train_on_aws/keras_on_gpu.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Credits\n",
    "\n",
    "This notebook was developed based on example notebook provided by AWS Sagemaker -> https://github.com/awslabs/amazon-sagemaker-examples/tree/master/advanced_functionality/scikit_bring_your_own.\n",
    "We focus on improving the training part and add GPU support for computations and Keras library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Docker container\n",
    "\n",
    "This container will be needed to provide all learning and GPU-required dependencies as well as to expose simple API so that Sagemaker knows how to use it.\n",
    "\n",
    "We can see that Tensorflow has the following requirements (https://www.nvidia.com/en-us/data-center/gpu-accelerated-applications/tensorflow/):\n",
    "\n",
    "* 64-bit Linux\n",
    "* Python 2.7\n",
    "* CUDA 7.5 (CUDA 8.0 required for Pascal GPUs)\n",
    "* cuDNN v5.1 (cuDNN v6 if on TF v1.3)\n",
    "\n",
    "To fullfill them we can use prebuilt image provided by nvidia-docker project (see https://github.com/NVIDIA/nvidia-docker). We do not need to worry about nvidia-docker deamon since it is provided by Sagemaker."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Build an image that can do training  in SageMaker\r\n",
      "# This image contains CUDA 9.0 (CUDA libs are backward-compatible), cuddn version 7 and 64bit ubuntu\r\n",
      "FROM nvidia/cuda:9.0-cudnn7-runtime-ubuntu16.04\r\n",
      "\r\n",
      "# FROM ubuntu:16.04 - if we do not want cuda support\r\n",
      "MAINTAINER Amazon AI <sage-learner@amazon.com>\r\n",
      "\r\n",
      "RUN apt-get -y update && apt-get install -y --no-install-recommends \\\r\n",
      "         wget \\\r\n",
      "         python \\\r\n",
      "         nginx \\\r\n",
      "         ca-certificates \\\r\n",
      "         python-dev \\\r\n",
      "         python-tk \\\r\n",
      "         gcc \\\r\n",
      "         g++ \\\r\n",
      "         libopenblas-dev \\\r\n",
      "    && rm -rf /var/lib/apt/lists/*\r\n",
      "\r\n",
      "# Here we get all python packages.\r\n",
      "RUN wget https://bootstrap.pypa.io/get-pip.py && python get-pip.py && \\\r\n",
      "    pip install numpy scikit-learn pandas flask gevent gunicorn matplotlib tensorflow-gpu keras Pillow six\r\n",
      "\r\n",
      "# Set some environment variables. PYTHONUNBUFFERED keeps Python from buffering our standard\r\n",
      "# output stream, which means that logs can be delivered to the user quickly. PYTHONDONTWRITEBYTECODE\r\n",
      "# keeps Python from writing the .pyc files which are unnecessary in this case. We also update\r\n",
      "# PATH so that the train and serve programs are found when the container is invoked.\r\n",
      "\r\n",
      "ENV PYTHONUNBUFFERED=TRUE\r\n",
      "ENV PYTHONDONTWRITEBYTECODE=TRUE\r\n",
      "ENV PATH=\"/opt/program:${PATH}\"\r\n",
      "\r\n",
      "# Set up the program in the image\r\n",
      "COPY keras-nn /opt/program\r\n",
      "WORKDIR /opt/program\r\n"
     ]
    }
   ],
   "source": [
    "!cat container/Dockerfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Login Succeeded\n",
      "Sending build context to Docker daemon  76.29kB\r",
      "\r\n",
      "Step 1/9 : FROM nvidia/cuda:9.0-cudnn7-runtime-ubuntu16.04\n",
      " ---> 03608776d6cb\n",
      "Step 2/9 : MAINTAINER Amazon AI <sage-learner@amazon.com>\n",
      " ---> Using cache\n",
      " ---> c46e372d6308\n",
      "Step 3/9 : RUN apt-get -y update && apt-get install -y --no-install-recommends          wget          python          nginx          ca-certificates          python-dev          python-tk          gcc          g++          libopenblas-dev     && rm -rf /var/lib/apt/lists/*\n",
      " ---> Using cache\n",
      " ---> 332aac28bcee\n",
      "Step 4/9 : RUN wget https://bootstrap.pypa.io/get-pip.py && python get-pip.py &&     pip install numpy scikit-learn pandas flask gevent gunicorn matplotlib tensorflow-gpu keras Pillow six\n",
      " ---> Using cache\n",
      " ---> d89a824d5f90\n",
      "Step 5/9 : ENV PYTHONUNBUFFERED=TRUE\n",
      " ---> Using cache\n",
      " ---> 20be4641b434\n",
      "Step 6/9 : ENV PYTHONDONTWRITEBYTECODE=TRUE\n",
      " ---> Using cache\n",
      " ---> 65db18abe65b\n",
      "Step 7/9 : ENV PATH=\"/opt/program:${PATH}\"\n",
      " ---> Using cache\n",
