Hugging Face Jobs
Run any workload on fully managed Hugging Face infrastructure - data processing, batch inference, experiments, and scheduled tasks.
Prerequisites
- •HF_TOKEN environment variable
- •Hugging Face Pro/Team/Enterprise plan
Instructions
Common Use Cases
- •Data Processing - Transform, filter, 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)
- •Scheduled Jobs - Automate recurring tasks
UV Scripts (Recommended)
python
hf_jobs("uv", {
"script": """
# /// script
# dependencies = ["datasets", "transformers"]
# ///
from datasets import load_dataset
dataset = load_dataset("cais/mmlu", split="train[:100]")
print(f"Loaded {len(dataset)} examples")
""",
"flavor": "cpu-basic",
"timeout": "30m",
"secrets": {"HF_TOKEN": "$HF_TOKEN"}
})
Hardware Flavors
| Use Case | Flavor | Cost/hr |
|---|---|---|
| Basic tasks | cpu-basic | ~$0.10 |
| Light GPU | t4-small | ~$0.75 |
| Medium GPU | a10g-small | ~$3.50 |
| Large GPU | a10g-large | ~$5.00 |
| Heavy compute | a100-large | ~$10.00 |
CLI Commands
bash
# Submit job from URL hf jobs uv run \ --flavor cpu-basic \ --timeout 30m \ --secrets HF_TOKEN \ "https://example.com/script.py" # Check status hf jobs ps hf jobs logs <job-id> hf jobs inspect <job-id> hf jobs cancel <job-id>
Important Notes
- •Jobs run in isolated Docker containers
- •Local file paths don't work - use inline code or URLs
- •Always set timeout (default 30min may be too short)
- •Use
secretsparameter to pass HF_TOKEN for Hub access - •Results are lost unless pushed to Hub
Notes
- •For model training, see the huggingface-model-trainer skill
- •Uses PEP 723 inline dependencies
- •Supports CPU, GPU, and TPU hardware
Source: huggingface/skills