whizlabs-course/lab/regression-lab/linearlearner-regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import boto3\n",
    "import sagemaker\n",
    "import io\n",
    "import sagemaker.amazon.common as smac\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "# Read csv from s3.\n",
    "# Download from your S3 bucket the bike share data CSV file based on the publically available bike share data from the ML repository curated by the University of California, Irvine\n",
    "from io import StringIO\n",
    "s3 = boto3.resource('s3')\n",
    "bucket = 'machine-learning-exam' # place the day.csv file in a bucket in your account\n",
    "object_key = 'day.csv'\n",
    "\n",
    "# Load the data into a pandas dataframe \n",
    "csv_obj = s3.Object(bucket, object_key)\n",
    "csv_string = csv_obj.get()['Body'].read().decode('utf-8')\n",
    "\n",
    "dataset = pd.read_csv(StringIO(csv_string))\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>20110101</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20110102</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>20110103</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>20110104</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>20110105</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant    dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  20110101       1   0     1        0        6           0   \n",
       "1        2  20110102       1   0     1        0        0           0   \n",
       "2        3  20110103       1   0     1        0        1           1   \n",
       "3        4  20110104       1   0     1        0        2           1   \n",
       "4        5  20110105       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert categorical date field\n",
    "dataset['dteday'] = dataset['dteday'].str.replace(\"-\",\"\")\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(511, 16) (220, 16)\n"
     ]
    }
   ],
   "source": [
    "# Randomize the data and split it between train and test datasets on a 70% 30% split respectively\n",
    "train_data, test_data = np.split(dataset.sample(frac=1, random_state=1729), [int(0.7 * len(dataset))])\n",
    "print(train_data.shape, test_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the features and labels.\n",
    "feature_dataset = train_data[['instant', 'dteday', 'season', 'yr', 'mnth', 'holiday', 'weekday',\n",
    "                           'workingday', 'weathersit', 'temp', 'atemp', 'hum', 'windspeed', 'casual', 'registered' ]]\n",
    "features = np.array(feature_dataset.values).astype('float32')\n",
    "\n",
    "label_dataset= train_data[['cnt']]\n",
    "labels = np.array(label_dataset.values).astype('float32')\n",
    "labels_vec = np.squeeze(np.asarray(labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training dataset will be uploaded to: s3://machine-learning-exam/realestate/train/linearregression\n"
     ]
    }
   ],
   "source": [
    "# Setup protoBuf\n",
    "buffer = io.BytesIO()\n",
    "smac.write_numpy_to_dense_tensor(buffer, features, labels_vec)\n",
    "buffer.seek(0)\n",
    "\n",
    "prefix = 'realestate'\n",
    "key = 'linearregression'\n",
    "boto3.resource('s3').Bucket(bucket).Object(os.path.join(prefix, 'train', key)).upload_fileobj(buffer)\n",
    "s3_training_data_location = 's3://{}/{}/train/{}'.format(bucket, prefix, key)\n",
    "print('training dataset will be uploaded to: {}'.format(s3_training_data_location))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model artifacts will be uploaded to: s3://machine-learning-exam/realestate/output\n"
     ]
    }
   ],
   "source": [
    "output_location = 's3://{}/{}/output'.format(bucket, prefix)\n",
    "print('model artifacts will be uploaded to: {}'.format(output_location))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the Linear Learner container instance\n",
    "from sagemaker.amazon.amazon_estimator import get_image_uri\n",
    "linear_container = get_image_uri(boto3.Session().region_name, 'linear-learner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-02-14 19:06:55 Starting - Starting the training job...\n",
      "2020-02-14 19:06:56 Starting - Launching requested ML instances......\n",
      "2020-02-14 19:08:02 Starting - Preparing the instances for training......\n",
      "2020-02-14 19:09:24 Downloading - Downloading input data...\n",
      "2020-02-14 19:09:47 Training - Downloading the training image..\u001b[34mDocker entrypoint called with argument(s): train\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:09 INFO 140163623442240] Reading default configuration from /opt/amazon/lib/python2.7/site-packages/algorithm/resources/default-input.json: {u'loss_insensitivity': u'0.01', u'epochs': u'15', u'feature_dim': u'auto', u'init_bias': u'0.0', u'lr_scheduler_factor': u'auto', u'num_calibration_samples': u'10000000', u'accuracy_top_k': u'3', u'_num_kv_servers': u'auto', u'use_bias': u'true', u'num_point_for_scaler': u'10000', u'_log_level': u'info', u'quantile': u'0.5', u'bias_lr_mult': u'auto', u'lr_scheduler_step': u'auto', u'init_method': u'uniform', u'init_sigma': u'0.01', u'lr_scheduler_minimum_lr': u'auto', u'target_recall': u'0.8', u'num_models': u'auto', u'early_stopping_patience': u'3', u'momentum': u'auto', u'unbias_label': u'auto', u'wd': u'auto', u'optimizer': u'auto', u'_tuning_objective_metric': u'', u'early_stopping_tolerance': u'0.001', u'learning_rate': u'auto', u'_kvstore': u'auto', u'normalize_data': u'true', u'binary_classifier_model_selection_criteria': u'accuracy', u'use_lr_scheduler': u'true', u'target_precision': u'0.8', u'unbias_data': u'auto', u'init_scale': u'0.07', u'bias_wd_mult': u'auto', u'f_beta': u'1.0', u'mini_batch_size': u'1000', u'huber_delta': u'1.0', u'num_classes': u'1', u'beta_1': u'auto', u'loss': u'auto', u'beta_2': u'auto', u'_enable_profiler': u'false', u'normalize_label': u'auto', u'_num_gpus': u'auto', u'balance_multiclass_weights': u'false', u'positive_example_weight_mult': u'1.0', u'l1': u'auto', u'margin': u'1.0'}\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:09 INFO 140163623442240] Reading provided configuration from /opt/ml/input/config/hyperparameters.json: {u'feature_dim': u'15', u'mini_batch_size': u'4', u'predictor_type': u'regressor'}\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:09 INFO 140163623442240] Final configuration: {u'loss_insensitivity': u'0.01', u'epochs': u'15', u'feature_dim': u'15', u'init_bias': u'0.0', u'lr_scheduler_factor': u'auto', u'num_calibration_samples': u'10000000', u'accuracy_top_k': u'3', u'_num_kv_servers': u'auto', u'use_bias': u'true', u'num_point_for_scaler': u'10000', u'_log_level': u'info', u'quantile': u'0.5', u'bias_lr_mult': u'auto', u'lr_scheduler_step': u'auto', u'init_method': u'uniform', u'init_sigma': u'0.01', u'lr_scheduler_minimum_lr': u'auto', u'target_recall': u'0.8', u'num_models': u'auto', u'early_stopping_patience': u'3', u'momentum': u'auto', u'unbias_label': u'auto', u'wd': u'auto', u'optimizer': u'auto', u'_tuning_objective_metric': u'', u'early_stopping_tolerance': u'0.001', u'learning_rate': u'auto', u'_kvstore': u'auto', u'normalize_data': u'true', u'binary_classifier_model_selection_criteria': u'accuracy', u'use_lr_scheduler': u'true', u'target_precision': u'0.8', u'unbias_data': u'auto', u'init_scale': u'0.07', u'bias_wd_mult': u'auto', u'f_beta': u'1.0', u'mini_batch_size': u'4', u'huber_delta': u'1.0', u'num_classes': u'1', u'predictor_type': u'regressor', u'beta_1': u'auto', u'loss': u'auto', u'beta_2': u'auto', u'_enable_profiler': u'false', u'normalize_label': u'auto', u'_num_gpus': u'auto', u'balance_multiclass_weights': u'false', u'positive_example_weight_mult': u'1.0', u'l1': u'auto', u'margin': u'1.0'}\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:09 WARNING 140163623442240] Loggers have already been setup.\u001b[0m\n",
      "\u001b[34mProcess 1 is a worker.\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:09 INFO 140163623442240] Using default worker.\u001b[0m\n",
      "\u001b[34m[2020-02-14 19:10:09.806] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 0, \"duration\": 14, \"num_examples\": 1, \"num_bytes\": 416}\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:09 INFO 140163623442240] Create Store: local\u001b[0m\n",
      "\u001b[34m[2020-02-14 19:10:10.113] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 1, \"duration\": 305, \"num_examples\": 128, \"num_bytes\": 53144}\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:10 INFO 140163623442240] Scaler algorithm parameters\n",
      " <algorithm.scaler.ScalerAlgorithmStable object at 0x7f7a11f17190>\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:10 INFO 140163623442240] Scaling model computed with parameters:\n",
      " {'stdev_weight': \u001b[0m\n",
      "\u001b[34m[2.1629564e+02 5.0256758e+03 1.1230947e+00 4.9953094e-01 3.5005350e+00\n",
      " 1.5791032e-01 2.0275118e+00 4.6975526e-01 5.3916532e-01 1.8521297e-01\n",
      " 1.6500629e-01 1.4519949e-01 7.7296913e-02 6.8667987e+02 1.5956224e+03]\u001b[0m\n",
      "\u001b[34m<NDArray 15 @cpu(0)>, 'stdev_label': \u001b[0m\n",
      "\u001b[34m[1980.7329]\u001b[0m\n",
      "\u001b[34m<NDArray 1 @cpu(0)>, 'mean_label': \u001b[0m\n",
      "\u001b[34m[4518.012]\u001b[0m\n",
      "\u001b[34m<NDArray 1 @cpu(0)>, 'mean_weight': \u001b[0m\n",
      "\u001b[34m[3.7352954e+02 2.0115878e+07 2.4783459e+00 5.2165359e-01 6.5137796e+00\n",
      " 2.5590552e-02 3.0374017e+00 6.7125976e-01 1.3799214e+00 4.9031106e-01\n",
      " 4.6974412e-01 6.2344158e-01 1.8905917e-01 8.4455316e+02 3.6734587e+03]\u001b[0m\n",
      "\u001b[34m<NDArray 15 @cpu(0)>}\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:10 INFO 140163623442240] nvidia-smi took: 0.0251958370209 secs to identify 0 gpus\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:10 INFO 140163623442240] Number of GPUs being used: 0\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 129, \"sum\": 129.0, \"min\": 129}, \"Total Records Seen\": {\"count\": 1, \"max\": 515, \"sum\": 515.0, \"min\": 515}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Reset Count\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}}, \"EndTime\": 1581707410.21496, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"init_train_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\"}, \"StartTime\": 1581707410.214923}\n",
      "\u001b[0m\n",
      "\u001b[34m[2020-02-14 19:10:12.780] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 4, \"duration\": 2565, \"num_examples\": 128, \"num_bytes\": 53144}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.160528074874814, \"sum\": 0.160528074874814, \"min\": 0.160528074874814}}, \"EndTime\": 1581707412.780916, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.78084}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.19196426039048303, \"sum\": 0.19196426039048303, \"min\": 0.19196426039048303}}, \"EndTime\": 1581707412.781012, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.780993}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.22893976722202286, \"sum\": 0.22893976722202286, \"min\": 0.22893976722202286}}, \"EndTime\": 1581707412.781083, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.781064}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.19839401569540108, \"sum\": 0.19839401569540108, \"min\": 0.19839401569540108}}, \"EndTime\": 1581707412.781147, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.78113}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.18636412733036467, \"sum\": 0.18636412733036467, \"min\": 0.18636412733036467}}, \"EndTime\": 1581707412.781211, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.781193}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.20222969106068528, \"sum\": 0.20222969106068528, \"min\": 0.20222969106068528}}, \"EndTime\": 1581707412.781283, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.781264}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.17307614322612427, \"sum\": 0.17307614322612427, \"min\": 0.17307614322612427}}, \"EndTime\": 1581707412.781347, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.78133}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.1804158676023353, \"sum\": 0.1804158676023353, \"min\": 0.1804158676023353}}, \"EndTime\": 1581707412.781418, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.7814}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.16733681974844553, \"sum\": 0.16733681974844553, \"min\": 0.16733681974844553}}, \"EndTime\": 1581707412.781489, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.781471}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.19310326184564103, \"sum\": 0.19310326184564103, \"min\": 0.19310326184564103}}, \"EndTime\": 1581707412.78156, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.781543}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.2447582282213949, \"sum\": 0.2447582282213949, \"min\": 0.2447582282213949}}, \"EndTime\": 1581707412.781628, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.78161}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.19505900937576198, \"sum\": 0.19505900937576198, \"min\": 0.19505900937576198}}, \"EndTime\": 1581707412.781694, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.781676}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.1632423272932051, \"sum\": 0.1632423272932051, \"min\": 0.1632423272932051}}, \"EndTime\": 1581707412.781766, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.781748}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.2380537844508681, \"sum\": 0.2380537844508681, \"min\": 0.2380537844508681}}, \"EndTime\": 1581707412.781837, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.781819}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.18988527521293053, \"sum\": 0.18988527521293053, \"min\": 0.18988527521293053}}, \"EndTime\": 1581707412.781908, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.781889}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.1847545411621081, \"sum\": 0.1847545411621081, \"min\": 0.1847545411621081}}, \"EndTime\": 1581707412.781978, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.78196}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.23367460765399214, \"sum\": 0.23367460765399214, \"min\": 0.23367460765399214}}, \"EndTime\": 1581707412.78204, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.782024}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.2723946583028296, \"sum\": 0.2723946583028296, \"min\": 0.2723946583028296}}, \"EndTime\": 1581707412.7821, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.782083}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.2365193755299909, \"sum\": 0.2365193755299909, \"min\": 0.2365193755299909}}, \"EndTime\": 1581707412.78216, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.782143}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.23676525302209311, \"sum\": 0.23676525302209311, \"min\": 0.23676525302209311}}, \"EndTime\": 1581707412.782226, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.782209}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.2998791102495954, \"sum\": 0.2998791102495954, \"min\": 0.2998791102495954}}, \"EndTime\": 1581707412.782296, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.782279}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.2993108123709543, \"sum\": 0.2993108123709543, \"min\": 0.2993108123709543}}, \"EndTime\": 1581707412.78236, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.782344}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.28375148999424077, \"sum\": 0.28375148999424077, \"min\": 0.28375148999424077}}, \"EndTime\": 1581707412.78242, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.782403}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.2978336779111043, \"sum\": 0.2978336779111043, \"min\": 0.2978336779111043}}, \"EndTime\": 1581707412.782481, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.782464}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9358175256560872, \"sum\": 0.9358175256560872, \"min\": 0.9358175256560872}}, \"EndTime\": 1581707412.782546, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.782529}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9407277674538883, \"sum\": 0.9407277674538883, \"min\": 0.9407277674538883}}, \"EndTime\": 1581707412.782618, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.782599}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9414003652733142, \"sum\": 0.9414003652733142, \"min\": 0.9414003652733142}}, \"EndTime\": 1581707412.782685, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.782667}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9331118595588395, \"sum\": 0.9331118595588395, \"min\": 0.9331118595588395}}, \"EndTime\": 1581707412.782745, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.782728}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0353653943386134, \"sum\": 1.0353653943386134, \"min\": 1.0353653943386134}}, \"EndTime\": 1581707412.782806, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.782789}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0517654310706563, \"sum\": 1.0517654310706563, \"min\": 1.0517654310706563}}, \"EndTime\": 1581707412.782866, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.782849}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0379457746845062, \"sum\": 1.0379457746845062, \"min\": 1.0379457746845062}}, \"EndTime\": 1581707412.782925, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.78291}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0353998830468636, \"sum\": 1.0353998830468636, \"min\": 1.0353998830468636}}, \"EndTime\": 1581707412.782985, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707412.782968}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:12 INFO 140163623442240] #quality_metric: host=algo-1, epoch=0, train mse_objective <loss>=0.160528074875\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:12 INFO 140163623442240] #early_stopping_criteria_metric: host=algo-1, epoch=0, criteria=mse_objective, value=0.160528074875\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:12 INFO 140163623442240] Epoch 0: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:12 INFO 140163623442240] #progress_metric: host=algo-1, completed 6 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Total Batches Seen\": {\"count\": 1, \"max\": 257, \"sum\": 257.0, \"min\": 257}, \"Total Records Seen\": {\"count\": 1, \"max\": 1026, \"sum\": 1026.0, \"min\": 1026}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Reset Count\": {\"count\": 1, \"max\": 3, \"sum\": 3.0, \"min\": 3}}, \"EndTime\": 1581707412.786324, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1581707410.21514}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:12 INFO 140163623442240] #throughput_metric: host=algo-1, train throughput=198.731235183 records/second\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "2020-02-14 19:10:06 Training - Training image download completed. Training in progress.