Kubeflow Trainer

Latest News 🔥

• [2026/08] Kubeflow Trainer v2.3.0 is officially released with the runtime snapshot mechanism for decoupled runtime lifecycle, enhanced MPI support, and proposal Megatron-LM examples. Check out the GitHub release notes.
• [2026/03] Kubeflow Trainer v2.2 is officially released with support for JAX and XGBoost Training Runtimes, enhanced observability with metrics propagation to TrainJob status, and Flux Framework integration for HPC and MPI workloads. Check out the blog post announcement.
• [2025/11] Kubeflow Trainer v2.1 is officially released with support of Distributed Data Cache, topology aware scheduling with Kueue and Volcano, and LLM post-training enhancements. Check out the GitHub release notes.
• [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.

Overview

Kubeflow Trainer is a Kubernetes-native distributed AI platform for scalable large language model (LLM) fine-tuning and training of AI models across a wide range of frameworks, including PyTorch, MLX, HuggingFace, DeepSpeed, Megatron-LM, JAX, XGBoost, and more.

Kubeflow Trainer brings MPI to Kubernetes, orchestrating multi-node, multi-GPU distributed jobs efficiently across high-performance computing (HPC) clusters. This enables high-throughput communication between processes, making it ideal for large-scale AI training that requires ultra-fast synchronization between GPUs nodes.

Kubeflow Trainer seamlessly integrates with the Cloud Native AI ecosystem, including Kueue for topology-aware scheduling and multi-cluster job dispatching, Slurm Bridge for scheduling on hybrid Kubernetes and Slurm clusters, and KAI Scheduler for GPU aware scheduling.

Kubeflow Trainer reuses existing Kubernetes-native building blocks like JobSet and LeaderWorkerSet for AI workload orchestration.

Kubeflow Trainer provides a distributed data cache designed to stream large-scale data with zero-copy transfer directly to GPU nodes. This ensures memory-efficient training jobs while maximizing GPU utilization.

With the Kubeflow Python SDK, AI practitioners can effortlessly develop and fine-tune LLMs while leveraging the Kubeflow Trainer APIs: TrainJob and Runtimes.

Kubeflow Trainer Introduction

Checkout following KubeCon + CloudNativeCon talks for Kubeflow Trainer capabilities:

Additional talks:

• From High Performance Computing To AI Workloads on Kubernetes: MPI Runtime in Kubeflow TrainJob
• Streamline LLM Fine-tuning on Kubernetes With Kubeflow LLM Trainer

Getting Started

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

Community

The following links provide information on how to get involved with the Kubeflow Trainer community:

• Join our #kubeflow-trainer Slack channel.
• Attend the bi-weekly Kubeflow Trainer and Katib call.
• If you use Kubeflow Trainer, add yourself to the ADOPTERS file.

Kubeflow Trainer is a Kubeflow subproject. Refer to the Kubeflow community page for more information.

Contributing

Please refer to the CONTRIBUTING guide.

Changelog

Please refer to the CHANGELOG directory.

Kubeflow Training Operator V1

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.

Acknowledgement

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.