3_Training_a_Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting sagemaker==1.72.0\n",
      "  Downloading sagemaker-1.72.0.tar.gz (297 kB)\n",
      "\u001b[K     |████████████████████████████████| 297 kB 6.3 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: boto3>=1.14.12 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sagemaker==1.72.0) (1.16.19)\n",
      "Requirement already satisfied: numpy>=1.9.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sagemaker==1.72.0) (1.18.1)\n",
      "Requirement already satisfied: protobuf>=3.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sagemaker==1.72.0) (3.11.4)\n",
      "Requirement already satisfied: scipy>=0.19.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sagemaker==1.72.0) (1.4.1)\n",
      "Requirement already satisfied: protobuf3-to-dict>=0.1.5 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sagemaker==1.72.0) (0.1.5)\n",
      "Collecting smdebug-rulesconfig==0.1.4\n",
      "  Downloading smdebug_rulesconfig-0.1.4-py2.py3-none-any.whl (10 kB)\n",
      "Requirement already satisfied: importlib-metadata>=1.4.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sagemaker==1.72.0) (2.0.0)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sagemaker==1.72.0) (20.1)\n",
      "Requirement already satisfied: botocore<1.20.0,>=1.19.19 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from boto3>=1.14.12->sagemaker==1.72.0) (1.19.19)\n",
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      "Requirement already satisfied: s3transfer<0.4.0,>=0.3.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from boto3>=1.14.12->sagemaker==1.72.0) (0.3.3)\n",
      "Requirement already satisfied: six>=1.9 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from protobuf>=3.1->sagemaker==1.72.0) (1.14.0)\n",
      "Requirement already satisfied: setuptools in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from protobuf>=3.1->sagemaker==1.72.0) (45.2.0.post20200210)\n",
      "Requirement already satisfied: zipp>=0.5 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from importlib-metadata>=1.4.0->sagemaker==1.72.0) (2.2.0)\n",
      "Requirement already satisfied: pyparsing>=2.0.2 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from packaging>=20.0->sagemaker==1.72.0) (2.4.6)\n",
      "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from botocore<1.20.0,>=1.19.19->boto3>=1.14.12->sagemaker==1.72.0) (2.8.1)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.25.4 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from botocore<1.20.0,>=1.19.19->boto3>=1.14.12->sagemaker==1.72.0) (1.25.10)\n",
      "Building wheels for collected packages: sagemaker\n",
      "  Building wheel for sagemaker (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for sagemaker: filename=sagemaker-1.72.0-py2.py3-none-any.whl size=386358 sha256=a2638ae6d9922f8ca9b0c949cc1f4292de93a5d176dabe988b2b8a8ee1588d82\n",
      "  Stored in directory: /home/ec2-user/.cache/pip/wheels/c3/58/70/85faf4437568bfaa4c419937569ba1fe54d44c5db42406bbd7\n",
      "Successfully built sagemaker\n",
      "Installing collected packages: smdebug-rulesconfig, sagemaker\n",
      "  Attempting uninstall: smdebug-rulesconfig\n",
      "    Found existing installation: smdebug-rulesconfig 0.1.5\n",
      "    Uninstalling smdebug-rulesconfig-0.1.5:\n",
      "      Successfully uninstalled smdebug-rulesconfig-0.1.5\n",
      "  Attempting uninstall: sagemaker\n",
      "    Found existing installation: sagemaker 2.16.4.dev0\n",
      "    Uninstalling sagemaker-2.16.4.dev0:\n",
      "      Successfully uninstalled sagemaker-2.16.4.dev0\n",
      "Successfully installed sagemaker-1.72.0 smdebug-rulesconfig-0.1.4\n",
      "\u001b[33mWARNING: You are using pip version 20.0.2; however, version 20.3 is available.\n",
      "You should consider upgrading via the '/home/ec2-user/anaconda3/envs/pytorch_p36/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install sagemaker==1.72.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plagiarism Detection Model\n",
    "\n",
    "Now that you've created training and test data, you are ready to define and train a model. Your goal in this notebook, will be to train a binary classification model that learns to label an answer file as either plagiarized or not, based on the features you provide the model.\n",
    "\n",
    "This task will be broken down into a few discrete steps:\n",
    "\n",
    "* Upload your data to S3.\n",
    "* Define a binary classification model and a training script.\n",
    "* Train your model and deploy it.\n",
    "* Evaluate your deployed classifier and answer some questions about your approach.\n",
    "\n",
    "To complete this notebook, you'll have to complete all given exercises and answer all the questions in this notebook.\n",
    "> All your tasks will be clearly labeled **EXERCISE** and questions as **QUESTION**.\n",
    "\n",
    "It will be up to you to explore different classification models and decide on a model that gives you the best performance for this dataset.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data to S3\n",
    "\n",
    "In the last notebook, you should have created two files: a `training.csv` and `test.csv` file with the features and class labels for the given corpus of plagiarized/non-plagiarized text data. \n",
    "\n",
    ">The below cells load in some AWS SageMaker libraries and creates a default bucket. After creating this bucket, you can upload your locally stored data to S3.\n",
    "\n",
    "Save your train and test `.csv` feature files, locally. To do this you can run the second notebook \"2_Plagiarism_Feature_Engineering\" in SageMaker or you can manually upload your files to this notebook using the upload icon in Jupyter Lab. Then you can upload local files to S3 by using `sagemaker_session.upload_data` and pointing directly to where the training data is saved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import boto3\n",
    "import sagemaker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "# session and role\n",
    "sagemaker_session = sagemaker.Session()\n",
    "role = sagemaker.get_execution_role()\n",
    "\n",
    "# create an S3 bucket\n",
    "bucket = sagemaker_session.default_bucket()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EXERCISE: Upload your training data to S3\n",
    "\n",
    "Specify the `data_dir` where you've saved your `train.csv` file. Decide on a descriptive `prefix` that defines where your data will be uploaded in the default S3 bucket. Finally, create a pointer to your training data by calling `sagemaker_session.upload_data` and passing in the required parameters. It may help to look at the [Session documentation](https://sagemaker.readthedocs.io/en/stable/session.html#sagemaker.session.Session.upload_data) or previous SageMaker code examples.\n",
    "\n",
    "You are expected to upload your entire directory. Later, the training script will only access the `train.csv` file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# should be the name of directory you created to save your features data\n",
    "data_dir = 'plagiarism_data'\n",
    "\n",
    "# set prefix, a descriptive name for a directory  \n",
    "prefix = 'plagiarism-detection'\n",
    "\n",
    "# upload all data to S3\n",
    "data_path  = sagemaker_session.upload_data(data_dir, bucket=bucket, key_prefix=prefix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test cell\n",
    "\n",
    "Test that your data has been successfully uploaded. The below cell prints out the items in your S3 bucket and will throw an error if it is empty. You should see the contents of your `data_dir` and perhaps some checkpoints. If you see any other files listed, then you may have some old model files that you can delete via the S3 console (though, additional files shouldn't affect the performance of model developed in this notebook)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plagiarism-detection/test.csv\n",
      "plagiarism-detection/train.csv\n",
      "Test passed!\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "# confirm that data is in S3 bucket\n",
    "empty_check = []\n",
    "for obj in boto3.resource('s3').Bucket(bucket).objects.all():\n",
    "    empty_check.append(obj.key)\n",
    "    print(obj.key)\n",
    "\n",
    "assert len(empty_check) !=0, 'S3 bucket is empty.'\n",
    "print('Test passed!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# Modeling\n",
    "\n",
    "Now that you've uploaded your training data, it's time to define and train a model!\n",
    "\n",
    "The type of model you create is up to you. For a binary classification task, you can choose to go one of three routes:\n",
    "* Use a built-in classification algorithm, like LinearLearner.\n",
    "* Define a custom Scikit-learn classifier, a comparison of models can be found [here](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html).\n",
    "* Define a custom PyTorch neural network classifier. \n",
    "\n",
    "It will be up to you to test out a variety of models and choose the best one. Your project will be graded on the accuracy of your final model. \n",
    " \n",
    "---\n",
    "\n",
    "## EXERCISE: Complete a training script \n",
    "\n",
    "To implement a custom classifier, you'll need to complete a `train.py` script. You've been given the folders `source_sklearn` and `source_pytorch` which hold starting code for a custom Scikit-learn model and a PyTorch model, respectively. Each directory has a `train.py` training script. To complete this project **you only need to complete one of these scripts**; the script that is responsible for training your final model.\n",
    "\n",
    "A typical training script:\n",
    "* Loads training data from a specified directory\n",
    "* Parses any training & model hyperparameters (ex. nodes in a neural network, training epochs, etc.)\n",
    "* Instantiates a model of your design, with any specified hyperparams\n",
    "* Trains that model \n",
    "* Finally, saves the model so that it can be hosted/deployed, later\n",
    "\n",
    "### Defining and training a model\n",
    "Much of the training script code is provided for you. Almost all of your work will be done in the `if __name__ == '__main__':` section. To complete a `train.py` file, you will:\n",
    "1. Import any extra libraries you need\n",
    "2. Define any additional model training hyperparameters using `parser.add_argument`\n",
    "2. Define a model in the `if __name__ == '__main__':` section\n",
    "3. Train the model in that same section\n",
    "\n",
    "Below, you can use `!pygmentize` to display an existing `train.py` file. Read through the code; all of your tasks are marked with `TODO` comments. \n",
    "\n",
    "**Note: If you choose to create a custom PyTorch model, you will be responsible for defining the model in the `model.py` file,** and a `predict.py` file is provided. If you choose to use Scikit-learn, you only need a `train.py` file; you may import a classifier from the `sklearn` library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36margparse\u001b[39;49;00m\r\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mjson\u001b[39;49;00m\r\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mos\u001b[39;49;00m\r\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mpandas\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36mpd\u001b[39;49;00m\r\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\r\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36moptim\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36moptim\u001b[39;49;00m\r\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mutils\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mdata\u001b[39;49;00m\r\n",
      "\r\n",
      "\u001b[37m# imports the model in model.py by name\u001b[39;49;00m\r\n",
      "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mmodel\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m BinaryClassifier\r\n",
      "\r\n",
      "\u001b[34mdef\u001b[39;49;00m \u001b[32mmodel_fn\u001b[39;49;00m(model_dir):\r\n",
      "    \u001b[33m\"\"\"Load the PyTorch model from the `model_dir` directory.\"\"\"\u001b[39;49;00m\r\n",
      "    \u001b[36mprint\u001b[39;49;00m(\u001b[33m\"\u001b[39;49;00m\u001b[33mLoading model.\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\r\n",
      "\r\n",
      "    \u001b[37m# First, load the parameters used to create the model.\u001b[39;49;00m\r\n",
      "    model_info = {}\r\n",
      "    model_info_path = os.path.join(model_dir, \u001b[33m'\u001b[39;49;00m\u001b[33mmodel_info.pth\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\r\n",
      "    \u001b[34mwith\u001b[39;49;00m \u001b[36mopen\u001b[39;49;00m(model_info_path, \u001b[33m'\u001b[39;49;00m\u001b[33mrb\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m) \u001b[34mas\u001b[39;49;00m f:\r\n",
      "        model_info = torch.load(f)\r\n",
      "\r\n",
      "    \u001b[36mprint\u001b[39;49;00m(\u001b[33m\"\u001b[39;49;00m\u001b[33mmodel_info: \u001b[39;49;00m\u001b[33m{}\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m.format(model_info))\r\n",
      "\r\n",
      "    \u001b[37m# Determine the device and construct the model.\u001b[39;49;00m\r\n",
      "    device = torch.device(\u001b[33m\"\u001b[39;49;00m\u001b[33mcuda\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m \u001b[34mif\u001b[39;49;00m torch.cuda.is_available() \u001b[34melse\u001b[39;49;00m \u001b[33m\"\u001b[39;49;00m\u001b[33mcpu\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\r\n",
      "    model = BinaryClassifier(model_info[\u001b[33m'\u001b[39;49;00m\u001b[33minput_features\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m], model_info[\u001b[33m'\u001b[39;49;00m\u001b[33mhidden_dim\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m], model_info[\u001b[33m'\u001b[39;49;00m\u001b[33moutput_dim\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m])\r\n",
      "\r\n",
      "    \u001b[37m# Load the stored model parameters.\u001b[39;49;00m\r\n",
      "    model_path = os.path.join(model_dir, \u001b[33m'\u001b[39;49;00m\u001b[33mmodel.pth\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\r\n",
      "    \u001b[34mwith\u001b[39;49;00m \u001b[36mopen\u001b[39;49;00m(model_path, \u001b[33m'\u001b[39;49;00m\u001b[33mrb\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m) \u001b[34mas\u001b[39;49;00m f:\r\n",
      "        model.load_state_dict(torch.load(f))\r\n",
      "\r\n",
      "    \u001b[37m# set to eval mode, could use no_grad\u001b[39;49;00m\r\n",
      "    model.to(device).eval()\r\n",
      "\r\n",
      "    \u001b[36mprint\u001b[39;49;00m(\u001b[33m\"\u001b[39;49;00m\u001b[33mDone loading model.\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\r\n",
      "    \u001b[34mreturn\u001b[39;49;00m model\r\n",
      "\r\n",
      "\u001b[37m# Gets training data in batches from the train.csv file\u001b[39;49;00m\r\n",
      "\u001b[34mdef\u001b[39;49;00m \u001b[32m_get_train_data_loader\u001b[39;49;00m(batch_size, training_dir):\r\n",
      "    \u001b[36mprint\u001b[39;49;00m(\u001b[33m\"\u001b[39;49;00m\u001b[33mGet train data loader.\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\r\n",
      "\r\n",
      "    train_data = pd.read_csv(os.path.join(training_dir, \u001b[33m\"\u001b[39;49;00m\u001b[33mtrain.csv\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m), header=\u001b[34mNone\u001b[39;49;00m, names=\u001b[34mNone\u001b[39;49;00m)\r\n",
      "\r\n",
      "    train_y = torch.from_numpy(train_data[[\u001b[34m0\u001b[39;49;00m]].values).float().squeeze()\r\n",
      "    train_x = torch.from_numpy(train_data.drop([\u001b[34m0\u001b[39;49;00m], axis=\u001b[34m1\u001b[39;49;00m).values).float()\r\n",
      "\r\n",
      "    train_ds = torch.utils.data.TensorDataset(train_x, train_y)\r\n",
      "\r\n",
      "    \u001b[34mreturn\u001b[39;49;00m torch.utils.data.DataLoader(train_ds, batch_size=batch_size)\r\n",
      "\r\n",
      "\r\n",
      "\u001b[37m# Provided training function\u001b[39;49;00m\r\n",
      "\u001b[34mdef\u001b[39;49;00m \u001b[32mtrain\u001b[39;49;00m(model, train_loader, epochs, criterion, optimizer, device):\r\n",
      "    \u001b[33m\"\"\"\u001b[39;49;00m\r\n",
      "\u001b[33m    This is the training method that is called by the PyTorch training script. The parameters\u001b[39;49;00m\r\n",
      "\u001b[33m    passed are as follows:\u001b[39;49;00m\r\n",
      "\u001b[33m    model        - The PyTorch model that we wish to train.\u001b[39;49;00m\r\n",
      "\u001b[33m    train_loader - The PyTorch DataLoader that should be used during training.\u001b[39;49;00m\r\n",
      "\u001b[33m    epochs       - The total number of epochs to train for.\u001b[39;49;00m\r\n",
      "\u001b[33m    criterion    - The loss function used for training. \u001b[39;49;00m\r\n",
      "\u001b[33m    optimizer    - The optimizer to use during training.\u001b[39;49;00m\r\n",
      "\u001b[33m    device       - Where the model and data should be loaded (gpu or cpu).\u001b[39;49;00m\r\n",
      "\u001b[33m    \"\"\"\u001b[39;49;00m\r\n",
      "    \r\n",
      "    \u001b[37m# training loop is provided\u001b[39;49;00m\r\n",
      "    \u001b[34mfor\u001b[39;49;00m epoch \u001b[35min\u001b[39;49;00m \u001b[36mrange\u001b[39;49;00m(\u001b[34m1\u001b[39;49;00m, epochs + \u001b[34m1\u001b[39;49;00m):\r\n",
      "        model.train() \u001b[37m# Make sure that the model is in training mode.\u001b[39;49;00m\r\n",
      "\r\n",
      "        total_loss = \u001b[34m0\u001b[39;49;00m\r\n",
      "\r\n",
      "        \u001b[34mfor\u001b[39;49;00m batch \u001b[35min\u001b[39;49;00m train_loader:\r\n",
      "            \u001b[37m# get data\u001b[39;49;00m\r\n",
      "            batch_x, batch_y = batch\r\n",
      "\r\n",
      "            batch_x = batch_x.to(device)\r\n",
      "            batch_y = batch_y.to(device)\r\n",
      "\r\n",
      "            optimizer.zero_grad()\r\n",
      "\r\n",
      "            \u001b[37m# get predictions from model\u001b[39;49;00m\r\n",
      "            y_pred = model(batch_x)\r\n",
      "            \r\n",
      "            \u001b[37m# perform backprop\u001b[39;49;00m\r\n",
      "            loss = criterion(y_pred, batch_y)\r\n",
      "            loss.backward()\r\n",
      "            optimizer.step()\r\n",
      "            \r\n",
      "            total_loss += loss.data.item()\r\n",
      "\r\n",
      "        \u001b[36mprint\u001b[39;49;00m(\u001b[33m\"\u001b[39;49;00m\u001b[33mEpoch: \u001b[39;49;00m\u001b[33m{}\u001b[39;49;00m\u001b[33m, Loss: \u001b[39;49;00m\u001b[33m{:.4f}\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m.format(epoch, total_loss / \u001b[36mlen\u001b[39;49;00m(train_loader)))\r\n",
      "\r\n",
      "\r\n",
      "\u001b[37m## TODO: Complete the main code\u001b[39;49;00m\r\n",
      "\u001b[34mif\u001b[39;49;00m \u001b[31m__name__\u001b[39;49;00m == \u001b[33m'\u001b[39;49;00m\u001b[33m__main__\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m:\r\n",
      "    \r\n",
      "    \u001b[37m# All of the model parameters and training parameters are sent as arguments\u001b[39;49;00m\r\n",
      "    \u001b[37m# when this script is executed, during a training job\u001b[39;49;00m\r\n",
      "    \r\n",
      "    \u001b[37m# Here we set up an argument parser to easily access the parameters\u001b[39;49;00m\r\n",
      "    parser = argparse.ArgumentParser()\r\n",
      "\r\n",
      "    \u001b[37m# SageMaker parameters, like the directories for training data and saving models; set automatically\u001b[39;49;00m\r\n",
      "    \u001b[37m# Do not need to change\u001b[39;49;00m\r\n",
      "    parser.add_argument(\u001b[33m'\u001b[39;49;00m\u001b[33m--output-data-dir\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mstr\u001b[39;49;00m, default=os.environ[\u001b[33m'\u001b[39;49;00m\u001b[33mSM_OUTPUT_DATA_DIR\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m])\r\n",
      "    parser.add_argument(\u001b[33m'\u001b[39;49;00m\u001b[33m--model-dir\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mstr\u001b[39;49;00m, default=os.environ[\u001b[33m'\u001b[39;49;00m\u001b[33mSM_MODEL_DIR\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m])\r\n",
      "    parser.add_argument(\u001b[33m'\u001b[39;49;00m\u001b[33m--data-dir\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mstr\u001b[39;49;00m, default=os.environ[\u001b[33m'\u001b[39;49;00m\u001b[33mSM_CHANNEL_TRAIN\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m])\r\n",
      "    \r\n",
      "    \u001b[37m# Training Parameters, given\u001b[39;49;00m\r\n",
      "    parser.add_argument(\u001b[33m'\u001b[39;49;00m\u001b[33m--batch-size\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mint\u001b[39;49;00m, default=\u001b[34m10\u001b[39;49;00m, metavar=\u001b[33m'\u001b[39;49;00m\u001b[33mN\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m,\r\n",
      "                        help=\u001b[33m'\u001b[39;49;00m\u001b[33minput batch size for training (default: 10)\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\r\n",
      "    parser.add_argument(\u001b[33m'\u001b[39;49;00m\u001b[33m--epochs\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mint\u001b[39;49;00m, default=\u001b[34m10\u001b[39;49;00m, metavar=\u001b[33m'\u001b[39;49;00m\u001b[33mN\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m,\r\n",
      "                        help=\u001b[33m'\u001b[39;49;00m\u001b[33mnumber of epochs to train (default: 10)\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\r\n",
      "    parser.add_argument(\u001b[33m'\u001b[39;49;00m\u001b[33m--seed\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mint\u001b[39;49;00m, default=\u001b[34m1\u001b[39;49;00m, metavar=\u001b[33m'\u001b[39;49;00m\u001b[33mS\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m,\r\n",
      "                        help=\u001b[33m'\u001b[39;49;00m\u001b[33mrandom seed (default: 1)\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\r\n",
      "    \r\n",
      "    \u001b[37m## TODO: Add args for the three model parameters: input_features, hidden_dim, output_dim\u001b[39;49;00m\r\n",
      "    \u001b[37m# Model Parameters\u001b[39;49;00m\r\n",
      "    parser.add_argument(\u001b[33m'\u001b[39;49;00m\u001b[33m--input_features\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mint\u001b[39;49;00m, default=\u001b[34m3\u001b[39;49;00m, metavar=\u001b[33m'\u001b[39;49;00m\u001b[33mN\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m,\r\n",
      "                        help=\u001b[33m'\u001b[39;49;00m\u001b[33mNumber of input features (default: 3)\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\r\n",
      "    parser.add_argument(\u001b[33m'\u001b[39;49;00m\u001b[33m--hidden_dim\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mint\u001b[39;49;00m, default=\u001b[34m10\u001b[39;49;00m, metavar=\u001b[33m'\u001b[39;49;00m\u001b[33mN\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m,\r\n",
      "                        help=\u001b[33m'\u001b[39;49;00m\u001b[33mSize of hidden layer(s) (default: 10)\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\r\n",
      "    parser.add_argument(\u001b[33m'\u001b[39;49;00m\u001b[33m--output_dim\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mint\u001b[39;49;00m, default=\u001b[34m1\u001b[39;49;00m, metavar=\u001b[33m'\u001b[39;49;00m\u001b[33mN\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m,\r\n",
      "                        help=\u001b[33m'\u001b[39;49;00m\u001b[33mNumber of outputs (default: 1)\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)    \r\n",
      "    \r\n",
      "    parser.add_argument(\u001b[33m'\u001b[39;49;00m\u001b[33m--lr\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mfloat\u001b[39;49;00m, default=\u001b[34m0.001\u001b[39;49;00m, metavar=\u001b[33m'\u001b[39;49;00m\u001b[33mLR\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m,\r\n",
      "                        help=\u001b[33m'\u001b[39;49;00m\u001b[33mlearning rate (default: 0.001)\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m) \u001b[37m#set the learning rate for the optimizer\u001b[39;49;00m\r\n",
      "    \r\n",
      "    \u001b[37m# args holds all passed-in arguments\u001b[39;49;00m\r\n",
      "    args = parser.parse_args()\r\n",
      "\r\n",
      "    device = torch.device(\u001b[33m\"\u001b[39;49;00m\u001b[33mcuda\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m \u001b[34mif\u001b[39;49;00m torch.cuda.is_available() \u001b[34melse\u001b[39;49;00m \u001b[33m\"\u001b[39;49;00m\u001b[33mcpu\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\r\n",
      "    \u001b[36mprint\u001b[39;49;00m(\u001b[33m\"\u001b[39;49;00m\u001b[33mUsing device \u001b[39;49;00m\u001b[33m{}\u001b[39;49;00m\u001b[33m.\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m.format(device))\r\n",
      "\r\n",
      "    torch.manual_seed(args.seed)\r\n",
      "\r\n",
      "    \u001b[37m# Load the training data.\u001b[39;49;00m\r\n",
      "    train_loader = _get_train_data_loader(args.batch_size, args.data_dir)\r\n",
      "\r\n",
      "\r\n",
      "    \u001b[37m## --- Your code here --- ##\u001b[39;49;00m\r\n",
      "    \r\n",
      "    \u001b[37m## TODO:  Build the model by passing in the input params\u001b[39;49;00m\r\n",
      "    \u001b[37m# To get params from the parser, call args.argument_name, ex. args.epochs or ards.hidden_dim\u001b[39;49;00m\r\n",
      "    \u001b[37m# Don't forget to move your model .to(device) to move to GPU , if appropriate\u001b[39;49;00m\r\n",
      "    model = BinaryClassifier(args.input_features, args.hidden_dim, args.output_dim).to(device)\r\n",