\u001b[34m[2020-02-14 19:10:15.572] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 6, \"duration\": 2785, \"num_examples\": 128, \"num_bytes\": 53144}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.043587390780742245, \"sum\": 0.043587390780742245, \"min\": 0.043587390780742245}}, \"EndTime\": 1581707415.572849, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.572751}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.04950973400722375, \"sum\": 0.04950973400722375, \"min\": 0.04950973400722375}}, \"EndTime\": 1581707415.572937, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.572923}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.062185786754318226, \"sum\": 0.062185786754318226, \"min\": 0.062185786754318226}}, \"EndTime\": 1581707415.572995, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.57298}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.04684822581979761, \"sum\": 0.04684822581979761, \"min\": 0.04684822581979761}}, \"EndTime\": 1581707415.573044, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.573031}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.05650983661354527, \"sum\": 0.05650983661354527, \"min\": 0.05650983661354527}}, \"EndTime\": 1581707415.573095, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.573079}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.22719358432466003, \"sum\": 0.22719358432466003, \"min\": 0.22719358432466003}}, \"EndTime\": 1581707415.573149, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.573134}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.05869481171798474, \"sum\": 0.05869481171798474, \"min\": 0.05869481171798474}}, \"EndTime\": 1581707415.573209, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.573192}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0509948231972842, \"sum\": 0.0509948231972842, \"min\": 0.0509948231972842}}, \"EndTime\": 1581707415.573268, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.573251}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.047153956660593854, \"sum\": 0.047153956660593854, \"min\": 0.047153956660593854}}, \"EndTime\": 1581707415.573324, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.57331}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.042577795651338, \"sum\": 0.042577795651338, \"min\": 0.042577795651338}}, \"EndTime\": 1581707415.573372, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.573358}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.06116203427512756, \"sum\": 0.06116203427512756, \"min\": 0.06116203427512756}}, \"EndTime\": 1581707415.573428, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.573413}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.04716114846505518, \"sum\": 0.04716114846505518, \"min\": 0.04716114846505518}}, \"EndTime\": 1581707415.573491, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.573474}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.09495229360475169, \"sum\": 0.09495229360475169, \"min\": 0.09495229360475169}}, \"EndTime\": 1581707415.573547, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.573531}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.10289267320039235, \"sum\": 0.10289267320039235, \"min\": 0.10289267320039235}}, \"EndTime\": 1581707415.573605, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.573588}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.029095275865640403, \"sum\": 0.029095275865640403, \"min\": 0.029095275865640403}}, \"EndTime\": 1581707415.573659, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.573644}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.12367665367893046, \"sum\": 0.12367665367893046, \"min\": 0.12367665367893046}}, \"EndTime\": 1581707415.57372, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.573703}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11803877078121396, \"sum\": 0.11803877078121396, \"min\": 0.11803877078121396}}, \"EndTime\": 1581707415.573779, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.573763}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.12116106256189954, \"sum\": 0.12116106256189954, \"min\": 0.12116106256189954}}, \"EndTime\": 1581707415.573842, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.573824}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.12058079602218813, \"sum\": 0.12058079602218813, \"min\": 0.12058079602218813}}, \"EndTime\": 1581707415.573902, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.573884}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.12029577185231166, \"sum\": 0.12029577185231166, \"min\": 0.12029577185231166}}, \"EndTime\": 1581707415.573964, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.573947}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.24239074361077914, \"sum\": 0.24239074361077914, \"min\": 0.24239074361077914}}, \"EndTime\": 1581707415.574024, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.574007}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.25678706100929205, \"sum\": 0.25678706100929205, \"min\": 0.25678706100929205}}, \"EndTime\": 1581707415.574086, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.574069}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.2449350785244051, \"sum\": 0.2449350785244051, \"min\": 0.2449350785244051}}, \"EndTime\": 1581707415.574145, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.574128}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.25790282532088754, \"sum\": 0.25790282532088754, \"min\": 0.25790282532088754}}, \"EndTime\": 1581707415.574211, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.574193}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9385658386273412, \"sum\": 0.9385658386273412, \"min\": 0.9385658386273412}}, \"EndTime\": 1581707415.574269, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.574254}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.939466544140981, \"sum\": 0.939466544140981, \"min\": 0.939466544140981}}, \"EndTime\": 1581707415.574328, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.574312}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9385098137812117, \"sum\": 0.9385098137812117, \"min\": 0.9385098137812117}}, \"EndTime\": 1581707415.574377, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.574367}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9394941007586446, \"sum\": 0.9394941007586446, \"min\": 0.9394941007586446}}, \"EndTime\": 1581707415.574428, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.574413}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0265988336480039, \"sum\": 1.0265988336480039, \"min\": 1.0265988336480039}}, \"EndTime\": 1581707415.574495, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.574478}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.041619716578816, \"sum\": 1.041619716578816, \"min\": 1.041619716578816}}, \"EndTime\": 1581707415.574561, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.574544}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0270074446370283, \"sum\": 1.0270074446370283, \"min\": 1.0270074446370283}}, \"EndTime\": 1581707415.574631, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.574612}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0430710488769013, \"sum\": 1.0430710488769013, \"min\": 1.0430710488769013}}, \"EndTime\": 1581707415.574693, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707415.574677}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:15 INFO 140163623442240] #quality_metric: host=algo-1, epoch=1, train mse_objective <loss>=0.0435873907807\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:15 INFO 140163623442240] #early_stopping_criteria_metric: host=algo-1, epoch=1, criteria=mse_objective, value=0.0290952758656\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:15 INFO 140163623442240] Epoch 1: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:15 INFO 140163623442240] #progress_metric: host=algo-1, completed 13 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Total Batches Seen\": {\"count\": 1, \"max\": 385, \"sum\": 385.0, \"min\": 385}, \"Total Records Seen\": {\"count\": 1, \"max\": 1537, \"sum\": 1537.0, \"min\": 1537}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Reset Count\": {\"count\": 1, \"max\": 4, \"sum\": 4.0, \"min\": 4}}, \"EndTime\": 1581707415.577491, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1581707412.786628}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:15 INFO 140163623442240] #throughput_metric: host=algo-1, train throughput=183.088240121 records/second\u001b[0m\n",
      "\u001b[34m[2020-02-14 19:10:17.967] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 8, \"duration\": 2389, \"num_examples\": 128, \"num_bytes\": 53144}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.018964494905328217, \"sum\": 0.018964494905328217, \"min\": 0.018964494905328217}}, \"EndTime\": 1581707417.967173, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.967077}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.02209457935715886, \"sum\": 0.02209457935715886, \"min\": 0.02209457935715886}}, \"EndTime\": 1581707417.967267, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.96725}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.03115541301484359, \"sum\": 0.03115541301484359, \"min\": 0.03115541301484359}}, \"EndTime\": 1581707417.967323, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.967307}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.02142356520227164, \"sum\": 0.02142356520227164, \"min\": 0.02142356520227164}}, \"EndTime\": 1581707417.967384, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.967367}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.026360127170144868, \"sum\": 0.026360127170144868, \"min\": 0.026360127170144868}}, \"EndTime\": 1581707417.967444, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.967428}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.17403585122032897, \"sum\": 0.17403585122032897, \"min\": 0.17403585122032897}}, \"EndTime\": 1581707417.967503, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.967487}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.08101395120125586, \"sum\": 0.08101395120125586, \"min\": 0.08101395120125586}}, \"EndTime\": 1581707417.967552, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.967539}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.16095943209957184, \"sum\": 0.16095943209957184, \"min\": 0.16095943209957184}}, \"EndTime\": 1581707417.9676, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.967587}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.023530229232200665, \"sum\": 0.023530229232200665, \"min\": 0.023530229232200665}}, \"EndTime\": 1581707417.967663, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.967647}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.01924020535354759, \"sum\": 0.01924020535354759, \"min\": 0.01924020535354759}}, \"EndTime\": 1581707417.967728, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.96771}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.031402417194596896, \"sum\": 0.031402417194596896, \"min\": 0.031402417194596896}}, \"EndTime\": 1581707417.967789, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.967771}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.020192892503774335, \"sum\": 0.020192892503774335, \"min\": 0.020192892503774335}}, \"EndTime\": 1581707417.967858, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.96784}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.07715152316143752, \"sum\": 0.07715152316143752, \"min\": 0.07715152316143752}}, \"EndTime\": 1581707417.967925, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.967907}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.04061111411277762, \"sum\": 0.04061111411277762, \"min\": 0.04061111411277762}}, \"EndTime\": 1581707417.967987, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.967969}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.08661929132255512, \"sum\": 0.08661929132255512, \"min\": 0.08661929132255512}}, \"EndTime\": 1581707417.968046, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.968029}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.18598391714791848, \"sum\": 0.18598391714791848, \"min\": 0.18598391714791848}}, \"EndTime\": 1581707417.968107, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.96809}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11585506895285716, \"sum\": 0.11585506895285716, \"min\": 0.11585506895285716}}, \"EndTime\": 1581707417.968166, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.96815}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11726166886886157, \"sum\": 0.11726166886886157, \"min\": 0.11726166886886157}}, \"EndTime\": 1581707417.968232, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.968214}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.1161198624772731, \"sum\": 0.1161198624772731, \"min\": 0.1161198624772731}}, \"EndTime\": 1581707417.968286, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.96827}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11755012483959358, \"sum\": 0.11755012483959358, \"min\": 0.11755012483959358}}, \"EndTime\": 1581707417.96834, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.968324}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.23303119818437992, \"sum\": 0.23303119818437992, \"min\": 0.23303119818437992}}, \"EndTime\": 1581707417.968393, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.968377}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.26810676813461104, \"sum\": 0.26810676813461104, \"min\": 0.26810676813461104}}, \"EndTime\": 1581707417.968458, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.968441}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.23217665895409, \"sum\": 0.23217665895409, \"min\": 0.23217665895409}}, \"EndTime\": 1581707417.96851, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.968495}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.2678189647079658, \"sum\": 0.2678189647079658, \"min\": 0.2678189647079658}}, \"EndTime\": 1581707417.968561, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.968547}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9382113837673673, \"sum\": 0.9382113837673673, \"min\": 0.9382113837673673}}, \"EndTime\": 1581707417.968651, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.96861}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9391309535350856, \"sum\": 0.9391309535350856, \"min\": 0.9391309535350856}}, \"EndTime\": 1581707417.968708, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.968696}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9382782642854246, \"sum\": 0.9382782642854246, \"min\": 0.9382782642854246}}, \"EndTime\": 1581707417.968765, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.968748}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9393802980386367, \"sum\": 0.9393802980386367, \"min\": 0.9393802980386367}}, \"EndTime\": 1581707417.968817, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.968806}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0214215481257813, \"sum\": 1.0214215481257813, \"min\": 1.0214215481257813}}, \"EndTime\": 1581707417.968863, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.968849}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0433832367984799, \"sum\": 1.0433832367984799, \"min\": 1.0433832367984799}}, \"EndTime\": 1581707417.968913, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.968898}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.021518769637337, \"sum\": 1.021518769637337, \"min\": 1.021518769637337}}, \"EndTime\": 1581707417.968968, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.968953}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0446990938402536, \"sum\": 1.0446990938402536, \"min\": 1.0446990938402536}}, \"EndTime\": 1581707417.969026, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707417.96901}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:17 INFO 140163623442240] #quality_metric: host=algo-1, epoch=2, train mse_objective <loss>=0.0189644949053\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:17 INFO 140163623442240] #early_stopping_criteria_metric: host=algo-1, epoch=2, criteria=mse_objective, value=0.0189644949053\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:17 INFO 140163623442240] Epoch 2: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:17 INFO 140163623442240] #progress_metric: host=algo-1, completed 20 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Total Batches Seen\": {\"count\": 1, \"max\": 513, \"sum\": 513.0, \"min\": 513}, \"Total Records Seen\": {\"count\": 1, \"max\": 2048, \"sum\": 2048.0, \"min\": 2048}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Reset Count\": {\"count\": 1, \"max\": 5, \"sum\": 5.0, \"min\": 5}}, \"EndTime\": 1581707417.971824, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1581707415.577762}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:17 INFO 140163623442240] #throughput_metric: host=algo-1, train throughput=213.431752684 records/second\u001b[0m\n",
