kubeflow/trainer

Health v2 · maintenance only

Adoption not part of the score

2198 stars · 1034 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Kubeflow Trainer is a Kubernetes-native platform for distributed AI model training and LLM fine-tuning across frameworks like PyTorch, JAX, TensorFlow, HuggingFace, and XGBoost. It orchestrates multi-node, multi-GPU jobs (including MPI workloads) via TrainJob custom resources with a Python SDK.

Use cases

• fine-tune LLMs on Kubernetes with multiple GPUs
• run distributed PyTorch training jobs across nodes
• train models with JAX or TensorFlow on a cluster
• orchestrate MPI workloads on Kubernetes
• scale HuggingFace model training with DeepSpeed
• manage ML training jobs as Kubernetes resources

When to choose

• you already run Kubernetes and need scalable distributed training
• you want LLM fine-tuning with multi-node, multi-GPU orchestration
• you need framework-agnostic training runtimes (PyTorch, JAX, XGBoost, MPI)

When to avoid

• you train small models on a single machine without a cluster
• you have no Kubernetes infrastructure or ops expertise
• you only need inference/serving rather than training

Facets

framework · maturity active

machine-learning llm-training gpu-computing workflow-automation deployment machine-learning deep-learning large-language-models microservices python go cloud distributed-training llm-fine-tuning pytorch jax tensorflow huggingface mpi mlops trainjob kueue kubernetes docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="kubeflow/trainer")