1. Spotinst for Interruptible Machine Learning Pipelines


    In order to create interruptible machine learning pipelines, there are several considerations to account for, particularly regarding the handling of interruptions. Spot instances and their equivalents (e.g., AWS EC2 Spot Instances) can be used to reduce costs, but they can be interrupted with little notice when the demand for compute resources changes. To address this within Pulumi, one would need to architect a solution that can gracefully handle potential interruptions.

    Unfortunately, as of my knowledge cutoff in 2023, there isn't a direct Pulumi provider for Spotinst. However, Pulumi can manage AWS EC2 Spot Instances which can be a part of such an interruptible pipeline, and services like AWS SageMaker can be used for machine learning tasks.

    Let's go through creating an AWS interruptible machine learning pipeline with the following components using Pulumi and Python as the language of choice:

    1. EC2 Spot Instances for cost-effective compute power.
    2. AWS SageMaker for the machine learning tasks.
    3. AWS S3 buckets for storing persistent data, like the machine learning models and datasets.

    Here's a Pulumi program that sets up an example of such a pipeline:

    • It will create an S3 bucket to store any persistent data.
    • It will also set up a SageMaker pipeline that will define the machine learning workflow.
    import pulumi import pulumi_aws as aws import json # Create an S3 bucket for storing model data and other persistent state ml_bucket = aws.s3.Bucket("mlBucket") # Define a role for SageMaker to access AWS resources sagemaker_role = aws.iam.Role("sageMakerRole", assume_role_policy=json.dumps({ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": {"Service": "sagemaker.amazonaws.com"}, }] })) # Attach policies to the role so that SageMaker can access the S3 bucket sagemaker_policy = aws.iam.RolePolicy("sageMakerPolicy", role=sagemaker_role.id, policy=ml_bucket.arn.apply(lambda arn: json.dumps({ "Version": "2012-10-17", "Statement": [{ "Action": ["s3:GetObject", "s3:PutObject"], "Effect": "Allow", "Resource": f"{arn}/*", }] })) ) # Define the SageMaker Pipeline sagemaker_pipeline = aws.sagemaker.Pipeline("mlPipeline", role_arn=sagemaker_role.arn, pipeline_name="MyMLPipeline", pipeline_definition_s3_location={ "bucket": ml_bucket.id, "objectKey": "pipeline_definition.json" }, pipeline_description="My machine learning pipeline", pipeline_display_name="MyMLPipeline" ) # Export the S3 bucket name and SageMaker Pipeline ARN pulumi.export("bucket_name", ml_bucket.id) pulumi.export("sagemaker_pipeline_arn", sagemaker_pipeline.arn)

    In the code provided, your machine learning pipeline's logic would be defined in a JSON file called pipeline_definition.json, which needs to be created and uploaded to the created S3 bucket. SageMaker will use that definition to execute the machine learning tasks.

    It's important to note that this setup doesn't specifically handle the actual interruption. To handle interruptions effectively, you would need to design your machine learning algorithms and data processing tasks to be fault-tolerant and able to resume from checkpoints. SageMaker Pipelines provide features like step caching to help with this out-of-the-box, and EC2 Spot Instances can be configured to launch and terminate based on demand and pricing changes, potentially integrating with the pipeline's logic to save state and shutdown gracefully when interrupted.

    If you need guidance on how to construct pipeline_definition.json or how to integrate EC2 Spot Instances into your machine learning workflows, please provide more details on your specific needs and use case.