Training with Jobs

Training with Jobs · Hugging Face TRL documentation Training with Jobs TRL 🏡 View all docsAWS Trainium & InferentiaAccelerateArgillaAutoTrainBitsandbytesCLIChat UIDataset viewerDatasetsDeploying on AWSDiffusersDistilabelEvaluateGoogle CloudGoogle TPUsGradioHubHub Python LibraryHuggingface.jsInference Endpoints (dedicated)Inference ProvidersKernelsLeRobotLeaderboardsLightevalMicrosoft AzureOpenEnvOptimumPEFTReachy MiniSafetensorsSentence TransformersTRLTasksText Embeddings InferenceText Generation InferenceTokenizersTrackioTransformersTransformers.jsXetsmolagentstimm Search documentation Ctrl+K mainv1.9.0v1.8.0v1.7.1v1.6.0v1.5.1v1.4.0v1.3.0v1.2.0v1.1.0v1.0.0v0.29.1v0.28.0v0.27.2v0.26.2v0.25.1v0.24.0v0.23.1v0.22.2v0.21.0v0.20.0v0.19.1v0.18.1v0.17.0v0.16.1v0.15.2v0.14.0v0.13.0v0.12.2v0.11.4v0.10.1v0.9.6v0.8.6v0.7.11v0.6.0v0.5.0v0.4.7v0.3.1v0.2.1v0.1.1 EN Join the Hugging Face community and get access to the augmented documentation experience Collaborate on models, datasets and Spaces Faster examples with accelerated inference Switch between documentation themes to get started Copy page Training with Jobs Hugging Face Jobs lets you run training scripts on fully managed infrastructure—no need to manage GPUs or local environment setup. In this guide, you’ll learn how to: - Use TRL Jobs to easily run pre-optimized TRL training - Run any TRL training script with uv scripts For general details about Hugging Face Jobs (hardware selection, job monitoring, etc.), see the Jobs documentation. Requirements - A Hugging Face account with a positive credit balance. Jobs is pay-as-you-go—you only pay for the seconds you use. - Logged in to the Hugging Face Hub (`hf auth login`) Using TRL Jobs TRL Jobs is a high-level wrapper around Hugging Face Jobs and TRL that streamlines training. It provides optimized default configurations so you can start quickly without manually tuning parameters. Example: Copied ```bash pip install trl-jobs trl-jobs sft --mo