PickYourTechPickYourTech home
Kubeflow Trainer logo

Kubeflow Trainer

Kubernetes-native platform for scalable LLM fine‑tuning and distributed training

Model Training & Fine-Tuning Platforms

Kubeflow Trainer lets data scientists run large‑language‑model fine‑tuning and other ML workloads on Kubernetes, supporting PyTorch, TensorFlow, JAX, HuggingFace, DeepSpeed, and Megatron‑LM via a unified Python SDK.

Kubeflow Trainer banner

Overview

Overview

Kubeflow Trainer is a Kubernetes‑native project that enables large‑language‑model fine‑tuning and general distributed model training. It supports major frameworks such as PyTorch, TensorFlow, JAX, and can integrate libraries like HuggingFace, DeepSpeed, and Megatron‑LM.

Capabilities

The platform provides custom resource definitions (CRDs) and a Python SDK for building training runtimes. Users can choose between a CustomTrainer, BuiltinTrainer, or local PyTorch execution. An MPI runtime adds high‑performance computing support, and the project is compatible with the broader Kubeflow ecosystem.

Deployment

Training jobs are declared as Kubernetes resources and run on any K8s cluster with GPU support. While the project is currently in alpha and APIs may evolve, recent releases (v2.0) bring stability and community backing. Documentation, Slack community, and bi‑weekly working‑group meetings help teams adopt the solution quickly.

Highlights

Unified training CRDs for PyTorch, TensorFlow, JAX, and more
Native integration with HuggingFace, DeepSpeed, and Megatron‑LM
CustomTrainer and BuiltinTrainer options, including local execution
MPI runtime for high‑performance distributed training

Pros

  • Runs on any Kubernetes cluster with GPU scheduling
  • Multi‑framework support reduces toolchain complexity
  • Extensible via the Kubeflow Python SDK
  • Active community with frequent releases

Considerations

  • Alpha status; APIs may change
  • Documentation still maturing compared to older tools
  • Requires Kubernetes expertise to operate effectively
  • No built‑in graphical UI; relies on CLI/SDK

Managed products teams compare with

When teams consider Kubeflow Trainer, these hosted platforms usually appear on the same shortlist.

Amazon SageMaker JumpStart logo

Amazon SageMaker JumpStart

ML hub with curated foundation models, pretrained algorithms, and solution templates you can deploy and fine-tune in SageMaker

Cohere logo

Cohere

Enterprise AI platform providing LLMs (Command, Aya) plus Embed/Rerank for retrieval

Replicate logo

Replicate

API-first platform to run, fine-tune, and deploy AI models without managing infrastructure

Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.

Fit guide

Great for

  • Teams needing scalable LLM fine‑tuning on Kubernetes
  • Organizations standardizing on the Kubeflow stack
  • Researchers experimenting with multiple ML frameworks
  • Workloads that benefit from MPI‑based high‑performance training

Not ideal when

  • Small projects without Kubernetes infrastructure
  • Users requiring production‑grade stability (still alpha)
  • Teams that need an out‑of‑the‑box graphical training dashboard
  • Environments limited to CPU‑only training

How teams use it

Fine‑tune a GPT‑style LLM with DeepSpeed on a GPU cluster

Accelerated training completes in hours, leveraging DeepSpeed optimizations and Kubernetes auto‑scaling.

Run distributed TensorFlow training for image classification

Scales across multiple nodes using the TensorFlow operator, simplifying resource allocation via CRDs.

Integrate HuggingFace pipelines into a CI/CD MLOps workflow

Automated model updates are triggered by code changes, with training jobs managed as Kubernetes resources.

Leverage MPI runtime for large‑scale scientific simulations

High‑performance compute jobs execute efficiently on Kubernetes, reducing time‑to‑solution for HPC workloads.

Tech snapshot

Go77%
Rust11%
Python7%
Shell2%
Makefile1%
Smarty1%

Tags

kubeflowmlopsaikubernetesfine-tuningllmpytorchmachine-learningdistributedjaxpythongpuhuggingfacexgboosttensorflow

Frequently asked questions

What Kubernetes version is required?

Kubeflow Trainer works with any Kubernetes version that supports Custom Resource Definitions and GPU scheduling; the latest stable release is recommended.

Does it support GPU scheduling?

Yes, the platform integrates with Kubernetes device plugins to schedule GPUs for training pods.

How does the Python SDK interact with the CRDs?

The SDK generates and applies the appropriate custom resources, handling lifecycle management and status monitoring.

Can I use existing Kubeflow pipelines with Trainer?

Trainer jobs can be invoked from Kubeflow Pipelines as steps, allowing end‑to‑end MLOps workflows.

What is the roadmap for moving out of alpha?

The project aims to reach beta in the next release cycle, stabilizing APIs and expanding documentation.

Project at a glance

Active
Visit siteView repo
Stars
2,008
Watchers
2,008
Forks
877
LicenseApache-2.0
Repo age8 years old
Last commit17 hours ago
Primary languageGo

Last synced 3 hours ago