701, -70.9...|     US|{2017-05-25T10:00...|                LYNN| false|      no2|    AirNow|government| ppm|0.002|2017|\n",
      "+--------------------+---------------+--------------------+--------------------+-------+--------------------+--------------------+------+---------+----------+----------+----+-----+----+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "\n",
    "import pyspark.sql.functions as F\n",
    "\n",
    "'''\n",
    "Filtering Data ONLY for \n",
    "    City      : Boston\n",
    "    Parameter : no2\n",
    "\n",
    "Adding a new Column 'YEAR'    \n",
    "'''\n",
    "dfBos = df2.filter(F.lower((df2.city)) \\\n",
    "           .contains('boston')) \\\n",
    "           .filter(df2.parameter == \"no2\") \\\n",
    "           .withColumn(\"year\", F.substring(df2.date.utc, 1, 4)) \\\n",
    "           .cache()\n",
    "\n",
    "dfBos.show(5, truncate=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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     "text": [
      "+----------+--------------------+\n",
      "|       ymd|             no2_avg|\n",
      "+----------+--------------------+\n",
      "|2020-11-17|0.007710526315789475|\n",
      "|2021-01-10|0.007300000000000001|\n",
      "|2021-01-29|0.003769230769230...|\n",
      "|2021-02-06|0.011196428571428569|\n",
      "|2020-10-20|0.011890510948905112|\n",
      "|2021-02-02|0.005068627450980...|\n",
      "|2020-12-31|0.006067226890756304|\n",
      "|2021-01-01|0.009551136363636362|\n",
      "|2021-03-24|0.012571428571428572|\n",
      "|2020-12-19|             0.01975|\n",
      "+----------+--------------------+\n",
      "only showing top 10 rows"
     ]
    }
   ],
   "source": [
    "'''\n",
    "Aggregating the data day wise by taking the average of `no2` value across each day. \n",
    "Reducing the no. of data points from ~2.5M to ~ 2K \n",
    "'''\n",
    "\n",
    "dfNoAvg = dfBos.withColumn(\"ymd\", F.to_date(dfBos.date.utc)) \\\n",
    "               .groupBy(\"ymd\") \\\n",
    "               .avg(\"value\") \\\n",
    "               .withColumnRenamed(\"avg(value)\", \"no2_avg\")\n",
    "\n",
    "dfNoAvg.show(10, truncate=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While this is running, you can click the Spark UI link mentioned above to debug your job while it's running. Some useful pages to check out:\n",
    "- \"Jobs\" page shows you the current status of your job/task\n",
    "- \"Event Timeline\" on the Jobs page shows Spark Executors starting up or shutting down\n",
    "- The \"Executors\" tab shows you how many Executors are started, what the capacity is of each, and allows you to drill into logs\n",
    "\n",
    "Lets now more this `dfNoAvg` Spark dataframe in the %%local Python context as a Pandas dataframe with the same name, `dfNoAvg`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
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   ],
   "source": [
    "%%spark -o dfNoAvg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### In the local Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%local \n",
    "\n",
    "type(dfNoAvg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### In the EMR Spark Cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pyspark.sql.dataframe.DataFrame'>"
     ]
    }
   ],
   "source": [
    "type(dfNoAvg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Part 2: Bring Spark results into SageMaker Studio\n",
    "\n",
    "With the `%%spark -o` command above, we took the `dfNoAvg` dataframe from Spark and made it available in the `%%local` Python context as a Pandas dataframe with the same name. Now we can use local libraries to explore the data as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [20, 5]\n",
    "\n",
    "df_sample_2018_2022 = dfNoAvg[(dfNoAvg['ymd'] > '2018-01-01') & (dfNoAvg['ymd'] < '2021-12-31')]\n",
    "df_sample_2018_2022.plot(x='ymd')\n",
    "plt.ylabel('NO2 Conc. ppm')\n",
    "plt.xlabel('Daily Average')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ymd\n",
       "2018-10-13    0.004738\n",
       "2018-10-12    0.005288\n",
       "2018-10-11    0.007577\n",
       "2018-11-21    0.007528\n",
       "Name: no2_avg, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%local\n",
    "\n",
    "# There are some gaps in 2017 and 2018 we need to fill\n",
    "dfNoAvg = dfNoAvg.set_index('ymd')\n",
    "dfNoAvg.loc['2018-10-11':'2018-11-21']['no2_avg'].head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-10-11    0.007577\n",
       "2018-10-12    0.005288\n",
       "2018-10-13    0.004738\n",
       "2018-10-14         NaN\n",
       "2018-10-15         NaN\n",
       "2018-10-16         NaN\n",
       "2018-10-17         NaN\n",
       "2018-10-18         NaN\n",
       "2018-10-19         NaN\n",
       "2018-10-20         NaN\n",
       "Freq: D, Name: no2_avg, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%local\n",
    "\n",
    "# Fill in the date index first\n",
    "idx = pd.date_range(dfNoAvg.index.min(), dfNoAvg.index.max())\n",
    "dfNoAvg = dfNoAvg.reindex(idx, fill_value=None)\n",
    "\n",
    "dfNoAvg.loc['2018-10-11':'2018-11-20']['no2_avg'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-10-11    0.007577\n",
       "2018-10-12    0.005288\n",
       "2018-10-13    0.004738\n",
       "2018-10-14    0.004810\n",
       "2018-10-15    0.004881\n",
       "2018-10-16    0.004953\n",
       "2018-10-17    0.005024\n",
       "2018-10-18    0.005096\n",
       "2018-10-19    0.005167\n",
       "2018-10-20    0.005239\n",
       "Freq: D, Name: no2_avg, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%local\n",
    "\n",
    "# Then interpolate the values that are missing\n",
    "dfNoAvg = dfNoAvg.interpolate(method='time')\n",
    "dfNoAvg.loc['2018-10-11':'2018-10-20']['no2_avg'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('2016', '2023')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%local\n",
    "\n",
    "year_min, year_max = [f\"{dfNoAvg.index.min().year}\", f\"{dfNoAvg.index.max().year}\"]\n",
    "year_min, year_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully passed 'year_min' as 'year_min' to Spark kernel"
     ]
    }
   ],
   "source": [
    "%%send_to_spark -i year_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully passed 'year_max' as 'year_max' to Spark kernel"
     ]
    }
   ],
   "source": [
    "%%send_to_spark -i year_max"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Part 3: Data Prep in Amazon EMR with the second dataset\n",
    "\n",
    "Now that our first dataset looks good, we used the `%%send_to_spark` magic above to send the start and stop years we want to read data for back to the Spark driver on EMR. We can use those variables to limit the data we want to read."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### And now the weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyspark.sql.types import DoubleType\n",
    "from pyspark.sql import functions as F\n",
    "\n",
    "# Boston, MA, USA\n",
    "longLeft, latBottom, longRight, latTop = [-71.191162,42.227087,-70.986022,42.397057]\n",
    "\n",
    "dfSchema = spark.read.csv(\"s3://noaa-gsod-pds/2022/32509099999.csv\", header=True, inferSchema=True)\n",
    "\n",
    "# We read our first year, then union the rest of the years :)\n",
    "def read_year(year):\n",
    "    return spark.read.csv(f\"s3://noaa-gsod-pds/{year}/\", header=True, schema=dfSchema.schema)\n",
    "\n",
    "year_range = range(int(year_min), int(year_max)+1)\n",
    "df = read_year(year_range[0])\n",
    "for year in year_range[1:]:\n",
    "    df = df.union(read_year(year))\n",
    "\n",
    "df = df \\\n",
    "        .withColumn('LATITUDE', df.LATITUDE.cast(DoubleType())) \\\n",
    "        .withColumn('LONGITUDE', df.LONGITUDE.cast(DoubleType()))\n",
    "\n",
    "bostondf = df \\\n",
    "        .filter(df.LATITUDE >= latBottom) \\\n",
    "        .filter(df.LATITUDE <= latTop) \\\n",
    "        .filter(df.LONGITUDE >= longLeft) \\\n",
    "        .filter(df.LONGITUDE <= longRight)\n",
    "\n",
    "# Rename columns so they're easier to read\n",
    "bostonfeatures = bostondf.selectExpr(\"Date as date\", \"MAX as temp_max\", \"MIN as temp_min\", \"WDSP as wind_avg\", \"SLP as pressure_sea_level\", \"STP as pressure_station\", \"VISIB as visibility\")\n",
    "\n",
    "# Remove invalid readings\n",
    "no_data_mappings = [\n",
    "    [\"temp_max\", 9999.9],\n",
    "    [\"temp_min\", 9999.9],\n",
    "    [\"wind_avg\", 999.9],\n",
    "    [\"pressure_sea_level\", 9999.9],\n",
    "    [\"pressure_station\", 9999.9],\n",
    "    [\"visibility\", 999.9],\n",
    "]\n",
    "for [name, val] in no_data_mappings:\n",
    "    bostonfeatures = bostonfeatures \\\n",
    "                        .withColumn(name, F.when(F.col(name)==val, None) \\\n",
    "                                           .otherwise(F.col(name)))\n",
    "    \n",
    "# Now average each reading per day\n",
    "bostonfeatures = bostonfeatures \\\n",
    "                    .groupBy(\"date\") \\\n",
    "                    .agg(*[F.mean(c).alias(c) for c in bostonfeatures.columns[1:]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%spark -o bostonfeatures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Part 4: Data Analysis in SageMaker Studio\n",
    "\n",
    "We again use the `%%spark -o` magic to send the aggregate back to SageMaker so we can do some exploration.\n",
    "\n",
    "One thing to note is that you can certainly do some of this exploration with Spark as well. It just depends on the use case and the size of your data. Because we've aggregated our data down to a few thousand rows, it's relatively easy to manage in the notebook. But if you're unable to do this, you can still use Spark to split your training/test datasets or do other aggregations and write the results out to S3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%local\n",
    "\n",
    "bostonfeatures.plot(x='date', y=['temp_min', 'temp_max'])\n",
    "plt.ylabel('Max Temp (F)')\n",
    "plt.xlabel('Daily Average')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%local\n",
    "\n",
    "bostonfeatures.plot(x='date', y=['wind_avg'])\n",
    "plt.ylabel('Average Wind (mph)')\n",
    "plt.xlabel('Daily Average')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%local\n",
    "\n",
    "bostonfeatures.plot(x='date', y=['visibility'])\n",
    "plt.ylabel('Visibility (miles)')\n",
    "plt.xlabel('Daily Average')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Part 5: Marry the data\n",
    "\n",
    "Now that we've taken a quick look at our data and done some initial exploration, let's merge the two datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             no2_avg\n",
      "2016-03-10  0.007571\n",
      "2016-03-11  0.012281\n",
      "2016-03-12  0.007848\n",
      "2016-03-13  0.006382\n",
      "2016-03-14  0.007338\n",
      "...              ...\n",
      "2023-01-20  0.009812\n",
      "2023-01-21  0.007531\n",
      "2023-01-22  0.004969\n",
      "2023-01-23  0.008376\n",
      "2023-01-24  0.008586\n",
      "\n",
      "[2512 rows x 1 columns]\n",
      "            temp_max  temp_min  ...  pressure_station  visibility\n",
      "date                            ...                              \n",
      "2016-01-01     46.90     33.10  ...             13.40        10.0\n",
      "2016-01-02     41.00     30.90  ...             12.10         9.9\n",
      "2016-01-03     44.10     30.90  ...              8.60        10.0\n",
      "2016-01-04     44.10     19.90  ...             12.30         9.3\n",
      "2016-01-05     36.00      8.10  ...             30.00        10.0\n",
      "...              ...       ...  ...               ...         ...\n",
      "2023-01-17     50.20     31.45  ...            501.30        10.0\n",
      "2023-01-18     51.55     37.40  ...            500.95         9.9\n",
      "2023-01-19     46.00     36.25  ...            506.25         9.5\n",
      "2023-01-20     39.00     33.10  ...              3.40         2.9\n",
      "2023-01-21     33.10     32.00  ...              9.20         8.0\n",
      "\n",
      "[2500 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "%%local\n",
    "\n",
    "# First Dataset - NO2 Dataset\n",
    "print(dfNoAvg)\n",
    "\n",
    "# Second Dataset - Weather Dataset \n",
    "bostonfeaturesi = bostonfeatures.set_index('date').sort_index()\n",
    "\n",
    "print(bostonfeaturesi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Merging dataframes between 2016-03-10 00:00:00 and 2023-01-21 00:00:00\n",
      "            temp_max  temp_min  ...  visibility   no2_avg\n",
      "2016-03-10      66.0      55.0  ...         8.8  0.007571\n",
      "2016-03-11      66.0      42.1  ...         8.8  0.012281\n",
      "2016-03-12      60.1      36.0  ...        10.0  0.007848\n",
      "2016-03-13      63.0      36.0  ...        10.0  0.006382\n",
      "2016-03-14      63.0      39.0  ...        10.0  0.007338\n",
      "2016-03-15      45.0      39.0  ...         2.9  0.008294\n",
      "2016-03-16      48.0      41.0  ...         9.2  0.009250\n",
      "2016-03-17      64.0      41.0  ...         7.8  0.009163\n",
      "2016-03-18      64.0      39.0  ...         9.8  0.009076\n",
      "2016-03-19      52.0      28.9  ...        10.0  0.008989\n",
      "2016-03-20      45.0      26.1  ...        10.0  0.008902\n",
      "2016-03-21      44.1      26.1  ...         5.9  0.008814\n",
      "2016-03-22      48.9      33.1  ...        10.0  0.008727\n",
      "2016-03-23      62.1      42.1  ...        10.0  0.010813\n",
      "2016-03-24      43.0      37.0  ...         9.1  0.008273\n",
      "2016-03-25      45.0      36.0  ...         1.6  0.012933\n",
      "2016-03-26      46.0      36.0  ...         9.9  0.005028\n",
      "2016-03-27      43.0      34.0  ...        10.0  0.008208\n",
      "2016-03-29      51.1      37.9  ...        10.0  0.003349\n",
      "2016-03-30      59.0      34.0  ...        10.0  0.007962\n",
      "\n",
      "[20 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "%%local\n",
    "\n",
    "# We need to make sure the data frames line up, so we'll create new\n",
    "# dataframes from the min and max of the existing ones.\n",
    "\n",
    "# Try Catch for Spark2 / Spark3 Compatability\n",
    "try:\n",
    "    min_viable_date = max(dfNoAvg.index.min(), bostonfeaturesi.index.min())\n",
    "    max_viable_date = min(dfNoAvg.index.max(), bostonfeaturesi.index.max())\n",
    "except:\n",
    "    dfNoAvg = dfNoAvg.tz_localize(tz='UTC')\n",
    "    min_viable_date = max(dfNoAvg.index.min(), bostonfeaturesi.index.min())\n",
    "    max_viable_date = min(dfNoAvg.index.max(), bostonfeaturesi.index.max())\n",
    "    \n",
    "print(f\"Merging dataframes between {min_viable_date} and {max_viable_date}\")\n",
    "\n",
    "comp_df = pd.merge(\n",
    "    bostonfeaturesi[min_viable_date:max_viable_date],\n",
    "    dfNoAvg[min_viable_date:max_viable_date][['no2_avg']],\n",
    "    left_index=True, right_index=True\n",
    ")\n",
    "print(comp_df.sort_index().head(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            temp_max  temp_min  ...  visibility   no2_avg\n",
      "2018-10-11     80.85     58.00  ...         5.5  0.007577\n",
      "2018-10-12     67.55     56.85  ...         8.5  0.005288\n",
      "2018-10-13     60.35     47.55  ...         9.5  0.004738\n",
      "2018-10-14     57.90     44.80  ...        10.0  0.004810\n",
      "2018-10-15     66.85     46.35  ...        10.0  0.004881\n",
      "2018-10-16     69.10     46.05  ...        10.0  0.004953\n",
      "2018-10-17     62.60     44.15  ...        10.0  0.005024\n",
      "2018-10-18     57.40     39.00  ...         9.9  0.005096\n",
      "2018-10-19     61.90     37.20  ...        10.0  0.005167\n",
      "2018-10-20     63.85     54.65  ...        10.0  0.005239\n",
      "\n",
      "[10 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "%%local\n",
    "\n",
    "# Check some data we looked into previously\n",
    "print(comp_df.loc['2018-10-11':'2018-10-20'].sort_index())\n",
    "comp_df = comp_df.sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've merged them, we can do some quick correlation tests to see what the impact is of different weather events on NO2 readings.\n",
    "\n",
    "Please see the [afore-mentioned blog post](https://aws.amazon.com/blogs/machine-learning/build-a-model-to-predict-the-impact-of-weather-on-urban-air-quality-using-amazon-sagemaker/) for more in-depth explations of these different charts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%local\n",
    "\n",
    "mydata = comp_df[['wind_avg','no2_avg']]\n",
    "\n",
    "x = mydata['wind_avg']\n",
    "y = mydata['no2_avg']\n",
    "plt.scatter(x, y)\n",
    "\n",
    "z = np.polyfit(x, y, 1)\n",
    "p = np.poly1d(z)\n",
    "plt.plot(x,p(x),\"r--\")\n",
    "\n",
    "plt.ylabel('NO2 Conc. ppm')\n",
    "plt.xlabel('Wind Speed (mph)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%local\n",
    "\n",
    "mydata = comp_df[['visibility','no2_avg']].dropna()\n",
    "\n",
    "x = mydata['visibility']\n",
    "y = mydata['no2_avg']\n",
    "plt.scatter(x, y)\n",
    "\n",
    "z = np.polyfit(x, y, 1)\n",
    "p = np.poly1d(z)\n",
    "plt.plot(x,p(x),\"r--\")\n",
    "\n",
    "plt.ylabel('NO2 Conc. ppm')\n",
    "plt.xlabel('Visibility (miles)')\n",
    "plt.show()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1710, 5) (1710, 1)\n",
      "(354, 5) (354, 1)\n",
      "            temp_max  temp_min  wind_avg  pressure_sea_level  pressure_station\n",
      "2016-03-11      66.0      42.1       6.7              1014.9              13.4\n",
      "2016-03-12      60.1      36.0       8.6              1023.6              22.6\n",
      "2016-03-13      63.0      36.0      10.3              1014.5              13.5\n",
      "2016-03-14      63.0      39.0      12.7              1024.2              23.4\n",
      "2016-03-15      45.0      39.0      14.9              1014.9              13.5\n"
     ]
    }
   ],
   "source": [
    "%%local\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "\n",
    "# Drop the 1st row as NaN\n",
    "aq_df = comp_df.iloc[1:].copy()\n",
