modal

Run Python code in the cloud with autoscaling and GPU acceleration via Modal's serverless platform.

52|6|Updated Nov 24, 2025

Search Tags:#deployment #serverless #gpu #modal #autoscaling

Ready to run

npx skills add https://github.com/ovachiever/droid-tings --skill modal

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: modal
Source: https://github.com/ovachiever/droid-tings/tree/main/skills/modal
Command: npx skills add https://github.com/ovachiever/droid-tings --skill modal

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires modal, and includes references (resource) components.

What problem does it solve?

Modal provides serverless, on-demand Python execution in the cloud with autoscaling, GPU access, and simple deployment, enabling scalable ML workflows, batch processing, and API services without managing infrastructure.

Core Features & Use Cases

Serverless functions: Deploy Python code that runs in containers with autoscaling.
GPU acceleration: Attach GPUs for training/inference; on-demand compute.
Batch processing & APIs: Run large jobs and serve models via web endpoints.
Resource control: Fine-tune CPU, memory, disk, and secrets, with volume support.

Quick Start

Create a Modal app, define a function, and run it remotely or deploy as a service.

Frequently Asked Questions about modal

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I deploy Python code to the cloud with automatic scaling?▼

Modal enables you to deploy Python functions as serverless containers that automatically scale based on demand. Define your function, attach it to a Modal app, and deploy—Modal handles infrastructure, scaling, and execution without manual server management.

Can I run GPU-accelerated workloads serverless?▼

Yes, Modal supports GPU acceleration on-demand. Attach GPUs to individual functions for model training, inference, or batch processing. You pay only for compute time and resources used, with automatic provisioning and deprovisioning.

What's the best way to serve ML models as APIs?▼

Modal lets you deploy Python functions as web endpoints with autoscaling. Define your model inference logic, expose it via a web endpoint, and Modal handles routing, scaling, and concurrency—ideal for serving ML models at scale.

How do I configure CPU, memory, and storage for serverless functions?▼

Modal provides fine-grained resource control per function. Specify CPU, memory, and ephemeral disk requirements when defining your function. Modal allocates exactly what you request and scales instances independently based on load.

Can I run scheduled or batch compute jobs serverless?▼

Yes, Modal supports scheduled tasks and large batch jobs. Deploy functions that run on a schedule or trigger them for batch processing. Combine with autoscaling and GPU support for efficient, cost-effective large-scale computation.

Do I need to manage container images or infrastructure?▼

No. Modal handles containerization, dependency management, and infrastructure automatically. Write Python code, specify dependencies, and Modal packages and runs it in containers without manual Docker or infrastructure setup.