      "\u001b[34m[2020-02-14 19:10:20.520] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 10, \"duration\": 2548, \"num_examples\": 128, \"num_bytes\": 53144}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.008805848856082581, \"sum\": 0.008805848856082581, \"min\": 0.008805848856082581}}, \"EndTime\": 1581707420.521063, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.52097}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.010035734080322689, \"sum\": 0.010035734080322689, \"min\": 0.010035734080322689}}, \"EndTime\": 1581707420.521166, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.521145}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.016520435721558466, \"sum\": 0.016520435721558466, \"min\": 0.016520435721558466}}, \"EndTime\": 1581707420.521233, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.521214}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.00975347944905644, \"sum\": 0.00975347944905644, \"min\": 0.00975347944905644}}, \"EndTime\": 1581707420.52129, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.521275}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.04975664273901718, \"sum\": 0.04975664273901718, \"min\": 0.04975664273901718}}, \"EndTime\": 1581707420.521339, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.521326}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.022683958969386953, \"sum\": 0.022683958969386953, \"min\": 0.022683958969386953}}, \"EndTime\": 1581707420.521387, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.521376}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.006794041575344672, \"sum\": 0.006794041575344672, \"min\": 0.006794041575344672}}, \"EndTime\": 1581707420.521418, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.52141}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.4148000222667465, \"sum\": 0.4148000222667465, \"min\": 0.4148000222667465}}, \"EndTime\": 1581707420.521458, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.521444}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.012747020364187173, \"sum\": 0.012747020364187173, \"min\": 0.012747020364187173}}, \"EndTime\": 1581707420.521513, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.521497}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.008988852285428017, \"sum\": 0.008988852285428017, \"min\": 0.008988852285428017}}, \"EndTime\": 1581707420.521571, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.521555}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.017093684039386648, \"sum\": 0.017093684039386648, \"min\": 0.017093684039386648}}, \"EndTime\": 1581707420.521629, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.521613}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.009309374260959971, \"sum\": 0.009309374260959971, \"min\": 0.009309374260959971}}, \"EndTime\": 1581707420.521687, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.521671}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.013871593407940076, \"sum\": 0.013871593407940076, \"min\": 0.013871593407940076}}, \"EndTime\": 1581707420.521746, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.52173}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.49350809907232684, \"sum\": 0.49350809907232684, \"min\": 0.49350809907232684}}, \"EndTime\": 1581707420.521801, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.521786}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.04129735385040261, \"sum\": 0.04129735385040261, \"min\": 0.04129735385040261}}, \"EndTime\": 1581707420.521858, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.521842}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.21275528359197549, \"sum\": 0.21275528359197549, \"min\": 0.21275528359197549}}, \"EndTime\": 1581707420.521916, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.5219}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.115305544286013, \"sum\": 0.115305544286013, \"min\": 0.115305544286013}}, \"EndTime\": 1581707420.521977, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.52196}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11747504540491761, \"sum\": 0.11747504540491761, \"min\": 0.11747504540491761}}, \"EndTime\": 1581707420.522036, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.52202}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11529689198946506, \"sum\": 0.11529689198946506, \"min\": 0.11529689198946506}}, \"EndTime\": 1581707420.522095, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.522078}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11772895976901054, \"sum\": 0.11772895976901054, \"min\": 0.11772895976901054}}, \"EndTime\": 1581707420.522153, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.522136}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.22726002043685106, \"sum\": 0.22726002043685106, \"min\": 0.22726002043685106}}, \"EndTime\": 1581707420.522213, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.522196}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.2663217149376979, \"sum\": 0.2663217149376979, \"min\": 0.2663217149376979}}, \"EndTime\": 1581707420.52228, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.522262}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.22696897664701376, \"sum\": 0.22696897664701376, \"min\": 0.22696897664701376}}, \"EndTime\": 1581707420.522347, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.52233}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.2662210277626364, \"sum\": 0.2662210277626364, \"min\": 0.2662210277626364}}, \"EndTime\": 1581707420.522409, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.522391}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9377900025565323, \"sum\": 0.9377900025565323, \"min\": 0.9377900025565323}}, \"EndTime\": 1581707420.522468, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.522452}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9389449089882881, \"sum\": 0.9389449089882881, \"min\": 0.9389449089882881}}, \"EndTime\": 1581707420.522526, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.52251}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9378981478512287, \"sum\": 0.9378981478512287, \"min\": 0.9378981478512287}}, \"EndTime\": 1581707420.522582, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.522566}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.939213721448277, \"sum\": 0.939213721448277, \"min\": 0.939213721448277}}, \"EndTime\": 1581707420.522642, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.522625}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0199843368779018, \"sum\": 1.0199843368779018, \"min\": 1.0199843368779018}}, \"EndTime\": 1581707420.522698, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.522682}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.043101659000624, \"sum\": 1.043101659000624, \"min\": 1.043101659000624}}, \"EndTime\": 1581707420.522764, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.522746}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0203360832464976, \"sum\": 1.0203360832464976, \"min\": 1.0203360832464976}}, \"EndTime\": 1581707420.52281, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.5228}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0439449047127107, \"sum\": 1.0439449047127107, \"min\": 1.0439449047127107}}, \"EndTime\": 1581707420.522864, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707420.522847}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:20 INFO 140163623442240] #quality_metric: host=algo-1, epoch=3, train mse_objective <loss>=0.00880584885608\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:20 INFO 140163623442240] #early_stopping_criteria_metric: host=algo-1, epoch=3, criteria=mse_objective, value=0.00679404157534\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:20 INFO 140163623442240] Epoch 3: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:20 INFO 140163623442240] #progress_metric: host=algo-1, completed 26 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Total Batches Seen\": {\"count\": 1, \"max\": 641, \"sum\": 641.0, \"min\": 641}, \"Total Records Seen\": {\"count\": 1, \"max\": 2559, \"sum\": 2559.0, \"min\": 2559}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Reset Count\": {\"count\": 1, \"max\": 6, \"sum\": 6.0, \"min\": 6}}, \"EndTime\": 1581707420.525827, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1581707417.972097}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:20 INFO 140163623442240] #throughput_metric: host=algo-1, train throughput=200.088963059 records/second\u001b[0m\n",
      "\u001b[34m[2020-02-14 19:10:23.117] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 12, \"duration\": 2590, \"num_examples\": 128, \"num_bytes\": 53144}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.004411165624734117, \"sum\": 0.004411165624734117, \"min\": 0.004411165624734117}}, \"EndTime\": 1581707423.117218, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.117124}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.00451172419145839, \"sum\": 0.00451172419145839, \"min\": 0.00451172419145839}}, \"EndTime\": 1581707423.117312, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.117292}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.009376997430396347, \"sum\": 0.009376997430396347, \"min\": 0.009376997430396347}}, \"EndTime\": 1581707423.117376, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.117357}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.004348102992530693, \"sum\": 0.004348102992530693, \"min\": 0.004348102992530693}}, \"EndTime\": 1581707423.117439, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.117422}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.008781442114593842, \"sum\": 0.008781442114593842, \"min\": 0.008781442114593842}}, \"EndTime\": 1581707423.117501, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.117484}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.04466104145275473, \"sum\": 0.04466104145275473, \"min\": 0.04466104145275473}}, \"EndTime\": 1581707423.117563, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.117544}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0034010069773396786, \"sum\": 0.0034010069773396786, \"min\": 0.0034010069773396786}}, \"EndTime\": 1581707423.117621, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.117605}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0635285263415426, \"sum\": 0.0635285263415426, \"min\": 0.0635285263415426}}, \"EndTime\": 1581707423.117682, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.117665}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.007430567459283968, \"sum\": 0.007430567459283968, \"min\": 0.007430567459283968}}, \"EndTime\": 1581707423.117741, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.117724}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.004394863489016113, \"sum\": 0.004394863489016113, \"min\": 0.004394863489016113}}, \"EndTime\": 1581707423.117799, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.117782}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.010033894056412135, \"sum\": 0.010033894056412135, \"min\": 0.010033894056412135}}, \"EndTime\": 1581707423.117858, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.11784}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0045590766825425386, \"sum\": 0.0045590766825425386, \"min\": 0.0045590766825425386}}, \"EndTime\": 1581707423.117919, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.117902}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.006344853759265237, \"sum\": 0.006344853759265237, \"min\": 0.006344853759265237}}, \"EndTime\": 1581707423.117979, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.117963}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.16321035868700917, \"sum\": 0.16321035868700917, \"min\": 0.16321035868700917}}, \"EndTime\": 1581707423.118037, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.11802}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0062001707203279246, \"sum\": 0.0062001707203279246, \"min\": 0.0062001707203279246}}, \"EndTime\": 1581707423.118097, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.118081}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.08852420604648435, \"sum\": 0.08852420604648435, \"min\": 0.08852420604648435}}, \"EndTime\": 1581707423.118157, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.118139}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11479626782421815, \"sum\": 0.11479626782421815, \"min\": 0.11479626782421815}}, \"EndTime\": 1581707423.118217, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.1182}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11762506477388106, \"sum\": 0.11762506477388106, \"min\": 0.11762506477388106}}, \"EndTime\": 1581707423.118278, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.118261}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11476608165393136, \"sum\": 0.11476608165393136, \"min\": 0.11476608165393136}}, \"EndTime\": 1581707423.118336, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.11832}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11782048998297903, \"sum\": 0.11782048998297903, \"min\": 0.11782048998297903}}, \"EndTime\": 1581707423.118395, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.118377}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.22378716908219293, \"sum\": 0.22378716908219293, \"min\": 0.22378716908219293}}, \"EndTime\": 1581707423.118458, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.11844}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.26271347306195764, \"sum\": 0.26271347306195764, \"min\": 0.26271347306195764}}, \"EndTime\": 1581707423.118519, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.118502}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.22390662397690644, \"sum\": 0.22390662397690644, \"min\": 0.22390662397690644}}, \"EndTime\": 1581707423.118582, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.118565}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.2627195041217863, \"sum\": 0.2627195041217863, \"min\": 0.2627195041217863}}, \"EndTime\": 1581707423.118643, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.118626}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9372801301399554, \"sum\": 0.9372801301399554, \"min\": 0.9372801301399554}}, \"EndTime\": 1581707423.118704, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.118687}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9388683268903043, \"sum\": 0.9388683268903043, \"min\": 0.9388683268903043}}, \"EndTime\": 1581707423.118765, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.118748}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9373712774776802, \"sum\": 0.9373712774776802, \"min\": 0.9373712774776802}}, \"EndTime\": 1581707423.118835, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.118817}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9390981993747978, \"sum\": 0.9390981993747978, \"min\": 0.9390981993747978}}, \"EndTime\": 1581707423.118896, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.11888}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.019358312461789, \"sum\": 1.019358312461789, \"min\": 1.019358312461789}}, \"EndTime\": 1581707423.118955, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.118939}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0417559120774738, \"sum\": 1.0417559120774738, \"min\": 1.0417559120774738}}, \"EndTime\": 1581707423.119015, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.118999}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0195198854592842, \"sum\": 1.0195198854592842, \"min\": 1.0195198854592842}}, \"EndTime\": 1581707423.119076, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.11906}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.042095173560963, \"sum\": 1.042095173560963, \"min\": 1.042095173560963}}, \"EndTime\": 1581707423.119136, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707423.11912}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:23 INFO 140163623442240] #quality_metric: host=algo-1, epoch=4, train mse_objective <loss>=0.00441116562473\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:23 INFO 140163623442240] #early_stopping_criteria_metric: host=algo-1, epoch=4, criteria=mse_objective, value=0.00340100697734\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:23 INFO 140163623442240] Epoch 4: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:23 INFO 140163623442240] #progress_metric: host=algo-1, completed 33 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Total Batches Seen\": {\"count\": 1, \"max\": 769, \"sum\": 769.0, \"min\": 769}, \"Total Records Seen\": {\"count\": 1, \"max\": 3070, \"sum\": 3070.0, \"min\": 3070}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Reset Count\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}}, \"EndTime\": 1581707423.122058, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1581707420.526147}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:23 INFO 140163623442240] #throughput_metric: host=algo-1, train throughput=196.837193993 records/second\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m[2020-02-14 19:10:25.396] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 14, \"duration\": 2273, \"num_examples\": 128, \"num_bytes\": 53144}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.002343199140319828, \"sum\": 0.002343199140319828, \"min\": 0.002343199140319828}}, \"EndTime\": 1581707425.396479, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.396384}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0019308556938350502, \"sum\": 0.0019308556938350502, \"min\": 0.0019308556938350502}}, \"EndTime\": 1581707425.396573, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.396553}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.005624238548087191, \"sum\": 0.005624238548087191, \"min\": 0.005624238548087191}}, \"EndTime\": 1581707425.396671, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.39665}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.001832042761455477, \"sum\": 0.001832042761455477, \"min\": 0.001832042761455477}}, \"EndTime\": 1581707425.396729, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.396716}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.003335687304183742, \"sum\": 0.003335687304183742, \"min\": 0.003335687304183742}}, \"EndTime\": 1581707425.396773, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.396758}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.4393133001006025, \"sum\": 0.4393133001006025, \"min\": 0.4393133001006025}}, \"EndTime\": 1581707425.396831, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.396815}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0010932620926888468, \"sum\": 0.0010932620926888468, \"min\": 0.0010932620926888468}}, \"EndTime\": 1581707425.396885, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.39687}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.02155558593153191, \"sum\": 0.02155558593153191, \"min\": 0.02155558593153191}}, \"EndTime\": 1581707425.396948, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.396932}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.004592660909626765, \"sum\": 0.004592660909626765, \"min\": 0.004592660909626765}}, \"EndTime\": 1581707425.39702, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.397002}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0022582915067529733, \"sum\": 0.0022582915067529733, \"min\": 0.0022582915067529733}}, \"EndTime\": 