    "\n",
    "# Drop visibility as it didn't seem correlate much and has NaNs that break the training\n",
    "aq_df = aq_df.drop('visibility', 1)\n",
    "\n",
    "# Use the data from years 2016 up to 2020 as training, and the year 2021 as our candidate year for testing and validating our model.\n",
    "aq_train_df = aq_df[aq_df.index.year < 2021]\n",
    "aq_test_df = aq_df[aq_df.index.year == 2021]\n",
    "\n",
    "x_train = aq_train_df.drop('no2_avg',axis=1)\n",
    "x_test = aq_test_df.drop('no2_avg',axis=1)\n",
    "\n",
    "y_train = aq_train_df[[\"no2_avg\"]]\n",
    "y_test = aq_test_df[[\"no2_avg\"]]\n",
    "\n",
    "print(x_train.shape, y_train.shape)\n",
    "print(x_test.shape, y_test.shape)\n",
    "print(x_train.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%local\n",
    "\n",
    "from math import sqrt\n",
    "from sklearn.metrics import mean_squared_error, r2_score, explained_variance_score\n",
    "\n",
    "# sMAPE is used in KDD Air Quality challenge: https://biendata.com/competition/kdd_2018/evaluation/ \n",
    "def smape(actual, predicted):\n",
    "    dividend= np.abs(np.array(actual) - np.array(predicted))\n",
    "    denominator = np.array(actual) + np.array(predicted)\n",
    "    \n",
    "    return 2 * np.mean(np.divide(dividend, denominator, out=np.zeros_like(dividend), where=denominator!=0, casting='unsafe'))\n",
    "\n",
    "def print_metrics(y_test, y_pred):\n",
    "    print(\"RMSE: %.4f\" % sqrt(mean_squared_error(y_test, y_pred)))\n",
    "    print('Variance score: %.4f' % r2_score(y_test, y_pred))\n",
    "    print('Explained variance score: %.4f' % explained_variance_score(y_test, y_pred))\n",
    "    forecast_err = np.array(y_test) - np.array(y_pred)\n",
    "    print('Forecast bias: %.4f' % (np.sum(forecast_err) * 1.0/len(y_pred) ))\n",
    "    print('sMAPE: %.4f' % smape(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%local\n",
    "\n",
    "import boto3\n",
    "from sagemaker import get_execution_role, session\n",
    "\n",
    "sess = session.Session() \n",
    "bucket = sess.default_bucket()\n",
    "\n",
    "# This is used to run the LinearLearner training job\n",
    "role = get_execution_role()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Part 6: Train and Deploy a Machine Learning Model\n",
    "\n",
    "In the section below, we create a new training job using the Linear Learner algorithm. Once that job completes, we deploy an endpoint and run some validation tests against it.\n",
    "\n",
    "💁\n",
    "\n",
    "**NOTE**: You only need to create this training job and deploy it once. You can use the same endpoint, even in future runs of this notebook, without re-training or re-deploying.\n",
    "\n",
    "💁"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%local\n",
    "\n",
    "from sagemaker import LinearLearner\n",
    "\n",
    "data_location = f's3://{bucket}/aq-linearlearner/data/train'\n",
    "output_location = f's3://{bucket}/aq-linearlearner/output'\n",
    "\n",
    "llearner = LinearLearner(role=role,\n",
    "                predictor_type='regressor',\n",
    "                normalize_data=True,\n",
    "                normalize_label=True,\n",
    "                instance_count=1,\n",
    "                use_spot_instances = True,\n",
    "                max_run= 1800,\n",
    "                max_wait = 3600,\n",
    "                instance_type='ml.c5.xlarge',\n",
    "                output_path=output_location,\n",
    "                data_location=data_location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-01-24 17:54:04 Starting - Starting the training job...\n",
      "2023-01-24 17:54:29 Starting - Preparing the instances for trainingProfilerReport-1674582844: InProgress\n",
      ".........\n",
      "2023-01-24 17:55:49 Downloading - Downloading input data...\n",
      "2023-01-24 17:56:29 Training - Downloading the training image.....\u001b[34mDocker entrypoint called with argument(s): train\u001b[0m\n",
      "\u001b[34mRunning default environment configuration script\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:13 INFO 140289507411776] Reading default configuration from /opt/amazon/lib/python3.7/site-packages/algorithm/resources/default-input.json: {'mini_batch_size': '1000', 'epochs': '15', 'feature_dim': 'auto', 'use_bias': 'true', 'binary_classifier_model_selection_criteria': 'accuracy', 'f_beta': '1.0', 'target_recall': '0.8', 'target_precision': '0.8', 'num_models': 'auto', 'num_calibration_samples': '10000000', 'init_method': 'uniform', 'init_scale': '0.07', 'init_sigma': '0.01', 'init_bias': '0.0', 'optimizer': 'auto', 'loss': 'auto', 'margin': '1.0', 'quantile': '0.5', 'loss_insensitivity': '0.01', 'huber_delta': '1.0', 'num_classes': '1', 'accuracy_top_k': '3', 'wd': 'auto', 'l1': 'auto', 'momentum': 'auto', 'learning_rate': 'auto', 'beta_1': 'auto', 'beta_2': 'auto', 'bias_lr_mult': 'auto', 'bias_wd_mult': 'auto', 'use_lr_scheduler': 'true', 'lr_scheduler_step': 'auto', 'lr_scheduler_factor': 'auto', 'lr_scheduler_minimum_lr': 'auto', 'positive_example_weight_mult': '1.0', 'balance_multiclass_weights': 'false', 'normalize_data': 'true', 'normalize_label': 'auto', 'unbias_data': 'auto', 'unbias_label': 'auto', 'num_point_for_scaler': '10000', '_kvstore': 'auto', '_num_gpus': 'auto', '_num_kv_servers': 'auto', '_log_level': 'info', '_tuning_objective_metric': '', 'early_stopping_patience': '3', 'early_stopping_tolerance': '0.001', '_enable_profiler': 'false'}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:13 INFO 140289507411776] Merging with provided configuration from /opt/ml/input/config/hyperparameters.json: {'feature_dim': '5', 'mini_batch_size': '1000', 'normalize_data': 'True', 'normalize_label': 'True', 'predictor_type': 'regressor'}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:13 INFO 140289507411776] Final configuration: {'mini_batch_size': '1000', 'epochs': '15', 'feature_dim': '5', 'use_bias': 'true', 'binary_classifier_model_selection_criteria': 'accuracy', 'f_beta': '1.0', 'target_recall': '0.8', 'target_precision': '0.8', 'num_models': 'auto', 'num_calibration_samples': '10000000', 'init_method': 'uniform', 'init_scale': '0.07', 'init_sigma': '0.01', 'init_bias': '0.0', 'optimizer': 'auto', 'loss': 'auto', 'margin': '1.0', 'quantile': '0.5', 'loss_insensitivity': '0.01', 'huber_delta': '1.0', 'num_classes': '1', 'accuracy_top_k': '3', 'wd': 'auto', 'l1': 'auto', 'momentum': 'auto', 'learning_rate': 'auto', 'beta_1': 'auto', 'beta_2': 'auto', 'bias_lr_mult': 'auto', 'bias_wd_mult': 'auto', 'use_lr_scheduler': 'true', 'lr_scheduler_step': 'auto', 'lr_scheduler_factor': 'auto', 'lr_scheduler_minimum_lr': 'auto', 'positive_example_weight_mult': '1.0', 'balance_multiclass_weights': 'false', 'normalize_data': 'True', 'normalize_label': 'True', 'unbias_data': 'auto', 'unbias_label': 'auto', 'num_point_for_scaler': '10000', '_kvstore': 'auto', '_num_gpus': 'auto', '_num_kv_servers': 'auto', '_log_level': 'info', '_tuning_objective_metric': '', 'early_stopping_patience': '3', 'early_stopping_tolerance': '0.001', '_enable_profiler': 'false', 'predictor_type': 'regressor'}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:15 WARNING 140289507411776] Loggers have already been setup.\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:15 INFO 140289507411776] Final configuration: {'mini_batch_size': '1000', 'epochs': '15', 'feature_dim': '5', 'use_bias': 'true', 'binary_classifier_model_selection_criteria': 'accuracy', 'f_beta': '1.0', 'target_recall': '0.8', 'target_precision': '0.8', 'num_models': 'auto', 'num_calibration_samples': '10000000', 'init_method': 'uniform', 'init_scale': '0.07', 'init_sigma': '0.01', 'init_bias': '0.0', 'optimizer': 'auto', 'loss': 'auto', 'margin': '1.0', 'quantile': '0.5', 'loss_insensitivity': '0.01', 'huber_delta': '1.0', 'num_classes': '1', 'accuracy_top_k': '3', 'wd': 'auto', 'l1': 'auto', 'momentum': 'auto', 'learning_rate': 'auto', 'beta_1': 'auto', 'beta_2': 'auto', 'bias_lr_mult': 'auto', 'bias_wd_mult': 'auto', 'use_lr_scheduler': 'true', 'lr_scheduler_step': 'auto', 'lr_scheduler_factor': 'auto', 'lr_scheduler_minimum_lr': 'auto', 'positive_example_weight_mult': '1.0', 'balance_multiclass_weights': 'false', 'normalize_data': 'True', 'normalize_label': 'True', 'unbias_data': 'auto', 'unbias_label': 'auto', 'num_point_for_scaler': '10000', '_kvstore': 'auto', '_num_gpus': 'auto', '_num_kv_servers': 'auto', '_log_level': 'info', '_tuning_objective_metric': '', 'early_stopping_patience': '3', 'early_stopping_tolerance': '0.001', '_enable_profiler': 'false', 'predictor_type': 'regressor'}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:15 WARNING 140289507411776] Loggers have already been setup.\u001b[0m\n",
      "\u001b[34mProcess 7 is a worker.\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:15 INFO 140289507411776] Using default worker.\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:15 INFO 140289507411776] Checkpoint loading and saving are disabled.\u001b[0m\n",
      "\u001b[34m[2023-01-24 17:57:15.877] [tensorio] [warning] TensorIO is already initialized; ignoring the initialization routine.\u001b[0m\n",
      "\u001b[34m[2023-01-24 17:57:15.890] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 0, \"duration\": 15, \"num_examples\": 1, \"num_bytes\": 64000}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:15 INFO 140289507411776] Create Store: local\u001b[0m\n",
      "\u001b[34m[2023-01-24 17:57:15.902] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 1, \"duration\": 11, \"num_examples\": 2, \"num_bytes\": 109440}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:15 INFO 140289507411776] Scaler algorithm parameters\n",
      " <algorithm.scaler.ScalerAlgorithmStable object at 0x7f9728988750>\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:15 INFO 140289507411776] Scaling model computed with parameters:\n",
      " {'stdev_label': \u001b[0m\n",
      "\u001b[34m[0.00461993]\u001b[0m\n",
      "\u001b[34m<NDArray 1 @cpu(0)>, 'stdev_weight': \u001b[0m\n",
      "\u001b[34m[ 17.938156  16.365223   2.993088   7.910179 250.83063 ]\u001b[0m\n",
      "\u001b[34m<NDArray 5 @cpu(0)>, 'mean_label': \u001b[0m\n",
      "\u001b[34m[0.00845824]\u001b[0m\n",
      "\u001b[34m<NDArray 1 @cpu(0)>, 'mean_weight': \u001b[0m\n",
      "\u001b[34m[  63.4749    46.77145    6.265   1016.20166  384.38486]\u001b[0m\n",
      "\u001b[34m<NDArray 5 @cpu(0)>}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:15 INFO 140289507411776] nvidia-smi: took 0.031 seconds to run.\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:15 INFO 140289507411776] nvidia-smi identified 0 GPUs.\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:15 INFO 140289507411776] Number of GPUs being used: 0\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583035.9860897, \"EndTime\": 1674583035.9861188, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"Meta\": \"init_train_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 2710.0, \"count\": 1, \"min\": 2710, \"max\": 2710}, \"Total Batches Seen\": {\"sum\": 3.0, \"count\": 1, \"min\": 3, \"max\": 3}, \"Max Records Seen Between Resets\": {\"sum\": 1710.0, \"count\": 1, \"min\": 1710, \"max\": 1710}, \"Max Batches Seen Between Resets\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}, \"Reset Count\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}, \"Number of Records Since Last Reset\": {\"sum\": 0.0, \"count\": 1, \"min\": 0, \"max\": 0}, \"Number of Batches Since Last Reset\": {\"sum\": 0.0, \"count\": 1, \"min\": 0, \"max\": 0}}}\u001b[0m\n",
      "\u001b[34m[2023-01-24 17:57:16.028] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 4, \"duration\": 42, \"num_examples\": 2, \"num_bytes\": 109440}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0285606, \"EndTime\": 1674583036.0286293, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 0}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0075349731445313, \"count\": 1, \"min\": 1.0075349731445313, \"max\": 1.0075349731445313}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0287004, \"EndTime\": 1674583036.0287137, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 1}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.024355712890625, \"count\": 1, \"min\": 1.024355712890625, \"max\": 1.024355712890625}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0287437, \"EndTime\": 1674583036.028752, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 2}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0131227416992188, \"count\": 1, \"min\": 1.0131227416992188, \"max\": 1.0131227416992188}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0287864, \"EndTime\": 1674583036.0287938, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 3}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.99669775390625, \"count\": 1, \"min\": 0.99669775390625, \"max\": 0.99669775390625}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0288274, \"EndTime\": 1674583036.0288348, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 4}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0344573974609375, \"count\": 1, \"min\": 1.0344573974609375, \"max\": 1.0344573974609375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.028863, \"EndTime\": 1674583036.0288706, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 5}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0967271728515624, \"count\": 1, \"min\": 1.0967271728515624, \"max\": 1.0967271728515624}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0289013, \"EndTime\": 1674583036.02891, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 6}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.111934814453125, \"count\": 1, \"min\": 1.111934814453125, \"max\": 1.111934814453125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0289369, \"EndTime\": 1674583036.0289438, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 7}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9483638916015625, \"count\": 1, \"min\": 0.9483638916015625, \"max\": 0.9483638916015625}}}\u001b[0m\n",
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      "\u001b[34m#metrics {\"StartTime\": 1674583036.0290473, \"EndTime\": 1674583036.029056, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 10}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.058550537109375, \"count\": 1, \"min\": 1.058550537109375, \"max\": 1.058550537109375}}}\u001b[0m\n",
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      "\u001b[34m#metrics {\"StartTime\": 1674583036.029317, \"EndTime\": 1674583036.0293245, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 17}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0389844970703126, \"count\": 1, \"min\": 1.0389844970703126, \"max\": 1.0389844970703126}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0293536, \"EndTime\": 1674583036.0293612, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 18}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0669388427734374, \"count\": 1, \"min\": 1.0669388427734374, \"max\": 1.0669388427734374}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0293913, \"EndTime\": 1674583036.0293987, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 19}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.035308837890625, \"count\": 1, \"min\": 1.035308837890625, \"max\": 1.035308837890625}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.029426, \"EndTime\": 1674583036.029431, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 20}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.949421875, \"count\": 1, \"min\": 0.949421875, \"max\": 0.949421875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0294528, \"EndTime\": 1674583036.0294592, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 21}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9940736083984375, \"count\": 1, \"min\": 0.9940736083984375, \"max\": 0.9940736083984375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0294855, \"EndTime\": 1674583036.0294929, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 22}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9992052612304687, \"count\": 1, \"min\": 0.9992052612304687, \"max\": 0.9992052612304687}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0295272, \"EndTime\": 1674583036.0295346, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 23}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.03816796875, \"count\": 1, \"min\": 1.03816796875, \"max\": 1.03816796875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0295653, \"EndTime\": 1674583036.0295734, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 24}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.987958251953125, \"count\": 1, \"min\": 0.987958251953125, \"max\": 0.987958251953125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.029606, \"EndTime\": 1674583036.0296144, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 25}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.946248779296875, \"count\": 1, \"min\": 0.946248779296875, \"max\": 0.946248779296875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.029648, \"EndTime\": 1674583036.0296566, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 26}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9722654418945312, \"count\": 1, \"min\": 0.9722654418945312, \"max\": 0.9722654418945312}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0296912, \"EndTime\": 1674583036.0296996, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 27}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0100235595703124, \"count\": 1, \"min\": 1.0100235595703124, \"max\": 1.0100235595703124}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0297346, \"EndTime\": 1674583036.0297432, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 28}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.977877197265625, \"count\": 1, \"min\": 0.977877197265625, \"max\": 0.977877197265625}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0297778, \"EndTime\": 1674583036.029786, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 29}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0280936279296875, \"count\": 1, \"min\": 1.0280936279296875, \"max\": 1.0280936279296875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.029821, \"EndTime\": 1674583036.02983, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 30}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.006772705078125, \"count\": 1, \"min\": 1.006772705078125, \"max\": 1.006772705078125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0298638, \"EndTime\": 1674583036.0298724, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 31}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9509653930664063, \"count\": 1, \"min\": 0.9509653930664063, \"max\": 0.9509653930664063}}}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #quality_metric: host=algo-1, epoch=0, train mse_objective <loss>=1.0075349731445313\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #early_stopping_criteria_metric: host=algo-1, epoch=0, criteria=mse_objective, value=0.9243499755859375\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Epoch 0: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Saving model for epoch: 0\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Saved checkpoint to \"/tmp/tmp3bgdkhfy/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #progress_metric: host=algo-1, completed 6.666666666666667 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583035.9863405, \"EndTime\": 1674583036.0372822, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 4420.0, \"count\": 1, \"min\": 4420, \"max\": 4420}, \"Total Batches Seen\": {\"sum\": 5.0, \"count\": 1, \"min\": 5, \"max\": 5}, \"Max Records Seen Between Resets\": {\"sum\": 1710.0, \"count\": 1, \"min\": 1710, \"max\": 1710}, \"Max Batches Seen Between Resets\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}, \"Reset Count\": {\"sum\": 3.0, \"count\": 1, \"min\": 3, \"max\": 3}, \"Number of Records Since Last Reset\": {\"sum\": 1710.0, \"count\": 1, \"min\": 1710, \"max\": 1710}, \"Number of Batches Since Last Reset\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}}}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #throughput_metric: host=algo-1, train throughput=33500.04829586589 records/second\u001b[0m\n",