      "\r\n",
      "    \u001b[37m## TODO: Define an optimizer and loss function for training\u001b[39;49;00m\r\n",
      "    optimizer = optim.Adam(model.parameters(), lr=args.lr)\r\n",
      "    criterion = torch.nn.BCELoss()    \r\n",
      "\r\n",
      "    \u001b[37m# Trains the model (given line of code, which calls the above training function)\u001b[39;49;00m\r\n",
      "    train(model, train_loader, args.epochs, criterion, optimizer, device)\r\n",
      "\r\n",
      "    \u001b[37m## TODO: complete in the model_info by adding three argument names, the first is given\u001b[39;49;00m\r\n",
      "    \u001b[37m# Keep the keys of this dictionary as they are \u001b[39;49;00m\r\n",
      "    model_info_path = os.path.join(args.model_dir, \u001b[33m'\u001b[39;49;00m\u001b[33mmodel_info.pth\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\r\n",
      "    \u001b[34mwith\u001b[39;49;00m \u001b[36mopen\u001b[39;49;00m(model_info_path, \u001b[33m'\u001b[39;49;00m\u001b[33mwb\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m) \u001b[34mas\u001b[39;49;00m f:\r\n",
      "        model_info = {\r\n",
      "            \u001b[33m'\u001b[39;49;00m\u001b[33minput_features\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m: args.input_features,\r\n",
      "            \u001b[33m'\u001b[39;49;00m\u001b[33mhidden_dim\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m: args.hidden_dim,\r\n",
      "            \u001b[33m'\u001b[39;49;00m\u001b[33moutput_dim\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m: args.output_dim,\r\n",
      "        }\r\n",
      "        torch.save(model_info, f)\r\n",
      "        \r\n",
      "    \u001b[37m## --- End of your code  --- ##\u001b[39;49;00m\r\n",
      "    \r\n",
      "\r\n",
      "\t\u001b[37m# Save the model parameters\u001b[39;49;00m\r\n",
      "    model_path = os.path.join(args.model_dir, \u001b[33m'\u001b[39;49;00m\u001b[33mmodel.pth\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\r\n",
      "    \u001b[34mwith\u001b[39;49;00m \u001b[36mopen\u001b[39;49;00m(model_path, \u001b[33m'\u001b[39;49;00m\u001b[33mwb\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m) \u001b[34mas\u001b[39;49;00m f:\r\n",
      "        torch.save(model.cpu().state_dict(), f)\r\n"
     ]
    }
   ],
   "source": [
    "# directory can be changed to: source_sklearn or source_pytorch\n",
    "!pygmentize source_pytorch/train.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Provided code\n",
    "\n",
    "If you read the code above, you can see that the starter code includes a few things:\n",
    "* Model loading (`model_fn`) and saving code\n",
    "* Getting SageMaker's default hyperparameters\n",
    "* Loading the training data by name, `train.csv` and extracting the features and labels, `train_x`, and `train_y`\n",
    "\n",
    "If you'd like to read more about model saving with [joblib for sklearn](https://scikit-learn.org/stable/modules/model_persistence.html) or with [torch.save](https://pytorch.org/tutorials/beginner/saving_loading_models.html), click on the provided links."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Create an Estimator\n",
    "\n",
    "When a custom model is constructed in SageMaker, an entry point must be specified. This is the Python file which will be executed when the model is trained; the `train.py` function you specified above. To run a custom training script in SageMaker, construct an estimator, and fill in the appropriate constructor arguments:\n",
    "\n",
    "* **entry_point**: The path to the Python script SageMaker runs for training and prediction.\n",
    "* **source_dir**: The path to the training script directory `source_sklearn` OR `source_pytorch`.\n",
    "* **role**: Role ARN, which was specified, above.\n",
    "* **train_instance_count**: The number of training instances (should be left at 1).\n",
    "* **train_instance_type**: The type of SageMaker instance for training. Note: Because Scikit-learn does not natively support GPU training, Sagemaker Scikit-learn does not currently support training on GPU instance types.\n",
    "* **sagemaker_session**: The session used to train on Sagemaker.\n",
    "* **hyperparameters** (optional): A dictionary `{'name':value, ..}` passed to the train function as hyperparameters.\n",
    "\n",
    "Note: For a PyTorch model, there is another optional argument **framework_version**, which you can set to the latest version of PyTorch, `1.0`.\n",
    "\n",
    "## EXERCISE: Define a Scikit-learn or PyTorch estimator\n",
    "\n",
    "To import your desired estimator, use one of the following lines:\n",
    "```\n",
    "from sagemaker.sklearn.estimator import SKLearn\n",
    "```\n",
    "```\n",
    "from sagemaker.pytorch import PyTorch\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classifier-1: PyTorch Feedforward Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your import and estimator code, here\n",
    "from sagemaker.pytorch import PyTorch\n",
    "\n",
    "# specify an output path\n",
    "output_path = 's3://{}/{}'.format(bucket, prefix)\n",
    "\n",
    "# instantiate a pytorch estimator\n",
    "estimator = PyTorch(entry_point=\"train.py\",\n",
    "                    source_dir=\"source_pytorch\",\n",
    "                    role=role,\n",
    "                    framework_version='1.4.0',\n",
    "                    py_version='py3',\n",
    "                    train_instance_count=1,\n",
    "                    train_instance_type='ml.c4.xlarge',\n",
    "                    output_path=output_path,\n",
    "                    sagemaker_session=sagemaker_session,\n",
    "                    hyperparameters={\n",
    "                        'epochs': 10,\n",
    "                        'hidden_dim': 20,\n",
    "                        'batch-size': 10,\n",
    "                        'input_features': 3,\n",
    "                        'output_dim': 1,\n",
    "                    })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EXERCISE: Train the estimator\n",
    "\n",
    "Train your estimator on the training data stored in S3. This should create a training job that you can monitor in your SageMaker console."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'create_image_uri' will be deprecated in favor of 'ImageURIProvider' class in SageMaker Python SDK v2.\n",
      "'s3_input' class will be renamed to 'TrainingInput' in SageMaker Python SDK v2.\n",
      "'create_image_uri' will be deprecated in favor of 'ImageURIProvider' class in SageMaker Python SDK v2.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-12-02 23:40:34 Starting - Starting the training job...\n",
      "2020-12-02 23:40:36 Starting - Launching requested ML instances......\n",
      "2020-12-02 23:41:56 Starting - Preparing the instances for training.........\n",
      "2020-12-02 23:43:05 Downloading - Downloading input data...\n",
      "2020-12-02 23:44:04 Training - Training image download completed. Training in progress.\n",
      "2020-12-02 23:44:04 Uploading - Uploading generated training model\u001b[34mbash: cannot set terminal process group (-1): Inappropriate ioctl for device\u001b[0m\n",
      "\u001b[34mbash: no job control in this shell\u001b[0m\n",
      "\u001b[34m2020-12-02 23:43:55,282 sagemaker-containers INFO     Imported framework sagemaker_pytorch_container.training\u001b[0m\n",
      "\u001b[34m2020-12-02 23:43:55,285 sagemaker-containers INFO     No GPUs detected (normal if no gpus installed)\u001b[0m\n",
      "\u001b[34m2020-12-02 23:43:55,295 sagemaker_pytorch_container.training INFO     Block until all host DNS lookups succeed.\u001b[0m\n",
      "\u001b[34m2020-12-02 23:43:55,299 sagemaker_pytorch_container.training INFO     Invoking user training script.\u001b[0m\n",
      "\u001b[34m2020-12-02 23:43:55,611 sagemaker-containers INFO     Module default_user_module_name does not provide a setup.py. \u001b[0m\n",
      "\u001b[34mGenerating setup.py\u001b[0m\n",
      "\u001b[34m2020-12-02 23:43:55,611 sagemaker-containers INFO     Generating setup.cfg\u001b[0m\n",
      "\u001b[34m2020-12-02 23:43:55,612 sagemaker-containers INFO     Generating MANIFEST.in\u001b[0m\n",
      "\u001b[34m2020-12-02 23:43:55,612 sagemaker-containers INFO     Installing module with the following command:\u001b[0m\n",
      "\u001b[34m/opt/conda/bin/python -m pip install . \u001b[0m\n",
      "\u001b[34mProcessing /tmp/tmp7hdobt_g/module_dir\u001b[0m\n",
      "\u001b[34mBuilding wheels for collected packages: default-user-module-name\n",
      "  Building wheel for default-user-module-name (setup.py): started\n",
      "  Building wheel for default-user-module-name (setup.py): finished with status 'done'\n",
      "  Created wheel for default-user-module-name: filename=default_user_module_name-1.0.0-py2.py3-none-any.whl size=11945 sha256=1e37a15241fad18a1d49f56b5c59edcce8d56a7b4c54a1c969b251f90079f398\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-tfpkr2dr/wheels/5c/2d/42/0807f6ecaf6d80f930ba02ce46ed382cfd7246b56cf4efeae9\u001b[0m\n",
      "\u001b[34mSuccessfully built default-user-module-name\u001b[0m\n",
      "\u001b[34mInstalling collected packages: default-user-module-name\u001b[0m\n",
      "\u001b[34mSuccessfully installed default-user-module-name-1.0.0\u001b[0m\n",
      "\u001b[34mWARNING: You are using pip version 20.1.1; however, version 20.3 is available.\u001b[0m\n",
      "\u001b[34mYou should consider upgrading via the '/opt/conda/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
      "\u001b[34m2020-12-02 23:43:57,870 sagemaker-containers INFO     No GPUs detected (normal if no gpus installed)\u001b[0m\n",
      "\u001b[34m2020-12-02 23:43:57,883 sagemaker-containers INFO     No GPUs detected (normal if no gpus installed)\u001b[0m\n",
      "\u001b[34m2020-12-02 23:43:57,896 sagemaker-containers INFO     No GPUs detected (normal if no gpus installed)\u001b[0m\n",
      "\u001b[34m2020-12-02 23:43:57,906 sagemaker-containers INFO     Invoking user script\n",
      "\u001b[0m\n",
      "\u001b[34mTraining Env:\n",
      "\u001b[0m\n",
      "\u001b[34m{\n",
      "    \"additional_framework_parameters\": {},\n",
      "    \"channel_input_dirs\": {\n",
      "        \"train\": \"/opt/ml/input/data/train\"\n",
      "    },\n",
      "    \"current_host\": \"algo-1\",\n",
      "    \"framework_module\": \"sagemaker_pytorch_container.training:main\",\n",
      "    \"hosts\": [\n",
      "        \"algo-1\"\n",
      "    ],\n",
      "    \"hyperparameters\": {\n",
      "        \"batch-size\": 10,\n",
      "        \"hidden_dim\": 20,\n",
      "        \"input_features\": 3,\n",
      "        \"epochs\": 10,\n",
      "        \"output_dim\": 1\n",
      "    },\n",
      "    \"input_config_dir\": \"/opt/ml/input/config\",\n",
      "    \"input_data_config\": {\n",
      "        \"train\": {\n",
      "            \"TrainingInputMode\": \"File\",\n",
      "            \"S3DistributionType\": \"FullyReplicated\",\n",
      "            \"RecordWrapperType\": \"None\"\n",
      "        }\n",
      "    },\n",
      "    \"input_dir\": \"/opt/ml/input\",\n",
      "    \"is_master\": true,\n",
      "    \"job_name\": \"pytorch-training-2020-12-02-23-40-34-519\",\n",
      "    \"log_level\": 20,\n",
      "    \"master_hostname\": \"algo-1\",\n",
      "    \"model_dir\": \"/opt/ml/model\",\n",
      "    \"module_dir\": \"s3://sagemaker-us-east-2-793227728067/pytorch-training-2020-12-02-23-40-34-519/source/sourcedir.tar.gz\",\n",
      "    \"module_name\": \"train\",\n",
      "    \"network_interface_name\": \"eth0\",\n",
      "    \"num_cpus\": 4,\n",
      "    \"num_gpus\": 0,\n",
      "    \"output_data_dir\": \"/opt/ml/output/data\",\n",
      "    \"output_dir\": \"/opt/ml/output\",\n",
      "    \"output_intermediate_dir\": \"/opt/ml/output/intermediate\",\n",
      "    \"resource_config\": {\n",
      "        \"current_host\": \"algo-1\",\n",
      "        \"hosts\": [\n",
      "            \"algo-1\"\n",
      "        ],\n",
      "        \"network_interface_name\": \"eth0\"\n",
      "    },\n",
      "    \"user_entry_point\": \"train.py\"\u001b[0m\n",
      "\u001b[34m}\n",
      "\u001b[0m\n",
      "\u001b[34mEnvironment variables:\n",
      "\u001b[0m\n",
      "\u001b[34mSM_HOSTS=[\"algo-1\"]\u001b[0m\n",
      "\u001b[34mSM_NETWORK_INTERFACE_NAME=eth0\u001b[0m\n",
      "\u001b[34mSM_HPS={\"batch-size\":10,\"epochs\":10,\"hidden_dim\":20,\"input_features\":3,\"output_dim\":1}\u001b[0m\n",
      "\u001b[34mSM_USER_ENTRY_POINT=train.py\u001b[0m\n",
      "\u001b[34mSM_FRAMEWORK_PARAMS={}\u001b[0m\n",
      "\u001b[34mSM_RESOURCE_CONFIG={\"current_host\":\"algo-1\",\"hosts\":[\"algo-1\"],\"network_interface_name\":\"eth0\"}\u001b[0m\n",
      "\u001b[34mSM_INPUT_DATA_CONFIG={\"train\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"}}\u001b[0m\n",
      "\u001b[34mSM_OUTPUT_DATA_DIR=/opt/ml/output/data\u001b[0m\n",
      "\u001b[34mSM_CHANNELS=[\"train\"]\u001b[0m\n",
      "\u001b[34mSM_CURRENT_HOST=algo-1\u001b[0m\n",
      "\u001b[34mSM_MODULE_NAME=train\u001b[0m\n",
      "\u001b[34mSM_LOG_LEVEL=20\u001b[0m\n",
      "\u001b[34mSM_FRAMEWORK_MODULE=sagemaker_pytorch_container.training:main\u001b[0m\n",
      "\u001b[34mSM_INPUT_DIR=/opt/ml/input\u001b[0m\n",
      "\u001b[34mSM_INPUT_CONFIG_DIR=/opt/ml/input/config\u001b[0m\n",
      "\u001b[34mSM_OUTPUT_DIR=/opt/ml/output\u001b[0m\n",
      "\u001b[34mSM_NUM_CPUS=4\u001b[0m\n",
      "\u001b[34mSM_NUM_GPUS=0\u001b[0m\n",
      "\u001b[34mSM_MODEL_DIR=/opt/ml/model\u001b[0m\n",
      "\u001b[34mSM_MODULE_DIR=s3://sagemaker-us-east-2-793227728067/pytorch-training-2020-12-02-23-40-34-519/source/sourcedir.tar.gz\u001b[0m\n",
      "\u001b[34mSM_TRAINING_ENV={\"additional_framework_parameters\":{},\"channel_input_dirs\":{\"train\":\"/opt/ml/input/data/train\"},\"current_host\":\"algo-1\",\"framework_module\":\"sagemaker_pytorch_container.training:main\",\"hosts\":[\"algo-1\"],\"hyperparameters\":{\"batch-size\":10,\"epochs\":10,\"hidden_dim\":20,\"input_features\":3,\"output_dim\":1},\"input_config_dir\":\"/opt/ml/input/config\",\"input_data_config\":{\"train\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"}},\"input_dir\":\"/opt/ml/input\",\"is_master\":true,\"job_name\":\"pytorch-training-2020-12-02-23-40-34-519\",\"log_level\":20,\"master_hostname\":\"algo-1\",\"model_dir\":\"/opt/ml/model\",\"module_dir\":\"s3://sagemaker-us-east-2-793227728067/pytorch-training-2020-12-02-23-40-34-519/source/sourcedir.tar.gz\",\"module_name\":\"train\",\"network_interface_name\":\"eth0\",\"num_cpus\":4,\"num_gpus\":0,\"output_data_dir\":\"/opt/ml/output/data\",\"output_dir\":\"/opt/ml/output\",\"output_intermediate_dir\":\"/opt/ml/output/intermediate\",\"resource_config\":{\"current_host\":\"algo-1\",\"hosts\":[\"algo-1\"],\"network_interface_name\":\"eth0\"},\"user_entry_point\":\"train.py\"}\u001b[0m\n",
      "\u001b[34mSM_USER_ARGS=[\"--batch-size\",\"10\",\"--epochs\",\"10\",\"--hidden_dim\",\"20\",\"--input_features\",\"3\",\"--output_dim\",\"1\"]\u001b[0m\n",
      "\u001b[34mSM_OUTPUT_INTERMEDIATE_DIR=/opt/ml/output/intermediate\u001b[0m\n",
      "\u001b[34mSM_CHANNEL_TRAIN=/opt/ml/input/data/train\u001b[0m\n",
      "\u001b[34mSM_HP_BATCH-SIZE=10\u001b[0m\n",
      "\u001b[34mSM_HP_HIDDEN_DIM=20\u001b[0m\n",
      "\u001b[34mSM_HP_INPUT_FEATURES=3\u001b[0m\n",
      "\u001b[34mSM_HP_EPOCHS=10\u001b[0m\n",
      "\u001b[34mSM_HP_OUTPUT_DIM=1\u001b[0m\n",
      "\u001b[34mPYTHONPATH=/opt/ml/code:/opt/conda/bin:/opt/conda/lib/python36.zip:/opt/conda/lib/python3.6:/opt/conda/lib/python3.6/lib-dynload:/opt/conda/lib/python3.6/site-packages\n",
      "\u001b[0m\n",
      "\u001b[34mInvoking script with the following command:\n",
      "\u001b[0m\n",
      "\u001b[34m/opt/conda/bin/python train.py --batch-size 10 --epochs 10 --hidden_dim 20 --input_features 3 --output_dim 1\n",
      "\n",
      "\u001b[0m\n",
      "\u001b[34mUsing device cpu.\u001b[0m\n",
      "\u001b[34mGet train data loader.\u001b[0m\n",
      "\u001b[34m[2020-12-02 23:44:00.908 algo-1:43 INFO json_config.py:90] Creating hook from json_config at /opt/ml/input/config/debughookconfig.json.\u001b[0m\n",
      "\u001b[34m[2020-12-02 23:44:00.908 algo-1:43 INFO hook.py:191] tensorboard_dir has not been set for the hook. SMDebug will not be exporting tensorboard summaries.\u001b[0m\n",
      "\u001b[34m[2020-12-02 23:44:00.908 algo-1:43 INFO hook.py:236] Saving to /opt/ml/output/tensors\u001b[0m\n",
      "\u001b[34m[2020-12-02 23:44:00.908 algo-1:43 INFO state_store.py:67] The checkpoint config file /opt/ml/input/config/checkpointconfig.json does not exist.\u001b[0m\n",
      "\u001b[34m[2020-12-02 23:44:00.909 algo-1:43 INFO hook.py:376] Monitoring the collections: losses\u001b[0m\n",
      "\u001b[34m[2020-12-02 23:44:00.909 algo-1:43 INFO hook.py:437] Hook is writing from the hook with pid: 43\n",
      "\u001b[0m\n",
      "\u001b[34mEpoch: 1, Loss: 0.7936103429113116\u001b[0m\n",
      "\u001b[34mEpoch: 2, Loss: 0.7653089165687561\u001b[0m\n",
      "\u001b[34mEpoch: 3, Loss: 0.7500952141625541\u001b[0m\n",
      "\u001b[34mEpoch: 4, Loss: 0.7363540189606803\u001b[0m\n",
      "\u001b[34mEpoch: 5, Loss: 0.7168293169566563\u001b[0m\n",
      "\u001b[34mEpoch: 6, Loss: 0.7126985362597874\u001b[0m\n",
      "\u001b[34mEpoch: 7, Loss: 0.7075247423989433\u001b[0m\n",
      "\u001b[34mEpoch: 8, Loss: 0.6939623100417001\u001b[0m\n",
      "\u001b[34mEpoch: 9, Loss: 0.6965613194874355\u001b[0m\n",
      "\u001b[34mEpoch: 10, Loss: 0.6901018619537354\u001b[0m\n",
      "\u001b[34m[2020-12-02 23:44:01.158 algo-1:43 INFO utils.py:25] The end of training job file will not be written for jobs running under SageMaker.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m2020-12-02 23:44:01,372 sagemaker-containers INFO     Reporting training SUCCESS\u001b[0m\n",
      "\n",
      "2020-12-02 23:44:12 Completed - Training job completed\n",
      "Training seconds: 67\n",
      "Billable seconds: 67\n",
      "CPU times: user 540 ms, sys: 40.4 ms, total: 581 ms\n",
      "Wall time: 4min 12s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Train your estimator on S3 training data\n",
    "estimator.fit({'train': data_path})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classifier-2: LinearLearner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import LinearLearner\n",
    "from sagemaker import LinearLearner\n",
    "\n",
    "# define location to store model artifacts\n",
    "cl2_prefix = 'plagiarism-detection-linearlearner'\n",
    "output_path='s3://{}/{}/'.format(bucket, cl2_prefix)\n",
    "# print('Training artifacts will be uploaded to: {}'.format(output_path))\n",
    "\n",
    "# instantiate LinearLearner\n",
    "linear = LinearLearner(role=role,\n",
    "             train_instance_count=1,\n",
    "             train_instance_type='ml.c4.xlarge',\n",
    "             output_path=output_path,\n",
    "             sagemaker_session=sagemaker_session,\n",
    "             predictor_type='binary_classifier', # The type of predictor to learn\n",
    "             epochs=15) # Default value. The maximum number of passes to make over the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "# read in the train csv file\n",
    "train_df = pd.read_csv(os.path.join(data_dir, \"train.csv\"), header=None, names=None) #[num_files=70, num_columns=4]\n",
    "train_labels = train_df.loc[:, 0].values #[num_files=70]\n",
    "train_features = train_df.loc[:, 1:].values #[num_files=70, num_features=3]\n",
    "\n",
    "# create RecordSet of training data\n",
    "train_x_np = train_features.astype('float32')\n",
    "train_y_np = train_labels.astype('float32')\n",
    "\n",
    "formatted_train_data = linear.record_set(train=train_x_np, labels=train_y_np) #Build a RecordSet from a numpy ndarray matrix and label vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'get_image_uri' method will be deprecated in favor of 'ImageURIProvider' class in SageMaker Python SDK v2.\n",
      "'s3_input' class will be renamed to 'TrainingInput' in SageMaker Python SDK v2.\n",
      "'get_image_uri' method will be deprecated in favor of 'ImageURIProvider' class in SageMaker Python SDK v2.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-12-03 19:01:22 Starting - Starting the training job...\n",
      "2020-12-03 19:01:24 Starting - Launching requested ML instances......\n",
      "2020-12-03 19:02:29 Starting - Preparing the instances for training.........\n",
      "2020-12-03 19:04:04 Downloading - Downloading input data...\n",
      "2020-12-03 19:04:53 Training - Training image download completed. Training in progress.\n",
      "2020-12-03 19:04:53 Uploading - Uploading generated training model\u001b[34mDocker entrypoint called with argument(s): train\u001b[0m\n",
      "\u001b[34mRunning default environment configuration script\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] 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[12/03/2020 19:04:48 INFO 140670865348416] Merging with provided configuration from /opt/ml/input/config/hyperparameters.json: {u'epochs': u'15', u'feature_dim': u'3', u'mini_batch_size': u'70', u'predictor_type': u'binary_classifier'}\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Final configuration: {u'loss_insensitivity': u'0.01', u'epochs': u'15', u'feature_dim': u'3', 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'70', u'huber_delta': u'1.0', u'num_classes': u'1', u'predictor_type': u'binary_classifier', 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[12/03/2020 19:04:48 WARNING 140670865348416] Loggers have already been setup.\u001b[0m\n",
      "\u001b[34mProcess 1 is a worker.\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Using default worker.\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Checkpoint loading and saving are disabled.\u001b[0m\n",
      "\u001b[34m[2020-12-03 19:04:48.408] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 0, \"duration\": 14, \"num_examples\": 1, \"num_bytes\": 3920}\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Create Store: local\u001b[0m\n",
      "\u001b[34m[2020-12-03 19:04:48.443] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 1, \"duration\": 34, \"num_examples\": 1, \"num_bytes\": 3920}\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Scaler algorithm parameters\n",
      " <algorithm.scaler.ScalerAlgorithmStable object at 0x7ff01d786d10>\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Scaling model computed with parameters:\n",
      " {'stdev_weight': \u001b[0m\n",
      "\u001b[34m[0.19045913 0.266688   0.24899504]\u001b[0m\n",
      "\u001b[34m<NDArray 3 @cpu(0)>, 'stdev_label': None, 'mean_label': None, 'mean_weight': \u001b[0m\n",
      "\u001b[34m[0.64699847 0.19143711 0.44681004]\u001b[0m\n",
      "\u001b[34m<NDArray 3 @cpu(0)>}\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] nvidia-smi took: 0.0251970291138 secs to identify 0 gpus\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Number of GPUs being used: 0\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"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\": 2, \"sum\": 2.0, \"min\": 2}, \"Total Records Seen\": {\"count\": 1, \"max\": 140, \"sum\": 140.0, \"min\": 140}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Reset Count\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}}, \"EndTime\": 1607022288.555457, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"init_train_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\"}, \"StartTime\": 1607022288.555421}\n",
      "\u001b[0m\n",
