Overview

What is Kubeflow Trainer

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, 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, as well as 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.

Who is this for

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

User Personas

Kubeflow Trainer documentation is separated between these user personas:

• AI Practitioners: ML engineers and data scientists who develop AI models using the Kubeflow Python SDK and TrainJob.
• Platform Administrators: administrators and DevOps engineers responsible for managing Kubernetes clusters and Kubeflow Training Runtimes.
• Contributors: open source contributors working on Kubeflow Trainer project.

Kubeflow Trainer Introduction

Watch the following KubeCon + CloudNativeCon 2024 talk which provides an overview of Kubeflow Trainer:

Why use Kubeflow Trainer

The Kubeflow Trainer supports key phases on the AI lifecycle, including model training and LLMs fine-tuning, as shown in the diagram below:

Key Benefits

• 🚀 Simple, Scalable, and Built for LLM Fine-Tuning

Effortlessly scale from single-machine training to large, distributed Kubernetes clusters with Kubeflow’s Python APIs and supported Training Runtimes. Perfect for modern AI workloads.

• 🔧 Extensible and Portable

Run Kubeflow Trainer on any cloud or on-premises Kubernetes cluster. Easily integrate your own ML frameworks—regardless of language or runtime—through a flexible, extensible API layer.

• ⚡️ Distributed AI Data Caching

Powered by Apache Arrow and Apache DataFusion, Kubeflow Trainer streams tensors directly to GPU nodes via a distributed cache layer – enabling seamless access to large datasets, minimizing I/O overhead, and cutting GPU costs.

• 🧠 LLM Fine-Tuning Blueprints

Accelerate your generative AI use-cases with ready-to-use Kubeflow LLM blueprints designed for efficient fine-tuning and deployment of LLMs on Kubernetes.

• 💰 Optimized for GPU Efficiency

Reduce GPU costs through intelligent dataset streaming and model initialization. Kubeflow Trainer offloads data preprocessing and I/O to CPU workloads, ensuring GPUs stay focused on training.

• ☸️ Native Kubernetes Integrations

Achieve optimal GPU utilization and coordinated scheduling for large-scale AI workloads. Kubeflow Trainer seamlessly integrates with Kubernetes ecosystem projects like Kueue, Coscheduling, Volcano, or YuniKorn.

Next steps

Run your first Kubeflow TrainJob by following the Getting Started guide.