hf-jobs

You can run Python scripts on cloud GPUs without local setup by executing UV scripts or custom Docker images on Hugging Face's managed cloud infrastructure. This enables computationally intensive ML workloads like model training and batch inference on remote hardware.

Yes, you can use custom Docker images for ML workloads on Hugging Face. The infrastructure supports executing arbitrary code workloads via Docker containers, allowing you to run scalable data processing and model inference on CPUs, GPUs, or TPUs.

Hugging Face cloud compute supports TPU acceleration for batch inference. You can execute arbitrary code workloads across CPUs, GPUs, and TPUs, enabling scalable processing for thousands of samples and complex machine learning experiments.

The best way to persist batch inference results back to Hugging Face Hub is through automated result persistence during cloud compute execution. The infrastructure handles saving outputs directly to the Hub using secure token management for Hub interactions.

Secure token management for running data processing jobs on Hugging Face is handled natively by the cloud compute infrastructure. It ensures safe Hub interactions and allows automatic persistence of outputs like synthetic data generation results back to your repository.