Does this skill support deploying web APIs?

Yes, you can use the @modal.web_endpoint decorator to serve HTTP endpoints, FastAPI integrations, and webhooks with automatic HTTPS.

What types of GPUs are available through this skill?

Modal supports a wide range of GPUs including cost-effective T4/L4s, standard A100s, and high-performance H100 and B200 units.

Can I store data persistently between function runs?

Yes, Modal Volumes allow you to store data like model weights or datasets persistently across different function invocations.

Do I need a Modal account to use this skill?

Yes, you need a Modal account and an API token which can be set up via the Modal CLI using 'modal token new'.

How does autoscaling work with Modal functions?

Modal automatically scales your functions from zero to thousands of containers based on demand. You can configure limits using max_containers and min_containers parameters.

Modal Cloud Computing

Name: Modal Cloud Computing
Author: Microck

byMicrock

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Data Science & ML

Executes Python code in serverless cloud containers with automatic scaling and high-performance GPU support.

About

Modal is a specialized Claude Code skill that enables seamless deployment and execution of Python-based workloads on serverless infrastructure. It streamlines the process of running compute-intensive tasks, such as machine learning inference, batch data processing, and GPU-accelerated training, without the overhead of managing underlying servers. By providing managed container environments, persistent storage via Volumes, and secure secret management, this skill allows developers to scale from a single function to thousands of containers automatically, paying only for the exact compute used.

Key Features

Serverless Python execution with automatic scaling
High-performance GPU support including H100 and A100
81 GitHub stars
Built-in secret management and web endpoint deployment
Persistent storage via Modal Volumes
Managed container images and dependency handling

Use Cases

Running high-volume batch processing jobs across parallel containers
Deploying and serving large language models (LLMs) with GPU acceleration
Scheduling recurring data science pipelines and maintenance tasks

About

Key Features

Serverless Python execution with automatic scaling
High-performance GPU support including H100 and A100
81 GitHub stars
Built-in secret management and web endpoint deployment
Persistent storage via Modal Volumes
Managed container images and dependency handling

Use Cases

Running high-volume batch processing jobs across parallel containers
Deploying and serving large language models (LLMs) with GPU acceleration
Scheduling recurring data science pipelines and maintenance tasks