{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Allocate resources in remote container"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kubeflow import fairing\n",
    "from kubeflow.fairing import TrainJob\n",
    "from kubeflow.fairing.backends import KubeflowAWSBackend\n",
    "from kubeflow.fairing.kubernetes.utils import get_resource_mutator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import multiprocessing\n",
    "import os\n",
    "import sys\n",
    "\n",
    "def train():\n",
    "    print(\"CPU count: {}\".format(multiprocessing.cpu_count()))\n",
    "    print(\"Memory: {}\".format(os.sysconf('SC_PAGE_SIZE') * os.sysconf('SC_PHYS_PAGES')/(1024.**3)))\n",
    "\n",
    "train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "AWS_ACCOUNT_ID=fairing.cloud.aws.guess_account_id()\n",
    "AWS_REGION='us-west-2'\n",
    "DOCKER_REGISTRY = '{}.dkr.ecr.{}.amazonaws.com'.format(AWS_ACCOUNT_ID, AWS_REGION)\n",
    "PY_VERSION = \".\".join([str(x) for x in sys.version_info[0:3]])\n",
    "BASE_IMAGE = '{}/python:{}'.format(DOCKER_REGISTRY, PY_VERSION)\n",
    "# TODO: bug to fix. use tensorflow image temporarily\n",
    "BASE_IMAGE = 'tensorflow/tensorflow:1.14.0-py3'\n",
    "\n",
    "job = TrainJob(train, base_docker_image=BASE_IMAGE, docker_registry=DOCKER_REGISTRY,  backend=KubeflowAWSBackend(), \n",
    "              pod_spec_mutators=[get_resource_mutator(cpu=3, memory=4)])\n",
    "job.submit()"
   ]
  }
 ],
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