Latest News 🔥

• [2025/09] Kubeflow SDK v0.1 is officially released with support for CustomTrainer, BuiltinTrainer, and local PyTorch execution. Check out the GitHub release notes.
• [2025/07] PyTorch on Kubernetes: Kubeflow Trainer Joins the PyTorch Ecosystem. Find the announcement in the PyTorch blog post.
• [2025/07] Kubeflow Trainer v2.0 has been officially released. Check out the blog post announcement and the release notes.
• [2025/04] From High Performance Computing To AI Workloads on Kubernetes: MPI Runtime in Kubeflow TrainJob. See the KubeCon + CloudNativeCon London talk

Kubeflow Trainer is a Kubernetes-native project designed for large language models (LLMs) fine-tuning and enabling scalable, distributed training of machine learning (ML) models across various frameworks, including PyTorch, JAX, TensorFlow, and others.

You can integrate other ML libraries such as HuggingFace, DeepSpeed, or Megatron-LM with Kubeflow Trainer to run them on Kubernetes.

Kubeflow Trainer enables you to effortlessly develop your LLMs with the Kubeflow Python SDK, and build Kubernetes-native Training Runtimes using Kubernetes Custom Resource APIs.

The following KubeCon + CloudNativeCon 2024 talk provides an overview of Kubeflow Trainer capabilities:

Please check the official Kubeflow Trainer documentation to install and get started with Kubeflow Trainer.

The following links provide information on how to get involved in the community:

• Join our #kubeflow-trainer Slack channel.
• Attend the bi-weekly AutoML and Training Working Group community meeting.
• Check out who is using Kubeflow Trainer.

Please refer to the CONTRIBUTING guide.

Please refer to the CHANGELOG.

Kubeflow Trainer project is currently in alpha status, and APIs may change. If you are using Kubeflow Training Operator V1, please refer to this migration document.

Kubeflow Community will maintain the Training Operator V1 source code at the release-1.9 branch.

You can find the documentation for Kubeflow Training Operator V1 in these guides.

This project was originally started as a distributed training operator for TensorFlow and later we merged efforts from other Kubeflow Training Operators to provide a unified and simplified experience for both users and developers. We are very grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions. We'd also like to thank everyone who's contributed to and maintained the original operators.

• PyTorch Operator: list of contributors and maintainers.
• MPI Operator: list of contributors and maintainers.
• XGBoost Operator: list of contributors and maintainers.
• Common library: list of contributors and maintainers.