      "\u001b[34m[2020-12-03 19:04:48.616] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 4, \"duration\": 60, \"num_examples\": 1, \"num_bytes\": 3920}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6772598266601563, \"sum\": 0.6772598266601563, \"min\": 0.6772598266601563}}, \"EndTime\": 1607022288.616714, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.616628}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6656907217843192, \"sum\": 0.6656907217843192, \"min\": 0.6656907217843192}}, \"EndTime\": 1607022288.616797, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.616778}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7285283769880022, \"sum\": 0.7285283769880022, \"min\": 0.7285283769880022}}, \"EndTime\": 1607022288.616865, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.616846}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6631687709263393, \"sum\": 0.6631687709263393, \"min\": 0.6631687709263393}}, \"EndTime\": 1607022288.616941, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.616921}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6930412292480469, \"sum\": 0.6930412292480469, \"min\": 0.6930412292480469}}, \"EndTime\": 1607022288.617006, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.616988}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6718323843819755, \"sum\": 0.6718323843819755, \"min\": 0.6718323843819755}}, \"EndTime\": 1607022288.617067, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.61705}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.721415056501116, \"sum\": 0.721415056501116, \"min\": 0.721415056501116}}, \"EndTime\": 1607022288.61714, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.617121}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6581271580287389, \"sum\": 0.6581271580287389, \"min\": 0.6581271580287389}}, \"EndTime\": 1607022288.617223, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.617203}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7122693743024554, \"sum\": 0.7122693743024554, \"min\": 0.7122693743024554}}, \"EndTime\": 1607022288.617288, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.617271}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7032196044921875, \"sum\": 0.7032196044921875, \"min\": 0.7032196044921875}}, \"EndTime\": 1607022288.61735, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.617334}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.657168688092913, \"sum\": 0.657168688092913, \"min\": 0.657168688092913}}, \"EndTime\": 1607022288.617417, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.6174}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6809098379952567, \"sum\": 0.6809098379952567, \"min\": 0.6809098379952567}}, \"EndTime\": 1607022288.617482, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.617464}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6868578774588449, \"sum\": 0.6868578774588449, \"min\": 0.6868578774588449}}, \"EndTime\": 1607022288.617543, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.617526}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6859128679547991, \"sum\": 0.6859128679547991, \"min\": 0.6859128679547991}}, \"EndTime\": 1607022288.617599, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.617584}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7068556649344307, \"sum\": 0.7068556649344307, \"min\": 0.7068556649344307}}, \"EndTime\": 1607022288.617667, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.61765}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6859907967703683, \"sum\": 0.6859907967703683, \"min\": 0.6859907967703683}}, \"EndTime\": 1607022288.617722, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.617706}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6781888689313617, \"sum\": 0.6781888689313617, \"min\": 0.6781888689313617}}, \"EndTime\": 1607022288.617789, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.617771}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6745536804199219, \"sum\": 0.6745536804199219, \"min\": 0.6745536804199219}}, \"EndTime\": 1607022288.617857, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.617838}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7021205357142857, \"sum\": 0.7021205357142857, \"min\": 0.7021205357142857}}, \"EndTime\": 1607022288.617928, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.617909}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7309802464076451, \"sum\": 0.7309802464076451, \"min\": 0.7309802464076451}}, \"EndTime\": 1607022288.617991, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.617973}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6771411895751953, \"sum\": 0.6771411895751953, \"min\": 0.6771411895751953}}, \"EndTime\": 1607022288.618052, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.618035}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6589389256068638, \"sum\": 0.6589389256068638, \"min\": 0.6589389256068638}}, \"EndTime\": 1607022288.618117, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.618101}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6692342485700334, \"sum\": 0.6692342485700334, \"min\": 0.6692342485700334}}, \"EndTime\": 1607022288.618176, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.618159}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6730436052594866, \"sum\": 0.6730436052594866, \"min\": 0.6730436052594866}}, \"EndTime\": 1607022288.618246, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.618227}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6961332048688617, \"sum\": 0.6961332048688617, \"min\": 0.6961332048688617}}, \"EndTime\": 1607022288.61837, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.618294}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7019153050013951, \"sum\": 0.7019153050013951, \"min\": 0.7019153050013951}}, \"EndTime\": 1607022288.618443, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.618424}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6615815298897879, \"sum\": 0.6615815298897879, \"min\": 0.6615815298897879}}, \"EndTime\": 1607022288.618507, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.618489}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7173212323869977, \"sum\": 0.7173212323869977, \"min\": 0.7173212323869977}}, \"EndTime\": 1607022288.618574, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.618556}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7043078831263951, \"sum\": 0.7043078831263951, \"min\": 0.7043078831263951}}, \"EndTime\": 1607022288.618636, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.61862}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6968993050711495, \"sum\": 0.6968993050711495, \"min\": 0.6968993050711495}}, \"EndTime\": 1607022288.618703, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.618685}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7137779235839844, \"sum\": 0.7137779235839844, \"min\": 0.7137779235839844}}, \"EndTime\": 1607022288.618759, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.618742}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6934423174176897, \"sum\": 0.6934423174176897, \"min\": 0.6934423174176897}}, \"EndTime\": 1607022288.618816, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.6188}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #quality_metric: host=algo-1, epoch=0, train binary_classification_cross_entropy_objective <loss>=0.67725982666\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #early_stopping_criteria_metric: host=algo-1, epoch=0, criteria=binary_classification_cross_entropy_objective, value=0.657168688093\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Epoch 0: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Saving model for epoch: 0\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Saved checkpoint to \"/tmp/tmph56JfM/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #progress_metric: host=algo-1, completed 6 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Total Batches Seen\": {\"count\": 1, \"max\": 3, \"sum\": 3.0, \"min\": 3}, \"Total Records Seen\": {\"count\": 1, \"max\": 210, \"sum\": 210.0, \"min\": 210}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Reset Count\": {\"count\": 1, \"max\": 3, \"sum\": 3.0, \"min\": 3}}, \"EndTime\": 1607022288.629709, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1607022288.555733}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #throughput_metric: host=algo-1, train throughput=944.93669338 records/second\u001b[0m\n",
      "\u001b[34m[2020-12-03 19:04:48.662] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 6, \"duration\": 32, \"num_examples\": 1, \"num_bytes\": 3920}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6679467337472098, \"sum\": 0.6679467337472098, \"min\": 0.6679467337472098}}, \"EndTime\": 1607022288.663062, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.662981}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6565220424107143, \"sum\": 0.6565220424107143, \"min\": 0.6565220424107143}}, \"EndTime\": 1607022288.663143, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.663122}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7186637878417969, \"sum\": 0.7186637878417969, \"min\": 0.7186637878417969}}, \"EndTime\": 1607022288.66319, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.663179}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6540334974016462, \"sum\": 0.6540334974016462, \"min\": 0.6540334974016462}}, \"EndTime\": 1607022288.663243, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.663231}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6244100298200335, \"sum\": 0.6244100298200335, \"min\": 0.6244100298200335}}, \"EndTime\": 1607022288.663285, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.663271}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6064589364188058, \"sum\": 0.6064589364188058, \"min\": 0.6064589364188058}}, \"EndTime\": 1607022288.663351, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.663333}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.647956793648856, \"sum\": 0.647956793648856, \"min\": 0.647956793648856}}, \"EndTime\": 1607022288.663412, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.663395}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5947925022670201, \"sum\": 0.5947925022670201, \"min\": 0.5947925022670201}}, \"EndTime\": 1607022288.663482, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.663465}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7025650569370815, \"sum\": 0.7025650569370815, \"min\": 0.7025650569370815}}, \"EndTime\": 1607022288.663542, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.663525}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6936129433768136, \"sum\": 0.6936129433768136, \"min\": 0.6936129433768136}}, \"EndTime\": 1607022288.663601, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.663584}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6480704171316964, \"sum\": 0.6480704171316964, \"min\": 0.6480704171316964}}, \"EndTime\": 1607022288.663661, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.663644}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6715706961495536, \"sum\": 0.6715706961495536, \"min\": 0.6715706961495536}}, \"EndTime\": 1607022288.66372, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.663704}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6193251473563058, \"sum\": 0.6193251473563058, \"min\": 0.6193251473563058}}, \"EndTime\": 1607022288.663779, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.663763}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6183661324637276, \"sum\": 0.6183661324637276, \"min\": 0.6183661324637276}}, \"EndTime\": 1607022288.663839, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.663823}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6365579332624163, \"sum\": 0.6365579332624163, \"min\": 0.6365579332624163}}, \"EndTime\": 1607022288.663897, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.663881}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6185591016496931, \"sum\": 0.6185591016496931, \"min\": 0.6185591016496931}}, \"EndTime\": 1607022288.663957, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.663941}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6688556671142578, \"sum\": 0.6688556671142578, \"min\": 0.6688556671142578}}, \"EndTime\": 1607022288.664015, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.663999}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6652593885149275, \"sum\": 0.6652593885149275, \"min\": 0.6652593885149275}}, \"EndTime\": 1607022288.664074, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.664058}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.692511476789202, \"sum\": 0.692511476789202, \"min\": 0.692511476789202}}, \"EndTime\": 1607022288.664123, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.664107}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7211268833705358, \"sum\": 0.7211268833705358, \"min\": 0.7211268833705358}}, \"EndTime\": 1607022288.664181, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.664165}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6107442583356585, \"sum\": 0.6107442583356585, \"min\": 0.6107442583356585}}, \"EndTime\": 1607022288.66424, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.664223}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5956478663853236, \"sum\": 0.5956478663853236, \"min\": 0.5956478663853236}}, \"EndTime\": 1607022288.664299, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.664283}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6043927873883929, \"sum\": 0.6043927873883929, \"min\": 0.6043927873883929}}, \"EndTime\": 1607022288.664356, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.664339}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6076971871512277, \"sum\": 0.6076971871512277, \"min\": 0.6076971871512277}}, \"EndTime\": 1607022288.664416, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.6644}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6928302764892578, \"sum\": 0.6928302764892578, \"min\": 0.6928302764892578}}, \"EndTime\": 1607022288.664473, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.664457}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6992162431989397, \"sum\": 0.6992162431989397, \"min\": 0.6992162431989397}}, \"EndTime\": 1607022288.664529, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.664513}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6582503727504185, \"sum\": 0.6582503727504185, \"min\": 0.6582503727504185}}, \"EndTime\": 1607022288.664655, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.664635}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7075910840715681, \"sum\": 0.7075910840715681, \"min\": 0.7075910840715681}}, \"EndTime\": 1607022288.664713, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.664698}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6952552795410156, \"sum\": 0.6952552795410156, \"min\": 0.6952552795410156}}, \"EndTime\": 1607022288.664772, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.664755}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6899538312639509, \"sum\": 0.6899538312639509, \"min\": 0.6899538312639509}}, \"EndTime\": 1607022288.664832, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.664816}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6946611676897322, \"sum\": 0.6946611676897322, \"min\": 0.6946611676897322}}, \"EndTime\": 1607022288.66489, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.664874}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7541017804827009, \"sum\": 0.7541017804827009, \"min\": 0.7541017804827009}}, \"EndTime\": 1607022288.664948, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.664934}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #quality_metric: host=algo-1, epoch=1, train binary_classification_cross_entropy_objective <loss>=0.667946733747\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #early_stopping_criteria_metric: host=algo-1, epoch=1, criteria=binary_classification_cross_entropy_objective, value=0.594792502267\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Epoch 1: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Saving model for epoch: 1\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Saved checkpoint to \"/tmp/tmpFL2Omg/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #progress_metric: host=algo-1, completed 13 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Total Batches Seen\": {\"count\": 1, \"max\": 4, \"sum\": 4.0, \"min\": 4}, \"Total Records Seen\": {\"count\": 1, \"max\": 280, \"sum\": 280.0, \"min\": 280}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Reset Count\": {\"count\": 1, \"max\": 4, \"sum\": 4.0, \"min\": 4}}, \"EndTime\": 1607022288.671838, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1607022288.629953}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #throughput_metric: host=algo-1, train throughput=1666.62662844 records/second\u001b[0m\n",
      "\u001b[34m[2020-12-03 19:04:48.698] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 8, \"duration\": 26, \"num_examples\": 1, \"num_bytes\": 3920}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6593309129987445, \"sum\": 0.6593309129987445, \"min\": 0.6593309129987445}}, \"EndTime\": 1607022288.698799, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.698722}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6480481828962054, \"sum\": 0.6480481828962054, \"min\": 0.6480481828962054}}, \"EndTime\": 1607022288.698878, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.698862}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7094966888427734, \"sum\": 0.7094966888427734, \"min\": 0.7094966888427734}}, \"EndTime\": 1607022288.698955, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.698935}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.645592280796596, \"sum\": 0.645592280796596, \"min\": 0.645592280796596}}, \"EndTime\": 1607022288.699033, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.699014}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5334952218191964, \"sum\": 0.5334952218191964, \"min\": 0.5334952218191964}}, \"EndTime\": 1607022288.6991, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.699081}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5195252009800502, \"sum\": 0.5195252009800502, \"min\": 0.5195252009800502}}, \"EndTime\": 1607022288.699146, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.699135}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5518992832728795, \"sum\": 0.5518992832728795, \"min\": 0.5518992832728795}}, \"EndTime\": 1607022288.699209, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.699191}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5105896541050502, \"sum\": 0.5105896541050502, \"min\": 0.5105896541050502}}, \"EndTime\": 1607022288.699285, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.699265}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6935595921107701, \"sum\": 0.6935595921107701, \"min\": 0.6935595921107701}}, \"EndTime\": 1607022288.699353, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.699334}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6847054617745536, \"sum\": 0.6847054617745536, \"min\": 0.6847054617745536}}, \"EndTime\": 1607022288.699429, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.699409}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6396659851074219, \"sum\": 0.6396659851074219, \"min\": 0.6396659851074219}}, \"EndTime\": 1607022288.699499, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.69948}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6629289899553571, \"sum\": 0.6629289899553571, \"min\": 0.6629289899553571}}, \"EndTime\": 1607022288.699564, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.699546}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5296766008649554, \"sum\": 0.5296766008649554, \"min\": 0.5296766008649554}}, \"EndTime\": 1607022288.699629, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.699611}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5287593841552735, \"sum\": 0.5287593841552735, \"min\": 0.5287593841552735}}, \"EndTime\": 1607022288.699693, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.699674}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5436751229422433, \"sum\": 0.5436751229422433, \"min\": 0.5436751229422433}}, \"EndTime\": 1607022288.699759, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.699741}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5290428706577845, \"sum\": 0.5290428706577845, \"min\": 0.5290428706577845}}, \"EndTime\": 1607022288.699824, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.699806}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.660222680228097, \"sum\": 0.660222680228097, \"min\": 0.660222680228097}}, \"EndTime\": 1607022288.699885, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.699867}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6566648210797991, \"sum\": 0.6566648210797991, \"min\": 0.6566648210797991}}, \"EndTime\": 1607022288.699956, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.699937}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6836034502301898, \"sum\": 0.6836034502301898, \"min\": 0.6836034502301898}}, \"EndTime\": 1607022288.700048, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.700027}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7119735172816686, \"sum\": 0.7119735172816686, \"min\": 0.7119735172816686}}, \"EndTime\": 1607022288.700119, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.7001}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5265725816999163, \"sum\": 0.5265725816999163, \"min\": 0.5265725816999163}}, \"EndTime\": 1607022288.700189, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.700171}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5168163299560546, \"sum\": 0.5168163299560546, \"min\": 0.5168163299560546}}, \"EndTime\": 1607022288.70025, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.700232}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5225630078996931, \"sum\": 0.5225630078996931, \"min\": 0.5225630078996931}}, \"EndTime\": 1607022288.700307, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.700291}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5250070299421038, \"sum\": 0.5250070299421038, \"min\": 0.5250070299421038}}, \"EndTime\": 1607022288.700376, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.700358}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6901293618338449, \"sum\": 0.6901293618338449, \"min\": 0.6901293618338449}}, \"EndTime\": 1607022288.700438, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.70042}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.694703347342355, \"sum\": 0.694703347342355, \"min\": 0.694703347342355}}, \"EndTime\": 1607022288.700506, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.700489}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6582929338727679, \"sum\": 0.6582929338727679, \"min\": 0.6582929338727679}}, \"EndTime\": 1607022288.700576, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.700557}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6988558088030133, \"sum\": 0.6988558088030133, \"min\": 0.6988558088030133}}, \"EndTime\": 1607022288.700648, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.700629}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6771573747907366, \"sum\": 0.6771573747907366, \"min\": 0.6771573747907366}}, \"EndTime\": 1607022288.700717, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.700699}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6797694069998604, \"sum\": 0.6797694069998604, \"min\": 0.6797694069998604}}, \"EndTime\": 1607022288.700775, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.700759}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6713248116629464, \"sum\": 0.6713248116629464, \"min\": 0.6713248116629464}}, \"EndTime\": 1607022288.70084, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.700823}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6947935921805245, \"sum\": 0.6947935921805245, \"min\": 0.6947935921805245}}, \"EndTime\": 1607022288.700901, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.700883}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #quality_metric: host=algo-1, epoch=2, train binary_classification_cross_entropy_objective <loss>=0.659330912999\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #early_stopping_criteria_metric: host=algo-1, epoch=2, criteria=binary_classification_cross_entropy_objective, value=0.510589654105\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Epoch 2: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Saving model for epoch: 2\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Saved checkpoint to \"/tmp/tmpKw6cTB/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #progress_metric: host=algo-1, completed 20 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Total Batches Seen\": {\"count\": 1, \"max\": 5, \"sum\": 5.0, \"min\": 5}, \"Total Records Seen\": {\"count\": 1, \"max\": 350, \"sum\": 350.0, \"min\": 350}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Reset Count\": {\"count\": 1, \"max\": 5, \"sum\": 5.0, \"min\": 5}}, \"EndTime\": 1607022288.7077, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1607022288.672091}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #throughput_metric: host=algo-1, train throughput=1960.34773319 records/second\u001b[0m\n",
