Distributed AI Training Platform
2.1k 2026-04-26
kubeflow/trainer
A Kubernetes-native platform for scalable distributed AI model training and LLM fine-tuning across various frameworks.
Core Features
Kubernetes-native distributed AI model training and LLM fine-tuning.
Supports a wide range of AI frameworks including PyTorch, JAX, XGBoost, and HuggingFace.
Orchestrates multi-node, multi-GPU jobs efficiently using MPI for HPC clusters.
Provides a distributed data cache for zero-copy data transfer and maximized GPU utilization.
Seamlessly integrates with Cloud Native AI tools like Kueue, JobSet, and LeaderWorkerSet.
Detailed Introduction
Kubeflow Trainer is a powerful Kubernetes-native platform designed for scalable distributed AI model training and large language model (LLM) fine-tuning. It orchestrates multi-node, multi-GPU jobs efficiently across HPC clusters by bringing MPI to Kubernetes, ensuring high-throughput communication. Supporting a wide array of frameworks such as PyTorch, JAX, and HuggingFace, it also features a distributed data cache for memory-efficient training and optimal GPU utilization. Its deep integration with the Cloud Native AI ecosystem, including Kueue and JobSet, makes it an ideal solution for complex, large-scale AI workloads.