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hugging-face-jobs

Execute Python workloads on Hugging Face Jobs with CPU, GPU, or TPU hardware.

3|Updated Feb 21, 2026
Search Tags:#mlops#model training#gpu#huggingface#jobs#cloud compute#batch inference
One-click install
npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill hugging-face-jobs-ihatesea69
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hugging-face-jobs
Source: https://github.com/ihatesea69/HieuNghi-AI-Skills/tree/main/huggingface_skills/hugging-face-jobs
Command: npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill hugging-face-jobs-ihatesea69

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires huggingface-hub, datasets, transformers, torch, vllm, polars, scikit-learn, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This skill allows users to run any Python workload (data processing, inference, training, batch jobs) on Hugging Face's managed cloud infrastructure, eliminating the need for local setup and leveraging powerful hardware like GPUs and TPUs.

Core Features & Use Cases

  • Cloud Compute: Execute Python scripts on cloud CPUs, GPUs, or TPUs.
  • GPU/TPU Workloads: Ideal for tasks requiring significant compute power, such as model training, batch inference, or large-scale data processing.
  • Result Persistence: Automatically save outputs back to the Hugging Face Hub.
  • Authentication: Securely handles Hugging Face tokens for Hub interactions.
  • Use Case: You need to fine-tune a large language model on a dataset. Instead of using your local machine, you can use this skill to submit the training job to Hugging Face Jobs, specifying GPU hardware and ensuring the trained model is saved back to your Hugging Face Hub repository.

Quick Start

Use the hugging-face-jobs skill to run a Python script named 'train_model.py' on a 'a10g-large' GPU flavor for 2 hours.

Frequently Asked Questions about hugging-face-jobs

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

FAQPage Schema
How do I run Python model training jobs on Hugging Face cloud GPUs?▼

To run Python model training jobs on Hugging Face cloud GPUs, you submit an arbitrary Python workload to Hugging Face Jobs infrastructure. This eliminates local setup by abstracting hardware selection, timeout configuration, and authentication for tasks like model training.

Can I use Hugging Face Jobs for batch inference and large-scale data processing?▼

Yes, you can use Hugging Face Jobs for batch inference and large-scale data processing. The infrastructure supports executing Python scripts on cloud CPUs, GPUs, and TPUs, making it ideal for compute-intensive workloads without local hardware constraints.

Do I need to manually configure authentication tokens to save model training results to the Hub?▼

No, you do not need to manually configure authentication tokens to save results to the Hub. The execution environment securely handles Hugging Face token management and automatically persists outputs back to your Hugging Face Hub repository.

What is the best way to fine-tune a large language model using Hugging Face cloud compute?▼

The best way to fine-tune a large language model using Hugging Face cloud compute is submitting a training script to Hugging Face Jobs. You specify GPU hardware flavors, configure timeouts, and the trained model is saved directly to your Hub repository.

Does Hugging Face Jobs work with PyTorch and Transformers dependencies?▼

Yes, Hugging Face Jobs works seamlessly with PyTorch and Transformers dependencies. The environment supports arbitrary Python workloads, allowing you to leverage frameworks like torch, transformers, vllm, datasets, and scikit-learn for your compute tasks.

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