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https://github.com/ray-project/kuberay
A toolkit to run Ray applications on Kubernetes
https://github.com/ray-project/kuberay
apache deep-learning kubernetes machine-learning ray
Last synced: about 21 hours ago
JSON representation
A toolkit to run Ray applications on Kubernetes
- Host: GitHub
- URL: https://github.com/ray-project/kuberay
- Owner: ray-project
- License: apache-2.0
- Created: 2020-10-29T20:42:00.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2024-10-26T00:07:33.000Z (about 2 months ago)
- Last Synced: 2024-10-29T15:34:18.582Z (about 1 month ago)
- Topics: apache, deep-learning, kubernetes, machine-learning, ray
- Language: Go
- Homepage:
- Size: 147 MB
- Stars: 1,226
- Watchers: 22
- Forks: 397
- Open Issues: 283
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- awesome-ray - KubeRay
README
# KubeRay
[](https://github.com/ray-project/kuberay/actions)
[](https://goreportcard.com/report/github.com/ray-project/kuberay)
KubeRay is a powerful, open-source Kubernetes operator that simplifies the deployment and management of [Ray](https://github.com/ray-project/ray) applications on Kubernetes. It offers several key components:
**KubeRay core**: This is the official, fully-maintained component of KubeRay that provides three custom resource definitions, RayCluster, RayJob, and RayService. These resources are designed to help you run a wide range of workloads with ease.
* **RayCluster**: KubeRay fully manages the lifecycle of RayCluster, including cluster creation/deletion, autoscaling, and ensuring fault tolerance.
* **RayJob**: With RayJob, KubeRay automatically creates a RayCluster and submits a job when the cluster is ready. You can also configure RayJob to automatically delete the RayCluster once the job finishes.
* **RayService**: RayService is made up of two parts: a RayCluster and a Ray Serve deployment graph. RayService offers zero-downtime upgrades for RayCluster and high availability.
**Community-managed components (optional)**: Some components are maintained by the KubeRay community.
* **KubeRay APIServer**: It provides a layer of simplified configuration for KubeRay resources. The KubeRay API server is used internally
by some organizations to back user interfaces for KubeRay resource management.
* **KubeRay Python client**: This Python client library provides APIs to handle RayCluster from your Python application.
## Documentation
From September 2023, all user-facing KubeRay documentation will be hosted on the [Ray documentation](https://docs.ray.io/en/latest/cluster/kubernetes/index.html).
The KubeRay repository only contains documentation related to the development and maintenance of KubeRay.
## Quick Start
* [RayCluster Quickstart](https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html)
* [RayJob Quickstart](https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html)
* [RayService Quickstart](https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html)
## Examples
* [Ray Train XGBoostTrainer on Kubernetes](https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#kuberay-ml-example) (CPU-only)
* [Train PyTorch ResNet model with GPUs on Kubernetes](https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#kuberay-gpu-training-example)
* [Serve a MobileNet image classifier on Kubernetes](https://docs.ray.io/en/master/cluster/kubernetes/examples/mobilenet-rayservice.html#kuberay-mobilenet-rayservice-example) (CPU-only)
* [Serve a StableDiffusion text-to-image model on Kubernetes](https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#kuberay-stable-diffusion-rayservice-example)
* [Serve a text summarizer on Kubernetes](https://docs.ray.io/en/master/cluster/kubernetes/examples/text-summarizer-rayservice.html#kuberay-text-summarizer-rayservice-example)
* [RayJob Batch Inference Example](https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#kuberay-batch-inference-example)
## Kubernetes Ecosystem
* [Ingress: AWS Application Load Balancer, GKE Ingress, Nginx](https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#kuberay-ingress)
* [Using Prometheus and Grafana](https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/prometheus-grafana.html#kuberay-prometheus-grafana)
* [Profiling with py-spy](https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/pyspy.html#kuberay-pyspy-integration)
* [KubeRay integration with Volcano](https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/volcano.html#kuberay-volcano)
* [Kubeflow: an interactive development solution](https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/kubeflow.html#kuberay-kubeflow-integration)
* [MCAD: A Kubernetes Solution for Queuing and Gang Dispatching jobs on Single or Multi-Cluster environment](https://github.com/ray-project/kuberay/blob/master/docs/guidance/kuberay-with-MCAD.md)
