Run workloads on Hugging Face Jobs with managed CPUs, GPUs, TPUs, secrets, and Hub persistence.
Scanned 9/12/2026
Install to Claude Code
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---
source: "https://github.com/huggingface/skills/tree/main/skills/huggingface-jobs"
date_added: "2026-09-04"
name: hugging-face-jobs
description: Run workloads on Hugging Face Jobs with managed CPUs, GPUs, TPUs, secrets, and Hub persistence.
license: Complete terms in LICENSE.txt
risk: critical
---
# 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](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:**
```python
# hf_jobs MCP tool — $HF_TOKEN is auto-replaced with real token:
{"secrets": {"HF_TOKEN": "$HF_TOKEN"}}
# HfApi().run_uv_job() — MUST pass actual token:
from huggingface_hub import get_token
secrets={"HF_TOKEN": get_token()}
```
**⚠️ CRITICAL:** The `$HF_TOKEN` placeholder is ONLY auto-replaced by the `hf_jobs` MCP tool. When using `HfApi().run_uv_job()`, you MUST pass the real token via `get_token()`. Passing the literal string `"$HF_TOKEN"` results in a 9-character invalid token and 401 errors.
## 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)
```python
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 token from your logged-in session (`hf auth login`)
- Most secure and convenient method
- Token is encrypted server-side when passed as a secret
**Benefits:**
- No token exposure in code
- Uses your current login session
- Automatically updated if you re-login
- Works seamlessly with MCP tools
#### Method 2: Explicit Token (Not Recommended)
```python
hf_jobs("uv", {
"script": "your_script.py",
"secrets": {"HF_TOKEN": "hf_abc123..."} # ⚠️ Hardcoded token
})
```
**When to use:**
- Only if automatic token doesn't work
- Testing with a specific token
- Organization tokens (use with caution)
**Security concerns:**
- Token visible in code/logs
- Must manually update if token rotates
- Risk of token exposure
#### Method 3: Environment Variable (Less Secure)
```python
hf_jobs("uv", {
"script": "your_script.py",
"env": {"HF_TOKEN": "hf_abc123..."} # ⚠️ Less secure than secrets
})
```
**Difference from secrets:**
- `env` variables are visible in job logs
- `secrets` are encrypted server-side
- Always prefer `secrets` for tokens
### Using Tokens in Scripts
**In your Python script, tokens are available as environment variables:**
```python
# /// script
# dependencies = ["huggingface-hub"]
# ///
import os
from huggingface_hub import HfApi
# Token is automatically available if passed via secrets
token = os.environ.get("HF_TOKEN")
# Use with Hub API
api = HfApi(token=token)
# Or let huggingface_hub auto-detect
api = HfApi() # Automatically uses HF_TOKEN env var
```
**Best practices:**
- Don't hardcode tokens in scripts
- Use `os.environ.get("HF_TOKEN")` to access
- Let `huggingface_hub` auto-detect when possible
- Verify token exists before Hub operations
### Token Verification
**Check if you're logged in:**
```python
from huggingface_hub import whoami
user_info = whoami() # Returns your username if authenticated
```
**Verify token in job:**
```python
import os
assert "HF_TOKEN" in os.environ, "HF_TOKEN not found!"
token = os.environ["HF_TOKEN"]
print(f"Token starts with: {token[:7]}...") # Should start with "hf_"
```
### Common Token Issues
**Error: 401 Unauthorized**
- **Cause:** Token missing or invalid
- **Fix:** Add `secrets={"HF_TOKEN": "$HF_TOKEN"}` to job config
- **Verify:** Check `hf_whoami()` works locally
**Error: 403 Forbidden**
- **Cause:** Token lacks required permissions
- **Fix:** Ensure token has write permissions for push operations
- **Check:** Token type at https://huggingface.co/settings/tokens
**Error: Token not found in environment**
- **Cause:** `secrets` not passed or wrong key name
- **Fix:** Use `secrets={"HF_TOKEN": "$HF_TOKEN"}` (not `env`)
- **Verify:** Script checks `os.environ.get("HF_TOKEN")`
**Error: Repository access denied**
- **Cause:** Token doesn't have access to private repo
- **Fix:** Use token from account with access
- **Check:** Verify repo visibility and your permissions
### Token Security Best Practices
1. **Never commit tokens** - Use `$HF_TOKEN` placeholder or environment variables
2. **Use secrets, not env** - Secrets are encrypted server-side
3. **Rotate tokens regularly** - Generate new tokens periodically
4. **Use minimal permissions** - Create tokens with only needed permissions
5. **Don't share tokens** - Each user should use their own token
6. **Monitor token usage** - Check token activity in Hub settings
### Complete Token Example
```python
# Example: Push results to Hub
hf_jobs("uv", {
"script": """
# /// script
# dependencies = ["huggingface-hub", "datasets"]
# ///
import os
from huggingface_hub import HfApi
from datasets import Dataset
# Verify token is available
assert "HF_TOKEN" in os.environ, "HF_TOKEN required!"
# Use token for Hub operations
api = HfApi(token=os.environ["HF_TOKEN"])
# Create and push dataset
data = {"text": ["Hello", "World"]}
dataset = Dataset.from_dict(data)
dataset.push_to_hub("username/my-dataset", token=os.environ["HF_TOKEN"])
print("✅ Dataset pushed successfully!")
""",
"flavor": "cpu-basic",
"timeout": "30m",
"secrets": {"HF_TOKEN": "$HF_TOKEN"} # ✅ Token provided securely
})
```
## Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
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