      "\u001b[34m[2023-01-24 17:57:16.051] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 6, \"duration\": 13, \"num_examples\": 2, \"num_bytes\": 109440}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.051233, \"EndTime\": 1674583036.0512831, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 0}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9972905883789063, \"count\": 1, \"min\": 0.9972905883789063, \"max\": 0.9972905883789063}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.051335, \"EndTime\": 1674583036.0513473, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 1}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0133220825195313, \"count\": 1, \"min\": 1.0133220825195313, \"max\": 1.0133220825195313}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0513852, \"EndTime\": 1674583036.0513937, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 2}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0024252319335938, \"count\": 1, \"min\": 1.0024252319335938, \"max\": 1.0024252319335938}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0514417, \"EndTime\": 1674583036.0514495, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 3}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9865551147460937, \"count\": 1, \"min\": 0.9865551147460937, \"max\": 0.9865551147460937}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0514822, \"EndTime\": 1674583036.05149, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 4}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9375366821289063, \"count\": 1, \"min\": 0.9375366821289063, \"max\": 0.9375366821289063}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.051524, \"EndTime\": 1674583036.0515316, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 5}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9569931030273438, \"count\": 1, \"min\": 0.9569931030273438, \"max\": 0.9569931030273438}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0515592, \"EndTime\": 1674583036.0515654, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 6}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9227310180664062, \"count\": 1, \"min\": 0.9227310180664062, \"max\": 0.9227310180664062}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0515933, \"EndTime\": 1674583036.0516002, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 7}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8332882690429687, \"count\": 1, \"min\": 0.8332882690429687, \"max\": 0.8332882690429687}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0516322, \"EndTime\": 1674583036.05164, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 8}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9167484130859375, \"count\": 1, \"min\": 0.9167484130859375, \"max\": 0.9167484130859375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0516698, \"EndTime\": 1674583036.0516772, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 9}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9866842041015625, \"count\": 1, \"min\": 0.9866842041015625, \"max\": 0.9866842041015625}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0517094, \"EndTime\": 1674583036.051718, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 10}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0465341796875, \"count\": 1, \"min\": 1.0465341796875, \"max\": 1.0465341796875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0517519, \"EndTime\": 1674583036.05176, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 11}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.01682958984375, \"count\": 1, \"min\": 1.01682958984375, \"max\": 1.01682958984375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0517902, \"EndTime\": 1674583036.0517986, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 12}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8733480834960937, \"count\": 1, \"min\": 0.8733480834960937, \"max\": 0.8733480834960937}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0518327, \"EndTime\": 1674583036.0518403, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 13}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8966958618164063, \"count\": 1, \"min\": 0.8966958618164063, \"max\": 0.8966958618164063}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0518742, \"EndTime\": 1674583036.0518823, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 14}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9434656372070312, \"count\": 1, \"min\": 0.9434656372070312, \"max\": 0.9434656372070312}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0519123, \"EndTime\": 1674583036.0519202, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 15}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8912964477539063, \"count\": 1, \"min\": 0.8912964477539063, \"max\": 0.8912964477539063}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0519512, \"EndTime\": 1674583036.0519593, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 16}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0619422607421876, \"count\": 1, \"min\": 1.0619422607421876, \"max\": 1.0619422607421876}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0519881, \"EndTime\": 1674583036.0519967, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 17}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0278118896484374, \"count\": 1, \"min\": 1.0278118896484374, \"max\": 1.0278118896484374}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0520272, \"EndTime\": 1674583036.0520353, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 18}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0546661376953126, \"count\": 1, \"min\": 1.0546661376953126, \"max\": 1.0546661376953126}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.052066, \"EndTime\": 1674583036.0520742, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 19}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.024513671875, \"count\": 1, \"min\": 1.024513671875, \"max\": 1.024513671875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.052106, \"EndTime\": 1674583036.052114, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 20}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8352794799804687, \"count\": 1, \"min\": 0.8352794799804687, \"max\": 0.8352794799804687}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0521436, \"EndTime\": 1674583036.052152, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 21}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9113561401367187, \"count\": 1, \"min\": 0.9113561401367187, \"max\": 0.9113561401367187}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0521812, \"EndTime\": 1674583036.0521884, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 22}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8895918579101563, \"count\": 1, \"min\": 0.8895918579101563, \"max\": 0.8895918579101563}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0522192, \"EndTime\": 1674583036.0522273, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 23}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8736447143554688, \"count\": 1, \"min\": 0.8736447143554688, \"max\": 0.8736447143554688}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0522578, \"EndTime\": 1674583036.0522654, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 24}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9880286865234374, \"count\": 1, \"min\": 0.9880286865234374, \"max\": 0.9880286865234374}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0522957, \"EndTime\": 1674583036.0523033, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 25}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.949658203125, \"count\": 1, \"min\": 0.949658203125, \"max\": 0.949658203125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0523338, \"EndTime\": 1674583036.0523417, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 26}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9726026000976562, \"count\": 1, \"min\": 0.9726026000976562, \"max\": 0.9726026000976562}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.05237, \"EndTime\": 1674583036.0523775, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 27}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0081511840820312, \"count\": 1, \"min\": 1.0081511840820312, \"max\": 1.0081511840820312}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0524037, \"EndTime\": 1674583036.0524106, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 28}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0249134521484375, \"count\": 1, \"min\": 1.0249134521484375, \"max\": 1.0249134521484375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0524397, \"EndTime\": 1674583036.0524478, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 29}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0154549560546875, \"count\": 1, \"min\": 1.0154549560546875, \"max\": 1.0154549560546875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0524805, \"EndTime\": 1674583036.0524888, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 30}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.2011341552734376, \"count\": 1, \"min\": 1.2011341552734376, \"max\": 1.2011341552734376}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0525181, \"EndTime\": 1674583036.0525255, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 31}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.14403759765625, \"count\": 1, \"min\": 1.14403759765625, \"max\": 1.14403759765625}}}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #quality_metric: host=algo-1, epoch=1, train mse_objective <loss>=0.9972905883789063\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #early_stopping_criteria_metric: host=algo-1, epoch=1, criteria=mse_objective, value=0.8332882690429687\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Epoch 1: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Saving model for epoch: 1\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Saved checkpoint to \"/tmp/tmpzo4ydwq9/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #progress_metric: host=algo-1, completed 13.333333333333334 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0375252, \"EndTime\": 1674583036.0597968, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 6130.0, \"count\": 1, \"min\": 6130, \"max\": 6130}, \"Total Batches Seen\": {\"sum\": 7.0, \"count\": 1, \"min\": 7, \"max\": 7}, \"Max Records Seen Between Resets\": {\"sum\": 1710.0, \"count\": 1, \"min\": 1710, \"max\": 1710}, \"Max Batches Seen Between Resets\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}, \"Reset Count\": {\"sum\": 4.0, \"count\": 1, \"min\": 4, \"max\": 4}, \"Number of Records Since Last Reset\": {\"sum\": 1710.0, \"count\": 1, \"min\": 1710, \"max\": 1710}, \"Number of Batches Since Last Reset\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}}}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #throughput_metric: host=algo-1, train throughput=76429.10253404659 records/second\u001b[0m\n",
      "\u001b[34m[2023-01-24 17:57:16.075] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 8, \"duration\": 14, \"num_examples\": 2, \"num_bytes\": 109440}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.075077, \"EndTime\": 1674583036.075129, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 0}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9884190673828125, \"count\": 1, \"min\": 0.9884190673828125, \"max\": 0.9884190673828125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0751836, \"EndTime\": 1674583036.075195, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 1}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.00347998046875, \"count\": 1, \"min\": 1.00347998046875, \"max\": 1.00347998046875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0752265, \"EndTime\": 1674583036.0752354, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 2}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.993227783203125, \"count\": 1, \"min\": 0.993227783203125, \"max\": 0.993227783203125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0752656, \"EndTime\": 1674583036.0752742, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 3}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9772110595703125, \"count\": 1, \"min\": 0.9772110595703125, \"max\": 0.9772110595703125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0753052, \"EndTime\": 1674583036.0753133, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 4}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0182759399414063, \"count\": 1, \"min\": 1.0182759399414063, \"max\": 1.0182759399414063}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0753412, \"EndTime\": 1674583036.0753493, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 5}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0693206787109375, \"count\": 1, \"min\": 1.0693206787109375, \"max\": 1.0693206787109375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0753794, \"EndTime\": 1674583036.0753865, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 6}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.261870849609375, \"count\": 1, \"min\": 1.261870849609375, \"max\": 1.261870849609375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0754383, \"EndTime\": 1674583036.0754473, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 7}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.3263875732421875, \"count\": 1, \"min\": 1.3263875732421875, \"max\": 1.3263875732421875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.075477, \"EndTime\": 1674583036.0754848, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 8}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9102469482421875, \"count\": 1, \"min\": 0.9102469482421875, \"max\": 0.9102469482421875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.075513, \"EndTime\": 1674583036.0755212, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 9}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9769170532226562, \"count\": 1, \"min\": 0.9769170532226562, \"max\": 0.9769170532226562}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0755513, \"EndTime\": 1674583036.075559, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 10}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.03567919921875, \"count\": 1, \"min\": 1.03567919921875, \"max\": 1.03567919921875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0755875, \"EndTime\": 1674583036.0755944, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 11}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.00671435546875, \"count\": 1, \"min\": 1.00671435546875, \"max\": 1.00671435546875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0756218, \"EndTime\": 1674583036.0756302, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 12}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.1856204833984374, \"count\": 1, \"min\": 1.1856204833984374, \"max\": 1.1856204833984374}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0756605, \"EndTime\": 1674583036.0756686, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 13}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.230684814453125, \"count\": 1, \"min\": 1.230684814453125, \"max\": 1.230684814453125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.075697, \"EndTime\": 1674583036.0757043, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 14}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.044987060546875, \"count\": 1, \"min\": 1.044987060546875, \"max\": 1.044987060546875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0757327, \"EndTime\": 1674583036.07574, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 15}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.206111328125, \"count\": 1, \"min\": 1.206111328125, \"max\": 1.206111328125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.075769, \"EndTime\": 1674583036.0757766, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 16}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0518873291015625, \"count\": 1, \"min\": 1.0518873291015625, \"max\": 1.0518873291015625}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0758052, \"EndTime\": 1674583036.0758128, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 17}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0181625366210938, \"count\": 1, \"min\": 