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Trainer

Trainer is a Kubernetes-native framework that coordinates distributed AI model training and LLM fine-tuning through TrainJob and Runtime APIs.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Trainer — OpenViST project profile in AI Infrastructure & Serving.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Trainer when training workloads need Kubernetes-native orchestration across nodes or GPUs, or when you want a training component within a Kubeflow stack.

Who it is for

  • AI practitioners running distributed training workloads on Kubernetes.
  • Platform teams building Kubernetes-based machine-learning operations stacks.
  • Teams fine-tuning LLMs or training models across multiple nodes and GPUs.

Documented uses

  • Coordinate distributed model-training jobs on Kubernetes.
  • Run LLM fine-tuning workloads across GPU resources.
  • Use MPI to coordinate multi-node training processes.
  • Integrate training jobs with Kubernetes-oriented scheduling components.
Before you choose

Requirements & limits

  • Trainer requires an operational Kubernetes environment for its intended workload role.
  • It uses TrainJob and Runtime APIs and Kubernetes building blocks including JobSet and LeaderWorkerSet.
  • Integration options named by the project include Kueue, Volcano, Slurm Bridge and KAI Scheduler.
  • The project identifies its status as alpha and cautions that APIs may change.
  • The repository lists an Apache-2.0 license and Go as its primary language.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Run distributed PyTorch training with Kubeflow Trainer

Submit a four-node PyTorch training job using the torch-distributed runtime and inspect its progress and logs.

Before you start

  • Access to a Kubernetes cluster with the Kubeflow Trainer control plane installed.
  • A training function configured for PyTorch distributed training; the guide provides a Fashion-MNIST example.
  • The Kubeflow Python SDK, installed with pip install -U kubeflow.
  • An available torch-distributed Training Runtime; the example requests one GPU per node.

Steps

  1. Define a PyTorch training function that initializes the distributed process group, creates and trains a model, and synchronizes processes. The getting-started example uses Fashion-MNIST.

  2. Use TrainerClient().list_runtimes() to inspect available Training Runtimes and confirm that torch-distributed is available.

  3. Submit the function with TrainerClient().train(...), using CustomTrainer configured for four nodes and the desired per-node resources.

  4. Inspect the job with TrainerClient().get_job(name=job_id).steps and retrieve its master-node logs with TrainerClient().get_job_logs(job_id, follow=True).

Expected outcome

The example describes a four-node job with one GPU per node, and shows successful node statuses and training logs for three epochs.

Guide limitations

  • The example requires the Trainer control plane to already be installed in the Kubernetes cluster; installation is referred to a separate guide.
  • GPU is optional in the example, but the documented four-node outcome requests one GPU per node.
  • The repository states that the project is in alpha and APIs may change.
Documentation 1 ↗Documentation 2 ↗
Evidence and transparency

Sources & review

Official repository ↗Guide documentation ↗
Source record details
Source availability
Active
Sources checked
11 October 2026
Original website title
kubeflow/trainer · Official homepage title · retained in its source language
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