JulianaFontolan

# Call the estimator with the main parameters

from sagemaker.estimator import Estimator

estimator = Estimator(
    image_uri=ecr_image,
    role=role,
    instance_count=1,
    instance_type="ml.c4.xlarge",
    output_path=out_path

JulianaFontolan

import sagemaker
import json
from sagemaker import get_execution_role
import boto3

# Role to have access of reading and write inside S3
role = get_execution_role()

# Path files
train_path='s3://topicmodeling/train/base_treino.csv'

JulianaFontolan

%%sh
# Login in ECR
aws ecr get-login-password --region xxxxx | docker login --username AWS --password-stdin xxxxxx.dkr.ecr.xxxxx.amazonaws.com

JulianaFontolan

%%writefile Dockerfile
FROM python:3.7-buster

# Set a docker label to advertise multi-model support on the container
LABEL com.amazonaws.sagemaker.capabilities.multi-models=false
# Set a docker label to enable container to use SAGEMAKER_BIND_TO_PORT environment variable if present
LABEL com.amazonaws.sagemaker.capabilities.accept-bind-to-port=true

RUN apt-get update -y && apt-get -y install --no-install-recommends default-jdk
RUN rm -rf /var/lib/apt/lists/*