modal-serverless-gpu

What it does

This skill enables AI agents to deploy and manage GPU-accelerated workloads using Modal’s serverless infrastructure. It automates the provisioning of compute resources, allowing agents to run intensive tasks like model training or inference without managing underlying servers.

When to use it

• Running large language model inference or training jobs that require significant GPU memory.
• Scaling machine learning pipelines dynamically based on demand without manual intervention.
• Deploying data processing workflows that benefit from parallel GPU computation.
• Testing and prototyping AI applications with instant, on-demand GPU access.

Key capabilities

• Automatically provisions GPU instances via Modal’s serverless platform.
• Supports containerized environments for reproducible ML workloads.
• Handles resource scaling and teardown based on job completion.
• Integrates with Python-based workflows for seamless execution.

Example prompts

• "Deploy a PyTorch model inference service using a GPU instance."
• "Run a batch training job on a dataset and scale resources as needed."
• "Set up a serverless pipeline to process video frames with GPU acceleration."

Tips & gotchas

Ensure your environment has the necessary Modal SDK installed and configured before running GPU tasks. Be mindful of cost implications, as GPU usage is billed based on runtime and resource allocation.