      " ---> 6f9d18e96274\n",
      "Step 8/9 : COPY keras-nn /opt/program\n",
      " ---> c01ad3d8ca27\n",
      "Step 9/9 : WORKDIR /opt/program\n",
      "Removing intermediate container a6206c550352\n",
      " ---> 6ebe32dabf33\n",
      "Successfully built 6ebe32dabf33\n",
      "Successfully tagged keras-nn:latest\n",
      "The push refers to repository [525003307142.dkr.ecr.eu-central-1.amazonaws.com/keras-nn]\n",
      "947b947b835a: Preparing\n",
      "112d734fe1dc: Preparing\n",
      "3ed761d77187: Preparing\n",
      "1ae8753acdda: Preparing\n",
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      "df2d28798916: Preparing\n",
      "709bdd00b1a4: Preparing\n",
      "07b9c3c04cbd: Preparing\n",
      "6eaddaf493f1: Preparing\n",
      "a0e188d0e278: Preparing\n",
      "711e4cb62f50: Preparing\n",
      "df2d28798916: Waiting\n",
      "709bdd00b1a4: Waiting\n",
      "07b9c3c04cbd: Waiting\n",
      "6eaddaf493f1: Waiting\n",
      "a0e188d0e278: Waiting\n",
      "711e4cb62f50: Waiting\n",
      "9b245cb8493b: Waiting\n",
      "5a4e62ee5fac: Waiting\n",
      "3ed761d77187: Layer already exists\n",
      "5a936f924f59: Layer already exists\n",
      "112d734fe1dc: Layer already exists\n",
      "1ae8753acdda: Layer already exists\n",
      "9b245cb8493b: Layer already exists\n",
      "5a4e62ee5fac: Layer already exists\n",
      "709bdd00b1a4: Layer already exists\n",
      "df2d28798916: Layer already exists\n",
      "07b9c3c04cbd: Layer already exists\n",
      "6eaddaf493f1: Layer already exists\n",
      "711e4cb62f50: Layer already exists\n",
      "a0e188d0e278: Layer already exists\n",
      "947b947b835a: Pushed\n",
      "latest: digest: sha256:a4aa392d7f994100831400d061f7102fe97e3d76bdf51b29cad93d8896acb281 size: 3046\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING! Using --password via the CLI is insecure. Use --password-stdin.\n"
     ]
    }
   ],
   "source": [
    "%%sh\n",
    "\n",
    "# AWS script to run the build and deployment \n",
    "\n",
    "# The name of our algorithm\n",
    "algorithm_name=keras-nn\n",
    "\n",
    "cd container\n",
    "\n",
    "chmod +x keras-nn/train\n",
    "\n",
    "account=$(aws sts get-caller-identity --query Account --output text)\n",
    "\n",
    "# Get the region defined in the current configuration (default to us-west-2 if none defined)\n",
    "region=$(aws configure get region)\n",
    "region=${region:-us-west-2}\n",
    "\n",
    "fullname=\"${account}.dkr.ecr.${region}.amazonaws.com/${algorithm_name}:latest\"\n",
    "\n",
    "# If the repository doesn't exist in ECR, create it.\n",
    "\n",
    "aws ecr describe-repositories --repository-names \"${algorithm_name}\" > /dev/null 2>&1\n",
    "\n",
    "if [ $? -ne 0 ]\n",
    "then\n",
    "    aws ecr create-repository --repository-name \"${algorithm_name}\" > /dev/null\n",
    "fi\n",
    "\n",
    "# Get the login command from ECR and execute it directly\n",
    "$(aws ecr get-login --region ${region} --no-include-email)\n",
    "\n",
    "# Build the docker image locally with the image name and then push it to ECR\n",
    "# with the full name.\n",
    "docker build  -t ${algorithm_name} .\n",
    "docker tag ${algorithm_name} ${fullname}\n",
    "\n",
    "docker push ${fullname}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# S3 prefix\n",
    "prefix = 'DEMO-keras-nn'\n",
    "\n",
    "import boto3\n",
    "import re\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sagemaker import get_execution_role\n",
    "\n",
    "role = get_execution_role()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the session\n",
    "\n",
    "The session remembers our connection parameters to SageMaker. We'll use it to perform all of our SageMaker operations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sagemaker as sage\n",
    "from time import gmtime, strftime\n",
    "\n",
    "sess = sage.Session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Upload the train data. In this case we leave this directory empty, since the CIFAR data is builtin in Keras library.\n",
    "# Otherwise we could access all the data we put on S3 from our container.\n",
    "WORK_DIRECTORY = 'data'\n",
    "\n",
    "data_location = sess.upload_data(WORK_DIRECTORY, key_prefix=prefix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an estimator and fit the model\n",
    "\n",
