Huggingface Jobs

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)

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

CLI Commands

# 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 secrets parameter 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