What types of GPUs can I access with this skill?

This skill supports a wide range of NVIDIA GPUs including the H100, A100 (40GB and 80GB variants), L40S, L4, A10G, and T4.

Do I need Docker or Kubernetes to use this skill?

No, Modal handles containerization and orchestration automatically directly from your Python code, eliminating the need for Dockerfiles, YAML, or cluster management.

How does the autoscaling work?

Modal functions can scale from zero to hundreds of instances instantly based on demand. You can configure parameters like min_containers to eliminate cold starts or max_containers to control costs.

Can I store persistent data between function runs?

Yes, this skill utilizes Modal Volumes for high-performance persistent file storage and Modal Dicts or Queues for distributed key-value storage and task management.

Modal Cloud Deployment

Name: Modal Cloud Deployment
Author: ferdousbhai

byferdousbhai

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Cloud Infrastructure

Deploys serverless Python applications with instant GPU access, autoscaling, and zero-configuration infrastructure.

About

This skill enables Claude to architect and deploy high-performance Python applications to the Modal platform without writing YAML or managing Kubernetes. It provides specialized knowledge for setting up serverless containers, configuring various GPU types (from T4 to H100), managing persistent volumes, and deploying scalable web endpoints. Whether you are running batch data processing, fine-tuning LLMs, or hosting high-traffic APIs, this skill ensures best practices for image building, secret management, and observability using modern tools like Logfire and uv.

Key Features

High-performance image builds with uv_sync for faster cold starts
2 GitHub stars
Zero-config serverless containerization and deployment
On-demand GPU acceleration (H100, A100, L4) for ML workloads
Persistent storage via Modal Volumes and distributed state management
Automated scaling from zero to hundreds of concurrent containers

Use Cases

Running large-scale parallel data processing and batch jobs
Deploying LLM inference APIs with A100/H100 GPU acceleration
Scheduling recurring cron jobs for automated reports or system maintenance

About

Key Features

High-performance image builds with uv_sync for faster cold starts
2 GitHub stars
Zero-config serverless containerization and deployment
On-demand GPU acceleration (H100, A100, L4) for ML workloads
Persistent storage via Modal Volumes and distributed state management
Automated scaling from zero to hundreds of concurrent containers

Use Cases

Running large-scale parallel data processing and batch jobs
Deploying LLM inference APIs with A100/H100 GPU acceleration
Scheduling recurring cron jobs for automated reports or system maintenance