This skill should be used when users want to run any workload on Hugging Face Jobs infrastructure. Covers UV scripts, Docker-based jobs, hardware selection, cost estimation, authentication with tokens, secrets management, timeout configuration, and result persistence. Designed for general-purpose compute workloads including data processing, inference, experiments, batch jobs, and any Python-based tasks. Should be invoked for tasks involving cloud compute, GPU workloads, or when users mention running jobs on Hugging Face infrastructure without local setup.
This skill should be used when users want to run any workload on Hugging Face Jobs infrastructure. Covers UV scripts, Docker-based jobs, hardware selection, cost estimation, authentication with tokens, secrets management, timeout configuration, and result persistence. Designed for general-purpose compute workloads including data processing, inference, experiments, batch jobs, and any Python-based tasks. Should be invoked for tasks involving cloud compute, GPU workloads, or when users mention running jobs on Hugging Face infrastructure without local setup.
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:
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().
Always handle authentication - Jobs that interact with the Hub require HF_TOKEN via secrets. See Token Usage section below.
Provide job details after submission - After submitting, provide job ID, monitoring URL, estimated time, and note that the user can request status checks later.
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, 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)
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:
# /// 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:
from huggingface_hub import whoami
user_info = whoami() # Returns your username if authenticated
Verify token in job:
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
from huggingface_hub import run_uv_job
run_uv_job("my_script.py", python="3.11")
Working with Scripts
⚠️ Important: There are two "script path" stories depending on how you run Jobs:
Using the hf_jobs() MCP tool (recommended in this repo): the script value must be inline code (a string) or a URL. A local filesystem path (like "./scripts/foo.py") won't exist inside the remote container.
Using the hf jobs uv run CLI: local file paths do work (the CLI uploads your script).
Common mistake with hf_jobs() MCP tool:
# ❌ Will fail (remote container can't see your local path)
hf_jobs("uv", {"script": "./scripts/foo.py"})
Correct patterns with hf_jobs() MCP tool:
# ✅ Inline: read the local script file and pass its *contents*from pathlib import Path
script = Path("hf-jobs/scripts/foo.py").read_text()
hf_jobs("uv", {"script": script})
# ✅ URL: host the script somewhere reachable
hf_jobs("uv", {"script": "https://huggingface.co/datasets/uv-scripts/.../raw/main/foo.py"})
# ✅ URL from GitHub
hf_jobs("uv", {"script": "https://raw.githubusercontent.com/huggingface/trl/main/trl/scripts/sft.py"})
CLI equivalent (local paths supported):
hf jobs uv run ./scripts/foo.py -- --your --args
Adding Dependencies at Runtime
Add extra dependencies beyond what's in the PEP 723 header:
hf jobs run python:3.12 python -c "print('Hello from HF Jobs!')"
Python API:
from huggingface_hub import run_job
run_job(image="python:3.12", command=["python", "-c", "print('Hello!')"], flavor="cpu-basic")
Benefits: Full Docker control, use pre-built images, run any command
When to use: Need specific Docker images, non-Python workloads, complex environments
import os
from huggingface_hub import HfApi
# Token automatically available from secrets
api = HfApi(token=os.environ.get("HF_TOKEN"))
# Push your results
api.upload_file(...)
Verification Checklist
Before submitting:
Results persistence method chosen
secrets={"HF_TOKEN": "$HF_TOKEN"} if using Hub
Script handles missing token gracefully
Test persistence path works
See:references/hub_saving.md for detailed Hub persistence guide
Timeout Management
⚠️ DEFAULT: 30 MINUTES
Jobs automatically stop after the timeout. For long-running tasks like training, always set a custom timeout.
Setting Timeouts
MCP Tool:
{
"timeout": "2h"# 2 hours
}
Supported formats:
Integer/float: seconds (e.g., 300 = 5 minutes)
String with suffix: "5m" (minutes), "2h" (hours), "1d" (days)
Examples: "90m", "2h", "1.5h", 300, "1d"
Python API:
from huggingface_hub import run_job, run_uv_job
run_job(image="python:3.12", command=[...], timeout="2h")
run_uv_job("script.py", timeout=7200) # 2 hours in seconds
Timeout Guidelines
Scenario
Recommended
Notes
Quick test
10-30 min
Verify setup
Data processing
1-2 hours
Depends on data size
Batch inference
2-4 hours
Large batches
Experiments
4-8 hours
Multiple runs
Long-running
8-24 hours
Production workloads
Always add 20-30% buffer for setup, network delays, and cleanup.