    "In order to use SageMaker to fit our algorithm, we'll create an `Estimator` that defines how to use the container to train. This includes the configuration we need to invoke SageMaker training:\n",
    "\n",
    "* The __container name__. This is constructed as in the shell commands above.\n",
    "* The __role__. As defined above.\n",
    "* The __instance count__ which is the number of machines to use for training.\n",
    "* The __instance type__ which is the type of machine to use for training.\n",
    "* The __output path__ determines where the model artifact will be written.\n",
    "* The __session__ is the SageMaker session object that we defined above.\n",
    "\n",
    "Then we use fit() on the estimator to train against the data that we uploaded above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating training-job with name: keras-nn-2018-07-26-09-03-41-932\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "............................\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/h5py/__init__.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from ._conv import register_converters as _register_converters\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/h5py/__init__.py:45: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from . import h5a, h5d, h5ds, h5f, h5fd, h5g, h5r, h5s, h5t, h5p, h5z\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/h5py/_hl/group.py:22: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from .. import h5g, h5i, h5o, h5r, h5t, h5l, h5p\u001b[0m\n",
      "\u001b[31mUsing TensorFlow backend.\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/scipy/sparse/lil.py:19: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from . import _csparsetools\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/scipy/sparse/csgraph/__init__.py:165: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from ._shortest_path import shortest_path, floyd_warshall, dijkstra,\\\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/scipy/sparse/csgraph/_validation.py:5: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from ._tools import csgraph_to_dense, csgraph_from_dense,\\\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/scipy/sparse/csgraph/__init__.py:167: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from ._traversal import breadth_first_order, depth_first_order, \\\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/scipy/sparse/csgraph/__init__.py:169: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from ._min_spanning_tree import minimum_spanning_tree\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/scipy/sparse/csgraph/__init__.py:170: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from ._reordering import reverse_cuthill_mckee, maximum_bipartite_matching, \\\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/scipy/linalg/basic.py:17: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from ._solve_toeplitz import levinson\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/scipy/linalg/__init__.py:207: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from ._decomp_update import *\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/scipy/special/__init__.py:640: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from ._ufuncs import *\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/scipy/special/_ellip_harm.py:7: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from ._ellip_harm_2 import _ellipsoid, _ellipsoid_norm\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/scipy/interpolate/_bsplines.py:10: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from . import _bspl\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/scipy/spatial/__init__.py:95: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from .ckdtree import *\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/scipy/spatial/__init__.py:96: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from .qhull import *\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/scipy/spatial/_spherical_voronoi.py:18: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from . import _voronoi\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/scipy/spatial/distance.py:122: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from . import _hausdorff\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from . import _ni_label\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/pandas/_libs/__init__.py:4: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from .tslib import iNaT, NaT, Timestamp, Timedelta, OutOfBoundsDatetime\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/pandas/__init__.py:26: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from pandas._libs import (hashtable as _hashtable,\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/pandas/core/dtypes/common.py:6: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from pandas._libs import algos, lib\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/pandas/core/util/hashing.py:7: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from pandas._libs import hashing, tslib\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/pandas/core/indexes/base.py:7: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from pandas._libs import (lib, index as libindex, tslib as libts,\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/pandas/tseries/offsets.py:21: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  import pandas._libs.tslibs.offsets as liboffsets\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/pandas/core/ops.py:16: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from pandas._libs import algos as libalgos, ops as libops\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/pandas/core/indexes/interval.py:32: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from pandas._libs.interval import (\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/pandas/core/internals.py:14: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from pandas._libs import internals as libinternals\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/pandas/core/sparse/array.py:33: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  import pandas._libs.sparse as splib\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/pandas/core/window.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  import pandas._libs.window as _window\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/pandas/core/groupby/groupby.py:68: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from pandas._libs import (lib, reduction,\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/pandas/core/reshape/reshape.py:30: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from pandas._libs import algos as _algos, reshape as _reshape\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.py:45: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  import pandas._libs.parsers as parsers\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/pandas/io/pytables.py:50: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  from pandas._libs import algos, lib, writers as libwriters\u001b[0m\n",
      "\u001b[31m2018-07-26 09:08:08.973440: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\u001b[0m\n",
      "\u001b[31m2018-07-26 09:08:09.784170: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:897] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\u001b[0m\n",
      "\u001b[31m2018-07-26 09:08:09.784612: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1392] Found device 0 with properties: \u001b[0m\n",
      "\u001b[31mname: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8755\u001b[0m\n",
      "\u001b[31mpciBusID: 0000:00:1e.0\u001b[0m\n",
      "\u001b[31mtotalMemory: 11.17GiB freeMemory: 11.10GiB\u001b[0m\n",
      "\u001b[31m2018-07-26 09:08:09.784646: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1471] Adding visible gpu devices: 0\u001b[0m\n",
      "\u001b[31m2018-07-26 09:08:10.785058: I tensorflow/core/common_runtime/gpu/gpu_device.cc:952] Device interconnect StreamExecutor with strength 1 edge matrix:\u001b[0m\n",
      "\u001b[31m2018-07-26 09:08:10.785108: I tensorflow/core/common_runtime/gpu/gpu_device.cc:958]      0 \u001b[0m\n",
      "\u001b[31m2018-07-26 09:08:10.785120: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0:   N \u001b[0m\n",
      "\u001b[31m2018-07-26 09:08:10.785454: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1084] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10761 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:1e.0, compute capability: 3.7)\u001b[0m\n",
      "\u001b[31mDownloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\u001b[0m\n",
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      "\u001b[31m170500096/170498071 [==============================] - 16s 0us/step\n",
