Introduction to Functions

Snowcell's serverless functions enable you to run isolated workloads in the cloud without managing the underlying infrastructure. These functions, referred to as Handler Functions, are designed to process inputs and return outputs efficiently. Whether you're developing locally or in the cloud, Snowcell provides the tools to easily build, deploy, and scale these serverless functions for GPU-intensive tasks.

What Are Handler Functions?

A Handler Function is a piece of code that runs in response to an event or request. This function encapsulates your processing logic and is automatically triggered by Snowcell whenever a job is submitted. With Snowcell managing the infrastructure, you can focus entirely on writing and optimizing the logic that powers your workloads.

Building Your Handler Function

Building a Handler Function with Snowcell is straightforward. Here are the basic steps to create and deploy your function:

1. Setup Your Development Environment: Ensure your environment is configured with the Snowcell SDK and any required libraries. You'll also want to have Docker installed to help with containerization.

2. Write Your Function: Define the logic that will be executed when your function is triggered. The Handler Function is the entry point for all incoming requests. It processes inputs, runs computations, and returns the results.

3. Containerize Your Code: Package your function into a Docker container, allowing Snowcell to deploy and scale it across its infrastructure. Docker ensures your function runs consistently, no matter where it's deployed.

4. Deploy to Snowcell: Use Snowcell’s deployment tools to push your function to the cloud. This step involves configuring the function, specifying resource requirements, and setting up GPU configurations.

5. Test the Function: Trigger your function manually or simulate real-world workloads to ensure it performs as expected. You can monitor performance and make adjustments before moving to production.

Key Benefits of Handler Functions

Handler Functions are designed to simplify cloud-based computing by abstracting the server management layer. Here’s why using Handler Functions can make your workflow more efficient:

• On-Demand Execution: Your function runs only when it's needed, eliminating the need for dedicated servers. Snowcell automatically provisions resources to handle incoming tasks.

• Scalability: Snowcell’s platform ensures that your functions can scale dynamically, whether you need to handle a single request or thousands simultaneously.

• Cost Efficiency: Pay only for the compute resources used during function execution. Since there’s no idle time with serverless models, costs are kept to a minimum.

Structuring Job Input

To trigger your Handler Function, Snowcell sends a structured job request. This request contains an input that your function will process. Here’s an example of what a job input might look like:

{
  "job_id": "unique_job_identifier",
  "input_data": {
    "task": "data to process"
  }
}

Your function should be designed to handle this format, and depending on your use case, you can define more complex input data.

Developing with Snowcell

You can develop and test your functions locally before deploying them to Snowcell. The Snowcell SDK allows you to simulate the cloud environment on your own machine, ensuring the code works as expected before it runs at scale. The development flow generally follows these steps:

1. Install the SDK: Set up the Snowcell SDK in your development environment to build, test, and deploy your functions.

2. Write the Handler: Define your handler function and its logic. Here’s a simple example:

from snowcell import serverless

def handler(job_request):
    # Access job input data
    data = job_request["input_data"]
    
    # Perform task
    result = some_custom_function(data)
    
    # Return result
    return result

serverless.start({"handler": handler})

3. Run Locally: Use Docker to build your function locally and test it in a simulated environment.

Deploying to Snowcell

When your function is ready, deploy it to Snowcell’s serverless infrastructure by packaging it into a Docker container. Here's a simplified deployment flow:

1. Build Your Docker Image: Package your code along with all necessary dependencies.

docker build -t <your-username>/<image-name>:<tag> .

2. Push the Image to a Registry: Push your Docker image to a container registry like Docker Hub or a private registry.

docker push <your-username>/<image-name>:<tag>

3. Configure on Snowcell: Log in to the Snowcell dashboard and create a new serverless endpoint. Specify the Docker image and configure the necessary resource limits (memory, GPU, etc.).

4. Deploy: Once configured, click deploy. Your function will be live, and you’ll receive an endpoint to trigger it.

Testing and Monitoring

Once deployed, it’s important to test the function under various workloads. You can trigger jobs manually via the Snowcell dashboard or by using the API. Snowcell also provides monitoring tools to help you track performance metrics such as execution time, resource usage, and error rates.

Input/Output Limits

Keep in mind that both input and output payloads are subject to size limits:

Input Size: 5 MB Output Size: 10 MB If your task involves handling larger data, consider storing input/output data in cloud storage and passing references (e.g., URLs) as part of the job input/output.