On timeout: Job killed immediately, all unsaved progress lost
Cost Estimation
General guidelines:
Total Cost = (Hours of runtime) × (Cost per hour)
Example calculations:
Quick test:
Hardware: cpu-basic ($0.10/hour)
Time: 15 minutes (0.25 hours)
Cost: $0.03
Data processing:
Hardware: l4x1 ($2.50/hour)
Time: 2 hours
Cost: $5.00
Batch inference:
Hardware: a10g-large ($5/hour)
Time: 4 hours
Cost: $20.00
Cost optimization tips:
Start small - Test on cpu-basic or t4-small
Monitor runtime - Set appropriate timeouts
Use checkpoints - Resume if job fails
Optimize code - Reduce unnecessary compute
Choose right hardware - Don't over-provision
Monitoring and Tracking
Check Job Status
MCP Tool:
# List all jobs
hf_jobs("ps")
# Inspect specific job
hf_jobs("inspect", {"job_id": "your-job-id"})
# View logs
hf_jobs("logs", {"job_id": "your-job-id"})
# Cancel a job
hf_jobs("cancel", {"job_id": "your-job-id"})
Python API:
from huggingface_hub import list_jobs, inspect_job, fetch_job_logs, cancel_job
# List your jobs
jobs = list_jobs()
# List running jobs only
running = [j for j in list_jobs() if j.status.stage == "RUNNING"]
# Inspect specific job
job_info = inspect_job(job_id="your-job-id")
# View logsfor log in fetch_job_logs(job_id="your-job-id"):
print(log)
# Cancel a job
cancel_job(job_id="your-job-id")
CLI:
hf jobs ps # List jobs
hf jobs logs <job-id> # View logs
hf jobs cancel <job-id> # Cancel job
Remember: Wait for user to request status checks. Avoid polling repeatedly.
Job URLs
After submission, jobs have monitoring URLs:
https://huggingface.co/jobs/username/job-id
View logs, status, and details in the browser.
Wait for Multiple Jobs
import time
from huggingface_hub import inspect_job, run_job
# Run multiple jobs
jobs = [run_job(image=img, command=cmd) for img, cmd in workloads]
# Wait for all to completefor job in jobs:
while inspect_job(job_id=job.id).status.stage notin ("COMPLETED", "ERROR"):
time.sleep(10)
Scheduled Jobs
Run jobs on a schedule using CRON expressions or predefined schedules.
MCP Tool:
# Schedule a UV script that runs every hour
hf_jobs("scheduled uv", {
"script": "your_script.py",
"schedule": "@hourly",
"flavor": "cpu-basic"
})
# Schedule with CRON syntax
hf_jobs("scheduled uv", {
"script": "your_script.py",
"schedule": "0 9 * * 1", # 9 AM every Monday"flavor": "cpu-basic"
})
# Schedule a Docker-based job
hf_jobs("scheduled run", {
"image": "python:3.12",
"command": ["python", "-c", "print('Scheduled!')"],
"schedule": "@daily",
"flavor": "cpu-basic"
})
Python API:
from huggingface_hub import create_scheduled_job, create_scheduled_uv_job
# Schedule a Docker job
create_scheduled_job(
image="python:3.12",
command=["python", "-c", "print('Running on schedule!')"],
schedule="@hourly"
)
# Schedule a UV script
create_scheduled_uv_job("my_script.py", schedule="@daily", flavor="cpu-basic")
# Schedule with GPU
create_scheduled_uv_job(
"ml_inference.py",
schedule="0 */6 * * *", # Every 6 hours
flavor="a10g-small"
)
Available schedules:
@annually, @yearly - Once per year
@monthly - Once per month
@weekly - Once per week
@daily - Once per day
@hourly - Once per hour
CRON expression - Custom schedule (e.g., "*/5 * * * *" for every 5 minutes)
from huggingface_hub import (
list_scheduled_jobs,
inspect_scheduled_job,
suspend_scheduled_job,
resume_scheduled_job,
delete_scheduled_job
)
# List all scheduled jobs
scheduled = list_scheduled_jobs()
# Inspect a scheduled job
info = inspect_scheduled_job(scheduled_job_id)
# Suspend (pause) a scheduled job
suspend_scheduled_job(scheduled_job_id)
# Resume a scheduled job
resume_scheduled_job(scheduled_job_id)
# Delete a scheduled job
delete_scheduled_job(scheduled_job_id)
Webhooks: Trigger Jobs on Events
Trigger jobs automatically when changes happen in Hugging Face repositories.
Python API:
from huggingface_hub import create_webhook
# Create webhook that triggers a job when a repo changes
webhook = create_webhook(
job_id=job.id,
watched=[
{"type": "user", "name": "your-username"},
{"type": "org", "name": "your-org-name"}
],
domains=["repo", "discussion"],
secret="your-secret"
)
How it works:
Webhook listens for changes in watched repositories
When triggered, the job runs with WEBHOOK_PAYLOAD environment variable
Your script can parse the payload to understand what changed
This repository ships ready-to-run UV scripts in hf-jobs/scripts/. Prefer using them instead of inventing new templates.
Pattern 1: Dataset → Model Responses (vLLM) — scripts/generate-responses.py
What it does: loads a Hub dataset (chat messages or a prompt column), applies a model chat template, generates responses with vLLM, and pushes the output dataset + dataset card back to the Hub.
Requires: GPU + write token (it pushes a dataset).
Pattern 2: CoT Self-Instruct Synthetic Data — scripts/cot-self-instruct.py
What it does: generates synthetic prompts/answers via CoT Self-Instruct, optionally filters outputs (answer-consistency / RIP), then pushes the generated dataset + dataset card to the Hub.
Requires: GPU + write token (it pushes a dataset).