      "\u001b[0m\n",
      "\u001b[31m170508288/170498071 [==============================] - 16s 0us/step\u001b[0m\n",
      "\u001b[31mEpoch 1 of 240\n",
      "\u001b[0m\n",
      "\u001b[31m/usr/local/lib/python2.7/dist-packages/keras/engine/training.py:478: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
      "  'Discrepancy between trainable weights and collected trainable'\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "\u001b[31m499/500 [============================>.] - ETA: 0s\u001b[0m\n",
      "\u001b[31mTesting for epoch 7:\u001b[0m\n",
      "\u001b[31mcomponent              | loss | generation_loss | auxiliary_loss\u001b[0m\n",
      "\u001b[31m-----------------------------------------------------------------\u001b[0m\n",
      "\u001b[31mgenerator (train)      | 1.45 | 1.18            | 0.27 \u001b[0m\n",
      "\u001b[31mgenerator (test)       | 8.39 | 0.07            | 8.32 \u001b[0m\n",
      "\u001b[31mdiscriminator (train)  | 1.39 | 0.57            | 0.82 \u001b[0m\n",
      "\u001b[31mdiscriminator (test)   | 13.67 | 3.95            | 9.72 \u001b[0m\n",
      "\u001b[31mEpoch 8 of 240\n",
      "\u001b[0m\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31m122/500 [======>.......................] - ETA: 3:48\u001b[0m\n",
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      "\u001b[31m270/500 [===============>..............] - ETA: 2:19\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31m271/500 [===============>..............] - ETA: 2:18\u001b[0m\n",
      "\u001b[31m272/500 [===============>..............] - ETA: 2:18\u001b[0m\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "\u001b[31m411/500 [=======================>......] - ETA: 53s\u001b[0m\n",
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      "\u001b[31mTesting for epoch 8:\u001b[0m\n",
      "\u001b[31mcomponent              | loss | generation_loss | auxiliary_loss\u001b[0m\n",
      "\u001b[31m-----------------------------------------------------------------\u001b[0m\n",
      "\u001b[31mgenerator (train)      | 1.40 | 1.14            | 0.25 \u001b[0m\n",
      "\u001b[31mgenerator (test)       | 9.22 | 0.03            | 9.19 \u001b[0m\n",
      "\u001b[31mdiscriminator (train)  | 1.37 | 0.58            | 0.79 \u001b[0m\n",
      "\u001b[31mdiscriminator (test)   | 13.68 | 3.58            | 10.09\u001b[0m\n",
      "\u001b[31mEpoch 9 of 240\n",
      "\u001b[0m\n",
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      "\u001b[31m 46/500 [=>............................] - ETA: 4:35\u001b[0m\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\u001b[31m 47/500 [=>............................] - ETA: 4:34\n",
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      "\u001b[31m184/500 [==========>...................] - ETA: 3:11\u001b[0m\n",
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      "\u001b[31m190/500 [==========>...................] - ETA: 3:07\u001b[0m\n",
      "\u001b[31m191/500 [==========>...................] - ETA: 3:07\u001b[0m\n",
      "\u001b[31m192/500 [==========>...................] - ETA: 3:06\u001b[0m\n",
      "\u001b[31m193/500 [==========>...................] - ETA: 3:06\u001b[0m\n",
      "\u001b[31m194/500 [==========>...................] - ETA: 3:05\u001b[0m\n",
      "\u001b[31m195/500 [==========>...................] - ETA: 3:04\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31m196/500 [==========>...................] - ETA: 3:04\u001b[0m\n",
      "\u001b[31m197/500 [==========>...................] - ETA: 3:03\u001b[0m\n",
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      "\u001b[31m200/500 [===========>..................] - ETA: 3:01\u001b[0m\n",
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    },
    {
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     "output_type": "stream",
     "text": [
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      "\u001b[31m484/500 [============================>.] - ETA: 9s \u001b[0m\n",
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      "\u001b[31m489/500 [============================>.] - ETA: 6s\u001b[0m\n",
      "\u001b[31m490/500 [============================>.] - ETA: 6s\u001b[0m\n",
      "\u001b[31m491/500 [============================>.] - ETA: 5s\u001b[0m\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\u001b[31m492/500 [============================>.] - ETA: 4s\u001b[0m\n",
