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kubeflow/trainer

A Kubernetes-native platform for scalable distributed AI model training and large language model (LLM) fine-tuning across various frameworks.

Core Features

Kubernetes-native distributed AI training and LLM fine-tuning.

Supports multiple AI frameworks: PyTorch, MLX, HuggingFace, DeepSpeed, JAX, XGBoost.

Orchestrates multi-node, multi-GPU jobs efficiently using MPI on HPC clusters.

Integrates with Cloud Native AI ecosystem tools like Kueue, JobSet, and LeaderWorkerSet.

Provides a distributed data cache for memory-efficient, high-GPU-utilization training.

Detailed Introduction

Kubeflow Trainer is a robust, Kubernetes-native platform designed for orchestrating distributed AI model training and LLM fine-tuning at scale. It leverages Kubernetes to manage multi-node, multi-GPU workloads, bringing HPC capabilities like MPI to the cloud-native environment. The platform supports a wide array of popular AI frameworks and seamlessly integrates with other Kubernetes-native tools for advanced scheduling and job orchestration. Its distributed data cache optimizes data transfer, ensuring efficient GPU utilization and memory management for large-scale AI tasks, empowering AI practitioners to develop and fine-tune models with ease.