gpu-operator

README

AMD GPU Operator

📖 GPU Operator Documentation Site: https://instinct.docs.amd.com/projects/gpu-operator

Introduction

AMD GPU Operator simplifies the deployment and management of AMD Instinct GPU accelerators within Kubernetes clusters. This project enables seamless configuration and operation of GPU-accelerated workloads, including machine learning, Generative AI, and other GPU-intensive applications.

Components

• AMD GPU Operator Controller
• K8s Device Plugin
• K8s Node Labeller
• Device Metrics Exporter
• Device Test Runner
• Node Feature Discovery Operator
• Kernel Module Management Operator

Features

• Streamlined GPU driver installation and management
• Comprehensive metrics collection and export
• Easy deployment of AMD GPU device plugin for Kubernetes
• Automated labeling of nodes with AMD GPU capabilities
• Compatibility with standard Kubernetes environments
• Efficient GPU resource allocation for containerized workloads
• GPU health monitoring and troubleshooting

Compatibility

• ROCm DKMS Compatibility: Please refer to the ROCM official website for the compatability matrix for ROCM driver.
• Kubernetes: 1.29.0+

Prerequisites

• Kubernetes v1.29.0+
• Helm v3.2.0+
• kubectl CLI tool configured to access your cluster
• Cert Manager Install it by running these commands if not already installed in the cluster:

helm repo add jetstack https://charts.jetstack.io --force-update

helm install cert-manager jetstack/cert-manager \
  --namespace cert-manager \
  --create-namespace \
  --version v1.15.1 \
  --set crds.enabled=true

Quick Start

1. Add the AMD Helm Repository

helm repo add rocm https://rocm.github.io/gpu-operator
helm repo update

2. Install the Operator

Basic installation:

helm install amd-gpu-operator rocm/gpu-operator-charts \
  --namespace kube-amd-gpu \
  --create-namespace \
  --version=v1.2.2

Installation Options
  - Skip NFD installation: `--set node-feature-discovery.enabled=false`
  - Skip KMM installation: `--set kmm.enabled=false`

  It is strongly recommended to use AMD-optimized KMM images included in the operator release.

3. Install Custom Resource

After the installation of AMD GPU Operator, you need to create the DeviceConfig custom resource in order to trigger the operator to start to work. By preparing the DeviceConfig in the YAML file, you can create the resouce by running kubectl apply -f deviceconfigs.yaml. For custom resource definition and more detailed information, please refer to Custom Resource Installation Guide.

Grafana Dashboards

Following dashboards are provided for visualizing GPU metrics collected from device-metrics-exporter:

• Overview Dashboard: Provides a comprehensive view of the GPU cluster.
• GPU Detail Dashboard: Offers a detailed look at individual GPUs.
• Job Detail Dashboard: Presents detailed GPU usage for specific jobs in SLURM and Kubernetes environments.
• Node Detail Dashboard: Displays detailed GPU usage at the host level.

Contributing

Please refer to our Developer Guide.

Support

For bugs and feature requests, please file an issue on our GitHub Issues page.

License

The AMD GPU Operator is licensed under the Apache License 2.0.