Model Checkpointing in ML Training with GCP Storage.

Question

Pulumi · Accepted Answer

To implement model checkpointing in machine learning training with GCP Storage, we'll create a Google Cloud Storage Bucket to store our model checkpoints. This will ensure that our model's state can be saved periodically during training, and can be reloaded in case the training process is interrupted or we want to use the model state at a later time.

Here are the steps we'll take in the Pulumi program:

1. Import necessary Pulumi and GCP modules.
2. Create a new GCP Storage Bucket where we'll store our model checkpoints.
3. Configure the bucket for versioning to keep the history of our checkpoints.
4. Optionally, set the lifecycle policies to automatically manage the stored checkpoints (e.g., delete older versions after a set number of days).
5. Provide a bucket object to initialize a placeholder for where the checkpoints will be stored.

To run this program, you'll need to have Pulumi installed and configured for use with GCP.

Let's start by writing the Pulumi program in Python.

```python
import pulumi
import pulumi_gcp as gcp

# Create a GCP Storage Bucket to store the model checkpoints.
checkpoint_bucket = gcp.storage.Bucket('model-checkpoint-bucket',
    location='us-central1',  # Choose the storage location closest to your GCP services.
    versioning={'enabled': True},  # Enable versioning to keep the checkpoint history.
    # The lifecycle_rules can be set according to your needs, for example:
    lifecycle_rules=[{
        'condition': {
            'age': 30  # Automatically delete checkpoints older than 30 days.
        },
        'action': {
            'type': 'Delete',
        },
    }]
)

# Output the bucket URL to access it later.
pulumi.export('bucket_url', checkpoint_bucket.url)
```

In the code above:

- We import the required Pulumi and GCP modules.
- We create a new storage bucket using `gcp.storage.Bucket`. We enable versioning on it so that each checkpoint can be accessed even after new ones are created.
- The `location` option is set to 'us-central1'. You should set this to the region closest to where your machine learning training jobs will run.
- The `lifecycle_rules` are optional, and in this example, we've specified that objects older than 30 days should be automatically deleted. This is useful for keeping costs down by not storing old model checkpoints indefinitely.
- Finally, we export the `bucket_url` so that it can be easily accessed, for example, to configure your training job to save checkpoints directly to this bucket.

With the bucket URL exported, you would configure your ML training to periodically write checkpoint files to this bucket. The exact mechanism for this will depend on the ML framework you're using. For instance, with TensorFlow, you could use the `tf.train.Checkpoint` and `tf.train.CheckpointManager` classes within your training script to save and manage checkpoints directly in the bucket.