      "\u001b[34m[2020-12-03 19:04:48.733] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 10, \"duration\": 25, \"num_examples\": 1, \"num_bytes\": 3920}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6514161791120257, \"sum\": 0.6514161791120257, \"min\": 0.6514161791120257}}, \"EndTime\": 1607022288.733574, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.733498}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6402720860072545, \"sum\": 0.6402720860072545, \"min\": 0.6402720860072545}}, \"EndTime\": 1607022288.733664, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.733642}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7010335649762834, \"sum\": 0.7010335649762834, \"min\": 0.7010335649762834}}, \"EndTime\": 1607022288.73373, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.733716}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6378477913992745, \"sum\": 0.6378477913992745, \"min\": 0.6378477913992745}}, \"EndTime\": 1607022288.733805, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.733784}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.46524767194475447, \"sum\": 0.46524767194475447, \"min\": 0.46524767194475447}}, \"EndTime\": 1607022288.73388, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.73386}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.45459180559430806, \"sum\": 0.45459180559430806, \"min\": 0.45459180559430806}}, \"EndTime\": 1607022288.733965, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.733936}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4790439060756138, \"sum\": 0.4790439060756138, \"min\": 0.4790439060756138}}, \"EndTime\": 1607022288.734029, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.734012}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.44775148119245256, \"sum\": 0.44775148119245256, \"min\": 0.44775148119245256}}, \"EndTime\": 1607022288.734083, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.734072}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6852585928780692, \"sum\": 0.6852585928780692, \"min\": 0.6852585928780692}}, \"EndTime\": 1607022288.734128, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.734114}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6765022277832031, \"sum\": 0.6765022277832031, \"min\": 0.6765022277832031}}, \"EndTime\": 1607022288.734178, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.734163}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6319582257952009, \"sum\": 0.6319582257952009, \"min\": 0.6319582257952009}}, \"EndTime\": 1607022288.734236, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.734219}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6549886431012835, \"sum\": 0.6549886431012835, \"min\": 0.6549886431012835}}, \"EndTime\": 1607022288.734294, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.734277}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4625162942068917, \"sum\": 0.4625162942068917, \"min\": 0.4625162942068917}}, \"EndTime\": 1607022288.734393, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.734373}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4616663796561105, \"sum\": 0.4616663796561105, \"min\": 0.4616663796561105}}, \"EndTime\": 1607022288.734453, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.734435}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4736615862165179, \"sum\": 0.4736615862165179, \"min\": 0.4736615862165179}}, \"EndTime\": 1607022288.734516, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.734499}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4620166233607701, \"sum\": 0.4620166233607701, \"min\": 0.4620166233607701}}, \"EndTime\": 1607022288.734582, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.734564}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.652295412336077, \"sum\": 0.652295412336077, \"min\": 0.652295412336077}}, \"EndTime\": 1607022288.734642, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.734625}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6487754821777344, \"sum\": 0.6487754821777344, \"min\": 0.6487754821777344}}, \"EndTime\": 1607022288.734701, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.734684}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.675403322492327, \"sum\": 0.675403322492327, \"min\": 0.675403322492327}}, \"EndTime\": 1607022288.734767, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.73475}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7035277775355748, \"sum\": 0.7035277775355748, \"min\": 0.7035277775355748}}, \"EndTime\": 1607022288.734828, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.734811}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4715061732700893, \"sum\": 0.4715061732700893, \"min\": 0.4715061732700893}}, \"EndTime\": 1607022288.734886, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.73487}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.46830738612583706, \"sum\": 0.46830738612583706, \"min\": 0.46830738612583706}}, \"EndTime\": 1607022288.734943, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.734926}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.47022748674665177, \"sum\": 0.47022748674665177, \"min\": 0.47022748674665177}}, \"EndTime\": 1607022288.735005, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.734988}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4715627942766462, \"sum\": 0.4715627942766462, \"min\": 0.4715627942766462}}, \"EndTime\": 1607022288.735055, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.73504}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.687962886265346, \"sum\": 0.687962886265346, \"min\": 0.687962886265346}}, \"EndTime\": 1607022288.735112, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.735096}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.690019771030971, \"sum\": 0.690019771030971, \"min\": 0.690019771030971}}, \"EndTime\": 1607022288.735166, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.735151}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6589504786900111, \"sum\": 0.6589504786900111, \"min\": 0.6589504786900111}}, \"EndTime\": 1607022288.735224, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.735208}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6916110447474888, \"sum\": 0.6916110447474888, \"min\": 0.6916110447474888}}, \"EndTime\": 1607022288.735278, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.735258}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6690434047154018, \"sum\": 0.6690434047154018, \"min\": 0.6690434047154018}}, \"EndTime\": 1607022288.735339, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.735322}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6755419594900949, \"sum\": 0.6755419594900949, \"min\": 0.6755419594900949}}, \"EndTime\": 1607022288.735395, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.73538}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6639864240373884, \"sum\": 0.6639864240373884, \"min\": 0.6639864240373884}}, \"EndTime\": 1607022288.73545, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.735435}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.645075443812779, \"sum\": 0.645075443812779, \"min\": 0.645075443812779}}, \"EndTime\": 1607022288.735509, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.735493}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #quality_metric: host=algo-1, epoch=3, train binary_classification_cross_entropy_objective <loss>=0.651416179112\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #early_stopping_criteria_metric: host=algo-1, epoch=3, criteria=binary_classification_cross_entropy_objective, value=0.447751481192\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Epoch 3: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Saving model for epoch: 3\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Saved checkpoint to \"/tmp/tmpB9Gnpa/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #progress_metric: host=algo-1, completed 26 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Total Batches Seen\": {\"count\": 1, \"max\": 6, \"sum\": 6.0, \"min\": 6}, \"Total Records Seen\": {\"count\": 1, \"max\": 420, \"sum\": 420.0, \"min\": 420}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Reset Count\": {\"count\": 1, \"max\": 6, \"sum\": 6.0, \"min\": 6}}, \"EndTime\": 1607022288.741957, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1607022288.707937}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #throughput_metric: host=algo-1, train throughput=2051.52032645 records/second\u001b[0m\n",
      "\u001b[34m[2020-12-03 19:04:48.778] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 12, \"duration\": 35, \"num_examples\": 1, \"num_bytes\": 3920}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.644196047101702, \"sum\": 0.644196047101702, \"min\": 0.644196047101702}}, \"EndTime\": 1607022288.778255, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.77816}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.633186776297433, \"sum\": 0.633186776297433, \"min\": 0.633186776297433}}, \"EndTime\": 1607022288.778365, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.778344}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6932707105364119, \"sum\": 0.6932707105364119, \"min\": 0.6932707105364119}}, \"EndTime\": 1607022288.77843, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.778412}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6307929992675781, \"sum\": 0.6307929992675781, \"min\": 0.6307929992675781}}, \"EndTime\": 1607022288.778494, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.778475}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.44267087663922994, \"sum\": 0.44267087663922994, \"min\": 0.44267087663922994}}, \"EndTime\": 1607022288.778564, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.778537}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4337738309587751, \"sum\": 0.4337738309587751, \"min\": 0.4337738309587751}}, \"EndTime\": 1607022288.778627, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.77861}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.45392142704554966, \"sum\": 0.45392142704554966, \"min\": 0.45392142704554966}}, \"EndTime\": 1607022288.778686, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.778669}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.427963992527553, \"sum\": 0.427963992527553, \"min\": 0.427963992527553}}, \"EndTime\": 1607022288.778748, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.77873}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6776575360979352, \"sum\": 0.6776575360979352, \"min\": 0.6776575360979352}}, \"EndTime\": 1607022288.778809, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.778792}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.668998282296317, \"sum\": 0.668998282296317, \"min\": 0.668998282296317}}, \"EndTime\": 1607022288.778871, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.778854}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6249397822788784, \"sum\": 0.6249397822788784, \"min\": 0.6249397822788784}}, \"EndTime\": 1607022288.778932, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.778914}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6477432795933314, \"sum\": 0.6477432795933314, \"min\": 0.6477432795933314}}, \"EndTime\": 1607022288.778992, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.778976}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.440509033203125, \"sum\": 0.440509033203125, \"min\": 0.440509033203125}}, \"EndTime\": 1607022288.779049, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.779031}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4397697993687221, \"sum\": 0.4397697993687221, \"min\": 0.4397697993687221}}, \"EndTime\": 1607022288.779111, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.779093}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.44994495936802453, \"sum\": 0.44994495936802453, \"min\": 0.44994495936802453}}, \"EndTime\": 1607022288.779174, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.779155}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4401057379586356, \"sum\": 0.4401057379586356, \"min\": 0.4401057379586356}}, \"EndTime\": 1607022288.779234, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.779217}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.645069340297154, \"sum\": 0.645069340297154, \"min\": 0.645069340297154}}, \"EndTime\": 1607022288.779292, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.779275}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6415865216936384, \"sum\": 0.6415865216936384, \"min\": 0.6415865216936384}}, \"EndTime\": 1607022288.779352, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.779334}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6679074968610491, \"sum\": 0.6679074968610491, \"min\": 0.6679074968610491}}, \"EndTime\": 1607022288.779411, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.779395}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6957870483398437, \"sum\": 0.6957870483398437, \"min\": 0.6957870483398437}}, \"EndTime\": 1607022288.77947, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.779452}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.46883512224469864, \"sum\": 0.46883512224469864, \"min\": 0.46883512224469864}}, \"EndTime\": 1607022288.779532, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.779515}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4703022003173828, \"sum\": 0.4703022003173828, \"min\": 0.4703022003173828}}, \"EndTime\": 1607022288.779591, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.779574}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4693620954241071, \"sum\": 0.4693620954241071, \"min\": 0.4693620954241071}}, \"EndTime\": 1607022288.779653, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.779636}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4695934840611049, \"sum\": 0.4695934840611049, \"min\": 0.4695934840611049}}, \"EndTime\": 1607022288.779713, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.779696}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6862123761858259, \"sum\": 0.6862123761858259, \"min\": 0.6862123761858259}}, \"EndTime\": 1607022288.779773, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.779756}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6856512342180524, \"sum\": 0.6856512342180524, \"min\": 0.6856512342180524}}, \"EndTime\": 1607022288.779831, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.779814}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6595614842006139, \"sum\": 0.6595614842006139, \"min\": 0.6595614842006139}}, \"EndTime\": 1607022288.77989, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.779874}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6859880719866072, \"sum\": 0.6859880719866072, \"min\": 0.6859880719866072}}, \"EndTime\": 1607022288.779947, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.779932}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6956369127546038, \"sum\": 0.6956369127546038, \"min\": 0.6956369127546038}}, \"EndTime\": 1607022288.780005, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.779988}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7017699105398996, \"sum\": 0.7017699105398996, \"min\": 0.7017699105398996}}, \"EndTime\": 1607022288.780062, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.780047}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.694064712524414, \"sum\": 0.694064712524414, \"min\": 0.694064712524414}}, \"EndTime\": 1607022288.780119, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.780102}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.654430171421596, \"sum\": 0.654430171421596, \"min\": 0.654430171421596}}, \"EndTime\": 1607022288.780181, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.780164}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #quality_metric: host=algo-1, epoch=4, train binary_classification_cross_entropy_objective <loss>=0.644196047102\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #early_stopping_criteria_metric: host=algo-1, epoch=4, criteria=binary_classification_cross_entropy_objective, value=0.427963992528\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Epoch 4: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Saving model for epoch: 4\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Saved checkpoint to \"/tmp/tmpF3aR1J/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #progress_metric: host=algo-1, completed 33 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Total Batches Seen\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}, \"Total Records Seen\": {\"count\": 1, \"max\": 490, \"sum\": 490.0, \"min\": 490}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Reset Count\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}}, \"EndTime\": 1607022288.78786, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1607022288.742189}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #throughput_metric: host=algo-1, train throughput=1528.87870567 records/second\u001b[0m\n",
      "\u001b[34m[2020-12-03 19:04:48.818] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 14, \"duration\": 29, \"num_examples\": 1, \"num_bytes\": 3920}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6376512254987444, \"sum\": 0.6376512254987444, \"min\": 0.6376512254987444}}, \"EndTime\": 1607022288.818132, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.818047}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6267724173409598, \"sum\": 0.6267724173409598, \"min\": 0.6267724173409598}}, \"EndTime\": 1607022288.818219, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.818198}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6861912318638392, \"sum\": 0.6861912318638392, \"min\": 0.6861912318638392}}, \"EndTime\": 1607022288.818283, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.818264}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6244081224714007, \"sum\": 0.6244081224714007, \"min\": 0.6244081224714007}}, \"EndTime\": 1607022288.818399, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.818377}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4158933367047991, \"sum\": 0.4158933367047991, \"min\": 0.4158933367047991}}, \"EndTime\": 1607022288.818469, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.818449}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4085353578839983, \"sum\": 0.4085353578839983, \"min\": 0.4085353578839983}}, \"EndTime\": 1607022288.818544, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.818526}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.42516250610351564, \"sum\": 0.42516250610351564, \"min\": 0.42516250610351564}}, \"EndTime\": 1607022288.818607, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.81859}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.40370303562709264, \"sum\": 0.40370303562709264, \"min\": 0.40370303562709264}}, \"EndTime\": 1607022288.818661, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.818645}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.670738765171596, \"sum\": 0.670738765171596, \"min\": 0.670738765171596}}, \"EndTime\": 1607022288.818722, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.818705}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6621754237583706, \"sum\": 0.6621754237583706, \"min\": 0.6621754237583706}}, \"EndTime\": 1607022288.818781, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.818764}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6185908726283482, \"sum\": 0.6185908726283482, \"min\": 0.6185908726283482}}, \"EndTime\": 1607022288.818849, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.818831}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6411736079624721, \"sum\": 0.6411736079624721, \"min\": 0.6411736079624721}}, \"EndTime\": 1607022288.818911, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.818893}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4141623088291713, \"sum\": 0.4141623088291713, \"min\": 0.4141623088291713}}, \"EndTime\": 1607022288.818975, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.818956}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.41360468183244975, \"sum\": 0.41360468183244975, \"min\": 0.41360468183244975}}, \"EndTime\": 1607022288.819037, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.819019}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.42187314714704244, \"sum\": 0.42187314714704244, \"min\": 0.42187314714704244}}, \"EndTime\": 1607022288.819102, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.819084}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.41384337288992745, \"sum\": 0.41384337288992745, \"min\": 0.41384337288992745}}, \"EndTime\": 1607022288.819165, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.819148}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6385269165039062, \"sum\": 0.6385269165039062, \"min\": 0.6385269165039062}}, \"EndTime\": 1607022288.819227, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.81921}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6350806644984655, \"sum\": 0.6350806644984655, \"min\": 0.6350806644984655}}, \"EndTime\": 1607022288.819294, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.819277}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6610994611467634, \"sum\": 0.6610994611467634, \"min\": 0.6610994611467634}}, \"EndTime\": 1607022288.819355, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.819338}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6887357439313616, \"sum\": 0.6887357439313616, \"min\": 0.6887357439313616}}, \"EndTime\": 1607022288.819413, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.819396}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4683061327253069, \"sum\": 0.4683061327253069, \"min\": 0.4683061327253069}}, \"EndTime\": 1607022288.819477, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.81946}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.47447542463030135, \"sum\": 0.47447542463030135, \"min\": 0.47447542463030135}}, \"EndTime\": 1607022288.819537, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.81952}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4706260681152344, \"sum\": 0.4706260681152344, \"min\": 0.4706260681152344}}, \"EndTime\": 1607022288.819599, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.819582}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.46970678056989396, \"sum\": 0.46970678056989396, \"min\": 0.46970678056989396}}, \"EndTime\": 1607022288.819661, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.819644}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6847243172781808, \"sum\": 0.6847243172781808, \"min\": 0.6847243172781808}}, \"EndTime\": 1607022288.819724, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.819706}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6819837297712054, \"sum\": 0.6819837297712054, \"min\": 0.6819837297712054}}, \"EndTime\": 1607022288.819784, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.819766}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6601243155343192, \"sum\": 0.6601243155343192, \"min\": 0.6601243155343192}}, \"EndTime\": 1607022288.819845, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.819827}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6817916325160436, \"sum\": 0.6817916325160436, \"min\": 0.6817916325160436}}, \"EndTime\": 1607022288.819903, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.819886}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7000056675502232, \"sum\": 0.7000056675502232, \"min\": 0.7000056675502232}}, \"EndTime\": 1607022288.819963, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.819946}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7042544773646764, \"sum\": 0.7042544773646764, \"min\": 0.7042544773646764}}, \"EndTime\": 1607022288.82003, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.820012}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7011282784598214, \"sum\": 0.7011282784598214, \"min\": 0.7011282784598214}}, \"EndTime\": 1607022288.820097, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.820079}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6647792271205357, \"sum\": 0.6647792271205357, \"min\": 0.6647792271205357}}, \"EndTime\": 1607022288.820161, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.820144}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #quality_metric: host=algo-1, epoch=5, train binary_classification_cross_entropy_objective <loss>=0.637651225499\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #early_stopping_criteria_metric: host=algo-1, epoch=5, criteria=binary_classification_cross_entropy_objective, value=0.403703035627\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Epoch 5: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Saving model for epoch: 5\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Saved checkpoint to \"/tmp/tmpq_hMTI/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #progress_metric: host=algo-1, completed 40 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Total Batches Seen\": {\"count\": 1, \"max\": 8, \"sum\": 8.0, \"min\": 8}, \"Total Records Seen\": {\"count\": 1, \"max\": 560, \"sum\": 560.0, \"min\": 560}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Reset Count\": {\"count\": 1, \"max\": 8, \"sum\": 8.0, \"min\": 8}}, \"EndTime\": 1607022288.826288, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1607022288.788125}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #throughput_metric: host=algo-1, train throughput=1827.73134458 records/second\u001b[0m\n",
