Jobs Overview

Hugging Face Jobs runs your code on remote CPUs and GPUs. Use it to fine-tune models, run inference over datasets, or process data.

A Job runs a command in an environment on the hardware you choose. You can follow its status and logs from the terminal or on the Hub.

Start with the Quickstart to run your first CPU and GPU Jobs, or browse examples for a workload to adapt.

Run Jobs from anywhere

There are multiple tools you can use to run jobs:

• the hf Command Line Interface (see the CLI installation steps and the Jobs CLI documentation for more information)
• the huggingface_hub Python client (see the huggingface_hub Jobs documentation for more information)
• the Jobs HTTP API (see the Jobs OpenAPI for more information)

Run any workload

Python scripts

Use hf jobs uv run to run a Python script remotely. Specify its dependencies using --with or in a script header. Jobs uses uv to install those dependencies before running your code.

- uv run <script.py>
+ hf jobs uv run <script.py>

Docker images

Use hf jobs run with a Docker image and the command to run. The image provides the tools and libraries your workload needs, whether you’re using Python or another language. Choose an existing image, use one built from a Docker Space, or build your own.

- docker run <image> <command>
+ hf jobs run <image> <command>

Many Jobs can run in parallel, for tasks such as parameter tuning, inference and data processing.

Automate Jobs

Trigger Jobs automatically with a schedule or using webhooks.

With a schedule, you can run Jobs every X minutes, hours, days, weeks or months. Scheduling Jobs uses the cron syntax like "*/5 * * * *" for “every 5 minutes”, or aliases like "@hourly", "@daily", "weekly" or "@monthly".

With webhooks, Jobs can run whenever there is an update on a Hugging Face repository. For example you can configure webhooks to trigger for every model update under a given account, and retrieve the updated model from the webhook payload in the Job.