mvsusp

mvsusp

package org.example.basicapp;

import com.amazonaws.services.sagemakerruntime.AmazonSageMakerRuntime;
import com.amazonaws.services.sagemakerruntime.AmazonSageMakerRuntimeClientBuilder;
import com.amazonaws.services.sagemakerruntime.model.InvokeEndpointRequest;
import com.amazonaws.services.sagemakerruntime.model.InvokeEndpointResult;

import java.nio.ByteBuffer;

public class App

mvsusp

#!/usr/bin/env bash

MODEL_NAME=half-plus-three-v1

ENDPOINT_CONFIG_NAME=half-plus-three-config-v1

ENDPOINT_NAME=half-plus-three

PRODUCTION_VARIANTS="VariantName=Default,ModelName=${MODEL_NAME},"\
"InitialInstanceCount=1,InstanceType=ml.c4.large"

mvsusp

#!/usr/bin/env bash

MODEL_NAME=half-plus-three-v1

# the role named created with
# https://gist.github.com/mvsusp/599311cb9f4ee1091065f8206c026962
ROLE_NAME=SageMakerRole

# the name of the image created with
# https://gist.github.com/mvsusp/07610f9cfecbec13fb2b7c77a2e843c4

mvsusp

#!/usr/bin/env bash

# This script creates a role named SageMakerRole
# that can be used by SageMaker and has Full access to S3.

ROLE_NAME=SageMakerRole

# WARNING: this policy gives full S3 access to container that
# is running in SageMaker. You can change this policy to a more
# restrictive one, or create your own policy.

mvsusp

#!/usr/bin/env bash

IMAGE_NAME=sagemaker-tf-serving
REGION=$(aws configure get region)
ACCOUNT=$(aws sts get-caller-identity --query Account --output text)

# If the ECS repository doesn't exist, creates it.
aws ecr create-repository --repository-name ${IMAGE_NAME} > /dev/null 2>&1

# ECR requires the image name to be in this format:

mvsusp

#!/usr/bin/env bash

docker build -t sagemaker-byoc-tf-serving .

mvsusp

events {
    # determines how many requests can simultaneously be served
    # https://www.digitalocean.com/community/tutorials/how-to-optimize-nginx-configuration
    # for more information
    worker_connections 2048;
}

http {
  server {
    # configures the server to listen to the port 8080

mvsusp

# Using the official tensorflow serving image from docker hub as base image
FROM tensorflow/serving

# Installing NGINX, used to rever proxy the predictions from SageMaker to TF Serving
RUN apt-get update && apt-get install -y --no-install-recommends nginx git

# Copy our model folder to the container
COPY saved_model_half_plus_three /saved_model_half_plus_three

# Copy NGINX configuration to the container

mvsusp

git clone --single-branch -b r1.9 https://github.com/tensorflow/serving.git /tmp/serving

cp -r \
/tmp/serving/tensorflow_serving/servables/tensorflow/testdata/saved_model_half_plus_three \
saved_model_half_plus_three