      "\u001b[34m[2020-12-03 19:04:48.854] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 16, \"duration\": 27, \"num_examples\": 1, \"num_bytes\": 3920}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6317471095493862, \"sum\": 0.6317471095493862, \"min\": 0.6317471095493862}}, \"EndTime\": 1607022288.854263, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.854194}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6209945133754186, \"sum\": 0.6209945133754186, \"min\": 0.6209945133754186}}, \"EndTime\": 1607022288.854365, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.854342}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6797623770577567, \"sum\": 0.6797623770577567, \"min\": 0.6797623770577567}}, \"EndTime\": 1607022288.854429, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.85441}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.618658447265625, \"sum\": 0.618658447265625, \"min\": 0.618658447265625}}, \"EndTime\": 1607022288.854497, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.854478}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.37969512939453126, \"sum\": 0.37969512939453126, \"min\": 0.37969512939453126}}, \"EndTime\": 1607022288.854562, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.854543}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3738316672188895, \"sum\": 0.3738316672188895, \"min\": 0.3738316672188895}}, \"EndTime\": 1607022288.854625, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.854607}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3871094839913504, \"sum\": 0.3871094839913504, \"min\": 0.3871094839913504}}, \"EndTime\": 1607022288.854698, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.854681}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3699928283691406, \"sum\": 0.3699928283691406, \"min\": 0.3699928283691406}}, \"EndTime\": 1607022288.85476, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.854743}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6644689287458148, \"sum\": 0.6644689287458148, \"min\": 0.6644689287458148}}, \"EndTime\": 1607022288.854823, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.854805}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.655999755859375, \"sum\": 0.655999755859375, \"min\": 0.655999755859375}}, \"EndTime\": 1607022288.854877, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.854861}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6128768920898438, \"sum\": 0.6128768920898438, \"min\": 0.6128768920898438}}, \"EndTime\": 1607022288.854938, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.854921}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6352451869419643, \"sum\": 0.6352451869419643, \"min\": 0.6352451869419643}}, \"EndTime\": 1607022288.855, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.854983}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.37843044825962613, \"sum\": 0.37843044825962613, \"min\": 0.37843044825962613}}, \"EndTime\": 1607022288.855062, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.855045}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.37802042279924664, \"sum\": 0.37802042279924664, \"min\": 0.37802042279924664}}, \"EndTime\": 1607022288.855122, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.855105}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3844869886125837, \"sum\": 0.3844869886125837, \"min\": 0.3844869886125837}}, \"EndTime\": 1607022288.855177, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.855161}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3781768798828125, \"sum\": 0.3781768798828125, \"min\": 0.3781768798828125}}, \"EndTime\": 1607022288.855236, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.855219}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6326353890555245, \"sum\": 0.6326353890555245, \"min\": 0.6326353890555245}}, \"EndTime\": 1607022288.855307, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.855289}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6292249952043806, \"sum\": 0.6292249952043806, \"min\": 0.6292249952043806}}, \"EndTime\": 1607022288.855371, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.855355}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6549468449183873, \"sum\": 0.6549468449183873, \"min\": 0.6549468449183873}}, \"EndTime\": 1607022288.855433, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.855415}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6823422023228236, \"sum\": 0.6823422023228236, \"min\": 0.6823422023228236}}, \"EndTime\": 1607022288.855494, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.855478}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4656242915562221, \"sum\": 0.4656242915562221, \"min\": 0.4656242915562221}}, \"EndTime\": 1607022288.855553, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.855536}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4767305101667132, \"sum\": 0.4767305101667132, \"min\": 0.4767305101667132}}, \"EndTime\": 1607022288.855614, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.855596}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.47006683349609374, \"sum\": 0.47006683349609374, \"min\": 0.47006683349609374}}, \"EndTime\": 1607022288.855674, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.855657}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.46808547973632814, \"sum\": 0.46808547973632814, \"min\": 0.46808547973632814}}, \"EndTime\": 1607022288.855734, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.855718}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6833630153111049, \"sum\": 0.6833630153111049, \"min\": 0.6833630153111049}}, \"EndTime\": 1607022288.855795, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.855777}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6789751325334822, \"sum\": 0.6789751325334822, \"min\": 0.6789751325334822}}, \"EndTime\": 1607022288.855855, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.855839}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6608697073800224, \"sum\": 0.6608697073800224, \"min\": 0.6608697073800224}}, \"EndTime\": 1607022288.855921, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.855898}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6788111550467355, \"sum\": 0.6788111550467355, \"min\": 0.6788111550467355}}, \"EndTime\": 1607022288.855982, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.855965}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6812455313546317, \"sum\": 0.6812455313546317, \"min\": 0.6812455313546317}}, \"EndTime\": 1607022288.856043, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.856025}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6837137494768415, \"sum\": 0.6837137494768415, \"min\": 0.6837137494768415}}, \"EndTime\": 1607022288.856101, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.856085}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6828188759940012, \"sum\": 0.6828188759940012, \"min\": 0.6828188759940012}}, \"EndTime\": 1607022288.856162, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.856145}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6652095249720982, \"sum\": 0.6652095249720982, \"min\": 0.6652095249720982}}, \"EndTime\": 1607022288.856229, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.856211}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #quality_metric: host=algo-1, epoch=6, train binary_classification_cross_entropy_objective <loss>=0.631747109549\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #early_stopping_criteria_metric: host=algo-1, epoch=6, criteria=binary_classification_cross_entropy_objective, value=0.369992828369\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Epoch 6: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Saving model for epoch: 6\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Saved checkpoint to \"/tmp/tmpZHorb8/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #progress_metric: host=algo-1, completed 46 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Total Batches Seen\": {\"count\": 1, \"max\": 9, \"sum\": 9.0, \"min\": 9}, \"Total Records Seen\": {\"count\": 1, \"max\": 630, \"sum\": 630.0, \"min\": 630}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Reset Count\": {\"count\": 1, \"max\": 9, \"sum\": 9.0, \"min\": 9}}, \"EndTime\": 1607022288.862759, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1607022288.82654}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #throughput_metric: host=algo-1, train throughput=1925.88573303 records/second\u001b[0m\n",
      "\u001b[34m[2020-12-03 19:04:48.899] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 18, \"duration\": 36, \"num_examples\": 1, \"num_bytes\": 3920}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6264319283621652, \"sum\": 0.6264319283621652, \"min\": 0.6264319283621652}}, \"EndTime\": 1607022288.899349, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.899256}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6158013480050223, \"sum\": 0.6158013480050223, \"min\": 0.6158013480050223}}, \"EndTime\": 1607022288.899425, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.899411}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6739333561488561, \"sum\": 0.6739333561488561, \"min\": 0.6739333561488561}}, \"EndTime\": 1607022288.899488, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.899471}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6134925842285156, \"sum\": 0.6134925842285156, \"min\": 0.6134925842285156}}, \"EndTime\": 1607022288.899538, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.899526}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.35053542000906807, \"sum\": 0.35053542000906807, \"min\": 0.35053542000906807}}, \"EndTime\": 1607022288.899572, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.899563}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.34576388767787386, \"sum\": 0.34576388767787386, \"min\": 0.34576388767787386}}, \"EndTime\": 1607022288.899601, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.899593}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.35658607482910154, \"sum\": 0.35658607482910154, \"min\": 0.35658607482910154}}, \"EndTime\": 1607022288.899635, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.899622}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.34267501831054686, \"sum\": 0.34267501831054686, \"min\": 0.34267501831054686}}, \"EndTime\": 1607022288.899679, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.899669}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6587964739118304, \"sum\": 0.6587964739118304, \"min\": 0.6587964739118304}}, \"EndTime\": 1607022288.899724, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.89971}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6504198346819197, \"sum\": 0.6504198346819197, \"min\": 0.6504198346819197}}, \"EndTime\": 1607022288.899808, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.899789}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.607746342250279, \"sum\": 0.607746342250279, \"min\": 0.607746342250279}}, \"EndTime\": 1607022288.89987, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.899853}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6299061366489955, \"sum\": 0.6299061366489955, \"min\": 0.6299061366489955}}, \"EndTime\": 1607022288.899936, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.899918}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3497498103550502, \"sum\": 0.3497498103550502, \"min\": 0.3497498103550502}}, \"EndTime\": 1607022288.899997, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.899979}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.349402345929827, \"sum\": 0.349402345929827, \"min\": 0.349402345929827}}, \"EndTime\": 1607022288.900056, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.90004}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3546407972063337, \"sum\": 0.3546407972063337, \"min\": 0.3546407972063337}}, \"EndTime\": 1607022288.900107, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.900092}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3495261328560965, \"sum\": 0.3495261328560965, \"min\": 0.3495261328560965}}, \"EndTime\": 1607022288.900141, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.900132}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6273447854178292, \"sum\": 0.6273447854178292, \"min\": 0.6273447854178292}}, \"EndTime\": 1607022288.900168, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.900161}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.623969486781529, \"sum\": 0.623969486781529, \"min\": 0.623969486781529}}, \"EndTime\": 1607022288.900195, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.900188}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6493997301374163, \"sum\": 0.6493997301374163, \"min\": 0.6493997301374163}}, \"EndTime\": 1607022288.900232, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.900219}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.676556886945452, \"sum\": 0.676556886945452, \"min\": 0.676556886945452}}, \"EndTime\": 1607022288.900291, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.900274}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.47990052359444757, \"sum\": 0.47990052359444757, \"min\": 0.47990052359444757}}, \"EndTime\": 1607022288.900352, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.900335}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4946751185825893, \"sum\": 0.4946751185825893, \"min\": 0.4946751185825893}}, \"EndTime\": 1607022288.900411, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.900394}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.48605526515415737, \"sum\": 0.48605526515415737, \"min\": 0.48605526515415737}}, \"EndTime\": 1607022288.900472, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.900455}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.48322628566196985, \"sum\": 0.48322628566196985, \"min\": 0.48322628566196985}}, \"EndTime\": 1607022288.900536, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.900518}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6820521763392857, \"sum\": 0.6820521763392857, \"min\": 0.6820521763392857}}, \"EndTime\": 1607022288.900597, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.900579}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6764898572649275, \"sum\": 0.6764898572649275, \"min\": 0.6764898572649275}}, \"EndTime\": 1607022288.900657, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.900641}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6619307926722935, \"sum\": 0.6619307926722935, \"min\": 0.6619307926722935}}, \"EndTime\": 1607022288.900718, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.9007}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6768007550920759, \"sum\": 0.6768007550920759, \"min\": 0.6768007550920759}}, \"EndTime\": 1607022288.900779, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.900761}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6702972412109375, \"sum\": 0.6702972412109375, \"min\": 0.6702972412109375}}, \"EndTime\": 1607022288.90084, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.900823}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6710411071777344, \"sum\": 0.6710411071777344, \"min\": 0.6710411071777344}}, \"EndTime\": 1607022288.900901, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.900884}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6711051940917969, \"sum\": 0.6711051940917969, \"min\": 0.6711051940917969}}, \"EndTime\": 1607022288.900959, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.900943}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6775311061314174, \"sum\": 0.6775311061314174, \"min\": 0.6775311061314174}}, \"EndTime\": 1607022288.901019, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.901001}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #quality_metric: host=algo-1, epoch=7, train binary_classification_cross_entropy_objective <loss>=0.626431928362\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #early_stopping_criteria_metric: host=algo-1, epoch=7, criteria=binary_classification_cross_entropy_objective, value=0.342675018311\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Epoch 7: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Saving model for epoch: 7\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Saved checkpoint to \"/tmp/tmpeEZMCP/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #progress_metric: host=algo-1, completed 53 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Total Batches Seen\": {\"count\": 1, \"max\": 10, \"sum\": 10.0, \"min\": 10}, \"Total Records Seen\": {\"count\": 1, \"max\": 700, \"sum\": 700.0, \"min\": 700}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Reset Count\": {\"count\": 1, \"max\": 10, \"sum\": 10.0, \"min\": 10}}, \"EndTime\": 1607022288.907793, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1607022288.863017}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #throughput_metric: host=algo-1, train throughput=1559.33675366 records/second\u001b[0m\n",
      "\u001b[34m[2020-12-03 19:04:48.935] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 20, \"duration\": 27, \"num_examples\": 1, \"num_bytes\": 3920}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6216357639857701, \"sum\": 0.6216357639857701, \"min\": 0.6216357639857701}}, \"EndTime\": 1607022288.935672, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.935592}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.611123766217913, \"sum\": 0.611123766217913, \"min\": 0.611123766217913}}, \"EndTime\": 1607022288.935741, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.935722}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6686343056815011, \"sum\": 0.6686343056815011, \"min\": 0.6686343056815011}}, \"EndTime\": 1607022288.935815, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.935796}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6088414873395648, \"sum\": 0.6088414873395648, \"min\": 0.6088414873395648}}, \"EndTime\": 1607022288.935885, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.935868}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.33490774972098214, \"sum\": 0.33490774972098214, \"min\": 0.33490774972098214}}, \"EndTime\": 1607022288.935946, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.935929}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.33083313533238, \"sum\": 0.33083313533238, \"min\": 0.33083313533238}}, \"EndTime\": 1607022288.935992, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.935977}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3400643484933036, \"sum\": 0.3400643484933036, \"min\": 0.3400643484933036}}, \"EndTime\": 1607022288.936052, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.936035}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3282196589878627, \"sum\": 0.3282196589878627, \"min\": 0.3282196589878627}}, \"EndTime\": 1607022288.93612, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.936102}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6536513192313058, \"sum\": 0.6536513192313058, \"min\": 0.6536513192313058}}, \"EndTime\": 1607022288.936184, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.936169}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6453651973179408, \"sum\": 0.6453651973179408, \"min\": 0.6453651973179408}}, \"EndTime\": 1607022288.936245, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.936227}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6031305585588728, \"sum\": 0.6031305585588728, \"min\": 0.6031305585588728}}, \"EndTime\": 1607022288.9363, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.936284}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6250865391322544, \"sum\": 0.6250865391322544, \"min\": 0.6250865391322544}}, \"EndTime\": 1607022288.936361, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.936344}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.33459156581333704, \"sum\": 0.33459156581333704, \"min\": 0.33459156581333704}}, \"EndTime\": 1607022288.936416, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.9364}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.33427252088274273, \"sum\": 0.33427252088274273, \"min\": 0.33427252088274273}}, \"EndTime\": 1607022288.93647, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.936454}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.33873533521379745, \"sum\": 0.33873533521379745, \"min\": 0.33873533521379745}}, \"EndTime\": 1607022288.93653, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.936513}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3343833923339844, \"sum\": 0.3343833923339844, \"min\": 0.3343833923339844}}, \"EndTime\": 1607022288.93659, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.936574}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6225867135184152, \"sum\": 0.6225867135184152, \"min\": 0.6225867135184152}}, \"EndTime\": 1607022288.936647, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.93663}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.619246346609933, \"sum\": 0.619246346609933, \"min\": 0.619246346609933}}, \"EndTime\": 1607022288.9367, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.936684}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6443892887660435, \"sum\": 0.6443892887660435, \"min\": 0.6443892887660435}}, \"EndTime\": 1607022288.936752, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.936736}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6713110787527902, \"sum\": 0.6713110787527902, \"min\": 0.6713110787527902}}, \"EndTime\": 1607022288.936808, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.936793}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5088479178292411, \"sum\": 0.5088479178292411, \"min\": 0.5088479178292411}}, \"EndTime\": 1607022288.936868, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.936852}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.523868887765067, \"sum\": 0.523868887765067, \"min\": 0.523868887765067}}, \"EndTime\": 1607022288.936929, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.936912}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5152226039341518, \"sum\": 0.5152226039341518, \"min\": 0.5152226039341518}}, \"EndTime\": 1607022288.936981, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.936965}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5119940076555525, \"sum\": 0.5119940076555525, \"min\": 0.5119940076555525}}, \"EndTime\": 1607022288.937041, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.937024}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6807586124965123, \"sum\": 0.6807586124965123, \"min\": 0.6807586124965123}}, \"EndTime\": 1607022288.937095, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.937079}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.67436888558524, \"sum\": 0.67436888558524, \"min\": 0.67436888558524}}, \"EndTime\": 1607022288.937158, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.937141}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6634008135114398, \"sum\": 0.6634008135114398, \"min\": 0.6634008135114398}}, \"EndTime\": 1607022288.937217, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.937201}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.675480215890067, \"sum\": 0.675480215890067, \"min\": 0.675480215890067}}, \"EndTime\": 1607022288.937282, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.937265}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6724203927176339, \"sum\": 0.6724203927176339, \"min\": 0.6724203927176339}}, \"EndTime\": 1607022288.937334, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.937318}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6711588178362166, \"sum\": 0.6711588178362166, \"min\": 0.6711588178362166}}, \"EndTime\": 1607022288.937388, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.937374}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6724765232631138, \"sum\": 0.6724765232631138, \"min\": 0.6724765232631138}}, \"EndTime\": 1607022288.937442, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.937427}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6978802272251674, \"sum\": 0.6978802272251674, \"min\": 0.6978802272251674}}, \"EndTime\": 1607022288.937499, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.937483}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #quality_metric: host=algo-1, epoch=8, train binary_classification_cross_entropy_objective <loss>=0.621635763986\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #early_stopping_criteria_metric: host=algo-1, epoch=8, criteria=binary_classification_cross_entropy_objective, value=0.328219658988\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Epoch 8: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Saving model for epoch: 8\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Saved checkpoint to \"/tmp/tmpM4Tqxr/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #progress_metric: host=algo-1, completed 60 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Total Batches Seen\": {\"count\": 1, \"max\": 11, \"sum\": 11.0, \"min\": 11}, \"Total Records Seen\": {\"count\": 1, \"max\": 770, \"sum\": 770.0, \"min\": 770}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Reset Count\": {\"count\": 1, \"max\": 11, \"sum\": 11.0, \"min\": 11}}, \"EndTime\": 1607022288.944021, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1607022288.908052}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #throughput_metric: host=algo-1, train throughput=1939.28069909 records/second\u001b[0m\n",
