Containerized Model Training with Docker Images

Question

Pulumi · Accepted Answer

To create a containerized model training pipeline using Docker images with Pulumi, you will need to define a Docker image for your model training application, build it, optionally push it to a container registry, and then run it either locally or on a cloud provider.

Below is a step-by-step program written in Python that demonstrates how to use Pulumi to:

1. Define a Docker image with the required environment for training a model.
2. Build the Docker image.
3. Push the Docker image to Amazon Elastic Container Registry (ECR) – a managed AWS Docker registry service.
4. (Optionally) Run the image on an Amazon ECS or other container services – for this example, we will just define the image and push it to a registry.

Before you begin, make sure you have the following prerequisites in place:

- Docker installed on your machine where you're running the Pulumi program.
- Pulumi CLI installed and configured for use with AWS.
- AWS account with permissions to manage ECR and ECS.

Now let's dive into the Pulumi program:

```python
import pulumi
import pulumi_aws as aws
import pulumi_docker as docker

# Define a Docker image for model training.
# Replace `context` with the directory where your Dockerfile and related files are located.
# Replace `dockerfile` with the path to your Dockerfile within the context if it's not named "Dockerfile".
# Replace `imageName` with the name you wish for your Docker image.
model_training_image = docker.Image("model-training-image",
    build=docker.DockerBuildArgs(
        context="/path/to/your/model/training/app", # Path to the directory with your Dockerfile and source code
        dockerfile="Dockerfile", # Optional if you have `Dockerfile` within the context
    ),
    image_name="my-repo/model-training:v1.0.0", # Name of the built image (repository name with tag)
    skip_push=False, # Set to True if you don't want to push the image after building it
)

# Create an AWS ECR repository to store the Docker image.
ecr_repository = aws.ecr.Repository("model-training-repo",
    image_tag_mutability="MUTABLE", # or "IMMUTABLE"
    image_scanning_configuration=aws.ecr.RepositoryImageScanningConfigurationArgs(
        scan_on_push=True, # Enable scanning the image on push
    ),
)

# Credentials to access the AWS ECR repository.
def get_registry_info(rid):
    creds = aws.ecr.get_credentials(registry_id=rid)
    return docker.ImageRegistryArgs(
        server=creds.proxy_endpoint,
        username=creds.user_name,
        password=creds.password,
    )

# Build and push the Docker image to the ECR repository.
# Note that this step assumes the Docker daemon is running on the machine executing this Pulumi program.
push_image = docker.Image("push-model-training-image",
    build=docker.DockerBuildArgs(
        context="/path/to/your/model/training/app", # Path to the directory with your Dockerfile and source code
        dockerfile="Dockerfile", # Optional if you have `Dockerfile` within the context
    ),
    image_name=ecr_repository.repository_url.apply(lambda url: f"{url}:v1.0.0"), # Combine the repository URL with your desired tag
    skip_push=False,
    registry=ecr_repository.registry_id.apply(get_registry_info),
)

# Export the repository URL so you know where your Docker image is located.
pulumi.export("repository_url", ecr_repository.repository_url)
pulumi.export("image_name", push_image.image_name)
```

In the above program:

- We define a Docker image with the necessary training environment using `docker.Image`.
- We specify the context and Dockerfile for building the Docker image.
- We define an ECR repository using `aws.ecr.Repository`, where we will store our Docker image.
- We obtain the credentials for the ECR repository using `aws.ecr.get_credentials`.
- We push the Docker image to ECR after building it using a second `docker.Image` resource and linking it to the ECR repository with the `registry` property.
- We export the ECR repository URL and the image name as outputs, which can be useful for the next steps of deploying this image to a container service.

To run this program, you would execute it with `pulumi up` command in the CLI, and it would automatically perform the defined actions: build the Docker image, create an ECR repository, push the image to ECR, and give you the outputted repository URL and image name.

To continue with running this Docker image on a cloud service like ECS, you would create a task definition and a service with the necessary configuration. However, that is beyond the scope of this initial containerization task and could be your next step.