hugging-face-jobs
Run workloads on Hugging Face Jobs with managed CPUs, GPUs, TPUs, secrets, and Hub persistence.
Running Workloads on Hugging Face Jobs
Detailed Guide
Read [the detailed guide](references/detailed-guide.md) before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.
When to Use This Skill
Use this skill when users want to:
- Run Python workloads on cloud infrastructure
- Execute jobs without local GPU/TPU setup
- Process data at scale
- Run batch inference or experiments
- Schedule recurring tasks
- Use GPUs/TPUs for any workload
- Persist results to the Hugging Face Hub
Prerequisites Checklist
Before starting any job, verify:
✅ Account & Authentication
- Hugging Face Account with Pro, Team, or Enterprise plan (Jobs require paid plan)
- Authenticated login: Check with
hf_whoami() - HF_TOKEN for Hub Access ⚠️ CRITICAL - Required for any Hub operations (push models/datasets, download private repos, etc.)
- Token must have appropriate permissions (read for downloads, write for uploads)
✅ Token Usage (See Token Usage section for details)
When tokens are required:
- Pushing models/datasets to Hub
- Accessing private repositories
- Using Hub APIs in scripts
- Any a
Subscribers only
The full skill, its 10 bundled files and every download is included with every paid Complete AI plan.
Details
| Source | huggingface/skills |
|---|---|
| License | Complete terms in LICENSE.txt |
| Risk label | critical ("critical" means the skill may run commands or touch files — read before use) |
| Files | SKILL.md, index.html, references/detailed-guide.md, references/hardware_guide.md, references/hub_saving.md, references/token_usage.md, references/troubleshooting.md, scripts/cot-self-instruct.py, scripts/finepdfs-stats.py, scripts/generate-responses.py |
| Added | 2026-09-04 |
Related skills
advanced-evaluation
This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.
agent-creator
Create custom AI subagents with proper plugin structure, persona generation, and companion routing skills.
agent-framework-azure-ai-py
Build persistent agents on Azure AI Foundry using the Microsoft Agent Framework Python SDK.
agent-memory
A hybrid memory system that provides persistent, searchable knowledge management for AI agents.
agent-memory-mcp
A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
agent-orchestration-improve-agent
Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
