Model Checkpoint Storage during Training on AWS S3

Question

Pulumi · Accepted Answer

When training machine learning models, it's important to regularly save checkpoints to ensure that you don't lose progress if the training process is interrupted. AWS's Simple Storage Service (S3) is an excellent choice for storing these checkpoints due to its durability, scalability, and accessibility. To accomplish this with Pulumi, we can create an S3 bucket and configure permissions to allow our training jobs to write to it.

Here's how you can use Pulumi to create an S3 bucket suitable for storing model training checkpoints:

1. **Create an S3 bucket**: This will be the location where the model checkpoints are stored. Every checkpoint can be an object within the bucket.

2. **Set up an IAM policy for access**: If you're running your training jobs on AWS (e.g., on EC2 instances or SageMaker), you will need to grant these services permission to write to the S3 bucket. This is typically done with an IAM role that includes a policy allowing the necessary S3 actions.

3. **Export the Bucket Name**: After creating the bucket, export the bucket's DNS name so it can be easily accessed when configuring your training job.

Below is a Pulumi Python program that sets this up:

```python
import pulumi
import pulumi_aws as aws

# Create an AWS S3 bucket to store model checkpoints
checkpoint_bucket = aws.s3.Bucket("model-checkpoint-bucket")

# (Optional) Define an IAM policy that grants write access to the bucket for training jobs
# This assumes you have a separate IAM role that your training jobs assume.
# Replace the `role_arn` with the appropriate ARN for your use case.
write_policy_document = aws.iam.get_policy_document(
    statements=[
        aws.iam.GetPolicyDocumentStatementArgs(
            actions=["s3:PutObject", "s3:GetObject"],
            resources=[checkpoint_bucket.arn.apply(lambda arn: f"{arn}/*")],  # Grant permissions to all objects in the bucket
            effect="Allow",
            principals=[aws.iam.GetPolicyDocumentStatementPrincipalArgs(
                type="AWS",
                identifiers=["arn:aws:iam::123456789012:role/YourTrainingJobRole"], # Replace with your training job role ARN
            )],
        ),
    ]
)

write_policy = aws.iam.Policy("writeCheckpointsPolicy",
                              policy=write_policy_document.json)

# (Optional) Attach the policy to a role that your training services will assume
checkpoint_role_policy_attachment = aws.iam.RolePolicyAttachment("checkpointRolePolicyAttachment",
                                                                role="YourTrainingJobRole",  # Replace with your training job role name
                                                                policy_arn=write_policy.arn)

# Export the bucket name so that it can be used by training jobs
pulumi.export("checkpoint_bucket_name", checkpoint_bucket.bucket)
```

This program sets up a new S3 bucket and outputs the bucket name. The comments are placeholders where you would replace values with those specific to your AWS environment and training setup. The policy document provided in the `aws.iam.get_policy_document` function allows any principal that assumes the specified role to put and get objects from the bucket. If you're running training jobs that do not require IAM roles (not recommended), you could modify the policy to allow access from a specific AWS user or service.

To run your Pulumi program, you would execute it in a Pulumi environment, which would set up the described resources in your AWS account. Be sure to have your AWS credentials configured for Pulumi, which typically involves having the AWS CLI installed and configured with the necessary access keys or using environment variables.