age_script.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import sagemaker\n",
    "from sagemaker.tensorflow import TensorFlow\n",
    "\n",
    "model_dir = '/opt/ml/model'\n",
    "train_instance_type = 'local'\n",
    "hyperparameters = {'epochs': 2, \n",
    "                   'batch_size': 32 ,\n",
    "                   'train_input': \"input/LPGAN/input/\" ,\n",
    "                   'label_input': \"input/LPGAN/label\"\n",
    "                  }\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! conda install -c conda-forge opencv -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_estimator = TensorFlow(entry_point='train.py',\n",
    "                       source_dir='.',\n",
    "                       model_dir=model_dir,\n",
    "                       train_instance_type=train_instance_type,\n",
    "                       train_instance_count=1,\n",
    "                       hyperparameters=hyperparameters,\n",
    "                       role=sagemaker.get_execution_role(),\n",
    "                       base_job_name='tf-20-newsgroups',\n",
    "                       framework_version='1.13',\n",
    "                       py_version='py3',\n",
    "                       script_mode=True)\\"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating tmprisma4eo_algo-1-uxw8s_1 ... \n",
      "\u001b[1BAttaching to tmprisma4eo_algo-1-uxw8s_12mdone\u001b[0m\n",
      "\u001b[36malgo-1-uxw8s_1  |\u001b[0m 2020-04-09 19:14:49,000 sagemaker-containers INFO     Imported framework sagemaker_tensorflow_container.training\n",
      "\u001b[36malgo-1-uxw8s_1  |\u001b[0m 2020-04-09 19:14:49,006 sagemaker-containers INFO     No GPUs detected (normal if no gpus installed)\n",
      "\u001b[36malgo-1-uxw8s_1  |\u001b[0m 2020-04-09 19:14:51,642 sagemaker-containers INFO     Installing module with the following command:\n",
      "\u001b[36malgo-1-uxw8s_1  |\u001b[0m /usr/local/bin/python3.6 -m pip install -U . -r requirements.txt\n",
      "\u001b[36malgo-1-uxw8s_1  |\u001b[0m Processing /opt/ml/code\n",
      "\u001b[36malgo-1-uxw8s_1  |\u001b[0m \u001b[31mERROR: Files/directories not found in /tmp/pip-req-build-zq8d884j/pip-egg-info\u001b[0m\n",
      "\u001b[36malgo-1-uxw8s_1  |\u001b[0m \u001b[33mWARNING: You are using pip version 19.1.1, however version 20.0.2 is available.\n",
      "\u001b[36malgo-1-uxw8s_1  |\u001b[0m You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "\u001b[36malgo-1-uxw8s_1  |\u001b[0m 2020-04-09 19:14:53,108 sagemaker-containers ERROR    InstallModuleError:\n",
      "\u001b[36malgo-1-uxw8s_1  |\u001b[0m Command \"/usr/local/bin/python3.6 -m pip install -U . -r requirements.txt\"\n",
      "\u001b[36mtmprisma4eo_algo-1-uxw8s_1 exited with code 1\n",
      "\u001b[0mAborting on container exit...\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "Failed to run: ['docker-compose', '-f', '/tmp/tmprisma4eo/docker-compose.yaml', 'up', '--build', '--abort-on-container-exit'], Process exited with code: 1",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/sagemaker/local/image.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, input_data_config, output_data_config, hyperparameters, job_name)\u001b[0m\n\u001b[1;32m    154\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 155\u001b[0;31m             \u001b[0m_stream_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocess\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    156\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mRuntimeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/sagemaker/local/image.py\u001b[0m in \u001b[0;36m_stream_output\u001b[0;34m(process)\u001b[0m\n\u001b[1;32m    672\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mexit_code\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 673\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Process exited with code: %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mexit_code\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    674\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: Process exited with code: 1",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-057a275548e5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlocal_estimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/sagemaker/tensorflow/estimator.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, inputs, wait, logs, job_name, experiment_config, run_tensorboard_locally)\u001b[0m\n\u001b[1;32m    471\u001b[0m                 \u001b[0mtensorboard\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    472\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 473\u001b[0;31m             \u001b[0mfit_super\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    474\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    475\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/sagemaker/tensorflow/estimator.py\u001b[0m in \u001b[0;36mfit_super\u001b[0;34m()\u001b[0m\n\u001b[1;32m    450\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    451\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mfit_super\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 452\u001b[0;31m             \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTensorFlow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjob_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexperiment_config\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    453\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    454\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_tensorboard_locally\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mwait\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/sagemaker/estimator.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, inputs, wait, logs, job_name, experiment_config)\u001b[0m\n\u001b[1;32m    465\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_for_training\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjob_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    466\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 467\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlatest_training_job\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_TrainingJob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexperiment_config\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    468\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjobs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlatest_training_job\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    469\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/sagemaker/estimator.py\u001b[0m in \u001b[0;36mstart_new\u001b[0;34m(cls, estimator, inputs, experiment_config)\u001b[0m\n\u001b[1;32m   1013\u001b[0m             \u001b[0mtrain_args\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"enable_sagemaker_metrics\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_sagemaker_metrics\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1014\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1015\u001b[0;31m         \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msagemaker_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mtrain_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1016\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1017\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msagemaker_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_current_job_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/sagemaker/session.