kubeflow/mpi-operator

What it solves

It simplifies the process of running allreduce-style distributed training on Kubernetes, which is typically complex to orchestrate across multiple nodes and GPUs.

How it works

The project provides a Kubernetes operator that introduces a custom resource called MPIJob. Users define their training configuration—including the number of workers and GPU requirements—in a YAML file. The operator then manages the lifecycle of the job, coordinating the launcher and worker pods to execute the distributed training task.

Who it’s for

ML engineers and platform operators who need to scale AI training workloads across a Kubernetes cluster using MPI-based distributed training frameworks.

Highlights

• Supports multi-node distributed training with GPU acceleration.
• Integrates with common MPI implementations like Intel MPI and MPICH.
• Provides built-in monitoring via exposed metrics for job creation, success, and failure rates.
• Compatible with the broader Kubeflow ecosystem.