## External Blog Posts
1. [Evolving Niantic AR Mapping Infrastructures with Ray](https://nianticlabs.com/news/ray) Niantic (September 6, 2023)
2. [Building a Modern Machine Learning Platform with Ray at Samsara](https://www.samsara.com/blog/building-a-modern-machine-learning-platform-with-ray) Samsara (August 29, 2023)
3. [Using Ray on Kubernetes with KubeRay at Google Cloud](https://cloud.google.com/blog/products/containers-kubernetes/use-ray-on-kubernetes-with-kuberay) Google (August 15, 2023)
4. [How DoorDash Built an Ensemble Learning Model for Time Series Forecasting with KubeRay](https://doordash.engineering/2023/06/20/how-doordash-built-an-ensemble-learning-model-for-time-series-forecasting/) Doordash (June 20, 2023)
5. [AI/ML Models Batch Training at Scale with Open Data Hub](https://cloud.redhat.com/blog/ai/ml-models-batch-training-at-scale-with-open-data-hub) Red Hat (May 15, 2023)
6. [A cloud-native, open-source stack for accelerating foundation model innovation](https://research.ibm.com/blog/openshift-foundation-model-stack) IBM (May 9, 2023)
7. [Distributed Machine Learning at Instacart](https://tech.instacart.com/distributed-machine-learning-at-instacart-4b11d7569423) Instacart (March 17, 2023)
8. [Unleashing ML Innovation at Spotify with Ray](https://engineering.atspotify.com/2023/02/unleashing-ml-innovation-at-spotify-with-ray/) Spotify (February 1, 2023)
9. [Best Practices For Ray Cluster On ACK](https://www.alibabacloud.com/blog/best-practices-for-ray-clusters---ray-on-ack_600925) Alibaba Cloud (Mar 12, 2024)
## Talks
1. [Supercharge Your AI Platform with KubeRay](https://youtu.be/DgfJR6wR4BQ?si=QuK3j7VEkteSwglA) Anyscale + Google (November 8, 2023)
2. [Sailing Ray Workloads with KubeRay and Kueue in Kubernetes](https://www.youtube.com/watch?v=Q-sQLDMeJ8M) Volcano + DaoCloud (October 17, 2023)
3. [Serving Large Language Models with KubeRay on TPUs](https://raysummit.anyscale.com/agenda/sessions/135) Google (September 19, 2023)
4. [KubeRay: A Ray Cluster Management Solution on Kubernetes](https://raysummit.anyscale.com/agenda/sessions/184) Anyscale (September 18, 2023)
5. [The Different Shades of using KubeRay with Kubernetes](https://raysummit.anyscale.com/agenda/sessions/140) Microsoft (September 18, 2023)
6. [On-Demand Ray Clusters in ML Workflows via KubeRay & Sematic](https://raysummit.anyscale.com/agenda/sessions/164) Sematic (September 18, 2023)
7. [KubeRay - A Kubernetes Ray Clustering Solution](https://www.youtube.com/watch?v=tMEwSAeC1jo) Microsoft (February 8, 2023)
8. [KubeRay x Flyte Integration](https://www.youtube.com/watch?v=RmGynLp5u4Q) Flyte (August 24, 2022)
9. [Operationalizing Ray Serve on Kubernetes](https://youtu.be/NekkpRrcAWg?si=bpX7z64AuZiM_iUv) Anyscale (August 24, 2022)
## Helm Charts
KubeRay Helm charts are hosted on the [ray-project/kuberay-helm](https://github.com/ray-project/kuberay-helm) repository.
Please read [kuberay-operator](helm-chart/kuberay-operator/README.md) to deploy the operator and [ray-cluster](helm-chart/ray-cluster/README.md) to deploy a configurable Ray cluster.
To deploy the optional KubeRay API Server, see [kuberay-apiserver](helm-chart/kuberay-apiserver/README.md).
```sh
# Add the Helm repo
helm repo add kuberay https://ray-project.github.io/kuberay-helm/
helm repo update
# Confirm the repo exists
helm search repo kuberay --devel
# Install both CRDs and KubeRay operator v1.1.0.
helm install kuberay-operator kuberay/kuberay-operator --version 1.1.0
# Check the KubeRay operator Pod in `default` namespace
kubectl get pods
# NAME READY STATUS RESTARTS AGE
# kuberay-operator-6fcbb94f64-mbfnr 1/1 Running 0 17s
```
## Development
Please read our [CONTRIBUTING](CONTRIBUTING.md) guide before making a pull request. Refer to our [DEVELOPMENT](./ray-operator/DEVELOPMENT.md) to build and run tests locally.
## Getting Involved
Join [Ray's Slack workspace](https://docs.google.com/forms/d/e/1FAIpQLSfAcoiLCHOguOm8e7Jnn-JJdZaCxPGjgVCvFijHB5PLaQLeig/viewform), and search the following public channels:
* `#kuberay-questions` (KubeRay users): This channel aims to help KubeRay users with their questions. The messages will be closely monitored by the Ray and KubeRay maintainers.
* `#kuberay-discuss` (KubeRay contributors): This channel is for contributors to discuss what to do next with KubeRay (e.g. issues, pull requests, feature requests, design docs, KubeRay ecosystem integrations). All KubeRay maintainers and core contributors are in the channel.
## Security
If you discover a potential security issue in this project, or think you may
have discovered a security issue, we ask that you notify KubeRay Security via our
[Slack Channel](https://ray-distributed.slack.com/archives/C02GFQ82JPM).
Please do **not** create a public GitHub issue.
## License
This project is licensed under the [Apache-2.0 License](LICENSE).