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conda-env-kernel-image

README.md

Conda Environments as Kernels

Overview

This custom image sample demonstrates how to create a custom Conda environment in a Docker image and use it as a custom kernel in SageMaker Studio.

The Conda environment must have the appropriate kernel package installed, for e.g., ipykernel for a Python kernel. This example creates a Conda environment called myenv with a few Python packages (see environment.yml) and the ipykernel. SageMaker Studio will automatically recognize this Conda environment as a kernel named conda-env-myenv-py (See app-image-config-input.json)

Building the image

Build the Docker image.

# 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=<aws-account-id>
IMAGE_NAME=conda-env-kernel

# 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 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}

Local testing

Run the image locally to verify that the kernels in the image are visible to a Kernel Gateway.

docker run -it "$IMAGE_NAME" bash

Run the container with a KernelGateway to validate that the kernels are visible from the REST endpoint exposed to the host.

docker run -it -p 8888:8888 "$IMAGE_NAME" bash -c 'pip install jupyter_kernel_gateway  && jupyter-kernelgateway --ip 0.0.0.0 --debug --port 8888'

Verify the Kernel Gateway is started successfully (e.g., [KernelGatewayApp] Jupyter Kernel Gateway at http://0.0.0.0:8888 in the Docker logs) and validate that you can list the kernelspecs in the the running container

curl http://0.0.0.0:8888/api/kernelspecs

Pushing the image

Push the Docker image to Amazon ECR

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 SageMakerImage. Modify as required.

ROLE_ARN=arn:aws:iam::${ACCOUNT_ID}:role/RoleName
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 input. 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. Replace the placeholder for Domain ID in update-domain-input.json

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

Create a User Profile, and start a Notebook using the SageMaker Studio launcher.

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.