1. Host AI Model Checkpoints in DigitalOcean Spaces


    Hosting AI model checkpoints can be a critical aspect of a Machine Learning ops pipeline, as it allows for the opportunity to resume training, perform evaluations, or deploy models at different stages of development. DigitalOcean Spaces is an object storage service that's compatible with the S3 API, providing a convenient and cost-effective way to store and retrieve large amounts of data, such as AI model checkpoints.

    To host AI model checkpoints in DigitalOcean Spaces using Pulumi, we need to create a Space (similar to a bucket in AWS S3), set policies for access, and then upload objects into that Space. By default, all new Spaces are private, and you can control access through API keys or by setting up access control lists (ACLs) and bucket policies.

    Here's a Python program using Pulumi to create a DigitalOcean Space and then upload a model checkpoint to it:

    1. DigitalOcean SpacesBucket: This resource is analogous to a storage container, which will hold our AI model checkpoints. Each Space must be given a unique name and region.

    2. DigitalOcean SpacesBucketObject: This is the individual file or object that we'll store in our bucket. In this case, it will be our model checkpoint file. We'll specify the key, which is the name of the object, and bucket, which is the name of the bucket containing the object.

    3. DigitalOcean SpacesBucketPolicy: This optional resource allows us to define access policies for the bucket. For example, we could make the bucket publicly readable while keeping write access restricted.

    Below is the Pulumi program that sets up a DigitalOcean Space and uploads a single object:

    import pulumi import pulumi_digitalocean as digitalocean # Configure DigitalOcean provider with your access token pulumi_digitalocean.Provider("provider", token="YOUR_DIGITALOCEAN_ACCESS_TOKEN") # Create a DigitalOcean Space (equivalent to an S3 bucket) # Be sure to provide a unique name for your Space model_checkpoints_space = digitalocean.SpacesBucket("model-checkpoints-space", name="ai-model-checkpoints", region="nyc3", ) # Upload an AI model checkpoint to the Space previously created # Replace 'path-to-your-model-checkpoint-file' with the actual file path model_checkpoint = digitalocean.SpacesBucketObject("model-checkpoint", acl="private", key="my-model-checkpoint.pt", bucket=model_checkpoints_space.name, content_type="application/octet-stream", source=pulumi.FileAsset("path-to-your-model-checkpoint-file"), ) # Policy is optional: demonstrate how to make the bucket publicly readable (not recommended for sensitive data) # As AI model checkpoints can contain proprietary data, it's advisable to keep the default "private" setting # Uncomment the following lines to apply a public read policy to the bucket: # public_read_policy = digitalocean.SpacesBucketPolicy("public-read-policy", # bucket=model_checkpoints_space.id.apply(lambda bid: bid), # policy=pulumi.Output.all(model_checkpoints_space.id).apply(lambda args: json.dumps({ # "Version": "2012-10-17", # "Statement": [{ # "Effect": "Allow", # "Principal": "*", # "Action": ["s3:GetObject"], # "Resource": [f"arn:aws:s3:::{args[0]}/*"] # }] # })) # ) # Export the URL of the bucket pulumi.export("checkpoint_space_url", model_checkpoints_space.website_endpoint) # Export the URL of the uploaded model checkpoint pulumi.export("checkpoint_url", model_checkpoint.bucket.apply(lambda b: f"https://{b}.nyc3.digitaloceanspaces.com/{model_checkpoint.key}"))

    Make sure to replace "YOUR_DIGITALOCEAN_ACCESS_TOKEN" with your actual DigitalOcean access token, and "path-to-your-model-checkpoint-file" with the path to your AI model checkpoint file.

    To run this program, you'll need to install Pulumi and the DigitalOcean Pulumi provider. Once installed, you can execute the program with the pulumi up command, which will provision the resources as per the script. After it runs, it will output the URL of the Space and the direct link to the uploaded model checkpoint file.

    Remember to keep your model checkpoints and other sensitive data secure: set up appropriate ACLs and policies, and do not share your access tokens or API keys.