Does Modal support GPU-accelerated workloads?

Yes, Modal provides a wide array of NVIDIA GPUs ranging from cost-effective T4s to high-performance H100 and B200 instances for training and inference.

What is the Modal Claude Code Skill?

It is a specialized capability that allows Claude to help you architect, write, and deploy serverless Python applications specifically for the Modal platform.

Do I need to manage Dockerfiles with Modal?

No, Modal allows you to define your container environment and dependencies directly in your Python code using the modal.Image API.

How does autoscaling work with this skill?

Modal automatically scales your functions based on incoming demand, allowing you to process thousands of inputs in parallel and scale down to zero when idle.

Modal Serverless Cloud

Name: Modal Serverless Cloud
Author: ovachiever

byovachiever

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Infraestructura en la Nube

Deploys and runs Python functions in the cloud with automatic scaling and high-performance GPU acceleration.

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Modal is a specialized serverless platform that enables developers to execute Python code on powerful cloud infrastructure without managing servers. It provides instant access to high-performance GPUs like the H100 and B200, handles automatic horizontal scaling to thousands of containers, and offers integrated persistent storage and secret management. This skill is particularly valuable for data scientists and engineers who need to deploy machine learning models, execute massive batch processing jobs, or build scalable serverless APIs with minimal configuration and pay-as-you-go pricing.

Características Principales

Declarative container image definitions directly in Python code
Support for scheduled cron jobs and serverless web endpoints
Automatic horizontal scaling from zero to thousands of containers
Persistent storage via Volumes and secure secret management
Instant access to various GPU types including H100, A100, and B200
5 GitHub stars

Casos de Uso

Executing high-performance batch processing and scientific computing tasks
Training machine learning models on distributed multi-GPU clusters
Deploying and serving large language models (LLMs) and diffusion models

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Características Principales

Declarative container image definitions directly in Python code
Support for scheduled cron jobs and serverless web endpoints
Automatic horizontal scaling from zero to thousands of containers
Persistent storage via Volumes and secure secret management
Instant access to various GPU types including H100, A100, and B200
5 GitHub stars

Casos de Uso

Executing high-performance batch processing and scientific computing tasks
Training machine learning models on distributed multi-GPU clusters
Deploying and serving large language models (LLMs) and diffusion models