1581707425.39709, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.397072}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.006311858513559339, \"sum\": 0.006311858513559339, \"min\": 0.006311858513559339}}, \"EndTime\": 1581707425.397161, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.397143}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0023428881284803717, \"sum\": 0.0023428881284803717, \"min\": 0.0023428881284803717}}, \"EndTime\": 1581707425.39723, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.397212}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0049129729157744075, \"sum\": 0.0049129729157744075, \"min\": 0.0049129729157744075}}, \"EndTime\": 1581707425.397301, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.397283}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.04397436191225204, \"sum\": 0.04397436191225204, \"min\": 0.04397436191225204}}, \"EndTime\": 1581707425.397371, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.397352}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0047465733586586865, \"sum\": 0.0047465733586586865, \"min\": 0.0047465733586586865}}, \"EndTime\": 1581707425.397428, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.397413}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.04813231573546466, \"sum\": 0.04813231573546466, \"min\": 0.04813231573546466}}, \"EndTime\": 1581707425.397492, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.397475}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11426111849897548, \"sum\": 0.11426111849897548, \"min\": 0.11426111849897548}}, \"EndTime\": 1581707425.39755, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.397534}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11773895287123604, \"sum\": 0.11773895287123604, \"min\": 0.11773895287123604}}, \"EndTime\": 1581707425.397604, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.397588}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11423464927375024, \"sum\": 0.11423464927375024, \"min\": 0.11423464927375024}}, \"EndTime\": 1581707425.397667, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.39765}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11789148340669028, \"sum\": 0.11789148340669028, \"min\": 0.11789148340669028}}, \"EndTime\": 1581707425.397734, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.397715}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.20963513151940516, \"sum\": 0.20963513151940516, \"min\": 0.20963513151940516}}, \"EndTime\": 1581707425.397807, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.397788}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.2609003410215387, \"sum\": 0.2609003410215387, \"min\": 0.2609003410215387}}, \"EndTime\": 1581707425.397876, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.397859}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.21018992262107766, \"sum\": 0.21018992262107766, \"min\": 0.21018992262107766}}, \"EndTime\": 1581707425.397946, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.397928}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.2609194571855405, \"sum\": 0.2609194571855405, \"min\": 0.2609194571855405}}, \"EndTime\": 1581707425.398006, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.397989}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9366944629376329, \"sum\": 0.9366944629376329, \"min\": 0.9366944629376329}}, \"EndTime\": 1581707425.398071, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.398054}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9387724917733998, \"sum\": 0.9387724917733998, \"min\": 0.9387724917733998}}, \"EndTime\": 1581707425.398129, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.398112}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9367514845482477, \"sum\": 0.9367514845482477, \"min\": 0.9367514845482477}}, \"EndTime\": 1581707425.398189, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.398172}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9389635114747239, \"sum\": 0.9389635114747239, \"min\": 0.9389635114747239}}, \"EndTime\": 1581707425.39826, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.398242}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0141608691590978, \"sum\": 1.0141608691590978, \"min\": 1.0141608691590978}}, \"EndTime\": 1581707425.39833, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.398311}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0405838090897075, \"sum\": 1.0405838090897075, \"min\": 1.0405838090897075}}, \"EndTime\": 1581707425.398381, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.398367}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.014424465420678, \"sum\": 1.014424465420678, \"min\": 1.014424465420678}}, \"EndTime\": 1581707425.398446, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.398429}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0407243178292054, \"sum\": 1.0407243178292054, \"min\": 1.0407243178292054}}, \"EndTime\": 1581707425.398518, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707425.398499}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:25 INFO 140163623442240] #quality_metric: host=algo-1, epoch=5, train mse_objective <loss>=0.00234319914032\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:25 INFO 140163623442240] #early_stopping_criteria_metric: host=algo-1, epoch=5, criteria=mse_objective, value=0.00109326209269\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:25 INFO 140163623442240] Epoch 5: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:25 INFO 140163623442240] #progress_metric: host=algo-1, completed 40 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Total Batches Seen\": {\"count\": 1, \"max\": 897, \"sum\": 897.0, \"min\": 897}, \"Total Records Seen\": {\"count\": 1, \"max\": 3581, \"sum\": 3581.0, \"min\": 3581}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Reset Count\": {\"count\": 1, \"max\": 8, \"sum\": 8.0, \"min\": 8}}, \"EndTime\": 1581707425.401366, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1581707423.122366}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:25 INFO 140163623442240] #throughput_metric: host=algo-1, train throughput=224.206602919 records/second\u001b[0m\n",
      "\u001b[34m[2020-02-14 19:10:27.796] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 16, \"duration\": 2394, \"num_examples\": 128, \"num_bytes\": 53144}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0012971160254591533, \"sum\": 0.0012971160254591533, \"min\": 0.0012971160254591533}}, \"EndTime\": 1581707427.796352, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.796266}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0007804925096891344, \"sum\": 0.0007804925096891344, \"min\": 0.0007804925096891344}}, \"EndTime\": 1581707427.79642, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.796408}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0035088709895874878, \"sum\": 0.0035088709895874878, \"min\": 0.0035088709895874878}}, \"EndTime\": 1581707427.796472, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.796458}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0007143837783515901, \"sum\": 0.0007143837783515901, \"min\": 0.0007143837783515901}}, \"EndTime\": 1581707427.796524, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.796511}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0014082517053628997, \"sum\": 0.0014082517053628997, \"min\": 0.0014082517053628997}}, \"EndTime\": 1581707427.796575, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.796561}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.17334029729309397, \"sum\": 0.17334029729309397, \"min\": 0.17334029729309397}}, \"EndTime\": 1581707427.796655, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.796616}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0004804266403178505, \"sum\": 0.0004804266403178505, \"min\": 0.0004804266403178505}}, \"EndTime\": 1581707427.79672, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.796703}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.05035767363435699, \"sum\": 0.05035767363435699, \"min\": 0.05035767363435699}}, \"EndTime\": 1581707427.796774, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.796759}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.002991814234598327, \"sum\": 0.002991814234598327, \"min\": 0.002991814234598327}}, \"EndTime\": 1581707427.796832, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.796816}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0012588410073498036, \"sum\": 0.0012588410073498036, \"min\": 0.0012588410073498036}}, \"EndTime\": 1581707427.79689, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.796873}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.004206669256187367, \"sum\": 0.004206669256187367, \"min\": 0.004206669256187367}}, \"EndTime\": 1581707427.796949, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.796933}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.001300310841872674, \"sum\": 0.001300310841872674, \"min\": 0.001300310841872674}}, \"EndTime\": 1581707427.79701, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.796993}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0036475937414775037, \"sum\": 0.0036475937414775037, \"min\": 0.0036475937414775037}}, \"EndTime\": 1581707427.79707, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.797054}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.014466840953485468, \"sum\": 0.014466840953485468, \"min\": 0.014466840953485468}}, \"EndTime\": 1581707427.797134, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.797116}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0038171896085832414, \"sum\": 0.0038171896085832414, \"min\": 0.0038171896085832414}}, \"EndTime\": 1581707427.797197, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.797179}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0795249520355513, \"sum\": 0.0795249520355513, \"min\": 0.0795249520355513}}, \"EndTime\": 1581707427.797256, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.79724}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11373924361871864, \"sum\": 0.11373924361871864, \"min\": 0.11373924361871864}}, \"EndTime\": 1581707427.797316, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.797299}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11782339326927335, \"sum\": 0.11782339326927335, \"min\": 0.11782339326927335}}, \"EndTime\": 1581707427.797376, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.797359}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.113717589142373, \"sum\": 0.113717589142373, \"min\": 0.113717589142373}}, \"EndTime\": 1581707427.797434, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.797418}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11794409768905227, \"sum\": 0.11794409768905227, \"min\": 0.11794409768905227}}, \"EndTime\": 1581707427.797494, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.797478}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.19111515167279272, \"sum\": 0.19111515167279272, \"min\": 0.19111515167279272}}, \"EndTime\": 1581707427.797555, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.797538}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.2589132582070306, \"sum\": 0.2589132582070306, \"min\": 0.2589132582070306}}, \"EndTime\": 1581707427.797617, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.797601}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.19155508812560929, \"sum\": 0.19155508812560929, \"min\": 0.19155508812560929}}, \"EndTime\": 1581707427.797676, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.79766}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.25891476188691, \"sum\": 0.25891476188691, \"min\": 0.25891476188691}}, \"EndTime\": 1581707427.797741, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.797723}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9361313824637199, \"sum\": 0.9361313824637199, \"min\": 0.9361313824637199}}, \"EndTime\": 1581707427.797796, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.79778}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9387217677073685, \"sum\": 0.9387217677073685, \"min\": 0.9387217677073685}}, \"EndTime\": 1581707427.797854, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.797838}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9361504010826819, \"sum\": 0.9361504010826819, \"min\": 0.9361504010826819}}, \"EndTime\": 1581707427.797916, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.797901}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9388803889256293, \"sum\": 0.9388803889256293, \"min\": 0.9388803889256293}}, \"EndTime\": 1581707427.797975, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.797959}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.007632995097656, \"sum\": 1.007632995097656, \"min\": 1.007632995097656}}, \"EndTime\": 1581707427.798035, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.798019}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0396535749451852, \"sum\": 1.0396535749451852, \"min\": 1.0396535749451852}}, \"EndTime\": 1581707427.798096, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.79808}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0077778461176579, \"sum\": 1.0077778461176579, \"min\": 1.0077778461176579}}, \"EndTime\": 1581707427.798157, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.798139}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0397217518935993, \"sum\": 1.0397217518935993, \"min\": 1.0397217518935993}}, \"EndTime\": 1581707427.798218, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707427.798201}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:27 INFO 140163623442240] #quality_metric: host=algo-1, epoch=6, train mse_objective <loss>=0.00129711602546\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:27 INFO 140163623442240] #early_stopping_criteria_metric: host=algo-1, epoch=6, criteria=mse_objective, value=0.000480426640318\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:27 INFO 140163623442240] Epoch 6: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:27 INFO 140163623442240] #progress_metric: host=algo-1, completed 46 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Total Batches Seen\": {\"count\": 1, \"max\": 1025, \"sum\": 1025.0, \"min\": 1025}, \"Total Records Seen\": {\"count\": 1, \"max\": 4092, \"sum\": 4092.0, \"min\": 4092}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Reset Count\": {\"count\": 1, \"max\": 9, \"sum\": 9.0, \"min\": 9}}, \"EndTime\": 1581707427.801134, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1581707425.401663}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:27 INFO 140163623442240] #throughput_metric: host=algo-1, train throughput=212.951436736 records/second\u001b[0m\n",
      "\u001b[34m[2020-02-14 19:10:30.365] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 18, \"duration\": 2563, \"num_examples\": 128, \"num_bytes\": 53144}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0007404353094984334, \"sum\": 0.0007404353094984334, \"min\": 0.0007404353094984334}}, \"EndTime\": 1581707430.365589, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.365491}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.00030733019016546977, \"sum\": 0.00030733019016546977, \"min\": 0.00030733019016546977}}, \"EndTime\": 1581707430.365755, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.365662}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0022527453657808, \"sum\": 0.0022527453657808, \"min\": 0.0022527453657808}}, \"EndTime\": 1581707430.365887, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.365865}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0002559047938063493, \"sum\": 0.0002559047938063493, \"min\": 0.0002559047938063493}}, \"EndTime\": 1581707430.366008, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.365937}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.000753683931643393, \"sum\": 0.000753683931643393, \"min\": 0.000753683931643393}}, \"EndTime\": 1581707430.366086, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.366067}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.019995483721965763, \"sum\": 0.019995483721965763, \"min\": 0.019995483721965763}}, \"EndTime\": 1581707430.366147, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.36613}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0001213071615220399, \"sum\": 0.0001213071615220399, \"min\": 0.0001213071615220399}}, \"EndTime\": 1581707430.366202, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.366185}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.062439281844962974, \"sum\": 0.062439281844962974, \"min\": 0.062439281844962974}}, \"EndTime\": 1581707430.366262, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.366245}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0020522152042285885, \"sum\": 0.0020522152042285885, \"min\": 0.0020522152042285885}}, \"EndTime\": 1581707430.36632, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.366304}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0007930971034339585, \"sum\": 0.0007930971034339585, \"min\": 0.0007930971034339585}}, \"EndTime\": 1581707430.366376, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.366361}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.002944528131757672, \"sum\": 0.002944528131757672, \"min\": 0.002944528131757672}}, \"EndTime\": 1581707430.366434, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.366417}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0008136654545896317, \"sum\": 0.0008136654545896317, \"min\": 0.0008136654545896317}}, \"EndTime\": 1581707430.366495, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.366477}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.002959074702193616, \"sum\": 0.002959074702193616, \"min\": 0.002959074702193616}}, \"EndTime\": 1581707430.366556, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.36654}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.03317582366280404, \"sum\": 0.03317582366280404, \"min\": 0.03317582366280404}}, \"EndTime\": 1581707430.366619, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.366602}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0030734139109831523, \"sum\": 0.0030734139109831523, \"min\": 0.0030734139109831523}}, \"EndTime\": 1581707430.366681, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.366665}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.37206675687174157, \"sum\": 0.37206675687174157, \"min\": 0.37206675687174157}}, \"EndTime\": 