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
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Running Workloads on Hugging Face Jobs
Overview
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-trai
[Description truncada. Veja o README completo no GitHub.]