      "\u001b[31m493/500 [============================>.] - ETA: 4s\u001b[0m\n",
      "\u001b[31m494/500 [============================>.] - ETA: 3s\u001b[0m\n",
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      "\u001b[31m496/500 [============================>.] - ETA: 2s\u001b[0m\n",
      "\u001b[31m497/500 [============================>.] - ETA: 1s\u001b[0m\n",
      "\u001b[31m498/500 [============================>.] - ETA: 1s\u001b[0m\n",
      "\u001b[31m499/500 [============================>.] - ETA: 0s\u001b[0m\n",
      "\u001b[31mTesting for epoch 9:\u001b[0m\n",
      "\u001b[31mcomponent              | loss | generation_loss | auxiliary_loss\u001b[0m\n",
      "\u001b[31m-----------------------------------------------------------------\u001b[0m\n",
      "\u001b[31mgenerator (train)      | 1.36 | 1.11            | 0.25 \u001b[0m\n",
      "\u001b[31mgenerator (test)       | 9.50 | 0.01            | 9.49 \u001b[0m\n",
      "\u001b[31mdiscriminator (train)  | 1.34 | 0.58            | 0.77 \u001b[0m\n",
      "\u001b[31mdiscriminator (test)   | 14.82 | 4.68            | 10.15\u001b[0m\n",
      "\u001b[31mEpoch 10 of 240\n",
      "\u001b[0m\n",
      "\u001b[31m  0/500 [..............................] - ETA: 0s\n",
      "  1/500 [..............................] - ETA: 5:02\u001b[0m\n",
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      "\u001b[31m  5/500 [..............................] - ETA: 5:00\n",
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      "\u001b[31m  7/500 [..............................] - ETA: 4:59\u001b[0m\n",
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      "\u001b[31m 10/500 [..............................] - ETA: 4:57\n",
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      "\u001b[31m 15/500 [..............................] - ETA: 4:54\n",
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      "\u001b[31m 17/500 [>.............................] - ETA: 4:52\u001b[0m\n",
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      "\u001b[31m 20/500 [>.............................] - ETA: 4:51\n",
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      "\u001b[31m 25/500 [>.............................] - ETA: 4:48\n",
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      "\u001b[31m 27/500 [>.............................] - ETA: 4:46\u001b[0m\n",
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      "\u001b[31m 30/500 [>.............................] - ETA: 4:45\u001b[0m\n",
      "\u001b[31m 31/500 [>.............................] - ETA: 4:44\n",
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      "\u001b[31m 33/500 [>.............................] - ETA: 4:43\n",
      " 34/500 [=>............................] - ETA: 4:42\u001b[0m\n",
      "\u001b[31m 35/500 [=>............................] - ETA: 4:41\u001b[0m\n",
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      "\u001b[31m 38/500 [=>............................] - ETA: 4:40\n",
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      "\u001b[31m 40/500 [=>............................] - ETA: 4:38\u001b[0m\n",
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      "\u001b[31m 43/500 [=>............................] - ETA: 4:37\n",
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      "\u001b[31m 48/500 [=>............................] - ETA: 4:34\n",
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      "\u001b[31m 50/500 [==>...........................] - ETA: 4:32\u001b[0m\n",
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      "\u001b[31m 55/500 [==>...........................] - ETA: 4:29\u001b[0m\n",
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      "\u001b[31m 58/500 [==>...........................] - ETA: 4:27\n",
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      "\u001b[31m 60/500 [==>...........................] - ETA: 4:26\u001b[0m\n",
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      "\u001b[31m 66/500 [==>...........................] - ETA: 4:23\n",
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      "\u001b[31m 81/500 [===>..........................] - ETA: 4:13\n",
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      "\u001b[31m 83/500 [===>..........................] - ETA: 4:12\u001b[0m\n",
