Run Hugging Face Jobs compute workloads with UV scripts or Docker images.
npx skills add https://github.com/huggingface/skills --skill hugging-face-jobs
Please help me install this Agent Skill. Skill: hugging-face-jobs Source: https://github.com/huggingface/skills/tree/main/skills/hugging-face-jobs Command: npx skills add https://github.com/huggingface/skills --skill hugging-face-jobs
Hugging Face Jobs lets you run compute workloads in the cloud without local setup, enabling scalable experiments, data processing, and model deployment tasks.
Submit a lightweight UV job or a Docker-based job on HF Jobs, supply HF_TOKEN via secrets, and pick a CPU or GPU flavor to begin.
High-intent search queries and answers about installing and using this skill.
You can run Python compute workloads on Hugging Face Jobs by submitting UV scripts inline or via hosted scripts, allowing automatic scaling for data processing and experiments without local setup.
Yes, you can execute custom Docker environments on HF Jobs to run reproducible pipelines, ensuring deterministic execution for batch inference and data processing workloads in the cloud.
To manage HF tokens and secrets for cloud compute authentication, you must supply your HF_TOKEN securely via secrets, enabling safe Hub operations and access during job execution.
The best way to estimate costs and select hardware flavors for model training experiments is using the hardware selection and cost estimation features to budget CPU or GPU workloads before execution.
Yes, Hugging Face Jobs supports configuring timeouts to prevent runaway workloads and persisting results directly to the Hub or external storage after completing data processing pipelines.
You should avoid inline UV scripts for batch inference workflows requiring complex custom dependencies, and instead use Docker-based jobs to ensure reproducible environments and deterministic execution.
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