Is persistent storage supported?

Yes, the skill includes guidance on using Modal Volumes to persist data, cache datasets, and store model weights across different function invocations.

Can I use GPUs with this skill?

Yes, it supports a wide range of NVIDIA GPUs including cost-effective T4s, powerful A100s, H100s, and the flagship B200 for AI and ML tasks.

Does this skill handle dependency management?

Yes, it provides patterns for defining container images, installing system packages, and managing Python dependencies via Modal's optimized image builder.

How do I handle API keys and secrets?

The skill provides instructions for using Modal Secrets to securely manage environment variables and credentials without hardcoding them in your scripts.

What is the Modal Claude Code skill used for?

It is used to deploy Python functions to the cloud, specifically for workloads requiring high compute, GPUs, or massive scaling without managing servers.

Modal Cloud Compute

Name: Modal Cloud Compute
Author: Weiwei-Mao

byWeiwei-Mao

云基础设施

Deploys and scales Python code on cloud infrastructure with serverless containers and high-performance GPUs.

关于

Modal Cloud Compute is a specialized Claude Code skill designed to streamline the execution of Python-based workloads in the cloud without complex infrastructure management. It allows developers to define container environments, attach powerful GPUs (including H100s and B200s), and autoscale functions from zero to thousands of instances. Whether you are deploying machine learning models, running massive batch processing jobs, or building serverless APIs that require specialized hardware, this skill provides the necessary patterns and best practices to integrate Modal's high-performance compute capabilities directly into your development workflow.

主要功能

0 GitHub stars
Automated containerization and declarative dependency management
Persistent storage via Modal Volumes for model weights and datasets
Secure secret management and serverless web endpoint deployment
Serverless GPU acceleration with support for T4 through B200 hardware
Dynamic autoscaling from zero to thousands of concurrent containers

使用场景

Deploying and serving LLMs and image generation models with GPU inference
Parallel batch processing of massive datasets across distributed containers
Scheduling compute-intensive training jobs or recurring data transformations

关于

主要功能

0 GitHub stars
Automated containerization and declarative dependency management
Persistent storage via Modal Volumes for model weights and datasets
Secure secret management and serverless web endpoint deployment
Serverless GPU acceleration with support for T4 through B200 hardware
Dynamic autoscaling from zero to thousands of concurrent containers

使用场景

Deploying and serving LLMs and image generation models with GPU inference
Parallel batch processing of massive datasets across distributed containers
Scheduling compute-intensive training jobs or recurring data transformations