Hugging Face Jobs

# Running Workloads on Hugging Face Jobs

Overview

Run any workload on fully managed Hugging Face infrastructure. No local setup required—jobs run on cloud CPUs, GPUs, or TPUs and can persist results to the Hugging Face Hub.

**Common use cases:**

- **Data Processing** - Transform, filter, or analyze large datasets

- **Batch Inference** - Run inference on thousands of samples

- **Experiments & Benchmarks** - Reproducible ML experiments

- **Model Training** - Fine-tune models (see `model-trainer` skill for TRL-specific training)

- **Synthetic Data Generation** - Generate datasets using LLMs

- **Development & Testing** - Test code without local GPU setup

- **Scheduled Jobs** - Automate recurring tasks

**For model training specifically:** See the `model-trainer` skill for TRL-based training workflows.

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

Key Directives

When assisting with jobs:

1. **ALWAYS use `hf_jobs()` MCP tool** - Submit jobs using `hf_jobs("uv", {...})` or `hf_jobs("run", {...})`. The `script` parameter accepts Python code directly. Do NOT save to local files unless the user explicitly requests it. Pass the script content as a string to `hf_jobs()`.

2. **Always handle authentication** - Jobs that interact with the Hub require `HF_TOKEN` via secrets. See Token Usage section below.

3. **Provide job details after submission** - After submitting, provide job ID, monitoring URL, estimated time, and note that the user can request status checks later.

4. **Set appropriate timeouts** - Default 30min may be insufficient for long-running tasks.

Prerequisites Checklist

Before starting any job, verify:

✅ **Account & Authentication**

- Hugging Face Account with [Pro](https://hf.co/pro), [Team](https://hf.co/enterprise), or [Enterprise](https://hf.co/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 authenticated Hub operations

**How to provide tokens:**

{
    "secrets": {"HF_TOKEN": "$HF_TOKEN"}  # Recommended: automatic token
}

**⚠️ CRITICAL:** The `$HF_TOKEN` placeholder is automatically replaced with your logged-in token. Never hardcode tokens in scripts.

Token Usage Guide

Understanding Tokens

**What are HF Tokens?**

- Authentication credentials for Hugging Face Hub

- Required for authenticated operations (push, private repos, API access)

- Stored securely on your machine after `hf auth login`

**Token Types:**

- **Read Token** - Can download models/datasets, read private repos

- **Write Token** - Can push models/datasets, create repos, modify content

- **Organization Token** - Can act on behalf of an organization

When Tokens Are Required

**Always Required:**

- Pushing models/datasets to Hub

- Accessing private repositories

- Creating new repositories

- Modifying existing repositories

- Using Hub APIs programmatically

**Not Required:**

- Downloading public models/datasets

- Running jobs that don't interact with Hub

- Reading public repository information

How to Provide Tokens to Jobs

#### Method 1: Automatic Token (Recommended)

hf_jobs("uv", {
    "script": "your_script.py",
    "secrets": {"HF_TOKEN": "$HF_TOKEN"}  # ✅ Automatic replacement
})

**How it works:**

- `$HF_TOKEN` is a placeholder that gets replaced with your actual token

- Uses the toke