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huggingface-jobs

Run Python workloads on Hugging Face Jobs with GPU/CPU resources.

Updated Apr 5, 2026
Search Tags:#gpu#ml#huggingface#jobs#cloud compute
One-click install
npx skills add https://github.com/tony-ng-vn/Resume-agent --skill huggingface-jobs-tony-ng-vn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: huggingface-jobs
Source: https://github.com/tony-ng-vn/Resume-agent/tree/main/.agents/skills/huggingface-jobs
Command: npx skills add https://github.com/tony-ng-vn/Resume-agent --skill huggingface-jobs-tony-ng-vn

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires huggingface-hub, transformers, torch, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables running GPU/CPU workloads on Hugging Face Jobs, handling authentication tokens, and persisting results back to the Hub, streamlining complex computational tasks.

Core Features & Use Cases

  • Cloud Compute Workloads: Execute tasks on cloud infrastructure without local setup.
  • Job Management: Control job creation, monitoring, and result persistence.
  • Token Handling: Securely manage and utilize Hugging Face tokens for Hub access.
  • Use Case: For example, you can run a complex machine learning experiment on Hugging Face Jobs, using GPU resources, and have the results automatically stored in your Hub repository.

Quick Start

Use the huggingface-jobs skill to run a Python script 'inference.py' with GPU acceleration and 30 minutes of timeout.

huggingface-jobs run inference.py --flavor a10g-large --timeout 30m

Frequently Asked Questions about huggingface-jobs

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

FAQPage Schema
How do I run GPU workloads on Hugging Face Jobs from a Python script?▼

Yes, you can execute machine learning experiments on Hugging Face Jobs using GPU resources, and the results are automatically persisted directly to your Hub repository for later access and analysis.

Do I need to manage Hugging Face tokens manually for cloud compute jobs?▼

No, you do not need to manage tokens manually as the process handles authentication tokens securely, utilizing your Hugging Face Hub access to run Python-based tasks on cloud hardware seamlessly.

Can I use this to run machine learning experiments on cloud infrastructure without local setup?▼

Yes, you can execute machine learning experiments on Hugging Face Jobs using GPU resources, and the results are automatically persisted directly to your Hub repository for later access and analysis.

Does this approach work with torch and transformers for executing Python tasks?▼

Yes, executing Python tasks on Hugging Face Jobs works directly with torch and transformers dependencies, enabling you to run complex machine learning workloads on cloud hardware.

What is the best way to persist results from a Hugging Face Jobs experiment to the Hub?▼

The best way to persist results from a Hugging Face Jobs experiment is to use the built-in job management features, which automatically store outputs and results back into your Hub repository.

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