      "\u001b[34m[2020-12-03 19:04:48.972] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 22, \"duration\": 28, \"num_examples\": 1, \"num_bytes\": 3920}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6172722952706473, \"sum\": 0.6172722952706473, \"min\": 0.6172722952706473}}, \"EndTime\": 1607022288.972594, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.972518}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.606876700265067, \"sum\": 0.606876700265067, \"min\": 0.606876700265067}}, \"EndTime\": 1607022288.972681, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.97266}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6637775966099331, \"sum\": 0.6637775966099331, \"min\": 0.6637775966099331}}, \"EndTime\": 1607022288.972757, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.972738}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6046202523367745, \"sum\": 0.6046202523367745, \"min\": 0.6046202523367745}}, \"EndTime\": 1607022288.972825, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.972806}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3264845711844308, \"sum\": 0.3264845711844308, \"min\": 0.3264845711844308}}, \"EndTime\": 1607022288.972899, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.97288}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.32290169852120537, \"sum\": 0.32290169852120537, \"min\": 0.32290169852120537}}, \"EndTime\": 1607022288.972968, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.972949}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3310189383370536, \"sum\": 0.3310189383370536, \"min\": 0.3310189383370536}}, \"EndTime\": 1607022288.973033, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.973014}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.32060519627162387, \"sum\": 0.32060519627162387, \"min\": 0.32060519627162387}}, \"EndTime\": 1607022288.973082, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.973071}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6489457811628069, \"sum\": 0.6489457811628069, \"min\": 0.6489457811628069}}, \"EndTime\": 1607022288.973125, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.973116}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6407484872000558, \"sum\": 0.6407484872000558, \"min\": 0.6407484872000558}}, \"EndTime\": 1607022288.973181, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.973165}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.598944582257952, \"sum\": 0.598944582257952, \"min\": 0.598944582257952}}, \"EndTime\": 1607022288.97323, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.973219}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6206999097551619, \"sum\": 0.6206999097551619, \"min\": 0.6206999097551619}}, \"EndTime\": 1607022288.973266, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.973253}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3265328543526786, \"sum\": 0.3265328543526786, \"min\": 0.3265328543526786}}, \"EndTime\": 1607022288.973323, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.973307}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.32628419058663505, \"sum\": 0.32628419058663505, \"min\": 0.32628419058663505}}, \"EndTime\": 1607022288.97338, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.973364}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.33001605442592075, \"sum\": 0.33001605442592075, \"min\": 0.33001605442592075}}, \"EndTime\": 1607022288.973436, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.97342}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.32634615216936386, \"sum\": 0.32634615216936386, \"min\": 0.32634615216936386}}, \"EndTime\": 1607022288.973499, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.973482}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6182767595563616, \"sum\": 0.6182767595563616, \"min\": 0.6182767595563616}}, \"EndTime\": 1607022288.973553, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.973536}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6149711608886719, \"sum\": 0.6149711608886719, \"min\": 0.6149711608886719}}, \"EndTime\": 1607022288.973612, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.973596}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.639829581124442, \"sum\": 0.639829581124442, \"min\": 0.639829581124442}}, \"EndTime\": 1607022288.973668, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.973653}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6665184020996093, \"sum\": 0.6665184020996093, \"min\": 0.6665184020996093}}, \"EndTime\": 1607022288.973719, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.973704}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5324168613978795, \"sum\": 0.5324168613978795, \"min\": 0.5324168613978795}}, \"EndTime\": 1607022288.973777, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.97376}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5441662379673549, \"sum\": 0.5441662379673549, \"min\": 0.5441662379673549}}, \"EndTime\": 1607022288.973831, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.973814}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5375483921595983, \"sum\": 0.5375483921595983, \"min\": 0.5375483921595983}}, \"EndTime\": 1607022288.973899, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.973882}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5345013754708426, \"sum\": 0.5345013754708426, \"min\": 0.5345013754708426}}, \"EndTime\": 1607022288.973965, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.973947}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6794607434953962, \"sum\": 0.6794607434953962, \"min\": 0.6794607434953962}}, \"EndTime\": 1607022288.974024, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.974008}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6725454057965959, \"sum\": 0.6725454057965959, \"min\": 0.6725454057965959}}, \"EndTime\": 1607022288.974083, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.974068}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.665318843296596, \"sum\": 0.665318843296596, \"min\": 0.665318843296596}}, \"EndTime\": 1607022288.974141, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.974125}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6745801653180804, \"sum\": 0.6745801653180804, \"min\": 0.6745801653180804}}, \"EndTime\": 1607022288.974212, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.974194}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6730181012834822, \"sum\": 0.6730181012834822, \"min\": 0.6730181012834822}}, \"EndTime\": 1607022288.974277, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.97426}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6696710859026228, \"sum\": 0.6696710859026228, \"min\": 0.6696710859026228}}, \"EndTime\": 1607022288.974365, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.974347}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6729037693568638, \"sum\": 0.6729037693568638, \"min\": 0.6729037693568638}}, \"EndTime\": 1607022288.974416, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.974404}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7051561628069196, \"sum\": 0.7051561628069196, \"min\": 0.7051561628069196}}, \"EndTime\": 1607022288.974484, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.974465}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #quality_metric: host=algo-1, epoch=9, train binary_classification_cross_entropy_objective <loss>=0.617272295271\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #early_stopping_criteria_metric: host=algo-1, epoch=9, criteria=binary_classification_cross_entropy_objective, value=0.320605196272\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Epoch 9: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Saving model for epoch: 9\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] Saved checkpoint to \"/tmp/tmpodZJwj/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #progress_metric: host=algo-1, completed 66 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Total Batches Seen\": {\"count\": 1, \"max\": 12, \"sum\": 12.0, \"min\": 12}, \"Total Records Seen\": {\"count\": 1, \"max\": 840, \"sum\": 840.0, \"min\": 840}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Reset Count\": {\"count\": 1, \"max\": 12, \"sum\": 12.0, \"min\": 12}}, \"EndTime\": 1607022288.981016, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1607022288.944274}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:48 INFO 140670865348416] #throughput_metric: host=algo-1, train throughput=1898.77110725 records/second\u001b[0m\n",
      "\u001b[34m[2020-12-03 19:04:49.013] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 24, \"duration\": 31, \"num_examples\": 1, \"num_bytes\": 3920}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6132432120186943, \"sum\": 0.6132432120186943, \"min\": 0.6132432120186943}}, \"EndTime\": 1607022289.013481, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.013397}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6029629298618862, \"sum\": 0.6029629298618862, \"min\": 0.6029629298618862}}, \"EndTime\": 1607022289.013566, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.01355}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6592624119349888, \"sum\": 0.6592624119349888, \"min\": 0.6592624119349888}}, \"EndTime\": 1607022289.013655, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.013635}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6007323128836496, \"sum\": 0.6007323128836496, \"min\": 0.6007323128836496}}, \"EndTime\": 1607022289.013736, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.013716}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3165953499930246, \"sum\": 0.3165953499930246, \"min\": 0.3165953499930246}}, \"EndTime\": 1607022289.013804, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.013786}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3134284428187779, \"sum\": 0.3134284428187779, \"min\": 0.3134284428187779}}, \"EndTime\": 1607022289.013864, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.013848}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.32064364297049386, \"sum\": 0.32064364297049386, \"min\": 0.32064364297049386}}, \"EndTime\": 1607022289.013916, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.013891}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3114088875906808, \"sum\": 0.3114088875906808, \"min\": 0.3114088875906808}}, \"EndTime\": 1607022289.013981, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.013964}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6445794786725726, \"sum\": 0.6445794786725726, \"min\": 0.6445794786725726}}, \"EndTime\": 1607022289.014054, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.014037}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.636469486781529, \"sum\": 0.636469486781529, \"min\": 0.636469486781529}}, \"EndTime\": 1607022289.014124, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.014106}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5950921194893973, \"sum\": 0.5950921194893973, \"min\": 0.5950921194893973}}, \"EndTime\": 1607022289.014194, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.014176}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.616647938319615, \"sum\": 0.616647938319615, \"min\": 0.616647938319615}}, \"EndTime\": 1607022289.014244, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.014233}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3168504987444196, \"sum\": 0.3168504987444196, \"min\": 0.3168504987444196}}, \"EndTime\": 1607022289.014278, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.014266}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.316705322265625, \"sum\": 0.316705322265625, \"min\": 0.316705322265625}}, \"EndTime\": 1607022289.014375, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.014355}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.319628415788923, \"sum\": 0.319628415788923, \"min\": 0.319628415788923}}, \"EndTime\": 1607022289.014446, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.014428}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.31668178013392856, \"sum\": 0.31668178013392856, \"min\": 0.31668178013392856}}, \"EndTime\": 1607022289.014501, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.014489}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6143179757254464, \"sum\": 0.6143179757254464, \"min\": 0.6143179757254464}}, \"EndTime\": 1607022289.014563, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.014545}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.61104736328125, \"sum\": 0.61104736328125, \"min\": 0.61104736328125}}, \"EndTime\": 1607022289.014633, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.014615}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.635622079031808, \"sum\": 0.635622079031808, \"min\": 0.635622079031808}}, \"EndTime\": 1607022289.014692, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.014676}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6620796203613282, \"sum\": 0.6620796203613282, \"min\": 0.6620796203613282}}, \"EndTime\": 1607022289.014809, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.014738}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5442361014229911, \"sum\": 0.5442361014229911, \"min\": 0.5442361014229911}}, \"EndTime\": 1607022289.014878, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.014859}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5509570530482701, \"sum\": 0.5509570530482701, \"min\": 0.5509570530482701}}, \"EndTime\": 1607022289.014937, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.014922}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5474748883928572, \"sum\": 0.5474748883928572, \"min\": 0.5474748883928572}}, \"EndTime\": 1607022289.015, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.014982}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5450781141008649, \"sum\": 0.5450781141008649, \"min\": 0.5450781141008649}}, \"EndTime\": 1607022289.01506, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.015044}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6781370435442243, \"sum\": 0.6781370435442243, \"min\": 0.6781370435442243}}, \"EndTime\": 1607022289.015121, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.015105}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6710682460239955, \"sum\": 0.6710682460239955, \"min\": 0.6710682460239955}}, \"EndTime\": 1607022289.015173, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.015161}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6675961630684989, \"sum\": 0.6675961630684989, \"min\": 0.6675961630684989}}, \"EndTime\": 1607022289.015229, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.015217}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.673895754132952, \"sum\": 0.673895754132952, \"min\": 0.673895754132952}}, \"EndTime\": 1607022289.015269, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.015253}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6686928885323661, \"sum\": 0.6686928885323661, \"min\": 0.6686928885323661}}, \"EndTime\": 1607022289.015329, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.015312}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6636847359793526, \"sum\": 0.6636847359793526, \"min\": 0.6636847359793526}}, \"EndTime\": 1607022289.015391, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.015373}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6692362104143416, \"sum\": 0.6692362104143416, \"min\": 0.6692362104143416}}, \"EndTime\": 1607022289.015451, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.015433}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6956560407366071, \"sum\": 0.6956560407366071, \"min\": 0.6956560407366071}}, \"EndTime\": 1607022289.015522, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022289.015503}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #quality_metric: host=algo-1, epoch=10, train binary_classification_cross_entropy_objective <loss>=0.613243212019\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #early_stopping_criteria_metric: host=algo-1, epoch=10, criteria=binary_classification_cross_entropy_objective, value=0.311408887591\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] Epoch 10: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] Saving model for epoch: 10\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] Saved checkpoint to \"/tmp/tmpqxnCy5/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #progress_metric: host=algo-1, completed 73 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Total Batches Seen\": {\"count\": 1, \"max\": 13, \"sum\": 13.0, \"min\": 13}, \"Total Records Seen\": {\"count\": 1, \"max\": 910, \"sum\": 910.0, \"min\": 910}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Reset Count\": {\"count\": 1, \"max\": 13, \"sum\": 13.0, \"min\": 13}}, \"EndTime\": 1607022289.022075, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1607022288.981282}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #throughput_metric: host=algo-1, train throughput=1711.24239386 records/second\u001b[0m\n",
      "\u001b[34m[2020-12-03 19:04:49.050] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 26, \"duration\": 27, \"num_examples\": 1, \"num_bytes\": 3920}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6094824654715402, \"sum\": 0.6094824654715402, \"min\": 0.6094824654715402}}, \"EndTime\": 1607022289.050543, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.050467}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5992809840611049, \"sum\": 0.5992809840611049, \"min\": 0.5992809840611049}}, \"EndTime\": 1607022289.050618, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.050597}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.655024664742606, \"sum\": 0.655024664742606, \"min\": 0.655024664742606}}, \"EndTime\": 1607022289.050691, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.050672}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5970763615199498, \"sum\": 0.5970763615199498, \"min\": 0.5970763615199498}}, \"EndTime\": 1607022289.05077, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.050749}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.30386810302734374, \"sum\": 0.30386810302734374, \"min\": 0.30386810302734374}}, \"EndTime\": 1607022289.050846, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.050826}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3009605407714844, \"sum\": 0.3009605407714844, \"min\": 0.3009605407714844}}, \"EndTime\": 1607022289.050909, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.050896}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.30750449044363837, \"sum\": 0.30750449044363837, \"min\": 0.30750449044363837}}, \"EndTime\": 1607022289.050988, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.05096}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.29922011239188057, \"sum\": 0.29922011239188057, \"min\": 0.29922011239188057}}, \"EndTime\": 1607022289.05105, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.051032}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6404873439243861, \"sum\": 0.6404873439243861, \"min\": 0.6404873439243861}}, \"EndTime\": 1607022289.051122, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.051103}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6324235098702568, \"sum\": 0.6324235098702568, \"min\": 0.6324235098702568}}, \"EndTime\": 1607022289.051195, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.051176}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5915072304861886, \"sum\": 0.5915072304861886, \"min\": 0.5915072304861886}}, \"EndTime\": 1607022289.051256, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.051239}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6128275190080915, \"sum\": 0.6128275190080915, \"min\": 0.6128275190080915}}, \"EndTime\": 1607022289.051327, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.051308}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3042542321341378, \"sum\": 0.3042542321341378, \"min\": 0.3042542321341378}}, \"EndTime\": 1607022289.051388, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.051371}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.30406663077218193, \"sum\": 0.30406663077218193, \"min\": 0.30406663077218193}}, \"EndTime\": 1607022289.051445, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.05143}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.30642057146344864, \"sum\": 0.30642057146344864, \"min\": 0.30642057146344864}}, \"EndTime\": 1607022289.051512, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.051494}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3039734431675502, \"sum\": 0.3039734431675502, \"min\": 0.3039734431675502}}, \"EndTime\": 1607022289.051582, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.051563}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6106448582240513, \"sum\": 0.6106448582240513, \"min\": 0.6106448582240513}}, \"EndTime\": 1607022289.051642, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.051625}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6073740822928292, \"sum\": 0.6073740822928292, \"min\": 0.6073740822928292}}, \"EndTime\": 1607022289.051701, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.051684}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6317017691476005, \"sum\": 0.6317017691476005, \"min\": 0.6317017691476005}}, \"EndTime\": 1607022289.051766, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.051749}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6578899928501674, \"sum\": 0.6578899928501674, \"min\": 0.6578899928501674}}, \"EndTime\": 1607022289.051826, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.051809}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5527649470738002, \"sum\": 0.5527649470738002, \"min\": 0.5527649470738002}}, \"EndTime\": 1607022289.051888, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.051871}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.553630610874721, \"sum\": 0.553630610874721, \"min\": 0.553630610874721}}, \"EndTime\": 1607022289.051951, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.051933}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5536768232073103, \"sum\": 0.5536768232073103, \"min\": 0.5536768232073103}}, \"EndTime\": 1607022289.052005, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.051989}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.552414049421038, \"sum\": 0.552414049421038, \"min\": 0.552414049421038}}, \"EndTime\": 1607022289.052064, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.052047}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.676781245640346, \"sum\": 0.676781245640346, \"min\": 0.676781245640346}}, \"EndTime\": 1607022289.052127, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.052109}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6699925013950893, \"sum\": 0.6699925013950893, \"min\": 0.6699925013950893}}, \"EndTime\": 1607022289.052195, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.052178}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6700178963797433, \"sum\": 0.6700178963797433, \"min\": 0.6700178963797433}}, \"EndTime\": 1607022289.05226, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.052243}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6732963017054967, \"sum\": 0.6732963017054967, \"min\": 0.6732963017054967}}, \"EndTime\": 1607022289.052322, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.052305}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6682798113141741, \"sum\": 0.6682798113141741, \"min\": 0.6682798113141741}}, \"EndTime\": 1607022289.05238, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.052363}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6626073019845146, \"sum\": 0.6626073019845146, \"min\": 0.6626073019845146}}, \"EndTime\": 1607022289.052437, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.052421}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6699551718575614, \"sum\": 0.6699551718575614, \"min\": 0.6699551718575614}}, \"EndTime\": 1607022289.052496, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.052479}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6819654192243304, \"sum\": 0.6819654192243304, \"min\": 0.6819654192243304}}, \"EndTime\": 1607022289.052553, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.052536}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #quality_metric: host=algo-1, epoch=11, train binary_classification_cross_entropy_objective <loss>=0.609482465472\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #early_stopping_criteria_metric: host=algo-1, epoch=11, criteria=binary_classification_cross_entropy_objective, value=0.299220112392\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] Epoch 11: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] Saving model for epoch: 11\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] Saved checkpoint to \"/tmp/tmptHAvOT/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #progress_metric: host=algo-1, completed 80 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Total Batches Seen\": {\"count\": 1, \"max\": 14, \"sum\": 14.0, \"min\": 14}, \"Total Records Seen\": {\"count\": 1, \"max\": 980, \"sum\": 980.0, \"min\": 980}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Reset Count\": {\"count\": 1, \"max\": 14, \"sum\": 14.0, \"min\": 14}}, \"EndTime\": 1607022289.058993, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1607022289.022354}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #throughput_metric: host=algo-1, train throughput=1902.89373396 records/second\u001b[0m\n",
