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tf23-image

README.md

TF2 Image

Overview

This custom image uses the official TensorFlow 2.3 image from DockerHub as a custom image in SageMaker Studio and demostrates how to bundle a file along with the custom image and access it in SageMaker Studio.

Building the image

Build the Docker image and push to Amazon ECR.

# Modify these as required. The Docker registry endpoint can be tuned based on your current region from https://docs.aws.amazon.com/general/latest/gr/ecr.html#ecr-docker-endpoints
REGION=<aws-region>
ACCOUNT_ID=<account-id>

# Create ECR Repository. Ignore if it exists. For simplcity, all examples in the repo
# use same ECR repo with different image tags
aws --region ${REGION} ecr create-repository --repository-name smstudio-custom

# Build the image
IMAGE_NAME=custom-tf23
aws --region ${REGION} ecr get-login-password | docker login --username AWS --password-stdin ${ACCOUNT_ID}.dkr.ecr.${REGION}.amazonaws.com/smstudio-custom
docker build . -t ${IMAGE_NAME} -t ${ACCOUNT_ID}.dkr.ecr.${REGION}.amazonaws.com/smstudio-custom:${IMAGE_NAME}

docker push ${ACCOUNT_ID}.dkr.ecr.${REGION}.amazonaws.com/smstudio-custom:${IMAGE_NAME}

Using with SageMaker Studio

Create a SageMaker Image (SMI) with the image in ECR. Request parameter RoleArn value is used to get ECR image information when and Image version is created. After creating Image, create an Image Version during which SageMaker stores image metadata like SHA etc. Everytime an image is updated in ECR, a new image version should be created. See Update Image

# Role in your account to be used for the SageMaker Image
ROLE_ARN=<role-arn>

aws --region ${REGION} sagemaker create-image \
    --image-name ${IMAGE_NAME} \
    --role-arn ${ROLE_ARN}

aws --region ${REGION} sagemaker create-image-version \
    --image-name ${IMAGE_NAME} \
    --base-image "${ACCOUNT_ID}.dkr.ecr.${REGION}.amazonaws.com/smstudio-custom:${IMAGE_NAME}"

# Verify the image-version is created successfully. Do NOT proceed if image-version is in CREATE_FAILED state or in any other state apart from CREATED.
aws --region ${REGION} sagemaker describe-image-version --image-name ${IMAGE_NAME}

Create a AppImageConfig for this image

aws --region ${REGION} sagemaker create-app-image-config --cli-input-json file://app-image-config-input.json

Create a Domain, providing the SageMaker Image and AppImageConfig in the Domain creation. Replace the placeholders for VPC ID, Subnet IDs, and Execution Role in create-domain-input.json

aws --region ${REGION} sagemaker create-domain --cli-input-json file://create-domain-input.json

If you have an existing Domain, you can also use the update-domain

aws --region ${REGION} sagemaker update-domain --cli-input-json file://update-domain-input.json

Update Image with SageMaker Studio

If you found an issue with your image or want to update Image with new features, Use following steps

Re-Build and push the image to ECR

# Build and push the image
aws --region ${REGION} ecr get-login-password | docker login --username AWS --password-stdin ${ACCOUNT_ID}.dkr.ecr.${REGION}.amazonaws.com/smstudio-custom
docker build . -t ${IMAGE_NAME} -t ${ACCOUNT_ID}.dkr.ecr.${REGION}.amazonaws.com/smstudio-custom:${IMAGE_NAME}
docker push ${ACCOUNT_ID}.dkr.ecr.${REGION}.amazonaws.com/smstudio-custom:${IMAGE_NAME}

Create new App Image Version.

aws --region ${REGION} sagemaker create-image-version \
    --image-name ${IMAGE_NAME} \
    --base-image "${ACCOUNT_ID}.dkr.ecr.${REGION}.amazonaws.com/smstudio-custom:${IMAGE_NAME}"

# Verify the image-version is created successfully. Do NOT proceed if image-version is in CREATE_FAILED state or in any other state apart from CREATED.
aws --region ${REGION} sagemaker describe-image-version --image-name ${IMAGE_NAME}

Re-Create App in SageMaker studio.