serverless-modal

Run GPU workloads on Modal via generated launcher scripts.

Updated May 25, 2026
One-click install
npx skills add https://github.com/duypham2801/ThS_LLM --skill serverless-modal-duypham2801
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: serverless-modal
Source: https://github.com/duypham2801/ThS_LLM/tree/main/.claude/skills/serverless-modal
Command: npx skills add https://github.com/duypham2801/ThS_LLM --skill serverless-modal-duypham2801

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It eliminates the friction of provisioning and managing remote GPU infrastructure by letting you run training, fine-tuning, or inference code on Modal serverlessly, without SSH or Docker setup.

Core Features & Use Cases

  • One-shot GPU execution: Run experiment scripts for training, evaluation, and benchmarks with a single modal run command.
  • Managed remote environments: Define dependencies in the Modal image build step so your results are reproducible.
  • Persistent outputs via volumes: Save checkpoints and artifacts to Modal volumes so runs can reliably return results.
  • Service deployment for inference: Turn an endpoint into a persistent web API (e.g., FastAPI endpoint) or scale batch inference workloads.

Quick Start

Use the serverless-modal skill when you want to run GPU code by asking it to generate a Modal launcher that will execute your specified Python training or inference entrypoint on the best-fit GPU and save outputs to a volume.

Frequently Asked Questions about serverless-modal

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

FAQPage Schema
How do I run GPU training jobs without managing SSH or Docker?▼

Deploy an inference API by turning your endpoint into a persistent web service using Modal. The skill orchestrates cost-aware remote execution and scales the API automatically to zero when not receiving traffic.

Can I save model checkpoints to persistent storage during remote training?▼

Estimate GPU and VRAM costs before launch by selecting an execution pattern and specifying your hardware requirements. The skill calculates estimated compute expenses so you can choose the best-fit GPU for your budget.

Do I need Modal authentication to run serverless GPU workloads?▼

You need Modal authentication to run serverless GPU workloads. Additionally, configuring optional secrets for Hugging Face or W&B allows the remote environment to securely access your model repositories and experiment tracking accounts.

What execution patterns are available for serverless GPU compute?▼

Available execution patterns for serverless GPU compute include one-shot scripts, web APIs, vLLM, batch processing, LoRA fine-tuning, and distributed training. You select the pattern that matches your workload before launching the cost-aware execution.