1.0181625366210938, \"max\": 1.0181625366210938}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0758412, \"EndTime\": 1674583036.075849, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 18}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.04305517578125, \"count\": 1, \"min\": 1.04305517578125, \"max\": 1.04305517578125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0758767, \"EndTime\": 1674583036.0758853, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 19}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.015260498046875, \"count\": 1, \"min\": 1.015260498046875, \"max\": 1.015260498046875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0759137, \"EndTime\": 1674583036.075921, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 20}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.3280667724609374, \"count\": 1, \"min\": 1.3280667724609374, \"max\": 1.3280667724609374}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0759497, \"EndTime\": 1674583036.0759573, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 21}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0426873779296875, \"count\": 1, \"min\": 1.0426873779296875, \"max\": 1.0426873779296875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0759857, \"EndTime\": 1674583036.0759938, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 22}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0943408203125, \"count\": 1, \"min\": 1.0943408203125, \"max\": 1.0943408203125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.076022, \"EndTime\": 1674583036.0760298, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 23}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.378572998046875, \"count\": 1, \"min\": 1.378572998046875, \"max\": 1.378572998046875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0760586, \"EndTime\": 1674583036.076066, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 24}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9891775512695312, \"count\": 1, \"min\": 0.9891775512695312, \"max\": 0.9891775512695312}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0760942, \"EndTime\": 1674583036.0761013, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 25}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.954714599609375, \"count\": 1, \"min\": 0.954714599609375, \"max\": 0.954714599609375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0761302, \"EndTime\": 1674583036.0761373, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 26}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9737705688476562, \"count\": 1, \"min\": 0.9737705688476562, \"max\": 0.9737705688476562}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0761652, \"EndTime\": 1674583036.0761735, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 27}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.007256591796875, \"count\": 1, \"min\": 1.007256591796875, \"max\": 1.007256591796875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0762024, \"EndTime\": 1674583036.0762105, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 28}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.5479715576171875, \"count\": 1, \"min\": 1.5479715576171875, \"max\": 1.5479715576171875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0762389, \"EndTime\": 1674583036.0762463, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 29}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.2486680908203125, \"count\": 1, \"min\": 1.2486680908203125, \"max\": 1.2486680908203125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0762758, \"EndTime\": 1674583036.0762835, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 30}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.3141754150390625, \"count\": 1, \"min\": 1.3141754150390625, \"max\": 1.3141754150390625}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.076313, \"EndTime\": 1674583036.076321, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 31}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.590718994140625, \"count\": 1, \"min\": 1.590718994140625, \"max\": 1.590718994140625}}}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #quality_metric: host=algo-1, epoch=2, train mse_objective <loss>=0.9884190673828125\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #early_stopping_criteria_metric: host=algo-1, epoch=2, criteria=mse_objective, value=0.9102469482421875\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Saving model for epoch: 2\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Saved checkpoint to \"/tmp/tmp5prxs9vv/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #progress_metric: host=algo-1, completed 20.0 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.060026, \"EndTime\": 1674583036.0816896, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 7840.0, \"count\": 1, \"min\": 7840, \"max\": 7840}, \"Total Batches Seen\": {\"sum\": 9.0, \"count\": 1, \"min\": 9, \"max\": 9}, \"Max Records Seen Between Resets\": {\"sum\": 1710.0, \"count\": 1, \"min\": 1710, \"max\": 1710}, \"Max Batches Seen Between Resets\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}, \"Reset Count\": {\"sum\": 5.0, \"count\": 1, \"min\": 5, \"max\": 5}, \"Number of Records Since Last Reset\": {\"sum\": 1710.0, \"count\": 1, \"min\": 1710, \"max\": 1710}, \"Number of Batches Since Last Reset\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}}}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #throughput_metric: host=algo-1, train throughput=78574.27519719544 records/second\u001b[0m\n",
      "\u001b[34m[2023-01-24 17:57:16.095] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 10, \"duration\": 13, \"num_examples\": 2, \"num_bytes\": 109440}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0958126, \"EndTime\": 1674583036.095862, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 0}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9777484130859375, \"count\": 1, \"min\": 0.9777484130859375, \"max\": 0.9777484130859375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0959134, \"EndTime\": 1674583036.095925, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 1}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9921375122070313, \"count\": 1, \"min\": 0.9921375122070313, \"max\": 0.9921375122070313}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0959597, \"EndTime\": 1674583036.0959687, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 2}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9820086669921875, \"count\": 1, \"min\": 0.9820086669921875, \"max\": 0.9820086669921875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.095995, \"EndTime\": 1674583036.0960019, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 3}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9669618530273437, \"count\": 1, \"min\": 0.9669618530273437, \"max\": 0.9669618530273437}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0960298, \"EndTime\": 1674583036.0960374, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 4}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.875025146484375, \"count\": 1, \"min\": 0.875025146484375, \"max\": 0.875025146484375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0960658, \"EndTime\": 1674583036.0960743, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 5}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8599099731445312, \"count\": 1, \"min\": 0.8599099731445312, \"max\": 0.8599099731445312}}}\u001b[0m\n",
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      "\u001b[34m#metrics {\"StartTime\": 1674583036.0962322, \"EndTime\": 1674583036.096241, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 9}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9664207763671875, \"count\": 1, \"min\": 0.9664207763671875, \"max\": 0.9664207763671875}}}\u001b[0m\n",
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      "\u001b[34m#metrics {\"StartTime\": 1674583036.096416, \"EndTime\": 1674583036.0964227, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 14}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8634263305664063, \"count\": 1, \"min\": 0.8634263305664063, \"max\": 0.8634263305664063}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0964544, \"EndTime\": 1674583036.0964618, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 15}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.861343505859375, \"count\": 1, \"min\": 0.861343505859375, \"max\": 0.861343505859375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0964901, \"EndTime\": 1674583036.096497, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 16}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0402283935546874, \"count\": 1, \"min\": 1.0402283935546874, \"max\": 1.0402283935546874}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0965261, \"EndTime\": 1674583036.096533, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 17}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0064887084960938, \"count\": 1, \"min\": 1.0064887084960938, \"max\": 1.0064887084960938}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0965645, \"EndTime\": 1674583036.0965724, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 18}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0307548828125, \"count\": 1, \"min\": 1.0307548828125, \"max\": 1.0307548828125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0966024, \"EndTime\": 1674583036.09661, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 19}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.004136962890625, \"count\": 1, \"min\": 1.004136962890625, \"max\": 1.004136962890625}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0966427, \"EndTime\": 1674583036.0966508, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 20}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8229086303710937, \"count\": 1, \"min\": 0.8229086303710937, \"max\": 0.8229086303710937}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0966809, \"EndTime\": 1674583036.0966892, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 21}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8698284912109375, \"count\": 1, \"min\": 0.8698284912109375, \"max\": 0.8698284912109375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.096721, \"EndTime\": 1674583036.096729, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 22}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.833866455078125, \"count\": 1, \"min\": 0.833866455078125, \"max\": 0.833866455078125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0967596, \"EndTime\": 1674583036.0967677, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 23}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.833340576171875, \"count\": 1, \"min\": 0.833340576171875, \"max\": 0.833340576171875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0967977, \"EndTime\": 1674583036.0968053, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 24}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9883564453125, \"count\": 1, \"min\": 0.9883564453125, \"max\": 0.9883564453125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0968316, \"EndTime\": 1674583036.096839, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 25}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.957392333984375, \"count\": 1, \"min\": 0.957392333984375, \"max\": 0.957392333984375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0968688, \"EndTime\": 1674583036.0968776, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 26}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9734545288085937, \"count\": 1, \"min\": 0.9734545288085937, \"max\": 0.9734545288085937}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0969124, \"EndTime\": 1674583036.0969203, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 27}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0049672241210938, \"count\": 1, \"min\": 1.0049672241210938, \"max\": 1.0049672241210938}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.096951, \"EndTime\": 1674583036.0969582, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 28}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0302689208984375, \"count\": 1, \"min\": 1.0302689208984375, \"max\": 1.0302689208984375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0969887, \"EndTime\": 1674583036.0969963, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 29}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0199549560546874, \"count\": 1, \"min\": 1.0199549560546874, \"max\": 1.0199549560546874}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0970263, \"EndTime\": 1674583036.0970335, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 30}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9740653686523437, \"count\": 1, \"min\": 0.9740653686523437, \"max\": 0.9740653686523437}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0970657, \"EndTime\": 1674583036.0970736, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 31}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9800145874023437, \"count\": 1, \"min\": 0.9800145874023437, \"max\": 0.9800145874023437}}}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #quality_metric: host=algo-1, epoch=3, train mse_objective <loss>=0.9777484130859375\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #early_stopping_criteria_metric: host=algo-1, epoch=3, criteria=mse_objective, value=0.8229086303710937\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Epoch 3: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Saving model for epoch: 3\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Saved checkpoint to \"/tmp/tmp47vrtvhz/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #progress_metric: host=algo-1, completed 26.666666666666668 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.0819042, \"EndTime\": 1674583036.102515, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 9550.0, \"count\": 1, \"min\": 9550, \"max\": 9550}, \"Total Batches Seen\": {\"sum\": 11.0, \"count\": 1, \"min\": 11, \"max\": 11}, \"Max Records Seen Between Resets\": {\"sum\": 1710.0, \"count\": 1, \"min\": 1710, \"max\": 1710}, \"Max Batches Seen Between Resets\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}, \"Reset Count\": {\"sum\": 6.0, \"count\": 1, \"min\": 6, \"max\": 6}, \"Number of Records Since Last Reset\": {\"sum\": 1710.0, \"count\": 1, \"min\": 1710, \"max\": 1710}, \"Number of Batches Since Last Reset\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}}}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #throughput_metric: host=algo-1, train throughput=82588.80784864641 records/second\u001b[0m\n",
      "\u001b[34m[2023-01-24 17:57:16.115] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 12, \"duration\": 12, \"num_examples\": 2, \"num_bytes\": 109440}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.115482, \"EndTime\": 1674583036.1155307, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 0}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9689320068359375, \"count\": 1, \"min\": 0.9689320068359375, \"max\": 0.9689320068359375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1155825, \"EndTime\": 1674583036.1155927, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 1}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9824224243164063, \"count\": 1, \"min\": 0.9824224243164063, \"max\": 0.9824224243164063}}}\u001b[0m\n",
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      "\u001b[34m#metrics {\"StartTime\": 1674583036.1156657, \"EndTime\": 1674583036.1156738, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 3}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9578521728515625, \"count\": 1, \"min\": 0.9578521728515625, \"max\": 0.9578521728515625}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1157062, \"EndTime\": 1674583036.1157148, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 4}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8906975708007813, \"count\": 1, \"min\": 0.8906975708007813, \"max\": 0.8906975708007813}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1157484, \"EndTime\": 1674583036.1157565, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 5}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9156237182617187, \"count\": 1, \"min\": 0.9156237182617187, \"max\": 0.9156237182617187}}}\u001b[0m\n",
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      "\u001b[34m#metrics {\"StartTime\": 1674583036.1159077, \"EndTime\": 1674583036.1159158, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 9}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9569127807617187, \"count\": 1, \"min\": 0.9569127807617187, \"max\": 0.9569127807617187}}}\u001b[0m\n",
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      "\u001b[34m#metrics {\"StartTime\": 1674583036.116727, \"EndTime\": 1674583036.116735, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 30}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0468834228515624, \"count\": 1, \"min\": 1.0468834228515624, \"max\": 1.0468834228515624}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.116758, \"EndTime\": 1674583036.1167653, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 31}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.177399658203125, \"count\": 1, \"min\": 1.177399658203125, \"max\": 1.177399658203125}}}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #quality_metric: host=algo-1, epoch=4, train mse_objective <loss>=0.9689320068359375\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #early_stopping_criteria_metric: host=algo-1, epoch=4, criteria=mse_objective, value=0.87693701171875\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Saving model for epoch: 4\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Saved checkpoint to \"/tmp/tmpyh_n34e0/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #progress_metric: host=algo-1, completed 33.333333333333336 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1027162, \"EndTime\": 1674583036.1221, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 11260.0, \"count\": 1, \"min\": 11260, \"max\": 11260}, \"Total Batches Seen\": {\"sum\": 13.0, \"count\": 1, \"min\": 13, \"max\": 13}, \"Max Records Seen Between Resets\": {\"sum\": 1710.0, \"count\": 1, \"min\": 1710, \"max\": 1710}, \"Max Batches Seen Between Resets\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}, \"Reset Count\": {\"sum\": 7.0, \"count\": 1, \"min\": 7, \"max\": 7}, \"Number of Records Since Last Reset\": {\"sum\": 1710.0, \"count\": 1, \"min\": 1710, \"max\": 1710}, \"Number of Batches Since Last Reset\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}}}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #throughput_metric: host=algo-1, train throughput=87833.98655351042 records/second\u001b[0m\n",
