Serving Machine Learning Models via GCP Backend Buckets

Question

Pulumi · Accepted Answer

To serve machine learning models via GCP Backend Buckets, you would typically have the models stored in Google Cloud Storage (GCS) buckets and use Google Cloud Load Balancer (GCLB) to serve them through HTTP(S). Backend Buckets in GCP are a way to use GCS with HTTP(S) Load Balancing. This allows you to serve content directly from a storage bucket without needing to manage compute resources such as VM instances.

Here's a basic scenario: you have a machine learning model that you've saved in a GCS bucket, and you want to allow clients to download the model or perhaps interact with it via an API. Using a Backend Bucket with a HTTP(S) load balancer, you can route incoming traffic to the appropriate storage bucket where your model resides.

Below is a Pulumi program written in Python that sets this up. Specifically, we will:

1. Create a GCS bucket to store your models.
2. Upload a machine learning model to the bucket.
3. Create a Backend Bucket pointing to the GCS bucket.
4. Set up a URL map and target HTTP proxy to define how requests are routed.
5. Create a global forwarding rule to route incoming requests to the proxy.

```python
import pulumi
import pulumi_gcp as gcp

# Step 1: Create a GCS bucket for storing your machine learning model.
model_bucket = gcp.storage.Bucket('model-bucket')

# For this example, we assume you have a machine learning model saved locally as `model.pt`.
# This file will be uploaded to the GCS bucket created above.

# Step 2: Upload the machine learning model to the bucket.
model_object = gcp.storage.BucketObject('model-object',
    bucket=model_bucket.name,
    source=pulumi.FileAsset('model.pt')  # Replace 'model.pt' with the path to your model file.
)

# Step 3: Create a Backend Bucket that points to the GCS bucket.
backend_bucket = gcp.compute.BackendBucket('backend-bucket',
    bucket_name=model_bucket.id,
    enable_cdn=True,  # Enable CDN for cache benefits if necessary.
    # Additional options can be configured based on your needs. For more details, check the documentation.
)

# Step 4: Set up URL map and target HTTP proxy to define how requests are routed.
url_map = gcp.compute.URLMap('url-map',
    default_service=backend_bucket.self_link
)

target_http_proxy = gcp.compute.TargetHttpProxy('target-http-proxy',
    url_map=url_map.id
)

# Step 5: Create a Global Forwarding Rule to route incoming requests to the proxy.
global_forwarding_rule = gcp.compute.GlobalForwardingRule('global-forwarding-rule',
    target=target_http_proxy.self_link,
    port_range='80',  # The port range traffic will come in on, typically 80 for HTTP and 443 for HTTPS.
)

# Export the URL where the model can be accessed.
pulumi.export('model_serving_url', global_forwarding_rule.ip_address.apply(
    lambda ip: f'http://{ip}'
))
```

This is a simple setup that serves a machine learning model file using GCP's backend bucket feature. Note that in a production environment, you would typically have additional considerations such as:

- Implementing HTTPS for secure communication (which would require setting up SSL certificates).
- Detailed CDN configurations to manage cache behaviors.
- Fine-tuning access and permissions for the GCS bucket, possibly using IAM policies.

Additionally, depending on your use case, you may also need cloud functions or serverless compute environments such as Cloud Run or GKE to run your model if it is to be interactively used via an API.

The `pulumi.FileAsset('model.pt')` argument assumes that you have a file named `model.pt` in the same directory as your Pulumi program. Replace it with the appropriate file name and path to your machine learning model.

The `pulumi.export` snippet at the end will output the URL where your model can be accessed after deployment, so you can easily integrate it into client applications or services that need to use your ML model.

Remember to replace `'model.pt'` with the actual path to your machine learning model when running this code. The file would need to be accessible from the directory where you run `pulumi up`, Pulumi's deployment command.