modal

Solid

Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.

DevOps & Infrastructure 27,705 stars 2858 forks Updated today MIT

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Skill Content

# Modal ## Overview Modal is a serverless platform for running Python code in the cloud with minimal configuration. Execute functions on powerful GPUs, scale automatically to thousands of containers, and pay only for compute used. Modal is particularly suited for AI/ML workloads, high-performance batch processing, scheduled jobs, GPU inference, and serverless APIs. Sign up for free at https://modal.com and receive $30/month in credits. ## When to Use This Skill Use Modal for: - Deploying and serving ML models (LLMs, image generation, embedding models) - Running GPU-accelerated computation (training, inference, rendering) - Batch processing large datasets in parallel - Scheduling compute-intensive jobs (daily data processing, model training) - Building serverless APIs that need automatic scaling - Scientific computing requiring distributed compute or specialized hardware ## Authentication and Setup Modal requires authentication via API token. ### Initial Setup ```bash # Install Modal uv uv pip install modal # Authenticate (opens browser for login) modal token new ``` This creates a token stored in `~/.modal.toml`. The token authenticates all Modal operations. ### Verify Setup ```python import modal app = modal.App("test-app") @app.function() def hello(): print("Modal is working!") ``` Run with: `modal run script.py` ## Core Capabilities Modal provides serverless Python execution through Functions that run in containers. Define compute requirements, depend...

Details

Author
davila7
Repository
davila7/claude-code-templates
Created
11 months ago
Last Updated
today
Language
Python
License
MIT

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