#!/usr/bin/env bash
pysph test

# Build an image that can do training and inference in SageMaker
# This is a Python 2 image that uses the nginx, gunicorn, flask stack
# for serving inferences in a stable way.

FROM nvidia/opencl:devel-ubuntu16.04

MAINTAINER user-name <[email protected]>


RUN apt-get -y update && apt-get install -y\
         wget \
         python3 \
         python3-pip \
         ca-certificates \
         python3-dev 
RUN apt-get install -y build-essential 
RUN apt-get install -y python3-numpy
RUN apt-get install -y python3-pytest 
RUN apt-get install -y cython3

#For pysph
RUN pip3 install pytools
RUN pip3 install h5py
RUN pip3 install pysph

ENV PYTHONUNBUFFERED=TRUE
ENV PYTHONDONTWRITEBYTECODE=TRUE
ENV PATH="/opt/program:${PATH}"

# Set up the program in the image
COPY train /opt/program/
WORKDIR /opt/program

#!/usr/bin/env bash

# This script shows how to build the Docker image and push it to ECR to be ready for use
# by SageMaker.

# The argument to this script is the image name. This will be used as the image on the local
# machine and combined with the account and region to form the repository name for ECR.
image=$1

chmod +x train

if [ "$image" == "" ]
then
    echo "Usage: $0 <image-name>"
    exit 1
fi

# Get the account number associated with the current IAM credentials
account=$(aws sts get-caller-identity --query Account --output text)

if [ $? -ne 0 ]
then
    exit 255
fi


# Get the region defined in the current configuration (default to us-west-2 if none defined)
region=$(aws configure get region)
region=${region:-us-west-2}


fullname="${account}.dkr.ecr.${region}.amazonaws.com/${image}:latest"

# If the repository doesn't exist in ECR, create it.

aws ecr describe-repositories --repository-names "${image}" > /dev/null 2>&1

if [ $? -ne 0 ]
then
    aws ecr create-repository --repository-name "${image}" > /dev/null
fi

# Get the login command from ECR and execute it directly
$(aws ecr get-login --region ${region} --no-include-email)



# Build the docker image locally with the image name and then push it to ECR
# with the full name.

docker build  -t ${image} -f Dockerfile .
docker tag ${image} ${fullname}

docker push ${fullname}

import subprocess
print (subprocess.check_output(['./build_and_push.sh', 'pysph-sagemaker']).decode('utf-8'))

from sagemaker import get_execution_role
import sagemaker as sage
import os

sess = sage.Session()

imagename = 'pysph-sagemaker' #dockerイメージの名前を指定

account = sess.boto_session.client('sts').get_caller_identity()['Account']
region = sess.boto_session.region_name
image = '{}.dkr.ecr.{}.amazonaws.com/{}'.format(account, region, imagename)

# トレーニングジョブ
# 出力先、使用するインスタンスを指定
sph = sage.estimator.Estimator(image,
                       role, 1, 'ml.c5.4xlarge',
                       output_path="s3://sagemakerbucket-rk1/psph/output",
                       sagemaker_session=sess)

sph.fit()