What is Modal used for in Claude Code?

Modal allows Claude to help you write and deploy Python functions that run on high-performance cloud hardware like GPUs without manual server or Kubernetes management.

Does Modal support persistent storage?

Yes, Modal Volumes allow you to persist data between runs, store large model weights, and share files across different function invocations.

Can I run machine learning models with this skill?

Yes, the skill provides specific implementation patterns for deploying LLMs, embedding models, and image generators with full GPU acceleration support.

Is autoscaling supported automatically?

Yes, Modal functions scale from zero to thousands of containers based on demand, and you can manually configure the scaling behavior using max_containers and min_containers.

How do I authenticate Modal?

You can authenticate by running 'modal token new' in your terminal, which opens a browser to link your environment to your Modal account.

Modal Serverless Python

Name: Modal Serverless Python
Author: ricable

byricable

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Cloud Infrastructure

Executes Python code in the cloud using serverless containers with specialized GPU support and automatic scaling.

About

Modal is a serverless platform designed to run Python workloads in the cloud without the overhead of managing infrastructure. This skill enables Claude to help you define container images, request high-performance GPUs like H100s or A100s, and deploy scalable applications ranging from ML model inference to massive batch processing jobs. By using a declarative Python-first approach, it allows for seamless transitions from local development to distributed cloud execution with integrated support for persistent volumes, secrets management, and scheduled cron jobs.

Key Features

Automated container image building and dependency management
Dynamic autoscaling from zero to thousands of containers
Persistent cloud storage via Modal Volumes for model weights and datasets
Serverless GPU acceleration (H100, A100, L40S, etc.)
Built-in secret management for secure API keys and credentials
1 GitHub stars

Use Cases

Running parallel batch processing jobs on large scientific datasets
Creating serverless APIs and scheduled data processing pipelines
Deploying and serving ML models like LLMs and image generators

About

Key Features

Automated container image building and dependency management
Dynamic autoscaling from zero to thousands of containers
Persistent cloud storage via Modal Volumes for model weights and datasets
Serverless GPU acceleration (H100, A100, L40S, etc.)
Built-in secret management for secure API keys and credentials
1 GitHub stars

Use Cases

Running parallel batch processing jobs on large scientific datasets
Creating serverless APIs and scheduled data processing pipelines
Deploying and serving ML models like LLMs and image generators