      "\u001b[34m[2023-01-24 17:57:16.135] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 14, \"duration\": 12, \"num_examples\": 2, \"num_bytes\": 109440}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.135286, \"EndTime\": 1674583036.1353307, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 0}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9601898193359375, \"count\": 1, \"min\": 0.9601898193359375, \"max\": 0.9601898193359375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1353767, \"EndTime\": 1674583036.1353877, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 1}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9728319091796875, \"count\": 1, \"min\": 0.9728319091796875, \"max\": 0.9728319091796875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1354432, \"EndTime\": 1674583036.135453, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 2}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9636621704101562, \"count\": 1, \"min\": 0.9636621704101562, \"max\": 0.9636621704101562}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.135482, \"EndTime\": 1674583036.1354907, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 3}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9490821533203125, \"count\": 1, \"min\": 0.9490821533203125, \"max\": 0.9490821533203125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1355202, \"EndTime\": 1674583036.1355276, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 4}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9742615966796875, \"count\": 1, \"min\": 0.9742615966796875, \"max\": 0.9742615966796875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1355586, \"EndTime\": 1674583036.1355658, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 5}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.99816845703125, \"count\": 1, \"min\": 0.99816845703125, \"max\": 0.99816845703125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1355946, \"EndTime\": 1674583036.1356018, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 6}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.121197021484375, \"count\": 1, \"min\": 1.121197021484375, \"max\": 1.121197021484375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1356285, \"EndTime\": 1674583036.1356354, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 7}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.15507666015625, \"count\": 1, \"min\": 1.15507666015625, \"max\": 1.15507666015625}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1356661, \"EndTime\": 1674583036.1356738, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 8}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8898614501953125, \"count\": 1, \"min\": 0.8898614501953125, \"max\": 0.8898614501953125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.135701, \"EndTime\": 1674583036.1357083, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 9}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9478294677734375, \"count\": 1, \"min\": 0.9478294677734375, \"max\": 0.9478294677734375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1357362, \"EndTime\": 1674583036.135744, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 10}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0020751953125, \"count\": 1, \"min\": 1.0020751953125, \"max\": 1.0020751953125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1357725, \"EndTime\": 1674583036.1357808, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 11}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9757818603515624, \"count\": 1, \"min\": 0.9757818603515624, \"max\": 0.9757818603515624}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1358101, \"EndTime\": 1674583036.135818, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 12}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0522491455078125, \"count\": 1, \"min\": 1.0522491455078125, \"max\": 1.0522491455078125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1358473, \"EndTime\": 1674583036.1358554, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 13}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0985032958984375, \"count\": 1, \"min\": 1.0985032958984375, \"max\": 1.0985032958984375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1358855, \"EndTime\": 1674583036.135894, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 14}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.990612060546875, \"count\": 1, \"min\": 0.990612060546875, \"max\": 0.990612060546875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1359227, \"EndTime\": 1674583036.13593, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 15}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0428438720703126, \"count\": 1, \"min\": 1.0428438720703126, \"max\": 1.0428438720703126}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1359603, \"EndTime\": 1674583036.1359675, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 16}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.020805908203125, \"count\": 1, \"min\": 1.020805908203125, \"max\": 1.020805908203125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1359956, \"EndTime\": 1674583036.136003, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 17}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9873536376953125, \"count\": 1, \"min\": 0.9873536376953125, \"max\": 0.9873536376953125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1360314, \"EndTime\": 1674583036.1360393, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 18}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0085670166015626, \"count\": 1, \"min\": 1.0085670166015626, \"max\": 1.0085670166015626}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1360626, \"EndTime\": 1674583036.1360695, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 19}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9860531005859375, \"count\": 1, \"min\": 0.9860531005859375, \"max\": 0.9860531005859375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1360974, \"EndTime\": 1674583036.1361048, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 20}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.2102254638671874, \"count\": 1, \"min\": 1.2102254638671874, \"max\": 1.2102254638671874}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1361327, \"EndTime\": 1674583036.1361406, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 21}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9662091674804687, \"count\": 1, \"min\": 0.9662091674804687, \"max\": 0.9662091674804687}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.136167, \"EndTime\": 1674583036.1361744, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 22}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9881104736328125, \"count\": 1, \"min\": 0.9881104736328125, \"max\": 0.9881104736328125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1362026, \"EndTime\": 1674583036.13621, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 23}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.20029150390625, \"count\": 1, \"min\": 1.20029150390625, \"max\": 1.20029150390625}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1362357, \"EndTime\": 1674583036.1362438, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 24}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9885158081054688, \"count\": 1, \"min\": 0.9885158081054688, \"max\": 0.9885158081054688}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1362727, \"EndTime\": 1674583036.13628, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 25}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9668958740234375, \"count\": 1, \"min\": 0.9668958740234375, \"max\": 0.9668958740234375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1363091, \"EndTime\": 1674583036.136317, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 26}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.97559326171875, \"count\": 1, \"min\": 0.97559326171875, \"max\": 0.97559326171875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1363428, \"EndTime\": 1674583036.13635, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 27}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0030942993164063, \"count\": 1, \"min\": 1.0030942993164063, \"max\": 1.0030942993164063}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1363788, \"EndTime\": 1674583036.1363854, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 28}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.3437978515625, \"count\": 1, \"min\": 1.3437978515625, \"max\": 1.3437978515625}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1364138, \"EndTime\": 1674583036.1364214, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 29}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.1775517578125, \"count\": 1, \"min\": 1.1775517578125, \"max\": 1.1775517578125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1364489, \"EndTime\": 1674583036.1364572, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 30}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.11153271484375, \"count\": 1, \"min\": 1.11153271484375, \"max\": 1.11153271484375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1364846, \"EndTime\": 1674583036.1364915, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 31}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.3055399169921875, \"count\": 1, \"min\": 1.3055399169921875, \"max\": 1.3055399169921875}}}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #quality_metric: host=algo-1, epoch=5, train mse_objective <loss>=0.9601898193359375\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #early_stopping_criteria_metric: host=algo-1, epoch=5, criteria=mse_objective, value=0.8898614501953125\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Saving model for epoch: 5\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Saved checkpoint to \"/tmp/tmpbr6ca610/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #progress_metric: host=algo-1, completed 40.0 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1222894, \"EndTime\": 1674583036.141267, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 12970.0, \"count\": 1, \"min\": 12970, \"max\": 12970}, \"Total Batches Seen\": {\"sum\": 15.0, \"count\": 1, \"min\": 15, \"max\": 15}, \"Max Records Seen Between Resets\": {\"sum\": 1710.0, \"count\": 1, \"min\": 1710, \"max\": 1710}, \"Max Batches Seen Between Resets\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}, \"Reset Count\": {\"sum\": 8.0, \"count\": 1, \"min\": 8, \"max\": 8}, \"Number of Records Since Last Reset\": {\"sum\": 1710.0, \"count\": 1, \"min\": 1710, \"max\": 1710}, \"Number of Batches Since Last Reset\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}}}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #throughput_metric: host=algo-1, train throughput=89704.82827625885 records/second\u001b[0m\n",
      "\u001b[34m[2023-01-24 17:57:16.155] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 16, \"duration\": 14, \"num_examples\": 2, \"num_bytes\": 109440}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1555934, \"EndTime\": 1674583036.1556365, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 0}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9511286010742187, \"count\": 1, \"min\": 0.9511286010742187, \"max\": 0.9511286010742187}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1556761, \"EndTime\": 1674583036.155684, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 1}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9630125732421875, \"count\": 1, \"min\": 0.9630125732421875, \"max\": 0.9630125732421875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.155712, \"EndTime\": 1674583036.1557178, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 2}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9541261596679688, \"count\": 1, \"min\": 0.9541261596679688, \"max\": 0.9541261596679688}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.15574, \"EndTime\": 1674583036.1557448, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 3}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9402029418945312, \"count\": 1, \"min\": 0.9402029418945312, \"max\": 0.9402029418945312}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1557655, \"EndTime\": 1674583036.1557705, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 4}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8762369995117187, \"count\": 1, \"min\": 0.8762369995117187, \"max\": 0.8762369995117187}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1557908, \"EndTime\": 1674583036.1557958, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 5}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.88712646484375, \"count\": 1, \"min\": 0.88712646484375, \"max\": 0.88712646484375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1558158, \"EndTime\": 1674583036.1558208, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 6}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9278828125, \"count\": 1, \"min\": 0.9278828125, \"max\": 0.9278828125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1558454, \"EndTime\": 1674583036.1558502, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 7}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9337889404296875, \"count\": 1, \"min\": 0.9337889404296875, \"max\": 0.9337889404296875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.155871, \"EndTime\": 1674583036.155876, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 8}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8834804077148437, \"count\": 1, \"min\": 0.8834804077148437, \"max\": 0.8834804077148437}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1558983, \"EndTime\": 1674583036.1559033, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 9}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9386570434570313, \"count\": 1, \"min\": 0.9386570434570313, \"max\": 0.9386570434570313}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1559234, \"EndTime\": 1674583036.1559286, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 10}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9912820434570313, \"count\": 1, \"min\": 0.9912820434570313, \"max\": 0.9912820434570313}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1559508, \"EndTime\": 1674583036.1559558, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 11}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.965923828125, \"count\": 1, \"min\": 0.965923828125, \"max\": 0.965923828125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1559753, \"EndTime\": 1674583036.1559803, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 12}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8724654541015625, \"count\": 1, \"min\": 0.8724654541015625, \"max\": 0.8724654541015625}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1560006, \"EndTime\": 1674583036.1560056, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 13}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9094462890625, \"count\": 1, \"min\": 0.9094462890625, \"max\": 0.9094462890625}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1560254, \"EndTime\": 1674583036.1560304, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 14}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.882365478515625, \"count\": 1, \"min\": 0.882365478515625, \"max\": 0.882365478515625}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1560502, \"EndTime\": 1674583036.156055, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 15}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.863884765625, \"count\": 1, \"min\": 0.863884765625, \"max\": 0.863884765625}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1560745, \"EndTime\": 1674583036.1560795, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 16}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0109559936523438, \"count\": 1, \"min\": 1.0109559936523438, \"max\": 1.0109559936523438}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1560996, \"EndTime\": 1674583036.1561048, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 17}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9774775390625, \"count\": 1, \"min\": 0.9774775390625, \"max\": 0.9774775390625}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1561248, \"EndTime\": 1674583036.15613, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 18}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.997639404296875, \"count\": 1, \"min\": 0.997639404296875, \"max\": 0.997639404296875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1561494, \"EndTime\": 1674583036.1561544, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 19}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9767018432617187, \"count\": 1, \"min\": 0.9767018432617187, \"max\": 0.9767018432617187}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1561747, \"EndTime\": 1674583036.1561794, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 20}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0225924072265624, \"count\": 1, \"min\": 1.0225924072265624, \"max\": 1.0225924072265624}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1561995, \"EndTime\": 1674583036.1562042, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 21}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9056624755859375, \"count\": 1, \"min\": 0.9056624755859375, \"max\": 0.9056624755859375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1562245, \"EndTime\": 1674583036.1562293, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 22}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9061951904296875, \"count\": 1, \"min\": 0.9061951904296875, \"max\": 0.9061951904296875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1562514, \"EndTime\": 1674583036.1562562, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 23}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0028873901367188, \"count\": 