1581707430.366788, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.366726}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.1132620237107704, \"sum\": 0.1132620237107704, \"min\": 0.1132620237107704}}, \"EndTime\": 1581707430.36685, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.366832}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.1178207884865307, \"sum\": 0.1178207884865307, \"min\": 0.1178207884865307}}, \"EndTime\": 1581707430.366904, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.366888}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11324426278427947, \"sum\": 0.11324426278427947, \"min\": 0.11324426278427947}}, \"EndTime\": 1581707430.366959, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.366944}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11791825288078037, \"sum\": 0.11791825288078037, \"min\": 0.11791825288078037}}, \"EndTime\": 1581707430.367017, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.367001}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.17121296258657936, \"sum\": 0.17121296258657936, \"min\": 0.17121296258657936}}, \"EndTime\": 1581707430.367076, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.367059}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.2551383806693214, \"sum\": 0.2551383806693214, \"min\": 0.2551383806693214}}, \"EndTime\": 1581707430.367138, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.36712}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.17165452192398153, \"sum\": 0.17165452192398153, \"min\": 0.17165452192398153}}, \"EndTime\": 1581707430.367197, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.367182}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.25512770699762455, \"sum\": 0.25512770699762455, \"min\": 0.25512770699762455}}, \"EndTime\": 1581707430.367258, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.367241}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9356471375364253, \"sum\": 0.9356471375364253, \"min\": 0.9356471375364253}}, \"EndTime\": 1581707430.367326, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.367308}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9386697603668285, \"sum\": 0.9386697603668285, \"min\": 0.9386697603668285}}, \"EndTime\": 1581707430.367393, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.367376}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9356313359549665, \"sum\": 0.9356313359549665, \"min\": 0.9356313359549665}}, \"EndTime\": 1581707430.367452, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.367435}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9388040554541068, \"sum\": 0.9388040554541068, \"min\": 0.9388040554541068}}, \"EndTime\": 1581707430.367515, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.367498}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0033346011237365, \"sum\": 1.0033346011237365, \"min\": 1.0033346011237365}}, \"EndTime\": 1581707430.367576, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.367559}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0384180270076737, \"sum\": 1.0384180270076737, \"min\": 1.0384180270076737}}, \"EndTime\": 1581707430.367636, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.367619}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.003866387399163, \"sum\": 1.003866387399163, \"min\": 1.003866387399163}}, \"EndTime\": 1581707430.367709, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.36769}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0384802961443353, \"sum\": 1.0384802961443353, \"min\": 1.0384802961443353}}, \"EndTime\": 1581707430.367773, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707430.367756}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:30 INFO 140163623442240] #quality_metric: host=algo-1, epoch=7, train mse_objective <loss>=0.000740435309498\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:30 INFO 140163623442240] #early_stopping_criteria_metric: host=algo-1, epoch=7, criteria=mse_objective, value=0.000121307161522\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:30 INFO 140163623442240] Epoch 7: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:30 INFO 140163623442240] #progress_metric: host=algo-1, completed 53 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Total Batches Seen\": {\"count\": 1, \"max\": 1153, \"sum\": 1153.0, \"min\": 1153}, \"Total Records Seen\": {\"count\": 1, \"max\": 4603, \"sum\": 4603.0, \"min\": 4603}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Reset Count\": {\"count\": 1, \"max\": 10, \"sum\": 10.0, \"min\": 10}}, \"EndTime\": 1581707430.370988, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1581707427.801432}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:30 INFO 140163623442240] #throughput_metric: host=algo-1, train throughput=198.855971169 records/second\u001b[0m\n",
      "\u001b[34m[2020-02-14 19:10:32.903] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 20, \"duration\": 2531, \"num_examples\": 128, \"num_bytes\": 53144}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0004336663640447293, \"sum\": 0.0004336663640447293, \"min\": 0.0004336663640447293}}, \"EndTime\": 1581707432.903628, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.903565}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.00012828181551713632, \"sum\": 0.00012828181551713632, \"min\": 0.00012828181551713632}}, \"EndTime\": 1581707432.903717, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.903698}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0014796872534119986, \"sum\": 0.0014796872534119986, \"min\": 0.0014796872534119986}}, \"EndTime\": 1581707432.903758, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.903749}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 8.43603145295051e-05, \"sum\": 8.43603145295051e-05, \"min\": 8.43603145295051e-05}}, \"EndTime\": 1581707432.903805, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.903792}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0006289287359938649, \"sum\": 0.0006289287359938649, \"min\": 0.0006289287359938649}}, \"EndTime\": 1581707432.903863, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.903848}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.004175194019156574, \"sum\": 0.004175194019156574, \"min\": 0.004175194019156574}}, \"EndTime\": 1581707432.903921, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.903906}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.00015826782650696632, \"sum\": 0.00015826782650696632, \"min\": 0.00015826782650696632}}, \"EndTime\": 1581707432.90398, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.903964}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6461964180935028, \"sum\": 0.6461964180935028, \"min\": 0.6461964180935028}}, \"EndTime\": 1581707432.90404, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.904024}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.001481528757206498, \"sum\": 0.001481528757206498, \"min\": 0.001481528757206498}}, \"EndTime\": 1581707432.904101, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.904084}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0005750194256155895, \"sum\": 0.0005750194256155895, \"min\": 0.0005750194256155895}}, \"EndTime\": 1581707432.904159, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.904143}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.002152359717918329, \"sum\": 0.002152359717918329, \"min\": 0.002152359717918329}}, \"EndTime\": 1581707432.904216, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.9042}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.000585880844375324, \"sum\": 0.000585880844375324, \"min\": 0.000585880844375324}}, \"EndTime\": 1581707432.904272, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.904256}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.002533881569670341, \"sum\": 0.002533881569670341, \"min\": 0.002533881569670341}}, \"EndTime\": 1581707432.90433, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.904315}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.13280278519685124, \"sum\": 0.13280278519685124, \"min\": 0.13280278519685124}}, \"EndTime\": 1581707432.904388, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.904373}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.002634551254762007, \"sum\": 0.002634551254762007, \"min\": 0.002634551254762007}}, \"EndTime\": 1581707432.904446, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.90443}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.143953638990945, \"sum\": 0.143953638990945, \"min\": 0.143953638990945}}, \"EndTime\": 1581707432.904507, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.90449}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11280021809892157, \"sum\": 0.11280021809892157, \"min\": 0.11280021809892157}}, \"EndTime\": 1581707432.904564, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.904549}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11774965986888122, \"sum\": 0.11774965986888122, \"min\": 0.11774965986888122}}, \"EndTime\": 1581707432.904621, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.904605}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.1127856741622677, \"sum\": 0.1127856741622677, \"min\": 0.1127856741622677}}, \"EndTime\": 1581707432.904707, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.904689}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11782879804767023, \"sum\": 0.11782879804767023, \"min\": 0.11782879804767023}}, \"EndTime\": 1581707432.904768, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.90475}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.15551738737253692, \"sum\": 0.15551738737253692, \"min\": 0.15551738737253692}}, \"EndTime\": 1581707432.904834, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.904816}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.2525281124550292, \"sum\": 0.2525281124550292, \"min\": 0.2525281124550292}}, \"EndTime\": 1581707432.904899, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.904882}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.15575717012654608, \"sum\": 0.15575717012654608, \"min\": 0.15575717012654608}}, \"EndTime\": 1581707432.904963, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.904945}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.25251459551417804, \"sum\": 0.25251459551417804, \"min\": 0.25251459551417804}}, \"EndTime\": 1581707432.905028, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.90501}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9352047248032149, \"sum\": 0.9352047248032149, \"min\": 0.9352047248032149}}, \"EndTime\": 1581707432.905097, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.905079}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9386400478914028, \"sum\": 0.9386400478914028, \"min\": 0.9386400478914028}}, \"EndTime\": 1581707432.90516, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.905144}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9351566018844684, \"sum\": 0.9351566018844684, \"min\": 0.9351566018844684}}, \"EndTime\": 1581707432.905221, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.905204}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9387515239476218, \"sum\": 0.9387515239476218, \"min\": 0.9387515239476218}}, \"EndTime\": 1581707432.905288, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.90527}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9988484023300213, \"sum\": 0.9988484023300213, \"min\": 0.9988484023300213}}, \"EndTime\": 1581707432.905349, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.905333}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0369636040679582, \"sum\": 1.0369636040679582, \"min\": 1.0369636040679582}}, \"EndTime\": 1581707432.905409, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.905392}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9988044138675131, \"sum\": 0.9988044138675131, \"min\": 0.9988044138675131}}, \"EndTime\": 1581707432.905469, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.905452}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0370065036310454, \"sum\": 1.0370065036310454, \"min\": 1.0370065036310454}}, \"EndTime\": 1581707432.905528, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707432.905511}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:32 INFO 140163623442240] #quality_metric: host=algo-1, epoch=8, train mse_objective <loss>=0.000433666364045\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:32 INFO 140163623442240] #early_stopping_criteria_metric: host=algo-1, epoch=8, criteria=mse_objective, value=8.43603145295e-05\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:32 INFO 140163623442240] Epoch 8: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:32 INFO 140163623442240] #progress_metric: host=algo-1, completed 60 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Total Batches Seen\": {\"count\": 1, \"max\": 1281, \"sum\": 1281.0, \"min\": 1281}, \"Total Records Seen\": {\"count\": 1, \"max\": 5114, \"sum\": 5114.0, \"min\": 5114}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Reset Count\": {\"count\": 1, \"max\": 11, \"sum\": 11.0, \"min\": 11}}, \"EndTime\": 1581707432.908322, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1581707430.371259}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:32 INFO 140163623442240] #throughput_metric: host=algo-1, train throughput=201.400966105 records/second\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m[2020-02-14 19:10:35.228] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 22, \"duration\": 2319, \"num_examples\": 128, \"num_bytes\": 53144}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.00026026721050899555, \"sum\": 0.00026026721050899555, \"min\": 0.00026026721050899555}}, \"EndTime\": 1581707435.228513, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.228419}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 6.459823257805188e-05, \"sum\": 6.459823257805188e-05, \"min\": 6.459823257805188e-05}}, \"EndTime\": 1581707435.228719, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.228698}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0009920670591087601, \"sum\": 0.0009920670591087601, \"min\": 0.0009920670591087601}}, \"EndTime\": 1581707435.228777, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.228765}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 2.5876237342608302e-05, \"sum\": 2.5876237342608302e-05, \"min\": 2.5876237342608302e-05}}, \"EndTime\": 1581707435.22882, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.228809}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0005091809606331187, \"sum\": 0.0005091809606331187, \"min\": 0.0005091809606331187}}, \"EndTime\": 1581707435.228862, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.228852}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0030353006434844123, \"sum\": 0.0030353006434844123, \"min\": 0.0030353006434844123}}, \"EndTime\": 1581707435.228904, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.228894}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.00016850730332772923, \"sum\": 0.00016850730332772923, \"min\": 0.00016850730332772923}}, \"EndTime\": 1581707435.228951, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.22894}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.4150675588881406, \"sum\": 0.4150675588881406, \"min\": 0.4150675588881406}}, \"EndTime\": 1581707435.228991, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.228981}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.001124046954778243, \"sum\": 0.001124046954778243, \"min\": 0.001124046954778243}}, \"EndTime\": 1581707435.22903, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.22902}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.00047261486090394286, \"sum\": 0.00047261486090394286, \"min\": 0.00047261486090394286}}, \"EndTime\": 1581707435.229068, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.229058}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0016373283244434773, \"sum\": 0.0016373283244434773, \"min\": 0.0016373283244434773}}, \"EndTime\": 1581707435.229113, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.229102}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.00047881537192745417, \"sum\": 0.00047881537192745417, \"min\": 0.00047881537192745417}}, \"EndTime\": 1581707435.229154, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.229144}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0020505819785592923, \"sum\": 0.0020505819785592923, \"min\": 0.0020505819785592923}}, \"EndTime\": 1581707435.229198, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.229188}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.253069953200972, \"sum\": 0.253069953200972, \"min\": 0.253069953200972}}, \"EndTime\": 1581707435.229239, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.229229}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0021614410738482717, \"sum\": 0.0021614410738482717, \"min\": 0.0021614410738482717}}, \"EndTime\": 1581707435.229279, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.229269}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.030479008797556162, \"sum\": 0.030479008797556162, \"min\": 0.030479008797556162}}, \"EndTime\": 1581707435.229323, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.229312}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11237072052184756, \"sum\": 0.11237072052184756, \"min\": 0.11237072052184756}}, \"EndTime\": 1581707435.229362, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.229352}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11771501088881586, \"sum\": 0.11771501088881586, \"min\": 0.11771501088881586}}, \"EndTime\": 1581707435.229405, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.229394}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11235878105627328, \"sum\": 0.11235878105627328, \"min\": 0.11235878105627328}}, \"EndTime\": 1581707435.229445, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.229435}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11777968032791565, \"sum\": 0.11777968032791565, \"min\": 0.11777968032791565}}, \"EndTime\": 1581707435.229483, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.229474}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.1458915730179324, \"sum\": 0.1458915730179324, \"min\": 0.1458915730179324}}, \"EndTime\": 1581707435.229527, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.229517}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.25215858872246555, \"sum\": 0.25215858872246555, \"min\": 0.25215858872246555}}, \"EndTime\": 