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, input_mode, input_config, role, job_name, output_config, resource_config, vpc_config, hyperparameters, stop_condition, tags, metric_definitions, enable_network_isolation, image, algorithm_arn, encrypt_inter_container_traffic, train_use_spot_instances, checkpoint_s3_uri, checkpoint_local_path, experiment_config, debugger_rule_configs, debugger_hook_config, tensorboard_output_config, enable_sagemaker_metrics)\u001b[0m\n\u001b[1;32m    567\u001b[0m         \u001b[0mLOGGER\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Creating training-job with name: %s\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjob_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m         \u001b[0mLOGGER\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"train request: %s\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_request\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 569\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msagemaker_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_training_job\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mtrain_request\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    570\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m     def process(\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/sagemaker/local/local_session.py\u001b[0m in \u001b[0;36mcreate_training_job\u001b[0;34m(self, TrainingJobName, AlgorithmSpecification, OutputDataConfig, ResourceConfig, InputDataConfig, **kwargs)\u001b[0m\n\u001b[1;32m     99\u001b[0m         \u001b[0mtraining_job\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_LocalTrainingJob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontainer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m         \u001b[0mhyperparameters\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"HyperParameters\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m\"HyperParameters\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m         \u001b[0mtraining_job\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mInputDataConfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOutputDataConfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhyperparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTrainingJobName\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m         \u001b[0mLocalSagemakerClient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_training_jobs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mTrainingJobName\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtraining_job\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/sagemaker/local/entities.py\u001b[0m in \u001b[0;36mstart\u001b[0;34m(self, input_data_config, output_data_config, hyperparameters, job_name)\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m         self.model_artifacts = self.container.train(\n\u001b[0;32m---> 96\u001b[0;31m             \u001b[0minput_data_config\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_data_config\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhyperparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjob_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m         )\n\u001b[1;32m     98\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/sagemaker/local/image.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, input_data_config, output_data_config, hyperparameters, job_name)\u001b[0m\n\u001b[1;32m    158\u001b[0m             \u001b[0;31m# which contains the exit code and append the command line to it.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m             \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Failed to run: %s, %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcompose_command\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    161\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m             \u001b[0martifacts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve_artifacts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcompose_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_data_config\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjob_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: Failed to run: ['docker-compose', '-f', '/tmp/tmprisma4eo/docker-compose.yaml', 'up', '--build', '--abort-on-container-exit'], Process exited with code: 1"
     ]
    }
   ],
   "source": [
    "local_estimator.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = {'train': f'file://{train_dir}',\n",
    "          'val': f'file://{val_dir}',\n",
    "          'embedding': f'file://{embedding_dir}'}\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from zipfile import ZipFile\n",
    "with ZipFile('../input/LPGAN/input.zip', 'r') as zipObj:\n",
    "   # Extract all the contents of zip file in current directory\n",
    "   zipObj.extractall('../input/LPGAN/input')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess=sagemaker.Session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess.upload_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'input': 's3://sagemaker-us-east-2-555068367679//input/LPGAN/input', 'label': 's3://sagemaker-us-east-2-555068367679//input/LPGAN/label'}\n"
     ]
    }
   ],
   "source": [
    "s3_prefix = '/input/LPGAN'\n",
    "\n",
    "traindata_s3_prefix = '{}/input'.format(s3_prefix)\n",
    "valdata_s3_prefix = '{}/label'.format(s3_prefix)\n",
    "\n",
    "\n",
    "input_s3 = sagemaker.Session().upload_data(path='input/LPGAN/input/', key_prefix=traindata_s3_prefix)\n",
    "label_s3 = sagemaker.Session().upload_data(path='input/LPGAN/label/', key_prefix=valdata_s3_prefix)\n",
    "\n",
    "\n",
    "inputs = {'input':input_s3, 'label': label_s3}\n",
    "print(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_instance_type = 'ml.p3.2xlarge'\n",
    "hyperparameters = {'epochs': 20, \n",
    "                   'batch_size': 32 ,\n",
    "                   'train_input': \"input/LPGAN/input/\" ,\n",
    "                   'label_input': \"input/LPGAN/label\"\n",
    "                  }\n",
    "\n",
    "estimator = TensorFlow(entry_point='train.py',\n",
    "                       source_dir='deep-photo-enhance-t2',\n",
    "                       model_dir=model_dir,\n",
    "                       train_instance_type=train_instance_type,\n",
    "                       train_instance_count=1,\n",
    "                       hyperparameters=hyperparameters,\n",
    "                       role=sagemaker.get_execution_role(),\n",
    "                       base_job_name='tf-20-newsgroups',\n",
    "                       framework_version='1.13',\n",
    "                       py_version='py3',\n",
    "                       script_mode=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "ResourceLimitExceeded",