1, \"min\": 1.0028873901367188, \"max\": 1.0028873901367188}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1562762, \"EndTime\": 1674583036.156281, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 24}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9876197509765625, \"count\": 1, \"min\": 0.9876197509765625, \"max\": 0.9876197509765625}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.156301, \"EndTime\": 1674583036.156306, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 25}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9708438110351563, \"count\": 1, \"min\": 0.9708438110351563, \"max\": 0.9708438110351563}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1563272, \"EndTime\": 1674583036.156332, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 26}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9765747680664062, \"count\": 1, \"min\": 0.9765747680664062, \"max\": 0.9765747680664062}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.156352, \"EndTime\": 1674583036.1563568, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 27}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0016988525390624, \"count\": 1, \"min\": 1.0016988525390624, \"max\": 1.0016988525390624}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1563768, \"EndTime\": 1674583036.1563816, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 28}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.127980224609375, \"count\": 1, \"min\": 1.127980224609375, \"max\": 1.127980224609375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1564016, \"EndTime\": 1674583036.1564069, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 29}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0545081787109376, \"count\": 1, \"min\": 1.0545081787109376, \"max\": 1.0545081787109376}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1564286, \"EndTime\": 1674583036.1564336, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 30}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.002241943359375, \"count\": 1, \"min\": 1.002241943359375, \"max\": 1.002241943359375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1564553, \"EndTime\": 1674583036.1564603, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 31}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.119625, \"count\": 1, \"min\": 1.119625, \"max\": 1.119625}}}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #quality_metric: host=algo-1, epoch=6, train mse_objective <loss>=0.9511286010742187\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #early_stopping_criteria_metric: host=algo-1, epoch=6, criteria=mse_objective, value=0.863884765625\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Saving model for epoch: 6\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Saved checkpoint to \"/tmp/tmpx188afe2/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #progress_metric: host=algo-1, completed 46.666666666666664 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.141453, \"EndTime\": 1674583036.1619039, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 14680.0, \"count\": 1, \"min\": 14680, \"max\": 14680}, \"Total Batches Seen\": {\"sum\": 17.0, \"count\": 1, \"min\": 17, \"max\": 17}, \"Max Records Seen Between Resets\": {\"sum\": 1710.0, \"count\": 1, \"min\": 1710, \"max\": 1710}, \"Max Batches Seen Between Resets\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}, \"Reset Count\": {\"sum\": 9.0, \"count\": 1, \"min\": 9, \"max\": 9}, \"Number of Records Since Last Reset\": {\"sum\": 1710.0, \"count\": 1, \"min\": 1710, \"max\": 1710}, \"Number of Batches Since Last Reset\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}}}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #throughput_metric: host=algo-1, train throughput=83286.9980839575 records/second\u001b[0m\n",
      "\u001b[34m[2023-01-24 17:57:16.176] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 18, \"duration\": 14, \"num_examples\": 2, \"num_bytes\": 109440}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1770036, \"EndTime\": 1674583036.1770487, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 0}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.942655517578125, \"count\": 1, \"min\": 0.942655517578125, \"max\": 0.942655517578125}}}\u001b[0m\n",
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      "\u001b[34m#metrics {\"StartTime\": 1674583036.1771855, \"EndTime\": 1674583036.1771903, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 4}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8095338745117188, \"count\": 1, \"min\": 0.8095338745117188, \"max\": 0.8095338745117188}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1772134, \"EndTime\": 1674583036.1772182, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 5}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.83000927734375, \"count\": 1, \"min\": 0.83000927734375, \"max\": 0.83000927734375}}}\u001b[0m\n",
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      "\u001b[34m#metrics {\"StartTime\": 1674583036.1777894, \"EndTime\": 1674583036.1777945, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 27}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.000494873046875, \"count\": 1, \"min\": 1.000494873046875, \"max\": 1.000494873046875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1778145, \"EndTime\": 1674583036.1778195, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 28}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.988239990234375, \"count\": 1, \"min\": 0.988239990234375, \"max\": 0.988239990234375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1778395, \"EndTime\": 1674583036.1778445, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 29}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0125249633789062, \"count\": 1, \"min\": 1.0125249633789062, \"max\": 1.0125249633789062}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1778648, \"EndTime\": 1674583036.1778698, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 30}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0057586669921874, \"count\": 1, \"min\": 1.0057586669921874, \"max\": 1.0057586669921874}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1778913, \"EndTime\": 1674583036.1778965, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 31}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0135416259765626, \"count\": 1, \"min\": 1.0135416259765626, \"max\": 1.0135416259765626}}}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #quality_metric: host=algo-1, epoch=7, train mse_objective <loss>=0.942655517578125\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #early_stopping_criteria_metric: host=algo-1, epoch=7, criteria=mse_objective, value=0.794777587890625\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Epoch 7: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Saving model for epoch: 7\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Saved checkpoint to \"/tmp/tmpiklzzku3/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #progress_metric: host=algo-1, completed 53.333333333333336 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.16205, \"EndTime\": 1674583036.1846437, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 16390.0, \"count\": 1, \"min\": 16390, \"max\": 16390}, \"Total Batches Seen\": {\"sum\": 19.0, \"count\": 1, \"min\": 19, \"max\": 19}, \"Max Records Seen Between Resets\": {\"sum\": 1710.0, \"count\": 1, \"min\": 1710, \"max\": 1710}, \"Max Batches Seen Between Resets\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}, \"Reset Count\": {\"sum\": 10.0, \"count\": 1, \"min\": 10, \"max\": 10}, \"Number of Records Since Last Reset\": {\"sum\": 1710.0, \"count\": 1, \"min\": 1710, \"max\": 1710}, \"Number of Batches Since Last Reset\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}}}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #throughput_metric: host=algo-1, train throughput=75427.60222110046 records/second\u001b[0m\n",
      "\u001b[34m[2023-01-24 17:57:16.200] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 20, \"duration\": 15, \"num_examples\": 2, \"num_bytes\": 109440}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2006357, \"EndTime\": 1674583036.2006834, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 0}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.934990234375, \"count\": 1, \"min\": 0.934990234375, \"max\": 0.934990234375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2007775, \"EndTime\": 1674583036.2007887, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 1}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9453148803710938, \"count\": 1, \"min\": 0.9453148803710938, \"max\": 0.9453148803710938}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2008271, \"EndTime\": 1674583036.200834, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 2}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9370311279296875, \"count\": 1, \"min\": 0.9370311279296875, \"max\": 0.9370311279296875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.200857, \"EndTime\": 1674583036.200862, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 3}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9241635131835938, \"count\": 1, \"min\": 0.9241635131835938, \"max\": 0.9241635131835938}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.200897, \"EndTime\": 1674583036.200903, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 4}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.849623046875, \"count\": 1, \"min\": 0.849623046875, \"max\": 0.849623046875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2009385, \"EndTime\": 1674583036.2009547, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 5}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.874027099609375, \"count\": 1, \"min\": 0.874027099609375, \"max\": 0.874027099609375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2009773, \"EndTime\": 1674583036.2009828, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 6}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.906463623046875, \"count\": 1, \"min\": 0.906463623046875, \"max\": 0.906463623046875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2010033, \"EndTime\": 1674583036.2010374, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 7}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8879197387695312, \"count\": 1, \"min\": 0.8879197387695312, \"max\": 0.8879197387695312}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2010834, \"EndTime\": 1674583036.2010894, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 8}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8725816040039063, \"count\": 1, \"min\": 0.8725816040039063, \"max\": 0.8725816040039063}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.201121, \"EndTime\": 1674583036.2011263, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 9}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9219440307617187, \"count\": 1, \"min\": 0.9219440307617187, \"max\": 0.9219440307617187}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2011576, \"EndTime\": 1674583036.201163, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 10}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.97163720703125, \"count\": 1, \"min\": 0.97163720703125, \"max\": 0.97163720703125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2011838, \"EndTime\": 1674583036.2011888, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 11}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.94805517578125, \"count\": 1, \"min\": 0.94805517578125, \"max\": 0.94805517578125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2012212, \"EndTime\": 1674583036.201227, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 12}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8950509033203125, \"count\": 1, \"min\": 0.8950509033203125, \"max\": 0.8950509033203125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.201247, \"EndTime\": 1674583036.201252, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 13}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.901345703125, \"count\": 1, \"min\": 0.901345703125, \"max\": 0.901345703125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2012982, \"EndTime\": 1674583036.2013164, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 14}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8618399658203125, \"count\": 1, \"min\": 0.8618399658203125, \"max\": 0.8618399658203125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2013555, \"EndTime\": 1674583036.201361, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 15}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8979022827148437, \"count\": 1, \"min\": 0.8979022827148437, \"max\": 0.8979022827148437}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2013915, \"EndTime\": 1674583036.201397, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 16}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9933279418945312, \"count\": 1, \"min\": 0.9933279418945312, \"max\": 0.9933279418945312}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.201428, \"EndTime\": 1674583036.2014334, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 17}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9598604736328125, \"count\": 1, \"min\": 0.9598604736328125, \"max\": 0.9598604736328125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2014642, \"EndTime\": 1674583036.2014697, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 18}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9774879760742188, \"count\": 1, \"min\": 0.9774879760742188, \"max\": 0.9774879760742188}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2014904, \"EndTime\": 1674583036.2014956, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 19}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9598736572265625, \"count\": 1, \"min\": 0.9598736572265625, \"max\": 0.9598736572265625}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.201528, \"EndTime\": 1674583036.201534, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 20}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.950815673828125, \"count\": 1, \"min\": 0.950815673828125, \"max\": 0.950815673828125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2015538, \"EndTime\": 1674583036.2015588, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 21}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9663871459960938, \"count\": 1, \"min\": 0.9663871459960938, \"max\": 0.9663871459960938}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2016163, \"EndTime\": 1674583036.2016227, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 22}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.992750244140625, \"count\": 1, \"min\": 0.992750244140625, \"max\": 0.992750244140625}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2016537, \"EndTime\": 1674583036.2016592, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 23}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.949353759765625, \"count\": 1, \"min\": 0.949353759765625, \"max\": 0.949353759765625}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2016904, \"EndTime\": 1674583036.2016957, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 24}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.986981201171875, \"count\": 1, \"min\": 0.986981201171875, \"max\": 0.986981201171875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2017298, \"EndTime\": 1674583036.2017357, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 25}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9795442504882812, \"count\": 1, \"min\": 0.9795442504882812, \"max\": 0.9795442504882812}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.201774, \"EndTime\": 1674583036.2017791, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 26}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9807500610351563, \"count\": 1, \"min\": 0.9807500610351563, \"max\": 0.9807500610351563}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2018094, \"EndTime\": 1674583036.2018151, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 27}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9994667358398438, \"count\": 1, \"min\": 0.9994667358398438, \"max\": 0.9994667358398438}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2018359, \"EndTime\": 1674583036.201851, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 28}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0808746337890625, \"count\": 1, \"min\": 1.0808746337890625, \"max\": 1.0808746337890625}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.201872, \"EndTime\": 1674583036.2018774, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 29}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.076340576171875, \"count\": 1, \"min\": 1.076340576171875, \"max\": 1.076340576171875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2019093, \"EndTime\": 1674583036.2019145, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 30}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.1285750732421875, \"count\": 1, \"min\": 1.1285750732421875, \"max\": 1.1285750732421875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2019346, \"EndTime\": 1674583036.2019393, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 31}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.1332666015625, \"count\": 1, \"min\": 1.1332666015625, \"max\": 1.1332666015625}}}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #quality_metric: host=algo-1, epoch=8, train mse_objective <loss>=0.934990234375\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #early_stopping_criteria_metric: host=algo-1, epoch=8, criteria=mse_objective, value=0.849623046875\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Saving model for epoch: 8\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Saved checkpoint to \"/tmp/tmp2d80rsj9/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #progress_metric: host=algo-1, completed 60.0 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.1847966, \"EndTime\": 1674583036.208061, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 18100.0, \"count\": 1, \"min\": 18100, \"max\": 18100}, \"Total Batches Seen\": {\"sum\": 21.0, \"count\": 1, \"min\": 21, \"max\": 21}, \"Max Records Seen Between Resets\": {\"sum\": 1710.0, \"count\": 1, \"min\": 1710, \"max\": 1710}, \"Max Batches Seen Between Resets\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}, \"Reset Count\": {\"sum\": 11.0, \"count\": 1, \"min\": 11, \"max\": 11}, \"Number of Records Since Last Reset\": {\"sum\": 1710.0, \"count\": 1, \"min\": 1710, \"max\": 1710}, \"Number of Batches Since Last Reset\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}}}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #throughput_metric: host=algo-1, train throughput=73127.37527911173 records/second\u001b[0m\n",