1581707435.229567, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.229557}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.14601483781946692, \"sum\": 0.14601483781946692, \"min\": 0.14601483781946692}}, \"EndTime\": 1581707435.229605, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.229596}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.2521328354019701, \"sum\": 0.2521328354019701, \"min\": 0.2521328354019701}}, \"EndTime\": 1581707435.229643, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.229634}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9347966352081675, \"sum\": 0.9347966352081675, \"min\": 0.9347966352081675}}, \"EndTime\": 1581707435.229682, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.229672}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9386055535658842, \"sum\": 0.9386055535658842, \"min\": 0.9386055535658842}}, \"EndTime\": 1581707435.229724, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.229711}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9347254286014189, \"sum\": 0.9347254286014189, \"min\": 0.9347254286014189}}, \"EndTime\": 1581707435.229763, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.229754}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9387000004548257, \"sum\": 0.9387000004548257, \"min\": 0.9387000004548257}}, \"EndTime\": 1581707435.22979, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.229783}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9904993619681812, \"sum\": 0.9904993619681812, \"min\": 0.9904993619681812}}, \"EndTime\": 1581707435.229815, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.229808}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0355317155265902, \"sum\": 1.0355317155265902, \"min\": 1.0355317155265902}}, \"EndTime\": 1581707435.22984, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.229833}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.990384950148543, \"sum\": 0.990384950148543, \"min\": 0.990384950148543}}, \"EndTime\": 1581707435.22988, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.229865}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0355611278668164, \"sum\": 1.0355611278668164, \"min\": 1.0355611278668164}}, \"EndTime\": 1581707435.229945, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707435.229928}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:35 INFO 140163623442240] #quality_metric: host=algo-1, epoch=9, train mse_objective <loss>=0.000260267210509\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:35 INFO 140163623442240] #early_stopping_criteria_metric: host=algo-1, epoch=9, criteria=mse_objective, value=2.58762373426e-05\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:35 INFO 140163623442240] Epoch 9: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:35 INFO 140163623442240] #progress_metric: host=algo-1, completed 66 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Total Batches Seen\": {\"count\": 1, \"max\": 1409, \"sum\": 1409.0, \"min\": 1409}, \"Total Records Seen\": {\"count\": 1, \"max\": 5625, \"sum\": 5625.0, \"min\": 5625}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Reset Count\": {\"count\": 1, \"max\": 12, \"sum\": 12.0, \"min\": 12}}, \"EndTime\": 1581707435.232891, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1581707432.908662}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:35 INFO 140163623442240] #throughput_metric: host=algo-1, train throughput=219.844232617 records/second\u001b[0m\n",
      "\u001b[34m[2020-02-14 19:10:37.697] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 24, \"duration\": 2464, \"num_examples\": 128, \"num_bytes\": 53144}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.00016004189933661883, \"sum\": 0.00016004189933661883, \"min\": 0.00016004189933661883}}, \"EndTime\": 1581707437.698096, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.698006}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 4.185453059917348e-05, \"sum\": 4.185453059917348e-05, \"min\": 4.185453059917348e-05}}, \"EndTime\": 1581707437.698188, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.698168}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0006782212313830643, \"sum\": 0.0006782212313830643, \"min\": 0.0006782212313830643}}, \"EndTime\": 1581707437.698254, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.698237}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 7.65336990862196e-06, \"sum\": 7.65336990862196e-06, \"min\": 7.65336990862196e-06}}, \"EndTime\": 1581707437.698317, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.6983}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.00029649838516194197, \"sum\": 0.00029649838516194197, \"min\": 0.00029649838516194197}}, \"EndTime\": 1581707437.698385, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.698368}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.009552479706767408, \"sum\": 0.009552479706767408, \"min\": 0.009552479706767408}}, \"EndTime\": 1581707437.698451, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.698434}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.00011505298779479566, \"sum\": 0.00011505298779479566, \"min\": 0.00011505298779479566}}, \"EndTime\": 1581707437.69851, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.698493}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.08318404265492421, \"sum\": 0.08318404265492421, \"min\": 0.08318404265492421}}, \"EndTime\": 1581707437.69857, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.698553}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0008929835910419383, \"sum\": 0.0008929835910419383, \"min\": 0.0008929835910419383}}, \"EndTime\": 1581707437.698629, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.698613}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0004255642812552644, \"sum\": 0.0004255642812552644, \"min\": 0.0004255642812552644}}, \"EndTime\": 1581707437.69869, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.698674}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0012920644085241966, \"sum\": 0.0012920644085241966, \"min\": 0.0012920644085241966}}, \"EndTime\": 1581707437.69875, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.698735}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0004291884555082858, \"sum\": 0.0004291884555082858, \"min\": 0.0004291884555082858}}, \"EndTime\": 1581707437.698812, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.698796}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0018697286081952034, \"sum\": 0.0018697286081952034, \"min\": 0.0018697286081952034}}, \"EndTime\": 1581707437.698875, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.698859}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.10867157830365413, \"sum\": 0.10867157830365413, \"min\": 0.10867157830365413}}, \"EndTime\": 1581707437.698938, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.698921}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0019324190566330226, \"sum\": 0.0019324190566330226, \"min\": 0.0019324190566330226}}, \"EndTime\": 1581707437.698999, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.698982}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.03702040455362752, \"sum\": 0.03702040455362752, \"min\": 0.03702040455362752}}, \"EndTime\": 1581707437.699063, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.699046}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.1119813531303529, \"sum\": 0.1119813531303529, \"min\": 0.1119813531303529}}, \"EndTime\": 1581707437.699125, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.699107}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11770610784833122, \"sum\": 0.11770610784833122, \"min\": 0.11770610784833122}}, \"EndTime\": 1581707437.699185, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.699168}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11197159588740273, \"sum\": 0.11197159588740273, \"min\": 0.11197159588740273}}, \"EndTime\": 1581707437.699246, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.699229}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11775962628908163, \"sum\": 0.11775962628908163, \"min\": 0.11775962628908163}}, \"EndTime\": 1581707437.699307, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.69929}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.13946539572051425, \"sum\": 0.13946539572051425, \"min\": 0.13946539572051425}}, \"EndTime\": 1581707437.699368, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.699351}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.25112140458810517, \"sum\": 0.25112140458810517, \"min\": 0.25112140458810517}}, \"EndTime\": 1581707437.699428, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.699411}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.13953740525198735, \"sum\": 0.13953740525198735, \"min\": 0.13953740525198735}}, \"EndTime\": 1581707437.699472, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.699459}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.2510877857791392, \"sum\": 0.2510877857791392, \"min\": 0.2510877857791392}}, \"EndTime\": 1581707437.699529, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.699513}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.934429985713067, \"sum\": 0.934429985713067, \"min\": 0.934429985713067}}, \"EndTime\": 1581707437.699585, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.69957}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9385737899074874, \"sum\": 0.9385737899074874, \"min\": 0.9385737899074874}}, \"EndTime\": 1581707437.699641, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.699625}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9343539660312529, \"sum\": 0.9343539660312529, \"min\": 0.9343539660312529}}, \"EndTime\": 1581707437.6997, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.699683}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9386542983587802, \"sum\": 0.9386542983587802, \"min\": 0.9386542983587802}}, \"EndTime\": 1581707437.699759, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.699742}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9813819128403983, \"sum\": 0.9813819128403983, \"min\": 0.9813819128403983}}, \"EndTime\": 1581707437.699819, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.699802}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.034748264097088, \"sum\": 1.034748264097088, \"min\": 1.034748264097088}}, \"EndTime\": 1581707437.699877, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.699861}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9812984173809449, \"sum\": 0.9812984173809449, \"min\": 0.9812984173809449}}, \"EndTime\": 1581707437.699938, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.69992}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0347740624070636, \"sum\": 1.0347740624070636, \"min\": 1.0347740624070636}}, \"EndTime\": 1581707437.699999, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707437.699982}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:37 INFO 140163623442240] #quality_metric: host=algo-1, epoch=10, train mse_objective <loss>=0.000160041899337\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:37 INFO 140163623442240] #early_stopping_criteria_metric: host=algo-1, epoch=10, criteria=mse_objective, value=7.65336990862e-06\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:37 INFO 140163623442240] Epoch 10: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:37 INFO 140163623442240] #progress_metric: host=algo-1, completed 73 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Total Batches Seen\": {\"count\": 1, \"max\": 1537, \"sum\": 1537.0, \"min\": 1537}, \"Total Records Seen\": {\"count\": 1, \"max\": 6136, \"sum\": 6136.0, \"min\": 6136}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Reset Count\": {\"count\": 1, \"max\": 13, \"sum\": 13.0, \"min\": 13}}, \"EndTime\": 1581707437.702881, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1581707435.233131}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:37 INFO 140163623442240] #throughput_metric: host=algo-1, train throughput=206.892154527 records/second\u001b[0m\n",
      "\u001b[34m[2020-02-14 19:10:40.113] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 26, \"duration\": 2409, \"num_examples\": 128, \"num_bytes\": 53144}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.00010087095152475668, \"sum\": 0.00010087095152475668, \"min\": 0.00010087095152475668}}, \"EndTime\": 1581707440.113403, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.113306}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 3.2443825972674006e-05, \"sum\": 3.2443825972674006e-05, \"min\": 3.2443825972674006e-05}}, \"EndTime\": 1581707440.113497, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.113479}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.000472533352332516, \"sum\": 0.000472533352332516, \"min\": 0.000472533352332516}}, \"EndTime\": 1581707440.113564, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.113546}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 2.408144316216133e-06, \"sum\": 2.408144316216133e-06, \"min\": 2.408144316216133e-06}}, \"EndTime\": 1581707440.113632, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.113614}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 8.672578058047394e-05, \"sum\": 8.672578058047394e-05, \"min\": 8.672578058047394e-05}}, \"EndTime\": 1581707440.113698, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.113681}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6037110624996227, \"sum\": 0.6037110624996227, \"min\": 0.6037110624996227}}, \"EndTime\": 1581707440.113766, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.113748}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 4.732700007548195e-05, \"sum\": 4.732700007548195e-05, \"min\": 4.732700007548195e-05}}, \"EndTime\": 1581707440.113826, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.113809}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.01372308598388551, \"sum\": 0.01372308598388551, \"min\": 0.01372308598388551}}, \"EndTime\": 1581707440.113889, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.113872}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0007389135181564219, \"sum\": 0.0007389135181564219, \"min\": 0.0007389135181564219}}, \"EndTime\": 1581707440.113948, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.113932}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0004052997514452899, \"sum\": 0.0004052997514452899, \"min\": 0.0004052997514452899}}, \"EndTime\": 1581707440.11401, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.113993}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0010541711504686638, \"sum\": 0.0010541711504686638, \"min\": 0.0010541711504686638}}, \"EndTime\": 1581707440.114068, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.114052}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.00040730136626777717, \"sum\": 0.00040730136626777717, \"min\": 0.00040730136626777717}}, \"EndTime\": 1581707440.114128, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.114112}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0015643593846264802, \"sum\": 0.0015643593846264802, \"min\": 0.0015643593846264802}}, \"EndTime\": 1581707440.114188, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.114172}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.03527680238686854, \"sum\": 0.03527680238686854, \"min\": 0.03527680238686854}}, \"EndTime\": 1581707440.114235, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.114225}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0016511460034246373, \"sum\": 0.0016511460034246373, \"min\": 0.0016511460034246373}}, \"EndTime\": 1581707440.114263, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.114257}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.030693476683254846, \"sum\": 0.030693476683254846, \"min\": 0.030693476683254846}}, \"EndTime\": 1581707440.11429, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.114283}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11162445919155546, \"sum\": 0.11162445919155546, \"min\": 0.11162445919155546}}, \"EndTime\": 1581707440.114317, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.11431}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11761149772127548, \"sum\": 0.11761149772127548, \"min\": 0.11761149772127548}}, \"EndTime\": 1581707440.114364, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.114349}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11161655129674147, \"sum\": 0.11161655129674147, \"min\": 0.11161655129674147}}, \"EndTime\": 1581707440.114416, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.114402}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11765607208651527, \"sum\": 0.11765607208651527, \"min\": 0.11765607208651527}}, \"EndTime\": 1581707440.114462, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.114447}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.13480382879433317, \"sum\": 0.13480382879433317, \"min\": 0.13480382879433317}}, \"EndTime\": 1581707440.114503, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.114493}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.24824434733678272, \"sum\": 0.24824434733678272, \"min\": 0.24824434733678272}}, \"EndTime\": 1581707440.114561, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.114544}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.13484768874532593, \"sum\": 0.13484768874532593, \"min\": 0.13484768874532593}}, \"EndTime\": 1581707440.114617, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.114602}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.24820829046464812, \"sum\": 0.24820829046464812, \"min\": 0.24820829046464812}}, \"EndTime\": 1581707440.114679, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.114661}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9340922524199242, \"sum\": 0.9340922524199242, \"min\": 0.9340922524199242}}, \"EndTime\": 1581707440.114738, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.114721}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9385333548292635, \"sum\": 0.9385333548292635, \"min\": 0.9385333548292635}}, \"EndTime\": 1581707440.114798, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.11478}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9340246319770813, \"sum\": 0.9340246319770813, \"min\": 0.9340246319770813}}, \"EndTime\": 1581707440.114857, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.11484}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9386027602581527, \"sum\": 0.9386027602581527, \"min\": 0.9386027602581527}}, \"EndTime\": 1581707440.114921, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.114902}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9728060354456658, \"sum\": 0.9728060354456658, \"min\": 0.9728060354456658}}, \"EndTime\": 1581707440.114987, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.114968}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0335529842303963, \"sum\": 1.0335529842303963, \"min\": 1.0335529842303963}}, \"EndTime\": 1581707440.11505, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.115034}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9727436093774837, \"sum\": 0.9727436093774837, \"min\": 0.9727436093774837}}, \"EndTime\": 1581707440.11511, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.115094}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0335836697692478, \"sum\": 1.0335836697692478, \"min\": 1.0335836697692478}}, \"EndTime\": 1581707440.115168, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707440.115152}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:40 INFO 140163623442240] #quality_metric: host=algo-1, epoch=11, train mse_objective <loss>=0.000100870951525\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:40 INFO 140163623442240] #early_stopping_criteria_metric: host=algo-1, epoch=11, criteria=mse_objective, value=2.40814431622e-06\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:40 INFO 140163623442240] Epoch 11: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:40 INFO 140163623442240] #progress_metric: host=algo-1, completed 80 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Total Batches Seen\": {\"count\": 1, \"max\": 1665, \"sum\": 1665.0, \"min\": 1665}, \"Total Records Seen\": {\"count\": 1, \"max\": 6647, \"sum\": 6647.0, \"min\": 6647}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Reset Count\": {\"count\": 1, \"max\": 14, \"sum\": 14.0, \"min\": 14}}, \"EndTime\": 1581707440.118126, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1581707437.703186}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:40 INFO 140163623442240] #throughput_metric: host=algo-1, train throughput=211.586936966 records/second\u001b[0m\n",
