Serverless Model Inference with GCP Cloud Tasks

Question

Pulumi · Accepted Answer

Serverless model inference using Google Cloud Platform (GCP) involves running machine learning model predictions without managing any underlying compute infrastructure. This allows you to process data and run inferences as tasks, scaling automatically and paying only for the resources you consume during the execution of those tasks.

In the context of using Pulumi to deploy such an infrastructure, we'll use multiple components from GCP, including Cloud Functions for running the inference code, Cloud Tasks for queueing tasks, and potentially Cloud Storage for storing model data or inference results if needed.

Here's a step-by-step explanation of how you can achieve this:

1. **Cloud Function**: You'll deploy a GCP Cloud Function that takes data as input, makes a prediction using your pre-trained model, and returns the inference result.
   
2. **Cloud Task**: A task queue will be created using GCP Cloud Tasks which can receive requests (tasks) to process data. These tasks will invoke the Cloud Function.

3. **Cloud Storage (Optional)**: If your model requires access to large files or you need to store the results of the inferences for future use, GCP Cloud Storage buckets may be used.

Below is a Pulumi program that sets up a serverless environment for model inference using GCP Cloud Tasks and Cloud Functions. We'll define a Cloud Function that can perform the inference and a Cloud Tasks queue that can send requests to the Cloud Function.

```python
import pulumi
import pulumi_gcp as gcp

# Set up a GCP Cloud Function to perform the inference
# The actual implementation of the inference logic would need to be provided as a file (`main.py`)
# or as an inline source in the `source_archive_bucket` argument.
# For instance, it could load a TensorFlow or PyTorch model and execute predictions based on the input data.
inference_function = gcp.cloudfunctions.Function("inference-function",
                                                 entry_point="inference_handler",
                                                 runtime="python37",
                                                 available_memory_mb=256,
                                                 source_archive_bucket=gcp.storage.Bucket("source-bucket").name,
                                                 source_archive_object=gcp.storage.BucketObject("source-archive-object",
                                                                                                bucket="source-bucket",
                                                                                                source=pulumi.FileAsset("path_to_source_zip_archive")).name,
                                                 trigger_http=True,
                                                 event_trigger={
                                                     "event_type": "google.pubsub.topic.publish",
                                                     "resource": "projects/your-project/topics/your-topic"
                                                 })

# Export the URL of the Cloud Function which can be triggered via HTTP
pulumi.export("function_url", inference_function.https_trigger_url)

# Create a Cloud Tasks queue to manage and dispatch tasks
# The tasks in this queue can be configured to make requests to the HTTP trigger of the Cloud Function
tasks_queue = gcp.cloudtasks.Queue("tasks-queue",
                                   location="us-central1",
                                   rate_limits={
                                       "max_concurrent_dispatches": 5,
                                       "max_dispatches_per_second": 1
                                   },
                                   retry_config={
                                       "max_attempts": 5,
                                       "min_backoff": "5s",
                                       "max_backoff": "60s",
                                       "max_doublings": 2
                                   })

# Export the name of the Cloud Tasks queue
pulumi.export("tasks_queue_name", tasks_queue.name)
```

In this Pulumi program:

- The `gcp.cloudfunctions.Function` resource creates a new Cloud Function where `entry_point` specifies the name of the function to execute, and `runtime` specifies the execution environment.
- The function's source code is assumed to be zipped up in an archive file (referenced as `"path_to_source_zip_archive"` in the `pulumi.FileAsset`), which is uploaded to a Cloud Storage bucket created by `gcp.storage.Bucket`.
- In `gcp.cloudtasks.Queue`, we specify the location and the rate limits for task execution. `max_concurrent_dispatches` and `max_dispatches_per_second` manage the throughput of tasks being handled by the function, while `retry_config` specifies how the tasks should be retried in case of failure.

After deploying this program with Pulumi, you'll have a serverless infrastructure capable of handling model inferences at scale, with the benefit of extensive configuration options provided by GCP Cloud Tasks and the ability to leverage various machine learning frameworks compatible with Cloud Functions.