      "\u001b[34m[2023-01-24 17:57:16.222] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 22, \"duration\": 14, \"num_examples\": 2, \"num_bytes\": 109440}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.222572, \"EndTime\": 1674583036.2226157, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 0}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9275733032226563, \"count\": 1, \"min\": 0.9275733032226563, \"max\": 0.9275733032226563}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2227092, \"EndTime\": 1674583036.2227194, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 1}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9370055541992187, \"count\": 1, \"min\": 0.9370055541992187, \"max\": 0.9370055541992187}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2227576, \"EndTime\": 1674583036.2227643, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 2}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9292420654296875, \"count\": 1, \"min\": 0.9292420654296875, \"max\": 0.9292420654296875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.222786, \"EndTime\": 1674583036.2228265, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 3}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9165977783203125, \"count\": 1, \"min\": 0.9165977783203125, \"max\": 0.9165977783203125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.222852, \"EndTime\": 1674583036.222858, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 4}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.852243896484375, \"count\": 1, \"min\": 0.852243896484375, \"max\": 0.852243896484375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2229025, \"EndTime\": 1674583036.2229095, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 5}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8617423095703125, \"count\": 1, \"min\": 0.8617423095703125, \"max\": 0.8617423095703125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2229574, \"EndTime\": 1674583036.2229638, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 6}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9569478759765625, \"count\": 1, \"min\": 0.9569478759765625, \"max\": 0.9569478759765625}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2229846, \"EndTime\": 1674583036.2229896, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 7}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.00322998046875, \"count\": 1, \"min\": 1.00322998046875, \"max\": 1.00322998046875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2230546, \"EndTime\": 1674583036.2230713, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 8}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8675989990234375, \"count\": 1, \"min\": 0.8675989990234375, \"max\": 0.8675989990234375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2230942, \"EndTime\": 1674583036.2230995, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 9}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9140508422851562, \"count\": 1, \"min\": 0.9140508422851562, \"max\": 0.9140508422851562}}}\u001b[0m\n",
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      "\u001b[34m#metrics {\"StartTime\": 1674583036.2232327, \"EndTime\": 1674583036.223239, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 12}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9280791625976562, \"count\": 1, \"min\": 0.9280791625976562, \"max\": 0.9280791625976562}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2232597, \"EndTime\": 1674583036.2232647, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 13}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9454419555664062, \"count\": 1, \"min\": 0.9454419555664062, \"max\": 0.9454419555664062}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2233229, \"EndTime\": 1674583036.2233295, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 14}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8542842407226563, \"count\": 1, \"min\": 0.8542842407226563, \"max\": 0.8542842407226563}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.223361, \"EndTime\": 1674583036.2233665, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 15}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9306947021484375, \"count\": 1, \"min\": 0.9306947021484375, \"max\": 0.9306947021484375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2235315, \"EndTime\": 1674583036.223539, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 16}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.985125, \"count\": 1, \"min\": 0.985125, \"max\": 0.985125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.223574, \"EndTime\": 1674583036.2235806, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 17}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.951912109375, \"count\": 1, \"min\": 0.951912109375, \"max\": 0.951912109375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2236018, \"EndTime\": 1674583036.223621, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 18}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9679307250976562, \"count\": 1, \"min\": 0.9679307250976562, \"max\": 0.9679307250976562}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.223642, \"EndTime\": 1674583036.2236476, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 19}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9520965576171875, \"count\": 1, \"min\": 0.9520965576171875, \"max\": 0.9520965576171875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2236784, \"EndTime\": 1674583036.2236838, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 20}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0427532958984376, \"count\": 1, \"min\": 1.0427532958984376, \"max\": 1.0427532958984376}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2237253, \"EndTime\": 1674583036.2237308, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 21}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.957443115234375, \"count\": 1, \"min\": 0.957443115234375, \"max\": 0.957443115234375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.223751, \"EndTime\": 1674583036.2237663, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 22}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9761554565429688, \"count\": 1, \"min\": 0.9761554565429688, \"max\": 0.9761554565429688}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2237868, \"EndTime\": 1674583036.2237918, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 23}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0840489501953126, \"count\": 1, \"min\": 1.0840489501953126, \"max\": 1.0840489501953126}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2238233, \"EndTime\": 1674583036.2238293, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 24}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9871057739257812, \"count\": 1, \"min\": 0.9871057739257812, \"max\": 0.9871057739257812}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2238498, \"EndTime\": 1674583036.2238545, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 25}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9841270141601562, \"count\": 1, \"min\": 0.9841270141601562, \"max\": 0.9841270141601562}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2239158, \"EndTime\": 1674583036.2239223, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 26}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.98348974609375, \"count\": 1, \"min\": 0.98348974609375, \"max\": 0.98348974609375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.223954, \"EndTime\": 1674583036.2239592, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 27}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.99807568359375, \"count\": 1, \"min\": 0.99807568359375, \"max\": 0.99807568359375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2239897, \"EndTime\": 1674583036.2239952, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 28}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.203774169921875, \"count\": 1, \"min\": 1.203774169921875, \"max\": 1.203774169921875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2240255, \"EndTime\": 1674583036.224031, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 29}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.06387548828125, \"count\": 1, \"min\": 1.06387548828125, \"max\": 1.06387548828125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2240617, \"EndTime\": 1674583036.224067, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 30}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.155359130859375, \"count\": 1, \"min\": 1.155359130859375, \"max\": 1.155359130859375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2240977, \"EndTime\": 1674583036.2241032, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"model\": 31}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.26219921875, \"count\": 1, \"min\": 1.26219921875, \"max\": 1.26219921875}}}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #quality_metric: host=algo-1, epoch=9, train mse_objective <loss>=0.9275733032226563\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #early_stopping_criteria_metric: host=algo-1, epoch=9, criteria=mse_objective, value=0.852243896484375\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Saving model for epoch: 9\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Saved checkpoint to \"/tmp/tmpdac6pc_a/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #progress_metric: host=algo-1, completed 66.66666666666667 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2083125, \"EndTime\": 1674583036.230098, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 9, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 19810.0, \"count\": 1, \"min\": 19810, \"max\": 19810}, \"Total Batches Seen\": {\"sum\": 23.0, \"count\": 1, \"min\": 23, \"max\": 23}, \"Max Records Seen Between Resets\": {\"sum\": 1710.0, \"count\": 1, \"min\": 1710, \"max\": 1710}, \"Max Batches Seen Between Resets\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}, \"Reset Count\": {\"sum\": 12.0, \"count\": 1, \"min\": 12, \"max\": 12}, \"Number of Records Since Last Reset\": {\"sum\": 1710.0, \"count\": 1, \"min\": 1710, \"max\": 1710}, \"Number of Batches Since Last Reset\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}}}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #throughput_metric: host=algo-1, train throughput=78051.82052649335 records/second\u001b[0m\n",
      "\u001b[34m[2023-01-24 17:57:16.246] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 24, \"duration\": 15, \"num_examples\": 2, \"num_bytes\": 109440}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2461183, \"EndTime\": 1674583036.2461612, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 0}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.920159423828125, \"count\": 1, \"min\": 0.920159423828125, \"max\": 0.920159423828125}}}\u001b[0m\n",
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      "\u001b[34m#metrics {\"StartTime\": 1674583036.2469985, \"EndTime\": 1674583036.2470057, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 23}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0608284912109376, \"count\": 1, \"min\": 1.0608284912109376, \"max\": 1.0608284912109376}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2470343, \"EndTime\": 1674583036.247042, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 24}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9872840576171875, \"count\": 1, \"min\": 0.9872840576171875, \"max\": 0.9872840576171875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2470706, \"EndTime\": 1674583036.2470775, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 25}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9881679077148438, \"count\": 1, \"min\": 0.9881679077148438, \"max\": 0.9881679077148438}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2471056, \"EndTime\": 1674583036.2471135, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 26}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9863525390625, \"count\": 1, \"min\": 0.9863525390625, \"max\": 0.9863525390625}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2471414, \"EndTime\": 1674583036.2471492, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 27}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9963357543945313, \"count\": 1, \"min\": 0.9963357543945313, \"max\": 0.9963357543945313}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2471766, \"EndTime\": 1674583036.247229, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 28}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.1700732421875, \"count\": 1, \"min\": 1.1700732421875, \"max\": 1.1700732421875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.247265, \"EndTime\": 1674583036.2472744, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 29}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.992480224609375, \"count\": 1, \"min\": 0.992480224609375, \"max\": 0.992480224609375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.247303, \"EndTime\": 1674583036.2473106, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 30}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0850643310546875, \"count\": 1, \"min\": 1.0850643310546875, \"max\": 1.0850643310546875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2473385, \"EndTime\": 1674583036.2473454, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"model\": 31}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.20890087890625, \"count\": 1, \"min\": 1.20890087890625, \"max\": 1.20890087890625}}}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #quality_metric: host=algo-1, epoch=10, train mse_objective <loss>=0.920159423828125\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #early_stopping_criteria_metric: host=algo-1, epoch=10, criteria=mse_objective, value=0.8071461181640625\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Saving model for epoch: 10\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Saved checkpoint to \"/tmp/tmpv25f7qb8/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #progress_metric: host=algo-1, completed 73.33333333333333 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.230341, \"EndTime\": 1674583036.2524643, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 10, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 21520.0, \"count\": 1, \"min\": 21520, \"max\": 21520}, \"Total Batches Seen\": {\"sum\": 25.0, \"count\": 1, \"min\": 25, \"max\": 25}, \"Max Records Seen Between Resets\": {\"sum\": 1710.0, \"count\": 1, \"min\": 1710, \"max\": 1710}, \"Max Batches Seen Between Resets\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}, \"Reset Count\": {\"sum\": 13.0, \"count\": 1, \"min\": 13, \"max\": 13}, \"Number of Records Since Last Reset\": {\"sum\": 1710.0, \"count\": 1, \"min\": 1710, \"max\": 1710}, \"Number of Batches Since Last Reset\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}}}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #throughput_metric: host=algo-1, train throughput=76982.83555336118 records/second\u001b[0m\n",
      "\u001b[34m[2023-01-24 17:57:16.266] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 26, \"duration\": 13, \"num_examples\": 2, \"num_bytes\": 109440}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.266059, \"EndTime\": 1674583036.2661064, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 0}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9131237182617188, \"count\": 1, \"min\": 0.9131237182617188, \"max\": 0.9131237182617188}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.266147, \"EndTime\": 1674583036.2661562, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 1}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9209077758789063, \"count\": 1, \"min\": 0.9209077758789063, \"max\": 0.9209077758789063}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2661886, \"EndTime\": 1674583036.2661967, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 2}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9139334106445313, \"count\": 1, \"min\": 0.9139334106445313, \"max\": 0.9139334106445313}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2662306, \"EndTime\": 1674583036.266239, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 3}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.902323974609375, \"count\": 1, \"min\": 0.902323974609375, \"max\": 0.902323974609375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.266262, \"EndTime\": 1674583036.2662694, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 4}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8182810668945313, \"count\": 1, \"min\": 0.8182810668945313, \"max\": 0.8182810668945313}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2662973, \"EndTime\": 1674583036.266305, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 5}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8139191284179688, \"count\": 1, \"min\": 0.8139191284179688, \"max\": 0.8139191284179688}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2663333, \"EndTime\": 1674583036.2663405, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 6}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8177913818359375, \"count\": 1, \"min\": 0.8177913818359375, \"max\": 0.8177913818359375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2663677, \"EndTime\": 1674583036.2663746, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 7}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8446227416992188, \"count\": 1, \"min\": 0.8446227416992188, \"max\": 0.8446227416992188}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2664022, \"EndTime\": 1674583036.2664094, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 8}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8581744384765625, \"count\": 1, \"min\": 0.8581744384765625, \"max\": 