      "\u001b[34m[2020-02-14 19:10:42.591] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 28, \"duration\": 2473, \"num_examples\": 128, \"num_bytes\": 53144}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 6.522417305766465e-05, \"sum\": 6.522417305766465e-05, \"min\": 6.522417305766465e-05}}, \"EndTime\": 1581707442.592161, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.592071}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 2.7184870013237613e-05, \"sum\": 2.7184870013237613e-05, \"min\": 2.7184870013237613e-05}}, \"EndTime\": 1581707442.592283, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.592265}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0003355240714237278, \"sum\": 0.0003355240714237278, \"min\": 0.0003355240714237278}}, \"EndTime\": 1581707442.592335, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.592324}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 9.821510146550522e-07, \"sum\": 9.821510146550522e-07, \"min\": 9.821510146550522e-07}}, \"EndTime\": 1581707442.592378, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.592368}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 8.139577607521926e-06, \"sum\": 8.139577607521926e-06, \"min\": 8.139577607521926e-06}}, \"EndTime\": 1581707442.592419, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.592409}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.4537107835443118, \"sum\": 0.4537107835443118, \"min\": 0.4537107835443118}}, \"EndTime\": 1581707442.59246, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.59245}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.3019090042546414e-05, \"sum\": 1.3019090042546414e-05, \"min\": 1.3019090042546414e-05}}, \"EndTime\": 1581707442.592499, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.592489}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0020800125717210533, \"sum\": 0.0020800125717210533, \"min\": 0.0020800125717210533}}, \"EndTime\": 1581707442.592541, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.592531}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.000633262106515128, \"sum\": 0.000633262106515128, \"min\": 0.000633262106515128}}, \"EndTime\": 1581707442.592579, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.592569}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0003988126906239534, \"sum\": 0.0003988126906239534, \"min\": 0.0003988126906239534}}, \"EndTime\": 1581707442.592616, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.592607}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.00088629830340116, \"sum\": 0.00088629830340116, \"min\": 0.00088629830340116}}, \"EndTime\": 1581707442.592686, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.592673}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.00039960230113381584, \"sum\": 0.00039960230113381584, \"min\": 0.00039960230113381584}}, \"EndTime\": 1581707442.592726, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.592716}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0011741838591367165, \"sum\": 0.0011741838591367165, \"min\": 0.0011741838591367165}}, \"EndTime\": 1581707442.592764, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.592754}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.017429785359757182, \"sum\": 0.017429785359757182, \"min\": 0.017429785359757182}}, \"EndTime\": 1581707442.592801, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.592792}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0012270495132016069, \"sum\": 0.0012270495132016069, \"min\": 0.0012270495132016069}}, \"EndTime\": 1581707442.592839, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.592829}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.013880755904948295, \"sum\": 0.013880755904948295, \"min\": 0.013880755904948295}}, \"EndTime\": 1581707442.592876, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.592867}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11129377214751375, \"sum\": 0.11129377214751375, \"min\": 0.11129377214751375}}, \"EndTime\": 1581707442.59293, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.592919}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11752788034473055, \"sum\": 0.11752788034473055, \"min\": 0.11752788034473055}}, \"EndTime\": 1581707442.592969, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.592959}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11128736616068292, \"sum\": 0.11128736616068292, \"min\": 0.11128736616068292}}, \"EndTime\": 1581707442.593007, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.592997}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11756523549153404, \"sum\": 0.11756523549153404, \"min\": 0.11756523549153404}}, \"EndTime\": 1581707442.593044, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.593034}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.13147174683216167, \"sum\": 0.13147174683216167, \"min\": 0.13147174683216167}}, \"EndTime\": 1581707442.593081, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.593071}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.24706215679821536, \"sum\": 0.24706215679821536, \"min\": 0.24706215679821536}}, \"EndTime\": 1581707442.593118, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.593108}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.13149971191543997, \"sum\": 0.13149971191543997, \"min\": 0.13149971191543997}}, \"EndTime\": 1581707442.593155, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.593145}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.24702978800747574, \"sum\": 0.24702978800747574, \"min\": 0.24702978800747574}}, \"EndTime\": 1581707442.593191, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.593182}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9337534670696015, \"sum\": 0.9337534670696015, \"min\": 0.9337534670696015}}, \"EndTime\": 1581707442.593228, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.593219}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9385031012920882, \"sum\": 0.9385031012920882, \"min\": 0.9385031012920882}}, \"EndTime\": 1581707442.593265, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.593255}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9336982294623777, \"sum\": 0.9336982294623777, \"min\": 0.9336982294623777}}, \"EndTime\": 1581707442.593301, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.593292}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9385620000383516, \"sum\": 0.9385620000383516, \"min\": 0.9385620000383516}}, \"EndTime\": 1581707442.593337, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.593327}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9655417767390022, \"sum\": 0.9655417767390022, \"min\": 0.9655417767390022}}, \"EndTime\": 1581707442.593379, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.593366}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0323720367874687, \"sum\": 1.0323720367874687, \"min\": 1.0323720367874687}}, \"EndTime\": 1581707442.59342, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.593411}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9655219041925716, \"sum\": 0.9655219041925716, \"min\": 0.9655219041925716}}, \"EndTime\": 1581707442.593473, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.593457}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0324007312026549, \"sum\": 1.0324007312026549, \"min\": 1.0324007312026549}}, \"EndTime\": 1581707442.593534, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707442.593518}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:42 INFO 140163623442240] #quality_metric: host=algo-1, epoch=12, train mse_objective <loss>=6.52241730577e-05\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:42 INFO 140163623442240] #early_stopping_criteria_metric: host=algo-1, epoch=12, criteria=mse_objective, value=9.82151014655e-07\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:42 INFO 140163623442240] Epoch 12: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:42 INFO 140163623442240] #progress_metric: host=algo-1, completed 86 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Total Batches Seen\": {\"count\": 1, \"max\": 1793, \"sum\": 1793.0, \"min\": 1793}, \"Total Records Seen\": {\"count\": 1, \"max\": 7158, \"sum\": 7158.0, \"min\": 7158}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Reset Count\": {\"count\": 1, \"max\": 15, \"sum\": 15.0, \"min\": 15}}, \"EndTime\": 1581707442.596345, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1581707440.118423}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:42 INFO 140163623442240] #throughput_metric: host=algo-1, train throughput=206.210032981 records/second\u001b[0m\n",
      "\u001b[34m[2020-02-14 19:10:44.901] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 30, \"duration\": 2304, \"num_examples\": 128, \"num_bytes\": 53144}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 4.3298898672255174e-05, \"sum\": 4.3298898672255174e-05, \"min\": 4.3298898672255174e-05}}, \"EndTime\": 1581707444.901305, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.901212}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 2.3393990013896046e-05, \"sum\": 2.3393990013896046e-05, \"min\": 2.3393990013896046e-05}}, \"EndTime\": 1581707444.901401, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.901383}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.00024276453407901143, \"sum\": 0.00024276453407901143, \"min\": 0.00024276453407901143}}, \"EndTime\": 1581707444.901462, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.901446}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 5.968466229940596e-07, \"sum\": 5.968466229940596e-07, \"min\": 5.968466229940596e-07}}, \"EndTime\": 1581707444.901519, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.901502}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 3.9327915835437e-06, \"sum\": 3.9327915835437e-06, \"min\": 3.9327915835437e-06}}, \"EndTime\": 1581707444.901582, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.901565}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.016212860601505, \"sum\": 0.016212860601505, \"min\": 0.016212860601505}}, \"EndTime\": 1581707444.901647, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.901629}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 7.076909400625231e-06, \"sum\": 7.076909400625231e-06, \"min\": 7.076909400625231e-06}}, \"EndTime\": 1581707444.901712, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.901694}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0008171603172583465, \"sum\": 0.0008171603172583465, \"min\": 0.0008171603172583465}}, \"EndTime\": 1581707444.901775, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.901758}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0005588231782837291, \"sum\": 0.0005588231782837291, \"min\": 0.0005588231782837291}}, \"EndTime\": 1581707444.901837, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.90182}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0003996093462874556, \"sum\": 0.0003996093462874556, \"min\": 0.0003996093462874556}}, \"EndTime\": 1581707444.901896, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.90188}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0007651194365874784, \"sum\": 0.0007651194365874784, \"min\": 0.0007651194365874784}}, \"EndTime\": 1581707444.901955, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.901939}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0003993632825245365, \"sum\": 0.0003993632825245365, \"min\": 0.0003993632825245365}}, \"EndTime\": 1581707444.902013, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.901998}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0010005450934640547, \"sum\": 0.0010005450934640547, \"min\": 0.0010005450934640547}}, \"EndTime\": 1581707444.902073, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.902056}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.007145476327451885, \"sum\": 0.007145476327451885, \"min\": 0.007145476327451885}}, \"EndTime\": 1581707444.902145, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.902126}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.001023133871803018, \"sum\": 0.001023133871803018, \"min\": 0.001023133871803018}}, \"EndTime\": 1581707444.902214, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.902198}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.4822079271708769, \"sum\": 0.4822079271708769, \"min\": 0.4822079271708769}}, \"EndTime\": 1581707444.902283, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.902265}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.1109966861879761, \"sum\": 0.1109966861879761, \"min\": 0.1109966861879761}}, \"EndTime\": 1581707444.902354, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.902335}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11747683269727066, \"sum\": 0.11747683269727066, \"min\": 0.11747683269727066}}, \"EndTime\": 1581707444.902424, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.902406}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11099156452430867, \"sum\": 0.11099156452430867, \"min\": 0.11099156452430867}}, \"EndTime\": 1581707444.902495, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.902477}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11750793528163761, \"sum\": 0.11750793528163761, \"min\": 0.11750793528163761}}, \"EndTime\": 1581707444.902557, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.90254}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.12904559927955855, \"sum\": 0.12904559927955855, \"min\": 0.12904559927955855}}, \"EndTime\": 1581707444.902615, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.9026}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.24761669460948058, \"sum\": 0.24761669460948058, \"min\": 0.24761669460948058}}, \"EndTime\": 1581707444.902647, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.902639}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.12906364424110162, \"sum\": 0.12906364424110162, \"min\": 0.12906364424110162}}, \"EndTime\": 1581707444.902679, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.902667}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.2475864405516447, \"sum\": 0.2475864405516447, \"min\": 0.2475864405516447}}, \"EndTime\": 1581707444.902734, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.902718}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9334304701155565, \"sum\": 0.9334304701155565, \"min\": 0.9334304701155565}}, \"EndTime\": 1581707444.902792, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.902776}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9384713920610627, \"sum\": 0.9384713920610627, \"min\": 0.9384713920610627}}, \"EndTime\": 1581707444.902853, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.902837}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9333871504453224, \"sum\": 0.9333871504453224, \"min\": 0.9333871504453224}}, \"EndTime\": 1581707444.902913, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.902896}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9385221758930702, \"sum\": 0.9385221758930702, \"min\": 0.9385221758930702}}, \"EndTime\": 1581707444.902972, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.902956}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.96002008786469, \"sum\": 0.96002008786469, \"min\": 0.96002008786469}}, \"EndTime\": 1581707444.903033, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.903017}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0313133728492447, \"sum\": 1.0313133728492447, \"min\": 1.0313133728492447}}, \"EndTime\": 1581707444.903093, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.903077}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9600242601898242, \"sum\": 0.9600242601898242, \"min\": 0.9600242601898242}}, \"EndTime\": 1581707444.903161, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.903142}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.031344234826058, \"sum\": 1.031344234826058, \"min\": 1.031344234826058}}, \"EndTime\": 1581707444.903223, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707444.903206}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:44 INFO 140163623442240] #quality_metric: host=algo-1, epoch=13, train mse_objective <loss>=4.32988986723e-05\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:44 INFO 140163623442240] #early_stopping_criteria_metric: host=algo-1, epoch=13, criteria=mse_objective, value=5.96846622994e-07\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:44 INFO 140163623442240] Epoch 13: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:44 INFO 140163623442240] #progress_metric: host=algo-1, completed 93 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Total Batches Seen\": {\"count\": 1, \"max\": 1921, \"sum\": 1921.0, \"min\": 1921}, \"Total Records Seen\": {\"count\": 1, \"max\": 7669, \"sum\": 7669.0, \"min\": 7669}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Reset Count\": {\"count\": 1, \"max\": 16, \"sum\": 16.0, \"min\": 16}}, \"EndTime\": 1581707444.90614, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1581707442.59658}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:44 INFO 140163623442240] #throughput_metric: host=algo-1, train throughput=221.241035322 records/second\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "2020-02-14 19:10:54 Uploading - Uploading generated training model\n",