      "\u001b[34m[2020-12-03 19:04:49.087] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 28, \"duration\": 27, \"num_examples\": 1, \"num_bytes\": 3920}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.605852290562221, \"sum\": 0.605852290562221, \"min\": 0.605852290562221}}, \"EndTime\": 1607022289.087222, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.087143}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5957338060651507, \"sum\": 0.5957338060651507, \"min\": 0.5957338060651507}}, \"EndTime\": 1607022289.087306, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.087284}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6509185246058873, \"sum\": 0.6509185246058873, \"min\": 0.6509185246058873}}, \"EndTime\": 1607022289.087378, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.087358}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.593555668422154, \"sum\": 0.593555668422154, \"min\": 0.593555668422154}}, \"EndTime\": 1607022289.087447, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.087426}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.29213019779750277, \"sum\": 0.29213019779750277, \"min\": 0.29213019779750277}}, \"EndTime\": 1607022289.087511, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.087493}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.2894872392926897, \"sum\": 0.2894872392926897, \"min\": 0.2894872392926897}}, \"EndTime\": 1607022289.08758, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.087561}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.2954008919852121, \"sum\": 0.2954008919852121, \"min\": 0.2954008919852121}}, \"EndTime\": 1607022289.087657, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.087628}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.28800626482282365, \"sum\": 0.28800626482282365, \"min\": 0.28800626482282365}}, \"EndTime\": 1607022289.087721, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.087702}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6365258353097099, \"sum\": 0.6365258353097099, \"min\": 0.6365258353097099}}, \"EndTime\": 1607022289.087786, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.087768}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6285105024065291, \"sum\": 0.6285105024065291, \"min\": 0.6285105024065291}}, \"EndTime\": 1607022289.087848, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.08783}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5880555289132254, \"sum\": 0.5880555289132254, \"min\": 0.5880555289132254}}, \"EndTime\": 1607022289.087918, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.087898}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6091402326311384, \"sum\": 0.6091402326311384, \"min\": 0.6091402326311384}}, \"EndTime\": 1607022289.08798, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.087963}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.2926745550973075, \"sum\": 0.2926745550973075, \"min\": 0.2926745550973075}}, \"EndTime\": 1607022289.08804, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.088022}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.29244076865059987, \"sum\": 0.29244076865059987, \"min\": 0.29244076865059987}}, \"EndTime\": 1607022289.088101, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.088085}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.29443370274135044, \"sum\": 0.29443370274135044, \"min\": 0.29443370274135044}}, \"EndTime\": 1607022289.088165, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.088147}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.2923253740583147, \"sum\": 0.2923253740583147, \"min\": 0.2923253740583147}}, \"EndTime\": 1607022289.088225, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.088208}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6071219308035715, \"sum\": 0.6071219308035715, \"min\": 0.6071219308035715}}, \"EndTime\": 1607022289.088287, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.088269}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6038548605782645, \"sum\": 0.6038548605782645, \"min\": 0.6038548605782645}}, \"EndTime\": 1607022289.088348, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.088331}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6279286520821707, \"sum\": 0.6279286520821707, \"min\": 0.6279286520821707}}, \"EndTime\": 1607022289.088409, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.088392}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6538496834891183, \"sum\": 0.6538496834891183, \"min\": 0.6538496834891183}}, \"EndTime\": 1607022289.08847, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.088454}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5625371660505022, \"sum\": 0.5625371660505022, \"min\": 0.5625371660505022}}, \"EndTime\": 1607022289.088528, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.088512}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5560408455984933, \"sum\": 0.5560408455984933, \"min\": 0.5560408455984933}}, \"EndTime\": 1607022289.088591, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.088574}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5601160866873605, \"sum\": 0.5601160866873605, \"min\": 0.5601160866873605}}, \"EndTime\": 1607022289.088653, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.088636}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.560398428780692, \"sum\": 0.560398428780692, \"min\": 0.560398428780692}}, \"EndTime\": 1607022289.088721, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.088704}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6753709520612444, \"sum\": 0.6753709520612444, \"min\": 0.6753709520612444}}, \"EndTime\": 1607022289.088784, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.088767}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6693418775285993, \"sum\": 0.6693418775285993, \"min\": 0.6693418775285993}}, \"EndTime\": 1607022289.088844, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.088827}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6723941802978516, \"sum\": 0.6723941802978516, \"min\": 0.6723941802978516}}, \"EndTime\": 1607022289.088892, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.088877}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6727003370012555, \"sum\": 0.6727003370012555, \"min\": 0.6727003370012555}}, \"EndTime\": 1607022289.088949, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.088933}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6759942190987723, \"sum\": 0.6759942190987723, \"min\": 0.6759942190987723}}, \"EndTime\": 1607022289.089009, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.088993}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6713983808244978, \"sum\": 0.6713983808244978, \"min\": 0.6713983808244978}}, \"EndTime\": 1607022289.089069, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.089052}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6784776960100446, \"sum\": 0.6784776960100446, \"min\": 0.6784776960100446}}, \"EndTime\": 1607022289.08913, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.089113}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6735097067696708, \"sum\": 0.6735097067696708, \"min\": 0.6735097067696708}}, \"EndTime\": 1607022289.08919, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.089173}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #quality_metric: host=algo-1, epoch=12, train binary_classification_cross_entropy_objective <loss>=0.605852290562\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #early_stopping_criteria_metric: host=algo-1, epoch=12, criteria=binary_classification_cross_entropy_objective, value=0.288006264823\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] Epoch 12: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] Saving model for epoch: 12\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] Saved checkpoint to \"/tmp/tmphpw_yJ/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #progress_metric: host=algo-1, completed 86 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Total Batches Seen\": {\"count\": 1, \"max\": 15, \"sum\": 15.0, \"min\": 15}, \"Total Records Seen\": {\"count\": 1, \"max\": 1050, \"sum\": 1050.0, \"min\": 1050}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Reset Count\": {\"count\": 1, \"max\": 15, \"sum\": 15.0, \"min\": 15}}, \"EndTime\": 1607022289.095767, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1607022289.059268}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #throughput_metric: host=algo-1, train throughput=1910.58351934 records/second\u001b[0m\n",
      "\u001b[34m[2020-12-03 19:04:49.127] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 30, \"duration\": 31, \"num_examples\": 1, \"num_bytes\": 3920}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6022701263427734, \"sum\": 0.6022701263427734, \"min\": 0.6022701263427734}}, \"EndTime\": 1607022289.127786, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.127713}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5922376905168806, \"sum\": 0.5922376905168806, \"min\": 0.5922376905168806}}, \"EndTime\": 1607022289.127875, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.127853}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6468590872628348, \"sum\": 0.6468590872628348, \"min\": 0.6468590872628348}}, \"EndTime\": 1607022289.127949, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.127929}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5900865827287947, \"sum\": 0.5900865827287947, \"min\": 0.5900865827287947}}, \"EndTime\": 1607022289.128017, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.127998}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.2845994404384068, \"sum\": 0.2845994404384068, \"min\": 0.2845994404384068}}, \"EndTime\": 1607022289.128092, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.128072}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.2822238922119141, \"sum\": 0.2822238922119141, \"min\": 0.2822238922119141}}, \"EndTime\": 1607022289.128149, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.128132}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.2875640596662249, \"sum\": 0.2875640596662249, \"min\": 0.2875640596662249}}, \"EndTime\": 1607022289.128211, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.128193}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.28094684055873326, \"sum\": 0.28094684055873326, \"min\": 0.28094684055873326}}, \"EndTime\": 1607022289.128293, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.128274}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6326107570103237, \"sum\": 0.6326107570103237, \"min\": 0.6326107570103237}}, \"EndTime\": 1607022289.128356, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.12834}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6246447426932199, \"sum\": 0.6246447426932199, \"min\": 0.6246447426932199}}, \"EndTime\": 1607022289.128408, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.128394}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5846554892403739, \"sum\": 0.5846554892403739, \"min\": 0.5846554892403739}}, \"EndTime\": 1607022289.128464, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.128453}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6055016653878348, \"sum\": 0.6055016653878348, \"min\": 0.6055016653878348}}, \"EndTime\": 1607022289.128519, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.128502}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.2853470393589565, \"sum\": 0.2853470393589565, \"min\": 0.2853470393589565}}, \"EndTime\": 1607022289.128583, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.128565}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.28510900224958147, \"sum\": 0.28510900224958147, \"min\": 0.28510900224958147}}, \"EndTime\": 1607022289.128635, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.128619}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.28686534336635044, \"sum\": 0.28686534336635044, \"min\": 0.28686534336635044}}, \"EndTime\": 1607022289.128698, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.12868}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.28500028337751115, \"sum\": 0.28500028337751115, \"min\": 0.28500028337751115}}, \"EndTime\": 1607022289.12876, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.128742}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6036680494035993, \"sum\": 0.6036680494035993, \"min\": 0.6036680494035993}}, \"EndTime\": 1607022289.128822, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.128805}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6004070826939174, \"sum\": 0.6004070826939174, \"min\": 0.6004070826939174}}, \"EndTime\": 1607022289.128881, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.128865}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6242202213832311, \"sum\": 0.6242202213832311, \"min\": 0.6242202213832311}}, \"EndTime\": 1607022289.128941, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.128925}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6498731340680803, \"sum\": 0.6498731340680803, \"min\": 0.6498731340680803}}, \"EndTime\": 1607022289.129004, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.128987}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5653652736118862, \"sum\": 0.5653652736118862, \"min\": 0.5653652736118862}}, \"EndTime\": 1607022289.129068, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.12905}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.551168714250837, \"sum\": 0.551168714250837, \"min\": 0.551168714250837}}, \"EndTime\": 1607022289.129137, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.129118}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5591250828334263, \"sum\": 0.5591250828334263, \"min\": 0.5591250828334263}}, \"EndTime\": 1607022289.129198, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.129181}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5608312334333148, \"sum\": 0.5608312334333148, \"min\": 0.5608312334333148}}, \"EndTime\": 1607022289.129269, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.129251}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.67391357421875, \"sum\": 0.67391357421875, \"min\": 0.67391357421875}}, \"EndTime\": 1607022289.129339, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.12932}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6690959930419922, \"sum\": 0.6690959930419922, \"min\": 0.6690959930419922}}, \"EndTime\": 1607022289.129407, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.12939}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6745228358677455, \"sum\": 0.6745228358677455, \"min\": 0.6745228358677455}}, \"EndTime\": 1607022289.129463, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.129447}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6720585414341518, \"sum\": 0.6720585414341518, \"min\": 0.6720585414341518}}, \"EndTime\": 1607022289.129519, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.129503}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.683530752999442, \"sum\": 0.683530752999442, \"min\": 0.683530752999442}}, \"EndTime\": 1607022289.129573, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.129556}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6817708696637835, \"sum\": 0.6817708696637835, \"min\": 0.6817708696637835}}, \"EndTime\": 1607022289.129635, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.129617}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6857584272112165, \"sum\": 0.6857584272112165, \"min\": 0.6857584272112165}}, \"EndTime\": 1607022289.129693, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.129677}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.667268807547433, \"sum\": 0.667268807547433, \"min\": 0.667268807547433}}, \"EndTime\": 1607022289.129753, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.129741}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #quality_metric: host=algo-1, epoch=13, train binary_classification_cross_entropy_objective <loss>=0.602270126343\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #early_stopping_criteria_metric: host=algo-1, epoch=13, criteria=binary_classification_cross_entropy_objective, value=0.280946840559\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] Epoch 13: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] Saving model for epoch: 13\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] Saved checkpoint to \"/tmp/tmp964OGC/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #progress_metric: host=algo-1, completed 93 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Total Batches Seen\": {\"count\": 1, \"max\": 16, \"sum\": 16.0, \"min\": 16}, \"Total Records Seen\": {\"count\": 1, \"max\": 1120, \"sum\": 1120.0, \"min\": 1120}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Reset Count\": {\"count\": 1, \"max\": 16, \"sum\": 16.0, \"min\": 16}}, \"EndTime\": 1607022289.13604, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1607022289.096019}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #throughput_metric: host=algo-1, train throughput=1744.37672669 records/second\u001b[0m\n",
      "\u001b[34m[2020-12-03 19:04:49.167] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 32, \"duration\": 31, \"num_examples\": 1, \"num_bytes\": 3920}\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5986738477434431, \"sum\": 0.5986738477434431, \"min\": 0.5986738477434431}}, \"EndTime\": 1607022289.167612, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.167535}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5887290954589843, \"sum\": 0.5887290954589843, \"min\": 0.5887290954589843}}, \"EndTime\": 1607022289.167687, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.167673}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6427825927734375, \"sum\": 0.6427825927734375, \"min\": 0.6427825927734375}}, \"EndTime\": 1607022289.167733, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.167718}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.586605725969587, \"sum\": 0.586605725969587, \"min\": 0.586605725969587}}, \"EndTime\": 1607022289.167796, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.167778}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.2808119092668806, \"sum\": 0.2808119092668806, \"min\": 0.2808119092668806}}, \"EndTime\": 1607022289.167865, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.167846}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.2786384582519531, \"sum\": 0.2786384582519531, \"min\": 0.2786384582519531}}, \"EndTime\": 1607022289.167928, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.16791}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.28354838235037666, \"sum\": 0.28354838235037666, \"min\": 0.28354838235037666}}, \"EndTime\": 1607022289.168002, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.167984}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.2775005613054548, \"sum\": 0.2775005613054548, \"min\": 0.2775005613054548}}, \"EndTime\": 1607022289.168062, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.168045}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6286788940429687, \"sum\": 0.6286788940429687, \"min\": 0.6286788940429687}}, \"EndTime\": 1607022289.168124, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.168106}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6207619258335658, \"sum\": 0.6207619258335658, \"min\": 0.6207619258335658}}, \"EndTime\": 1607022289.168183, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.168167}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.581244877406529, \"sum\": 0.581244877406529, \"min\": 0.581244877406529}}, \"EndTime\": 1607022289.168247, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.168229}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6018481663295201, \"sum\": 0.6018481663295201, \"min\": 0.6018481663295201}}, \"EndTime\": 1607022289.168322, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.168303}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.2817504337855748, \"sum\": 0.2817504337855748, \"min\": 0.2817504337855748}}, \"EndTime\": 1607022289.168384, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.168367}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.28152942657470703, \"sum\": 0.28152942657470703, \"min\": 0.28152942657470703}}, \"EndTime\": 1607022289.168455, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.168436}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.2830617359706334, \"sum\": 0.2830617359706334, \"min\": 0.2830617359706334}}, \"EndTime\": 1607022289.168517, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.1685}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.2814167022705078, \"sum\": 0.2814167022705078, \"min\": 0.2814167022705078}}, \"EndTime\": 1607022289.168575, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.16856}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6002223423549107, \"sum\": 0.6002223423549107, \"min\": 0.6002223423549107}}, \"EndTime\": 1607022289.16863, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.168616}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5969687325613839, \"sum\": 0.5969687325613839, \"min\": 0.5969687325613839}}, \"EndTime\": 1607022289.168685, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.168668}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6205147334507534, \"sum\": 0.6205147334507534, \"min\": 0.6205147334507534}}, \"EndTime\": 1607022289.168746, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.168729}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6458962031773159, \"sum\": 0.6458962031773159, \"min\": 0.6458962031773159}}, \"EndTime\": 1607022289.168806, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.168788}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5552855900355748, \"sum\": 0.5552855900355748, \"min\": 0.5552855900355748}}, \"EndTime\": 1607022289.168867, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.168851}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5368739536830357, \"sum\": 0.5368739536830357, \"min\": 0.5368739536830357}}, \"EndTime\": 1607022289.168929, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.168911}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5467831202915736, \"sum\": 0.5467831202915736, \"min\": 0.5467831202915736}}, \"EndTime\": 1607022289.169002, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.168983}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5492850712367466, \"sum\": 0.5492850712367466, \"min\": 0.5492850712367466}}, \"EndTime\": 1607022289.169065, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.169048}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6724337986537389, \"sum\": 0.6724337986537389, \"min\": 0.6724337986537389}}, \"EndTime\": 1607022289.169138, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.169119}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.669156265258789, \"sum\": 0.669156265258789, \"min\": 0.669156265258789}}, \"EndTime\": 1607022289.1692, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.169182}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6762316022600446, \"sum\": 0.6762316022600446, \"min\": 0.6762316022600446}}, \"EndTime\": 1607022289.169271, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.169253}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6713534218924386, \"sum\": 0.6713534218924386, \"min\": 0.6713534218924386}}, \"EndTime\": 1607022289.169333, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.169315}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6821127210344587, \"sum\": 0.6821127210344587, \"min\": 0.6821127210344587}}, \"EndTime\": 1607022289.169396, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.169378}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6840759822300502, \"sum\": 0.6840759822300502, \"min\": 0.6840759822300502}}, \"EndTime\": 1607022289.169453, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.169438}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6828911917550223, \"sum\": 0.6828911917550223, \"min\": 0.6828911917550223}}, \"EndTime\": 1607022289.169486, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.169478}\n",
      "\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"train_binary_classification_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6587217058454241, \"sum\": 0.6587217058454241, \"min\": 0.6587217058454241}}, \"EndTime\": 1607022289.169514, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.169507}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #quality_metric: host=algo-1, epoch=14, train binary_classification_cross_entropy_objective <loss>=0.598673847743\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #early_stopping_criteria_metric: host=algo-1, epoch=14, criteria=binary_classification_cross_entropy_objective, value=0.277500561305\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] Epoch 14: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] Saving model for epoch: 14\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] Saved checkpoint to \"/tmp/tmpnX8dzr/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #progress_metric: host=algo-1, completed 100 % of epochs\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Total Batches Seen\": {\"count\": 1, \"max\": 17, \"sum\": 17.0, \"min\": 17}, \"Total Records Seen\": {\"count\": 1, \"max\": 1190, \"sum\": 1190.0, \"min\": 1190}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Reset Count\": {\"count\": 1, \"max\": 17, \"sum\": 17.0, \"min\": 17}}, \"EndTime\": 1607022289.175906, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1607022289.1363}\n",
      "\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #throughput_metric: host=algo-1, train throughput=1760.69563966 records/second\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 WARNING 140670865348416] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 WARNING 140670865348416] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n",
      "\u001b[34m[2020-12-03 19:04:49.177] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 34, \"duration\": 0, \"num_examples\": 1, \"num_bytes\": 3920}\u001b[0m\n",
      "\u001b[34m[2020-12-03 19:04:49.284] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 37, \"duration\": 104, \"num_examples\": 1, \"num_bytes\": 3920}\u001b[0m\n",
      "\u001b[34m[2020-12-03 19:04:49.296] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 39, \"duration\": 4, \"num_examples\": 1, \"num_bytes\": 3920}\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #train_score (algo-1) : ('binary_classification_cross_entropy_objective', 0.27488743918282643)\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #train_score (algo-1) : ('binary_classification_accuracy', 0.9142857142857143)\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #train_score (algo-1) : ('binary_f_1.000', 0.9285714285714286)\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #train_score (algo-1) : ('precision', 0.9285714285714286)\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #train_score (algo-1) : ('recall', 0.9285714285714286)\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #train_score (algo-1) : ('roc_auc_score', 0.9540816326530612)\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #quality_metric: host=algo-1, train binary_classification_cross_entropy_objective <loss>=0.274887439183\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #quality_metric: host=algo-1, train binary_classification_accuracy <score>=0.914285714286\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #quality_metric: host=algo-1, train binary_f_1.000 <score>=0.928571428571\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #quality_metric: host=algo-1, train precision <score>=0.928571428571\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #quality_metric: host=algo-1, train recall <score>=0.928571428571\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] #quality_metric: host=algo-1, train roc_auc_score <score>=0.954081632653\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] Best model found for hyperparameters: {\"lr_scheduler_step\": 100, \"wd\": 0.0001, \"optimizer\": \"adam\", \"lr_scheduler_factor\": 0.99, \"l1\": 0.0, \"learning_rate\": 0.1, \"lr_scheduler_minimum_lr\": 0.0001}\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] Saved checkpoint to \"/tmp/tmpHjqI0Z/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[34m[12/03/2020 19:04:49 INFO 140670865348416] Test data is not provided.\u001b[0m\n",