0.8581744384765625}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2664378, \"EndTime\": 1674583036.266446, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 9}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8991619262695313, \"count\": 1, \"min\": 0.8991619262695313, \"max\": 0.8991619262695313}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.266476, \"EndTime\": 1674583036.2664835, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 10}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9444498901367188, \"count\": 1, \"min\": 0.9444498901367188, \"max\": 0.9444498901367188}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2665088, \"EndTime\": 1674583036.266516, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 11}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9234203491210937, \"count\": 1, \"min\": 0.9234203491210937, \"max\": 0.9234203491210937}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.266546, \"EndTime\": 1674583036.266553, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 12}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8157457885742188, \"count\": 1, \"min\": 0.8157457885742188, \"max\": 0.8157457885742188}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2665813, \"EndTime\": 1674583036.266589, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 13}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8145921020507813, \"count\": 1, \"min\": 0.8145921020507813, \"max\": 0.8145921020507813}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2666168, \"EndTime\": 1674583036.266624, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 14}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8175601806640626, \"count\": 1, \"min\": 0.8175601806640626, \"max\": 0.8175601806640626}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2666605, \"EndTime\": 1674583036.2666678, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 15}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8079725952148438, \"count\": 1, \"min\": 0.8079725952148438, \"max\": 0.8079725952148438}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2666965, \"EndTime\": 1674583036.2667036, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 16}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9691217651367188, \"count\": 1, \"min\": 0.9691217651367188, \"max\": 0.9691217651367188}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2667346, \"EndTime\": 1674583036.2667422, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 17}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9363903198242187, \"count\": 1, \"min\": 0.9363903198242187, \"max\": 0.9363903198242187}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2667649, \"EndTime\": 1674583036.2667718, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 18}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9499277954101563, \"count\": 1, \"min\": 0.9499277954101563, \"max\": 0.9499277954101563}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2668002, \"EndTime\": 1674583036.2668078, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 19}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.936627197265625, \"count\": 1, \"min\": 0.936627197265625, \"max\": 0.936627197265625}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2668383, \"EndTime\": 1674583036.2668462, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 20}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.874100830078125, \"count\": 1, \"min\": 0.874100830078125, \"max\": 0.874100830078125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2668698, \"EndTime\": 1674583036.2668767, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 21}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8566612548828125, \"count\": 1, \"min\": 0.8566612548828125, \"max\": 0.8566612548828125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.266904, \"EndTime\": 1674583036.2669113, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 22}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8505274658203125, \"count\": 1, \"min\": 0.8505274658203125, \"max\": 0.8505274658203125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2669394, \"EndTime\": 1674583036.2669468, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 23}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9352428588867188, \"count\": 1, \"min\": 0.9352428588867188, \"max\": 0.9352428588867188}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.266972, \"EndTime\": 1674583036.2669792, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 24}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9878472290039062, \"count\": 1, \"min\": 0.9878472290039062, \"max\": 0.9878472290039062}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2670069, \"EndTime\": 1674583036.267015, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 25}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.99178466796875, \"count\": 1, \"min\": 0.99178466796875, \"max\": 0.99178466796875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2670476, \"EndTime\": 1674583036.2670548, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 26}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.989513427734375, \"count\": 1, \"min\": 0.989513427734375, \"max\": 0.989513427734375}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.267083, \"EndTime\": 1674583036.26709, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 27}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9946681518554688, \"count\": 1, \"min\": 0.9946681518554688, \"max\": 0.9946681518554688}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2671204, \"EndTime\": 1674583036.267128, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 28}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.053731201171875, \"count\": 1, \"min\": 1.053731201171875, \"max\": 1.053731201171875}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2671578, \"EndTime\": 1674583036.2671652, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 29}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9852141723632812, \"count\": 1, \"min\": 0.9852141723632812, \"max\": 0.9852141723632812}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2671938, \"EndTime\": 1674583036.267202, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 30}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0490001220703125, \"count\": 1, \"min\": 1.0490001220703125, \"max\": 1.0490001220703125}}}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2672298, \"EndTime\": 1674583036.2672377, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"model\": 31}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0576839599609376, \"count\": 1, \"min\": 1.0576839599609376, \"max\": 1.0576839599609376}}}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #quality_metric: host=algo-1, epoch=11, train mse_objective <loss>=0.9131237182617188\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #early_stopping_criteria_metric: host=algo-1, epoch=11, criteria=mse_objective, value=0.8079725952148438\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Saving model for epoch: 11\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Saved checkpoint to \"/tmp/tmp2rtdxrig/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Early stop condition met. Stopping training.\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #progress_metric: host=algo-1, completed 100 % epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.2526667, \"EndTime\": 1674583036.2723515, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 11, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 23230.0, \"count\": 1, \"min\": 23230, \"max\": 23230}, \"Total Batches Seen\": {\"sum\": 27.0, \"count\": 1, \"min\": 27, \"max\": 27}, \"Max Records Seen Between Resets\": {\"sum\": 1710.0, \"count\": 1, \"min\": 1710, \"max\": 1710}, \"Max Batches Seen Between Resets\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}, \"Reset Count\": {\"sum\": 14.0, \"count\": 1, \"min\": 14, \"max\": 14}, \"Number of Records Since Last Reset\": {\"sum\": 1710.0, \"count\": 1, \"min\": 1710, \"max\": 1710}, \"Number of Batches Since Last Reset\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}}}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #throughput_metric: host=algo-1, train throughput=86438.80494124736 records/second\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 WARNING 140289507411776] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 WARNING 140289507411776] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n",
      "\u001b[34m[2023-01-24 17:57:16.274] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 28, \"duration\": 1, \"num_examples\": 1, \"num_bytes\": 64000}\u001b[0m\n",
      "\u001b[34m[2023-01-24 17:57:16.279] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 30, \"duration\": 3, \"num_examples\": 2, \"num_bytes\": 109440}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #train_score (algo-1) : ('mse_objective', 1.646260028345543e-05)\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #train_score (algo-1) : ('mse', 1.646260028345543e-05)\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #train_score (algo-1) : ('absolute_loss', 0.0028858671411436205)\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #train_score (algo-1) : ('rmse', 0.0040574130038061726)\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #train_score (algo-1) : ('r2', 0.2066470948488437)\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #train_score (algo-1) : ('mae', 0.002885867183829238)\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #quality_metric: host=algo-1, train mse_objective <loss>=1.646260028345543e-05\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #quality_metric: host=algo-1, train mse <loss>=1.646260028345543e-05\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #quality_metric: host=algo-1, train absolute_loss <loss>=0.0028858671411436205\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #quality_metric: host=algo-1, train rmse <loss>=0.0040574130038061726\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #quality_metric: host=algo-1, train r2 <loss>=0.2066470948488437\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #quality_metric: host=algo-1, train mae <loss>=0.002885867183829238\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Best model found for hyperparameters: {\"optimizer\": \"adam\", \"learning_rate\": 0.1, \"wd\": 0.0001, \"l1\": 0.0, \"lr_scheduler_step\": 100, \"lr_scheduler_factor\": 0.99, \"lr_scheduler_minimum_lr\": 0.0001}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] Saved checkpoint to \"/tmp/tmp3akhhm_h/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[2023-01-24 17:57:16.283] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/test\", \"epoch\": 0, \"duration\": 406, \"num_examples\": 1, \"num_bytes\": 22656}\u001b[0m\n",
      "\u001b[34m[2023-01-24 17:57:16.285] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/test\", \"epoch\": 1, \"duration\": 1, \"num_examples\": 1, \"num_bytes\": 22656}\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583036.283729, \"EndTime\": 1674583036.2860317, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"Meta\": \"test_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 354.0, \"count\": 1, \"min\": 354, \"max\": 354}, \"Total Batches Seen\": {\"sum\": 1.0, \"count\": 1, \"min\": 1, \"max\": 1}, \"Max Records Seen Between Resets\": {\"sum\": 354.0, \"count\": 1, \"min\": 354, \"max\": 354}, \"Max Batches Seen Between Resets\": {\"sum\": 1.0, \"count\": 1, \"min\": 1, \"max\": 1}, \"Reset Count\": {\"sum\": 1.0, \"count\": 1, \"min\": 1, \"max\": 1}, \"Number of Records Since Last Reset\": {\"sum\": 354.0, \"count\": 1, \"min\": 354, \"max\": 354}, \"Number of Batches Since Last Reset\": {\"sum\": 1.0, \"count\": 1, \"min\": 1, \"max\": 1}}}\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #test_score (algo-1) : ('mse_objective', 1.1116567483477e-05)\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #test_score (algo-1) : ('mse', 1.1116567483477e-05)\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #test_score (algo-1) : ('absolute_loss', 0.002530753444143608)\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #test_score (algo-1) : ('rmse', 0.003334151688732383)\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #test_score (algo-1) : ('r2', 0.3769300521351635)\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #test_score (algo-1) : ('mae', 0.002530753394157368)\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #quality_metric: host=algo-1, test mse_objective <loss>=1.1116567483477e-05\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #quality_metric: host=algo-1, test mse <loss>=1.1116567483477e-05\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #quality_metric: host=algo-1, test absolute_loss <loss>=0.002530753444143608\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #quality_metric: host=algo-1, test rmse <loss>=0.003334151688732383\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #quality_metric: host=algo-1, test r2 <loss>=0.3769300521351635\u001b[0m\n",
      "\u001b[34m[01/24/2023 17:57:16 INFO 140289507411776] #quality_metric: host=algo-1, test mae <loss>=0.002530753394157368\u001b[0m\n",
      "\u001b[34m#metrics {\"StartTime\": 1674583035.87467, \"EndTime\": 1674583036.2890363, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\"}, \"Metrics\": {\"initialize.time\": {\"sum\": 107.3918342590332, \"count\": 1, \"min\": 107.3918342590332, \"max\": 107.3918342590332}, \"epochs\": {\"sum\": 15.0, \"count\": 1, \"min\": 15, \"max\": 15}, \"check_early_stopping.time\": {\"sum\": 4.202365875244141, \"count\": 12, \"min\": 0.12278556823730469, \"max\": 0.8075237274169922}, \"update.time\": {\"sum\": 260.4835033416748, \"count\": 12, \"min\": 17.336368560791016, \"max\": 48.51055145263672}, \"finalize.time\": {\"sum\": 9.396076202392578, \"count\": 1, \"min\": 9.396076202392578, \"max\": 9.396076202392578}, \"setuptime\": {\"sum\": 1.8472671508789062, \"count\": 1, \"min\": 1.8472671508789062, \"max\": 1.8472671508789062}, \"totaltime\": {\"sum\": 503.8747787475586, \"count\": 1, \"min\": 503.8747787475586, \"max\": 503.8747787475586}}}\u001b[0m\n",
      "\n",
      "2023-01-24 17:57:33 Uploading - Uploading generated training model\n",
      "2023-01-24 17:57:33 Completed - Training job completed\n",
      "Training seconds: 107\n",
      "Billable seconds: 46\n",
      "Managed Spot Training savings: 57.0%\n"
     ]
    }
   ],
   "source": [
    "%%local\n",
    "\n",
    "llearner.fit([\n",
    "    llearner.record_set(x_train.values.astype('float32'), y_train.values[:, 0].astype('float32'), channel='train'),\n",
    "    llearner.record_set(x_test.values.astype('float32'), y_test.values[:, 0].astype('float32'), channel='test')\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create our estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------!"
     ]
    }
   ],
   "source": [
    "%%local\n",
    "\n",
    "llearner_predictor = llearner.deploy(initial_instance_count=1,\n",
    "                                 instance_type='ml.t2.medium')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.0033\n",
      "Variance score: 0.3769\n",
      "Explained variance score: 0.3770\n",
      "Forecast bias: 0.0000\n",
      "sMAPE: 0.3617\n"
     ]
    }
   ],
   "source": [
    "# %%local\n",
    "\n",
    "# result = llearner_predictor.predict(x_test.values.astype('float32'))\n",
    "# y_sm_pred = [r.label[\"score\"].float32_tensor.values[0] for r in result]\n",
    "# y_sm_test = y_test.values[:, 0].astype('float32')\n",
    "# print_metrics(y_sm_test, y_sm_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%local\n",
    "\n",
    "y_sm_pred_df = pd.DataFrame(y_sm_pred, columns=y_train.columns).set_index(y_test.index).sort_index()\n",
    "y_sm_test_df = pd.DataFrame(y_sm_test, columns=y_train.columns).set_index(y_test.index).sort_index()\n",
    "\n",
    "plt.plot(y_sm_test_df, label='actual')\n",
    "plt.plot(y_sm_pred_df, label='forecast')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear-learner-2023-01-24-17-59-00-156\n"
     ]
    }
   ],
   "source": [
    "%%local\n",
    "\n",
    "endpoint_name = llearner_predictor.endpoint_name\n",
    "print(endpoint_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reuse an existing estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.0033\n",
      "Variance score: 0.3769\n",
      "Explained variance score: 0.3770\n",
      "Forecast bias: 0.0000\n",
      "sMAPE: 0.3617\n"
     ]
    }
   ],
   "source": [
    "%%local\n",
    "\n",
    "# The endpoint can take a while to create, so we'll use a previously created one.\n",
    "\n",
    "# Can specify if there is an existing endpoint\n",
    "# endpoint_name = \"\"\n",
    "\n",
    "from sagemaker import LinearLearnerPredictor\n",
    "\n",
    "llearner_predictor = LinearLearnerPredictor(endpoint_name)\n",
    "result = llearner_predictor.predict(x_test.values.astype('float32'))\n",
    "y_sm_pred = [r.label[\"score\"].float32_tensor.values[0] for r in result]\n",
    "y_sm_test = y_test.values[:, 0].astype('float32')\n",
    "\n",
    "print_metrics(y_sm_test, y_sm_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%local\n",
    "\n",
    "y_sm_pred_df = pd.DataFrame(y_sm_pred, columns=y_train.columns).set_index(y_test.index).sort_index()\n",
    "y_sm_test_df = pd.DataFrame(y_sm_test, columns=y_train.columns).set_index(y_test.index).sort_index()\n",
    "\n",
    "plt.plot(y_sm_test_df, label='actual')\n",
    "plt.plot(y_sm_pred_df, label='forecast')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Clean Up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%cleanup -f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%local\n",
    "llearner_predictor.delete_endpoint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "instance_type": "ml.m5.large",
  "kernelspec": {
   "display_name": "PySpark (SparkMagic)",
   "language": "python",
   "name": "pysparkkernel__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/sagemaker-sparkmagic"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "python",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "pyspark",
   "pygments_lexer": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}