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      "\u001b[31m 86/500 [====>.........................] - ETA: 4:10\n",
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      "\u001b[31m 88/500 [====>.........................] - ETA: 4:09\u001b[0m\n",
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      "\u001b[31m 91/500 [====>.........................] - ETA: 4:07\n",
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      "\u001b[31m129/500 [======>.......................] - ETA: 3:45\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31m130/500 [======>.......................] - ETA: 3:44\u001b[0m\n",
      "\u001b[31m131/500 [======>.......................] - ETA: 3:43\u001b[0m\n",
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      "\u001b[31m212/500 [===========>..................] - ETA: 2:54\u001b[0m\n",
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      "\u001b[31m214/500 [===========>..................] - ETA: 2:53\u001b[0m\n",
      "\u001b[31m215/500 [===========>..................] - ETA: 2:52\u001b[0m\n",
      "\u001b[31m216/500 [===========>..................] - ETA: 2:52\u001b[0m\n",
      "\u001b[31m217/500 [============>.................] - ETA: 2:51\u001b[0m\n",
      "\u001b[31m218/500 [============>.................] - ETA: 2:51\u001b[0m\n",
      "\u001b[31m219/500 [============>.................] - ETA: 2:50\u001b[0m\n",
      "\u001b[31m220/500 [============>.................] - ETA: 2:49\u001b[0m\n",
      "\u001b[31m221/500 [============>.................] - ETA: 2:49\u001b[0m\n",
      "\u001b[31m222/500 [============>.................] - ETA: 2:48\u001b[0m\n",
      "\u001b[31m223/500 [============>.................] - ETA: 2:48\u001b[0m\n",
      "\u001b[31m224/500 [============>.................] - ETA: 2:47\u001b[0m\n",
      "\u001b[31m225/500 [============>.................] - ETA: 2:46\u001b[0m\n",
      "\u001b[31m226/500 [============>.................] - ETA: 2:46\u001b[0m\n",
      "\u001b[31m227/500 [============>.................] - ETA: 2:45\u001b[0m\n",
      "\u001b[31m228/500 [============>.................] - ETA: 2:44\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "account = sess.boto_session.client('sts').get_caller_identity()['Account']\n",
    "region = sess.boto_session.region_name\n",
    "image = '{}.dkr.ecr.{}.amazonaws.com/keras-nn:latest'.format(account, region)\n",
    "\n",
    "tree = sage.estimator.Estimator(image,\n",
    "                       role, 1, 'ml.p2.xlarge',\n",
    "                       output_path=\"s3://{}/output_keras_gpu\".format(sess.default_bucket()),\n",
    "                       sagemaker_session=sess)\n",
    "\n",
    "tree.fit(data_location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "container/\r\n",
      "container/ReadMe.md\r\n",
      "container/keras-nn/\r\n",
      "container/keras-nn/train_cpu\r\n",
      "container/keras-nn/.ipynb_checkpoints/\r\n",
      "container/keras-nn/train_gpu\r\n",
      "container/keras-nn/train_old\r\n",
      "container/keras-nn/Minibatch.py\r\n",
      "container/keras-nn/train\r\n",
      "container/Dockerfile\r\n",
      "container/.ipynb_checkpoints/\r\n",
      "container/Dockerfile_cpu\r\n",
      "container/local_test/\r\n",
      "container/local_test/test_dir/\r\n",
      "container/local_test/test_dir/model/\r\n",
      "container/local_test/test_dir/model/decision-tree-model.pkl\r\n",
      "container/local_test/test_dir/output/\r\n",
      "container/local_test/test_dir/output/success\r\n",
      "container/local_test/test_dir/input/\r\n",
      "container/local_test/test_dir/input/config/\r\n",
      "container/local_test/test_dir/input/config/hyperparameters.json\r\n",
      "container/local_test/test_dir/input/config/resourceConfig.json\r\n",
      "container/local_test/test_dir/input/data/\r\n",
      "container/local_test/test_dir/input/data/training/\r\n",
      "container/local_test/test_dir/input/data/training/iris.csv\r\n",
      "container/local_test/.ipynb_checkpoints/\r\n",
      "data/\r\n",
      "data/iris.csv\r\n",
      "keras_on_gpu.ipynb\r\n",
      "notebook.tar.gz\r\n",
      "stack.png\r\n"
     ]
    }
   ],
   "source": [
    "!tar chvfz notebook2.tar.gz *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "conda_tensorflow_p27",
   "language": "python",
   "name": "conda_tensorflow_p27"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}