      "2020-02-14 19:10:54 Completed - Training job completed\n",
      "\u001b[34m[2020-02-14 19:10:47.046] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 32, \"duration\": 2140, \"num_examples\": 128, \"num_bytes\": 53144}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 2.952944395578173e-05, \"sum\": 2.952944395578173e-05, \"min\": 2.952944395578173e-05}}, \"EndTime\": 1581707447.046762, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.04668}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 2.023877109476466e-05, \"sum\": 2.023877109476466e-05, \"min\": 2.023877109476466e-05}}, \"EndTime\": 1581707447.046859, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.046841}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.00017893210314662246, \"sum\": 0.00017893210314662246, \"min\": 0.00017893210314662246}}, \"EndTime\": 1581707447.046922, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.04691}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 4.863436618399098e-07, \"sum\": 4.863436618399098e-07, \"min\": 4.863436618399098e-07}}, \"EndTime\": 1581707447.046971, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.046957}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 2.246470229464439e-06, \"sum\": 2.246470229464439e-06, \"min\": 2.246470229464439e-06}}, \"EndTime\": 1581707447.047035, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.047019}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0026446445083097136, \"sum\": 0.0026446445083097136, \"min\": 0.0026446445083097136}}, \"EndTime\": 1581707447.047098, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.047082}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 5.454267658135038e-06, \"sum\": 5.454267658135038e-06, \"min\": 5.454267658135038e-06}}, \"EndTime\": 1581707447.047157, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.047141}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0013979538224108599, \"sum\": 0.0013979538224108599, \"min\": 0.0013979538224108599}}, \"EndTime\": 1581707447.047221, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.047204}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0005050565002785856, \"sum\": 0.0005050565002785856, \"min\": 0.0005050565002785856}}, \"EndTime\": 1581707447.04728, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.047265}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.00040421099837422076, \"sum\": 0.00040421099837422076, \"min\": 0.00040421099837422076}}, \"EndTime\": 1581707447.047339, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.047324}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0006758099886546128, \"sum\": 0.0006758099886546128, \"min\": 0.0006758099886546128}}, \"EndTime\": 1581707447.047398, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.047383}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.000403004206970126, \"sum\": 0.000403004206970126, \"min\": 0.000403004206970126}}, \"EndTime\": 1581707447.04746, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.047444}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0009424964205030073, \"sum\": 0.0009424964205030073, \"min\": 0.0009424964205030073}}, \"EndTime\": 1581707447.047519, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.047503}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.1903082765959658, \"sum\": 0.1903082765959658, \"min\": 0.1903082765959658}}, \"EndTime\": 1581707447.047582, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.047565}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.0009506119665783494, \"sum\": 0.0009506119665783494, \"min\": 0.0009506119665783494}}, \"EndTime\": 1581707447.047639, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.047622}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.3836715535194738, \"sum\": 0.3836715535194738, \"min\": 0.3836715535194738}}, \"EndTime\": 1581707447.04769, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.047674}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.1107414045451781, \"sum\": 0.1107414045451781, \"min\": 0.1107414045451781}}, \"EndTime\": 1581707447.047764, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.047733}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11742992914010456, \"sum\": 0.11742992914010456, \"min\": 0.11742992914010456}}, \"EndTime\": 1581707447.047826, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.047809}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11073726832896001, \"sum\": 0.11073726832896001, \"min\": 0.11073726832896001}}, \"EndTime\": 1581707447.047875, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.04786}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.11745627141946297, \"sum\": 0.11745627141946297, \"min\": 0.11745627141946297}}, \"EndTime\": 1581707447.047936, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.047919}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.12720057892239117, \"sum\": 0.12720057892239117, \"min\": 0.12720057892239117}}, \"EndTime\": 1581707447.047996, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.047979}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.24625974966169106, \"sum\": 0.24625974966169106, \"min\": 0.24625974966169106}}, \"EndTime\": 1581707447.048051, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.048036}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.127211913052125, \"sum\": 0.127211913052125, \"min\": 0.127211913052125}}, \"EndTime\": 1581707447.048119, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.048101}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.24623323309667935, \"sum\": 0.24623323309667935, \"min\": 0.24623323309667935}}, \"EndTime\": 1581707447.048178, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.048162}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9331531198768634, \"sum\": 0.9331531198768634, \"min\": 0.9331531198768634}}, \"EndTime\": 1581707447.048238, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.048221}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9384565825214771, \"sum\": 0.9384565825214771, \"min\": 0.9384565825214771}}, \"EndTime\": 1581707447.048297, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.048281}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9331208143353932, \"sum\": 0.9331208143353932, \"min\": 0.9331208143353932}}, \"EndTime\": 1581707447.048366, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.048349}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9385006287699844, \"sum\": 0.9385006287699844, \"min\": 0.9385006287699844}}, \"EndTime\": 1581707447.04844, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.048421}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.955635018705383, \"sum\": 0.955635018705383, \"min\": 0.955635018705383}}, \"EndTime\": 1581707447.048508, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.04849}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0306256004851164, \"sum\": 1.0306256004851164, \"min\": 1.0306256004851164}}, \"EndTime\": 1581707447.048576, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.048557}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9556347071303157, \"sum\": 0.9556347071303157, \"min\": 0.9556347071303157}}, \"EndTime\": 1581707447.048662, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.048615}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0306614030768553, \"sum\": 1.0306614030768553, \"min\": 1.0306614030768553}}, \"EndTime\": 1581707447.048739, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707447.04872}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:47 INFO 140163623442240] #quality_metric: host=algo-1, epoch=14, train mse_objective <loss>=2.95294439558e-05\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:47 INFO 140163623442240] #early_stopping_criteria_metric: host=algo-1, epoch=14, criteria=mse_objective, value=4.8634366184e-07\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:47 INFO 140163623442240] Epoch 14: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:47 INFO 140163623442240] #progress_metric: host=algo-1, completed 100 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Total Batches Seen\": {\"count\": 1, \"max\": 2049, \"sum\": 2049.0, \"min\": 2049}, \"Total Records Seen\": {\"count\": 1, \"max\": 8180, \"sum\": 8180.0, \"min\": 8180}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 511, \"sum\": 511.0, \"min\": 511}, \"Reset Count\": {\"count\": 1, \"max\": 17, \"sum\": 17.0, \"min\": 17}}, \"EndTime\": 1581707447.051605, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1581707444.906443}\n",
      "\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:47 INFO 140163623442240] #throughput_metric: host=algo-1, train throughput=238.193889879 records/second\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:47 WARNING 140163623442240] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:47 WARNING 140163623442240] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n",
      "\u001b[34m[2020-02-14 19:10:47.052] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 34, \"duration\": 0, \"num_examples\": 1, \"num_bytes\": 416}\u001b[0m\n",
      "\u001b[34m[2020-02-14 19:10:47.196] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 36, \"duration\": 140, \"num_examples\": 128, \"num_bytes\": 53144}\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:47 INFO 140163623442240] #train_score (algo-1) : ('mse_objective', 1.6779703935298433)\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:47 INFO 140163623442240] #train_score (algo-1) : ('mse', 1.6779703935298433)\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:47 INFO 140163623442240] #train_score (algo-1) : ('absolute_loss', 1.043970156555773)\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:47 INFO 140163623442240] #quality_metric: host=algo-1, train mse_objective <loss>=1.67797039353\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:47 INFO 140163623442240] #quality_metric: host=algo-1, train mse <loss>=1.67797039353\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:47 INFO 140163623442240] #quality_metric: host=algo-1, train absolute_loss <loss>=1.04397015656\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:47 INFO 140163623442240] Best model found for hyperparameters: {\"lr_scheduler_step\": 100, \"wd\": 0.0001, \"optimizer\": \"adam\", \"lr_scheduler_factor\": 0.99, \"l1\": 0.0, \"learning_rate\": 0.005, \"lr_scheduler_minimum_lr\": 0.0001}\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:47 INFO 140163623442240] Saved checkpoint to \"/tmp/tmpRVYAEU/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[02/14/2020 19:10:47 INFO 140163623442240] Test data is not provided.\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"totaltime\": {\"count\": 1, \"max\": 37502.739906311035, \"sum\": 37502.739906311035, \"min\": 37502.739906311035}, \"finalize.time\": {\"count\": 1, \"max\": 145.23792266845703, \"sum\": 145.23792266845703, \"min\": 145.23792266845703}, \"initialize.time\": {\"count\": 1, \"max\": 421.84996604919434, \"sum\": 421.84996604919434, \"min\": 421.84996604919434}, \"check_early_stopping.time\": {\"count\": 15, \"max\": 1.1441707611083984, \"sum\": 15.458822250366211, \"min\": 0.90789794921875}, \"setuptime\": {\"count\": 1, \"max\": 26.974916458129883, \"sum\": 26.974916458129883, \"min\": 26.974916458129883}, \"update.time\": {\"count\": 15, \"max\": 2790.6999588012695, \"sum\": 36829.829931259155, \"min\": 2144.9930667877197}, \"epochs\": {\"count\": 1, \"max\": 15, \"sum\": 15.0, \"min\": 15}}, \"EndTime\": 1581707447.206031, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\"}, \"StartTime\": 1581707409.791645}\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training seconds: 90\n",
      "Billable seconds: 90\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "from sagemaker import get_execution_role\n",
    "\n",
    "role = get_execution_role()\n",
    "\n",
    "sagemaker_session = sagemaker.Session()\n",
    "\n",
    "# Provide the container, role, instance type and model output location\n",
    "linear = sagemaker.estimator.Estimator(linear_container,\n",
    "                                       role=role, \n",
    "                                       train_instance_count=1, \n",
    "                                       train_instance_type='ml.c4.xlarge',\n",
    "                                       output_path=output_location,\n",
    "                                       sagemaker_session=sagemaker_session)\n",
    "\n",
    "# Provide the number of features identified during data preparation\n",
    "# Provide the predictor_type \n",
    "\n",
    "linear.set_hyperparameters(feature_dim=15,\n",
    "                           mini_batch_size=4,\n",
    "                           predictor_type='regressor')\n",
    "\n",
    "# Train the model using the previously prepared test data and validate the \n",
    "# data by providing the validation data.\n",
    "\n",
    "linear.fit({'train': s3_training_data_location})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------"
     ]
    }
   ],
   "source": [
    "# Deploy the model\n",
    "linear_predictor = linear.deploy(initial_instance_count=1,\n",
    "                                 instance_type='ml.c4.xlarge',\n",
    "                                 endpoint_name='bikeshare-sagemaker-regression-v1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction:  799.390625 \t\tactual:  801.0\n",
      "prediction:  5216.703125 \t\tactual:  5217.0\n",
      "prediction:  7765.5625 \t\tactual:  7767.0\n",
      "prediction:  6850.9375 \t\tactual:  6852.0\n",
      "prediction:  2209.953125 \t\tactual:  2209.0\n",
      "prediction:  6289.296875 \t\tactual:  6290.0\n",
      "prediction:  4790.71875 \t\tactual:  4792.0\n",
      "prediction:  1866.8125 \t\tactual:  1865.0\n",
      "prediction:  5670.359375 \t\tactual:  5668.0\n",
      "prediction:  4492.265625 \t\tactual:  4492.0\n",
      "prediction:  4368.203125 \t\tactual:  4367.0\n",
      "prediction:  2402.453125 \t\tactual:  2402.0\n",
      "prediction:  3847.546875 \t\tactual:  3846.0\n",
      "prediction:  4787.96875 \t\tactual:  4788.0\n",
      "prediction:  3189.03125 \t\tactual:  3190.0\n",
      "prediction:  3006.515625 \t\tactual:  3005.0\n",
      "prediction:  7284.65625 \t\tactual:  7286.0\n",
      "prediction:  2131.25 \t\tactual:  2132.0\n",
      "prediction:  5462.21875 \t\tactual:  5464.0\n",
      "prediction:  4984.09375 \t\tactual:  4985.0\n",
      "prediction:  6304.640625 \t\tactual:  6304.0\n",
      "prediction:  5532.03125 \t\tactual:  5532.0\n",
      "prediction:  8008.15625 \t\tactual:  8009.0\n",
      "prediction:  3000.46875 \t\tactual:  2999.0\n",
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      "prediction:  2802.53125 \t\tactual:  2802.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction:  3749.1875 \t\tactual:  3747.0\n",
      "prediction:  3572.734375 \t\tactual:  3574.0\n",
      "prediction:  2594.296875 \t\tactual:  2594.0\n",
      "prediction:  7040.453125 \t\tactual:  7040.0\n",
      "prediction:  2235.21875 \t\tactual:  2236.0\n",
      "prediction:  3785.796875 \t\tactual:  3784.0\n",
      "prediction:  2733.140625 \t\tactual:  2732.0\n",
      "prediction:  5255.671875 \t\tactual:  5255.0\n",
      "prediction:  4341.09375 \t\tactual:  4342.0\n",
      "prediction:  5528.203125 \t\tactual:  5531.0\n",
      "prediction:  4066.546875 \t\tactual:  4067.0\n",
      "prediction:  1890.6875 \t\tactual:  1891.0\n",
      "prediction:  3376.5625 \t\tactual:  3376.0\n",
      "prediction:  6977.0625 \t\tactual:  6978.0\n",
      "prediction:  6657.6875 \t\tactual:  6660.0\n",
      "prediction:  5112.59375 \t\tactual:  5115.0\n",
      "prediction:  2162.65625 \t\tactual:  2162.0\n",
      "prediction:  5875.5 \t\tactual:  5875.0\n"
     ]
    }
   ],
   "source": [
    "# Get prediction using the test data\n",
    "from sagemaker.predictor import csv_serializer, json_deserializer\n",
    "\n",
    "linear_predictor.content_type = 'text/csv'\n",
    "linear_predictor.serializer = csv_serializer\n",
    "linear_predictor.deserializer = json_deserializer\n",
    "\n",
    "test_feature_dataset = test_data[['instant', 'dteday', 'season', 'yr', 'mnth', 'holiday', 'weekday',\n",
    "                           'workingday', 'weathersit', 'temp', 'atemp', 'hum', 'windspeed', 'casual', 'registered']]\n",
    "\n",
    "test_actuals = np.array(test_data['cnt'].astype('float32'))\n",
    "test_features = np.array(test_feature_dataset.values).astype('float32')\n",
    "\n",
    "predictions = []\n",
    "actuals = []\n",
    "for tf, actual in zip(test_features, test_actuals):\n",
    "    prediction = linear_predictor.predict(tf)\n",
    "    predictions.append(prediction['predictions'][0]['score'])\n",
    "    actuals.append(actual)\n",
    "    print('prediction: ', prediction['predictions'][0]['score'], '\\t\\tactual: ', str(actual))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:  100.00000313584616\n"
     ]
    }
   ],
   "source": [
    "# Get accuracy using Cosine Similarity method \n",
    "from numpy import dot\n",
    "from numpy.linalg import norm\n",
    "tolerance = 1e-10\n",
    "accuracy = (dot(actuals, predictions)/(norm(actuals)*norm(predictions))) * 100\n",
    "print('accuracy: ', accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# delete the endpoint\n",
    "sagemaker.Session().delete_endpoint(linear_predictor.endpoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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