     "evalue": "An error occurred (ResourceLimitExceeded) when calling the CreateTrainingJob operation: The account-level service limit 'ml.p3.2xlarge for training job usage' is 0 Instances, with current utilization of 0 Instances and a request delta of 1 Instances. Please contact AWS support to request an increase for this limit.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mResourceLimitExceeded\u001b[0m                     Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-0731e612c7b1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/sagemaker/tensorflow/estimator.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, inputs, wait, logs, job_name, experiment_config, run_tensorboard_locally)\u001b[0m\n\u001b[1;32m    477\u001b[0m                 \u001b[0mtensorboard\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    478\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 479\u001b[0;31m             \u001b[0mfit_super\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/sagemaker/tensorflow/estimator.py\u001b[0m in \u001b[0;36mfit_super\u001b[0;34m()\u001b[0m\n\u001b[1;32m    456\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    457\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mfit_super\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 458\u001b[0;31m             \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTensorFlow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjob_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexperiment_config\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    459\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    460\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_tensorboard_locally\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mwait\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/sagemaker/estimator.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, inputs, wait, logs, job_name, experiment_config)\u001b[0m\n\u001b[1;32m    465\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_for_training\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjob_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    466\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 467\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlatest_training_job\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_TrainingJob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexperiment_config\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    468\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjobs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlatest_training_job\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    469\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/sagemaker/estimator.py\u001b[0m in \u001b[0;36mstart_new\u001b[0;34m(cls, estimator, inputs, experiment_config)\u001b[0m\n\u001b[1;32m   1013\u001b[0m             \u001b[0mtrain_args\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"enable_sagemaker_metrics\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_sagemaker_metrics\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1014\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1015\u001b[0;31m         \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msagemaker_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mtrain_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1016\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1017\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msagemaker_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_current_job_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/sagemaker/session.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, input_mode, input_config, role, job_name, output_config, resource_config, vpc_config, hyperparameters, stop_condition, tags, metric_definitions, enable_network_isolation, image, algorithm_arn, encrypt_inter_container_traffic, train_use_spot_instances, checkpoint_s3_uri, checkpoint_local_path, experiment_config, debugger_rule_configs, debugger_hook_config, tensorboard_output_config, enable_sagemaker_metrics)\u001b[0m\n\u001b[1;32m    588\u001b[0m         \u001b[0mLOGGER\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Creating training-job with name: %s\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjob_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    589\u001b[0m         \u001b[0mLOGGER\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"train request: %s\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_request\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 590\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msagemaker_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_training_job\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mtrain_request\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    591\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    592\u001b[0m     def process(\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/botocore/client.py\u001b[0m in \u001b[0;36m_api_call\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    314\u001b[0m                     \"%s() only accepts keyword arguments.\" % py_operation_name)\n\u001b[1;32m    315\u001b[0m             \u001b[0;31m# The \"self\" in this scope is referring to the BaseClient.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 316\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_api_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moperation_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    317\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    318\u001b[0m         \u001b[0m_api_call\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpy_operation_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/botocore/client.py\u001b[0m in \u001b[0;36m_make_api_call\u001b[0;34m(self, operation_name, api_params)\u001b[0m\n\u001b[1;32m    624\u001b[0m             \u001b[0merror_code\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsed_response\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Error\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Code\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    625\u001b[0m             \u001b[0merror_class\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_code\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror_code\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 626\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0merror_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparsed_response\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moperation_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    627\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    628\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mparsed_response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mResourceLimitExceeded\u001b[0m: An error occurred (ResourceLimitExceeded) when calling the CreateTrainingJob operation: The account-level service limit 'ml.p3.2xlarge for training job usage' is 0 Instances, with current utilization of 0 Instances and a request delta of 1 Instances. Please contact AWS support to request an increase for this limit."
     ]
    }
   ],
   "source": [
    "estimator.fit(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor = TensorFlow(entry_point='predict.py',\n",
    "                       source_dir='code',\n",
    "                       model_dir=model_dir,\n",
    "                       train_instance_type=train_instance_type,\n",
    "                       train_instance_count=1,\n",
    "                       hyperparameters=hyperparameters,\n",
    "                       role=sagemaker.get_execution_role(),\n",
    "                       base_job_name='tf-20-newsgroups',\n",
    "                       framework_version='1.13',\n",
    "                       py_version='py3',\n",
    "                       script_mode=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor = estimator.deploy(initial_instance_count=1,instance_type='ml.m5.xlarge')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor.predict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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