1. Portable Deep Learning Workflows in Docker Containers


    To create portable deep learning workflows in Docker containers, we can utilize the Docker provider in Pulumi to define and deploy containers that package our deep learning application and its dependencies. We will also set up a Docker network to enable communication between containers if necessary.

    For deep learning, you usually need to choose an image that contains the required deep learning libraries, such as TensorFlow or PyTorch. For this example, I'll use a generic tensorflow/tensorflow:latest-gpu image to demonstrate how you might run a deep learning workload with GPU support. We'll assume that your machine is properly set up with the necessary GPU drivers and Docker configurations to support GPU usage within containers.

    Here's a step-by-step description of the program we're about to write:

    1. Import Modules: We'll start by importing the required Pulumi and pulumi_docker modules.
    2. Define Image: We'll specify the Docker image we plan to use. In our case, it's a TensorFlow image that comes with GPU support.
    3. Create a Container: We'll create a Docker container resource, specifying necessary properties such as the image to be used, the name of the container, any environment variables, and the command to run.
    4. Configure Networking (if needed): We'll set up Docker networking to allow our container to communicate with other containers or services as required.
    5. Exports: We'll export any necessary information, such as the container ID or the ports being used, for reference or to be used by other resources or services.

    The following Pulumi Python code realizes the above steps. Please make sure you've already installed Pulumi and the Docker provider (pulumi_docker), and that Docker is running on your machine.

    import pulumi import pulumi_docker as docker # Specify the image for the deep learning task. # This example uses TensorFlow with GPU support, but you can replace with the one that suits your requirements. image_name = 'tensorflow/tensorflow:latest-gpu' # Define the Docker image from a remote repository. image = docker.RemoteImage('deep-learning-image', name=image_name, # Keep the image locally so that we don't need to pull it every time we start a container. keep_locally=True) # Define the container where the deep learning task will run. container = docker.Container('deep-learning-container', image=image.name, name='deep_learning_workflow', # Set the command or entrypoint to the deep learning script or command you need to run. command=['python', '-u', 'train_model.py'], # Mount any necessary volumes, pass the required environment variables, etc. # For GPU support, ensure the runtime is set correctly and the host system is configured. mounts=[docker.ContainerMountArgs( type='bind', source='/path/on/host/machine/to/dataset', target='/container/path/to/dataset' )], # Enable GPU access (ensure that Docker and your system are correctly configured for this) runtime='nvidia', # If the container needs to interact with other services, configure networking. networks_advanced=[docker.ContainerNetworksAdvancedArgs( name='my-network' )]) # Optional: A Docker network if the container needs to communicate with other containers/services. network = docker.Network('network', name='my-network', attachable=True) # Export the container ID and any other information you need. pulumi.export('container_id', container.id) # If you exposed any ports, export them as well. # pulumi.export('service_port', container.ports['8080/tcp'].get())

    This Pulumi program does the following:

    • Pulls a TensorFlow Docker image designed for GPU usage.
    • Creates a Docker container from this image, ready to execute a hypothetical deep learning script called train_model.py.
    • Attaches a volume from the host to the container for dataset access.
    • Sets up the container to use a specified network, which is also created in the program.
    • Exports the container's runtime ID for reference.

    Before you run this program using Pulumi, make sure to replace /path/on/host/machine/to/dataset with the actual path to your dataset, and train_model.py with your actual training script file name. Additionally, if you're using specific ports or environment variables, you should include them in the Container resource's properties.

    Please note that enabling GPU support requires that Docker and the host machine be configured accordingly. Docker must be set up with the NVIDIA Container Toolkit or similar for GPU support in containers. The runtime option must also correspond to what's available on your system.