      "\u001b[34m#metrics {\"Metrics\": {\"totaltime\": {\"count\": 1, \"max\": 1176.149845123291, \"sum\": 1176.149845123291, \"min\": 1176.149845123291}, \"finalize.time\": {\"count\": 1, \"max\": 124.57108497619629, \"sum\": 124.57108497619629, \"min\": 124.57108497619629}, \"initialize.time\": {\"count\": 1, \"max\": 160.7508659362793, \"sum\": 160.7508659362793, \"min\": 160.7508659362793}, \"check_early_stopping.time\": {\"count\": 15, \"max\": 0.9999275207519531, \"sum\": 12.083292007446289, \"min\": 0.7140636444091797}, \"setuptime\": {\"count\": 1, \"max\": 25.476932525634766, \"sum\": 25.476932525634766, \"min\": 25.476932525634766}, \"update.time\": {\"count\": 15, \"max\": 68.24994087219238, \"sum\": 579.0574550628662, \"min\": 31.836986541748047}, \"epochs\": {\"count\": 1, \"max\": 15, \"sum\": 15.0, \"min\": 15}}, \"EndTime\": 1607022289.305691, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\"}, \"StartTime\": 1607022288.392841}\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "2020-12-03 19:05:01 Completed - Training job completed\n",
      "Training seconds: 57\n",
      "Billable seconds: 57\n",
      "CPU times: user 607 ms, sys: 23.9 ms, total: 631 ms\n",
      "Wall time: 4min 12s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "# train the estimator on formatted training data\n",
    "linear.fit(formatted_train_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classifier-3: Scikit-learn Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'s3_input' class will be renamed to 'TrainingInput' in SageMaker Python SDK v2.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training artifacts will be uploaded to: s3://sagemaker-us-east-2-793227728067/plagiarism-detection-nb/\n",
      "2020-12-03 20:42:47 Starting - Starting the training job...\n",
      "2020-12-03 20:42:50 Starting - Launching requested ML instances......\n",
      "2020-12-03 20:43:56 Starting - Preparing the instances for training...\n",
      "2020-12-03 20:44:42 Downloading - Downloading input data...\n",
      "2020-12-03 20:45:11 Training - Downloading the training image..\u001b[34m2020-12-03 20:45:25,810 sagemaker-training-toolkit INFO     Imported framework sagemaker_sklearn_container.training\u001b[0m\n",
      "\u001b[34m2020-12-03 20:45:25,812 sagemaker-training-toolkit INFO     No GPUs detected (normal if no gpus installed)\u001b[0m\n",
      "\u001b[34m2020-12-03 20:45:25,821 sagemaker_sklearn_container.training INFO     Invoking user training script.\u001b[0m\n",
      "\u001b[34m2020-12-03 20:45:26,164 sagemaker-training-toolkit INFO     No GPUs detected (normal if no gpus installed)\u001b[0m\n",
      "\u001b[34m2020-12-03 20:45:26,826 sagemaker-training-toolkit INFO     No GPUs detected (normal if no gpus installed)\u001b[0m\n",
      "\u001b[34m2020-12-03 20:45:26,837 sagemaker-training-toolkit INFO     No GPUs detected (normal if no gpus installed)\u001b[0m\n",
      "\u001b[34m2020-12-03 20:45:26,847 sagemaker-training-toolkit INFO     Invoking user script\n",
      "\u001b[0m\n",
      "\u001b[34mTraining Env:\n",
      "\u001b[0m\n",
      "\u001b[34m{\n",
      "    \"additional_framework_parameters\": {},\n",
      "    \"channel_input_dirs\": {\n",
      "        \"train\": \"/opt/ml/input/data/train\"\n",
      "    },\n",
      "    \"current_host\": \"algo-1\",\n",
      "    \"framework_module\": \"sagemaker_sklearn_container.training:main\",\n",
      "    \"hosts\": [\n",
      "        \"algo-1\"\n",
      "    ],\n",
      "    \"hyperparameters\": {},\n",
      "    \"input_config_dir\": \"/opt/ml/input/config\",\n",
      "    \"input_data_config\": {\n",
      "        \"train\": {\n",
      "            \"TrainingInputMode\": \"File\",\n",
      "            \"S3DistributionType\": \"FullyReplicated\",\n",
      "            \"RecordWrapperType\": \"None\"\n",
      "        }\n",
      "    },\n",
      "    \"input_dir\": \"/opt/ml/input\",\n",
      "    \"is_master\": true,\n",
      "    \"job_name\": \"sagemaker-scikit-learn-2020-12-03-20-42-47-093\",\n",
      "    \"log_level\": 20,\n",
      "    \"master_hostname\": \"algo-1\",\n",
      "    \"model_dir\": \"/opt/ml/model\",\n",
      "    \"module_dir\": \"s3://sagemaker-us-east-2-793227728067/sagemaker-scikit-learn-2020-12-03-20-42-47-093/source/sourcedir.tar.gz\",\n",
      "    \"module_name\": \"train\",\n",
      "    \"network_interface_name\": \"eth0\",\n",
      "    \"num_cpus\": 4,\n",
      "    \"num_gpus\": 0,\n",
      "    \"output_data_dir\": \"/opt/ml/output/data\",\n",
      "    \"output_dir\": \"/opt/ml/output\",\n",
      "    \"output_intermediate_dir\": \"/opt/ml/output/intermediate\",\n",
      "    \"resource_config\": {\n",
      "        \"current_host\": \"algo-1\",\n",
      "        \"hosts\": [\n",
      "            \"algo-1\"\n",
      "        ],\n",
      "        \"network_interface_name\": \"eth0\"\n",
      "    },\n",
      "    \"user_entry_point\": \"train.py\"\u001b[0m\n",
      "\u001b[34m}\n",
      "\u001b[0m\n",
      "\u001b[34mEnvironment variables:\n",
      "\u001b[0m\n",
      "\u001b[34mSM_HOSTS=[\"algo-1\"]\u001b[0m\n",
      "\u001b[34mSM_NETWORK_INTERFACE_NAME=eth0\u001b[0m\n",
      "\u001b[34mSM_HPS={}\u001b[0m\n",
      "\u001b[34mSM_USER_ENTRY_POINT=train.py\u001b[0m\n",
      "\u001b[34mSM_FRAMEWORK_PARAMS={}\u001b[0m\n",
      "\u001b[34mSM_RESOURCE_CONFIG={\"current_host\":\"algo-1\",\"hosts\":[\"algo-1\"],\"network_interface_name\":\"eth0\"}\u001b[0m\n",
      "\u001b[34mSM_INPUT_DATA_CONFIG={\"train\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"}}\u001b[0m\n",
      "\u001b[34mSM_OUTPUT_DATA_DIR=/opt/ml/output/data\u001b[0m\n",
      "\u001b[34mSM_CHANNELS=[\"train\"]\u001b[0m\n",
      "\u001b[34mSM_CURRENT_HOST=algo-1\u001b[0m\n",
      "\u001b[34mSM_MODULE_NAME=train\u001b[0m\n",
      "\u001b[34mSM_LOG_LEVEL=20\u001b[0m\n",
      "\u001b[34mSM_FRAMEWORK_MODULE=sagemaker_sklearn_container.training:main\u001b[0m\n",
      "\u001b[34mSM_INPUT_DIR=/opt/ml/input\u001b[0m\n",
      "\u001b[34mSM_INPUT_CONFIG_DIR=/opt/ml/input/config\u001b[0m\n",
      "\u001b[34mSM_OUTPUT_DIR=/opt/ml/output\u001b[0m\n",
      "\u001b[34mSM_NUM_CPUS=4\u001b[0m\n",
      "\u001b[34mSM_NUM_GPUS=0\u001b[0m\n",
      "\u001b[34mSM_MODEL_DIR=/opt/ml/model\u001b[0m\n",
      "\u001b[34mSM_MODULE_DIR=s3://sagemaker-us-east-2-793227728067/sagemaker-scikit-learn-2020-12-03-20-42-47-093/source/sourcedir.tar.gz\u001b[0m\n",
      "\u001b[34mSM_TRAINING_ENV={\"additional_framework_parameters\":{},\"channel_input_dirs\":{\"train\":\"/opt/ml/input/data/train\"},\"current_host\":\"algo-1\",\"framework_module\":\"sagemaker_sklearn_container.training:main\",\"hosts\":[\"algo-1\"],\"hyperparameters\":{},\"input_config_dir\":\"/opt/ml/input/config\",\"input_data_config\":{\"train\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"}},\"input_dir\":\"/opt/ml/input\",\"is_master\":true,\"job_name\":\"sagemaker-scikit-learn-2020-12-03-20-42-47-093\",\"log_level\":20,\"master_hostname\":\"algo-1\",\"model_dir\":\"/opt/ml/model\",\"module_dir\":\"s3://sagemaker-us-east-2-793227728067/sagemaker-scikit-learn-2020-12-03-20-42-47-093/source/sourcedir.tar.gz\",\"module_name\":\"train\",\"network_interface_name\":\"eth0\",\"num_cpus\":4,\"num_gpus\":0,\"output_data_dir\":\"/opt/ml/output/data\",\"output_dir\":\"/opt/ml/output\",\"output_intermediate_dir\":\"/opt/ml/output/intermediate\",\"resource_config\":{\"current_host\":\"algo-1\",\"hosts\":[\"algo-1\"],\"network_interface_name\":\"eth0\"},\"user_entry_point\":\"train.py\"}\u001b[0m\n",
      "\u001b[34mSM_USER_ARGS=[]\u001b[0m\n",
      "\u001b[34mSM_OUTPUT_INTERMEDIATE_DIR=/opt/ml/output/intermediate\u001b[0m\n",
      "\u001b[34mSM_CHANNEL_TRAIN=/opt/ml/input/data/train\u001b[0m\n",
      "\u001b[34mPYTHONPATH=/opt/ml/code:/miniconda3/bin:/miniconda3/lib/python37.zip:/miniconda3/lib/python3.7:/miniconda3/lib/python3.7/lib-dynload:/miniconda3/lib/python3.7/site-packages\n",
      "\u001b[0m\n",
      "\u001b[34mInvoking script with the following command:\n",
      "\u001b[0m\n",
      "\u001b[34m/miniconda3/bin/python train.py\n",
      "\n",
      "\u001b[0m\n",
      "\u001b[34mtraining model\u001b[0m\n",
      "\u001b[34m2020-12-03 20:45:28,419 sagemaker-training-toolkit INFO     Reporting training SUCCESS\u001b[0m\n",
      "\n",
      "2020-12-03 20:45:37 Uploading - Uploading generated training model\n",
      "2020-12-03 20:45:37 Completed - Training job completed\n",
      "Training seconds: 55\n",
      "Billable seconds: 55\n"
     ]
    }
   ],
   "source": [
    "from sagemaker.sklearn.estimator import SKLearn\n",
    "\n",
    "# define location to store model artifacts\n",
    "cl3_prefix = 'plagiarism-detection-nb'\n",
    "output_path='s3://{}/{}/'.format(bucket, cl3_prefix)\n",
    "print('Training artifacts will be uploaded to: {}'.format(output_path))\n",
    "\n",
    "sklearn_estimator = SKLearn('train.py',\n",
    "                            train_instance_type='ml.c4.xlarge',\n",
    "                            py_version='py3',\n",
    "                            framework_version='0.23-1',\n",
    "                            source_dir='source_sklearn',\n",
    "                            role=role,\n",
    "                            output_path=output_path,\n",
    "                            sagemaker_session=sagemaker_session,)\n",
    "\n",
    "sklearn_estimator.fit({'train': data_path})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EXERCISE: Deploy the trained model\n",
    "\n",
    "After training, deploy your model to create a `predictor`. If you're using a PyTorch model, you'll need to create a trained `PyTorchModel` that accepts the trained `<model>.model_data` as an input parameter and points to the provided `source_pytorch/predict.py` file as an entry point. \n",
    "\n",
    "To deploy a trained model, you'll use `<model>.deploy`, which takes in two arguments:\n",
    "* **initial_instance_count**: The number of deployed instances (1).\n",
    "* **instance_type**: The type of SageMaker instance for deployment.\n",
    "\n",
    "Note: If you run into an instance error, it may be because you chose the wrong training or deployment instance_type. It may help to refer to your previous exercise code to see which types of instances we used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deploy Classifier-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parameter image will be renamed to image_uri in SageMaker Python SDK v2.\n",
      "'create_image_uri' will be deprecated in favor of 'ImageURIProvider' class in SageMaker Python SDK v2.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------!CPU times: user 558 ms, sys: 20.7 ms, total: 579 ms\n",
      "Wall time: 9min 33s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "from sagemaker.pytorch import PyTorchModel\n",
    "\n",
    "# Create a model from the trained estimator data and point to the prediction script\n",
    "model = PyTorchModel(model_data=estimator.model_data,\n",
    "                     role = role,\n",
    "                     framework_version='1.4.0',\n",
    "                     py_version='py3',\n",
    "                     entry_point='predict.py',\n",
    "                     source_dir='source_pytorch')\n",
    "\n",
    "# deploy the model\n",
    "predictor = model.deploy(initial_instance_count=1, instance_type='ml.t2.medium')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deploy Classifier-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parameter image will be renamed to image_uri in SageMaker Python SDK v2.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------!CPU times: user 336 ms, sys: 49 µs, total: 336 ms\n",
      "Wall time: 8min 32s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "# deploy and create a predictor\n",
    "linear_predictor = linear.deploy(initial_instance_count=1, \n",
    "                              instance_type='ml.t2.medium')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deploy Classifier-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parameter image will be renamed to image_uri in SageMaker Python SDK v2.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------!CPU times: user 271 ms, sys: 18.6 ms, total: 289 ms\n",
      "Wall time: 7min 32s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "# deploy and create a predictor\n",
    "nb_predictor = sklearn_estimator.deploy(initial_instance_count=1, \n",
    "                              instance_type='ml.t2.medium')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Evaluating Your Model\n",
    "\n",
    "Once your model is deployed, you can see how it performs when applied to our test data.\n",
    "\n",
    "The provided cell below, reads in the test data, assuming it is stored locally in `data_dir` and named `test.csv`. The labels and features are extracted from the `.csv` file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "import os\n",
    "\n",
    "# read in test data, assuming it is stored locally\n",
    "test_data = pd.read_csv(os.path.join(data_dir, \"test.csv\"), header=None, names=None)\n",
    "\n",
    "# labels are in the first column\n",
    "test_y = test_data.iloc[:,0]\n",
    "test_x = test_data.iloc[:,1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EXERCISE: Determine the accuracy of your model\n",
    "\n",
    "Use your deployed `predictor` to generate predicted, class labels for the test data. Compare those to the *true* labels, `test_y`, and calculate the accuracy as a value between 0 and 1.0 that indicates the fraction of test data that your model classified correctly. You may use [sklearn.metrics](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics) for this calculation.\n",
    "\n",
    "**To pass this project, your model should get at least 90% test accuracy.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test passed!\n"
     ]
    }
   ],
   "source": [
    "# First: generate predicted, class labels\n",
    "import numpy as np\n",
    "\n",
    "### Classifier-1 Results ###\n",
    "# test_y_preds = np.squeeze(np.round(predictor.predict(test_x.values)))\n",
    "### Classifier-1 Results ###\n",
    "\n",
    "### Classifier-2 Results ### Best Model\n",
    "result = linear_predictor.predict(test_x.values.astype('float32')) #returns a list of Records\n",
    "test_y_preds = [val.label['predicted_label'].float32_tensor.values[0] for val in result]\n",
    "### Classifier-2 Results ###\n",
    "\n",
    "### Classifier-3 Results ###\n",
    "# test_y_preds = np.squeeze(np.round(nb_predictor.predict(test_x.values)))\n",
    "### Classifier-3 Results ###\n",
    "\n",
    "test_y_preds = [int(value) for value in test_y_preds]\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "# test that your model generates the correct number of labels\n",
    "assert len(test_y_preds)==len(test_y), 'Unexpected number of predictions.'\n",
    "print('Test passed!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.96\n",
      "\n",
      "Predicted class labels: \n",
      "[1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0]\n",
      "\n",
      "True class labels: \n",
      "[1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 0 1 0 1 1 0 0]\n"
     ]
    }
   ],
   "source": [
    "# Second: calculate the test accuracy\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy = accuracy_score(test_y, test_y_preds)\n",
    "\n",
    "print(accuracy)\n",
    "\n",
    "## print out the array of predicted and true labels, if you want\n",
    "print('\\nPredicted class labels: ')\n",
    "print(test_y_preds)\n",
    "print('\\nTrue class labels: ')\n",
    "print(test_y.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction Test Results for all Classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Neural Network Model, 80% Accuracy\n",
    "classifier1_test_predictions = [1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#LinearLearner, 96% Accuracy, Best Model\n",
    "classifier2_test_predictions = [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Naive Bayes Model, 92% Accuracy\n",
    "classifier3_test_predictions = [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tabular Results Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='table'></a>\n",
    "\n",
    "| Classifier | Training Time | Accuracy |\n",
    "| --- | --- | --- |\n",
    "| Feedforward Neural Network | 67 seconds | .80 | \n",
    "| LinearLearner | 57 seconds | .96 | \n",
    "| Naive Bayes | 55 seconds | .92 | "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1: How many false positives and false negatives did your model produce, if any? And why do you think this is?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FP for document in index 20 \n",
      "\n",
      "TP: 15, FP: 1, FN: 0, TN: 9\n"
     ]
    }
   ],
   "source": [
    "def compute_values(y_actual, y_pred):\n",
    "    TP = 0\n",
    "    FP = 0\n",
    "    TN = 0\n",
    "    FN = 0\n",
    "\n",
    "    for i in range(len(y_pred)): \n",
    "        if y_actual[i]==y_pred[i]==1:\n",
    "           TP += 1\n",
    "        if y_pred[i]==1 and y_actual[i]==0:\n",
    "           FP += 1\n",
    "           print ('FP for document in index {} \\n'.format(i))\n",
    "        if y_actual[i]==y_pred[i]==0:\n",
    "           TN += 1\n",
    "        if y_pred[i]==0 and y_actual[i]==1:\n",
    "           FN += 1\n",
    "           print ('FN for document in index {} \\n'.format(i))\n",
    "\n",
    "    return(TP, FP, FN, TN)\n",
    "\n",
    "values = compute_values(test_y, classifier2_test_predictions) #use test prediction results of the best classifier\n",
    "\n",
    "tp, fp, fn, tn = values[0], values[1], values[2], values[3]\n",
    "\n",
    "print('TP: {}, FP: {}, FN: {}, TN: {}'.format(tp, fp, fn, tn))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Answer**: Out of the 25 test predictions, the best classifier LinearLearner generated 1 fase positive (FP) and 0 false negatives (FN), as shown above. The binary classifier was able to learn the different types of plagiarism present in the train documents, which resulted in 0 FN in the test prediction results. Only the test document at the dataframe index 20 was misclassified by the model as plagiarized, as shown in the cell below. One reason for this might be because the 1-gram containment feature value of that test document was quite high (0.613924) while the 1-gram containment feature values of the training documents, which the model was trained on, were mostly in the range from 0.3 to 0.5, as shown in the second cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.000000\n",
       "1    0.613924\n",
       "2    0.000000\n",
       "3    0.298343\n",
       "Name: 20, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(test_data.loc[20, :]) #misclassified document info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "    0         1         2         3\n",
       "0   0  0.398148  0.000000  0.191781\n",
       "3   0  0.544503  0.000000  0.242574\n",
       "4   0  0.329502  0.000000  0.161172\n",
       "5   0  0.590308  0.000000  0.301653\n",
       "7   0  0.516129  0.000000  0.270833\n",
       "8   0  0.440860  0.000000  0.223958\n",
       "13  0  0.441176  0.000000  0.305556\n",
       "14  0  0.488889  0.000000  0.282609\n",
       "16  0  0.611111  0.000000  0.324675\n",
       "21  0  0.420382  0.000000  0.218750\n",
       "28  0  0.332061  0.000000  0.153025\n",
       "32  0  0.572115  0.000000  0.229358\n",
       "33  0  0.319672  0.000000  0.165354\n",
       "34  0  0.530000  0.000000  0.260465\n",
       "36  0  0.652695  0.000000  0.355191\n",
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       "47  0  0.407035  0.000000  0.172249\n",
       "48  0  0.515464  0.000000  0.236842\n",
       "53  0  0.550562  0.000000  0.283333\n",
       "54  0  0.419355  0.000000  0.261682\n",
       "57  0  0.492891  0.000000  0.235023\n",
       "61  0  0.438017  0.000000  0.223077\n",
       "64  0  0.486111  0.000000  0.227679\n",
       "69  0  0.361257  0.000000  0.161765"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data = pd.read_csv(os.path.join(data_dir, \"train.csv\"), header=None, names=None)\n",
    "display(train_data[train_data.iloc[:, 0]==0]) #feature values for train documents with class label 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2: How did you decide on the type of model to use? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Answer**: For the binary classification problem of plagiarism detection, I decided on what learning algorithm to use based on the data distribution and features available. Since the data set is small with less than 100 training documents that are fairly distributed over the 2 classes and contains small feature space per sample, my intuition was that simplest models, like Linear and Naive Bayes classifiers, would give the best results as there isn't enough data to estimate many parameters in more complex classifiers like Neural Networks. So I trained 3 different classifiers on the same training data - (1) Feed Forward Neural Network Model, (2) LinearLearner Model, and (3) Naive Bayes Model. The evaluation results on the test data are shown in the summary [table](#table) above. The LinearLearner gave the best results, with an accuary of 96%, which means that the learned model is able to separate the two classes of data and effectively output labels as plagiarized or non-plagiarized.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## EXERCISE: Clean up Resources\n",
    "\n",
    "After you're done evaluating your model, **delete your model endpoint**. You can do this with a call to `.delete_endpoint()`. You need to show, in this notebook, that the endpoint was deleted. Any other resources, you may delete from the AWS console, and you will find more instructions on cleaning up all your resources, below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncomment and fill in the line below!\n",
    "linear_predictor.delete_endpoint()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deleting S3 bucket\n",
    "\n",
    "When you are *completely* done with training and testing models, you can also delete your entire S3 bucket. If you do this before you are done training your model, you'll have to recreate your S3 bucket and upload your training data again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'ResponseMetadata': {'RequestId': '6EF731FE163B0C5D',\n",
       "   'HostId': 'Ol/lMRX0i6/jmiK76LOUKqtLwpBZcu0C2WwEhPISPG164CjFHcoFEITbe3um/zwKt9nPntlqmic=',\n",
       "   'HTTPStatusCode': 200,\n",
       "   'HTTPHeaders': {'x-amz-id-2': 'Ol/lMRX0i6/jmiK76LOUKqtLwpBZcu0C2WwEhPISPG164CjFHcoFEITbe3um/zwKt9nPntlqmic=',\n",
       "    'x-amz-request-id': '6EF731FE163B0C5D',\n",
       "    'date': 'Fri, 04 Dec 2020 00:54:00 GMT',\n",
       "    'content-type': 'application/xml',\n",
       "    'transfer-encoding': 'chunked',\n",
       "    'server': 'AmazonS3',\n",
       "    'connection': 'close'},\n",
       "   'RetryAttempts': 0},\n",
       "  'Deleted': [{'Key': 'plagiarism-detection/test.csv'},\n",
       "   {'Key': 'plagiarism-detection-nb/sagemaker-scikit-learn-2020-12-03-20-42-47-093/output/model.tar.gz'},\n",
       "   {'Key': 'sagemaker-record-sets/LinearLearner-2020-12-03-19-01-13-845/.amazon.manifest'},\n",
       "   {'Key': 'sagemaker-scikit-learn-2020-12-03-20-20-38-669/source/sourcedir.tar.gz'},\n",
       "   {'Key': 'sagemaker-record-sets/LinearLearner-2020-12-03-19-01-13-845/matrix_0.pbr'},\n",
       "   {'Key': 'plagiarism-detection-linearlearner/linear-learner-2020-12-03-19-01-22-387/output/model.tar.gz'},\n",
       "   {'Key': 'plagiarism-detection-nb/sagemaker-scikit-learn-2020-12-03-20-42-47-093/debug-output/training_job_end.ts'},\n",
       "   {'Key': 'sagemaker-scikit-learn-2020-12-03-20-42-47-093/source/sourcedir.tar.gz'},\n",
       "   {'Key': 'plagiarism-detection/train.csv'}]}]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# deleting bucket, uncomment lines below\n",
    "\n",
    "bucket_to_delete = boto3.resource('s3').Bucket(bucket)\n",
    "bucket_to_delete.objects.all().delete()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deleting all your models and instances\n",
    "\n",
    "When you are _completely_ done with this project and do **not** ever want to revisit this notebook, you can choose to delete all of your SageMaker notebook instances and models by following [these instructions](https://docs.aws.amazon.com/sagemaker/latest/dg/ex1-cleanup.html). Before you delete this notebook instance, I recommend at least downloading a copy and saving it, locally."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Further Directions\n",
    "\n",
    "There are many ways to improve or add on to this project to expand your learning or make this more of a unique project for you. A few ideas are listed below:\n",
    "* Train a classifier to predict the *category* (1-3) of plagiarism and not just plagiarized (1) or not (0).\n",
    "* Utilize a different and larger dataset to see if this model can be extended to other types of plagiarism.\n",
    "* Use language or character-level analysis to find different (and more) similarity features.\n",
    "* Write a complete pipeline function that accepts a source text and submitted text file, and classifies the submitted text as plagiarized or not.\n",
    "* Use API Gateway and a lambda function to deploy your model to a web application.\n",
    "\n",
    "These are all just options for extending your work. If you've completed all the exercises in this notebook, you've completed a real-world application, and can proceed to submit your project. Great job!"
   ]
  }
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