Overview
- Full name
docker.io/anyscale/ray- Registry
- docker.io
- Namespace
- anyscale
- Repository
- ray
- Cached tags
- 9244
- Last synced
- 07/06/2026, 09:25 AM
Introduction
Anyscale Ray 分布式计算镜像,运行 Ray head/worker 支撑 Python 并行与 LLM 推理集群,需 GPU 节点可选。
Details
anyscale/ray 提供 Ray runtime,可 scale ML training、hyperparameter tuning 与 Ray Serve 推理。典型用于 LLM 批推理或 RL 训练集群,定位为分布式计算框架,需 head/worker 拓扑与 GCS;K8s 使用 KubeRay Operator 或 RayCluster CR。
快速启动
docker run -d --name ray-head \
-p 8265:8265 -p 10001:10001 \
docker.io/anyscale/ray:latest \
ray start --head --dashboard-host=0.0.0.0
推荐实践
docker run -d --name ray-head \
--restart unless-stopped \
-p 8265:8265 \
--gpus all \
docker.io/anyscale/ray:2.9.0-py310 \
ray start --head --dashboard-host=0.0.0.0 --num-gpus=1
核心参数说明
-p 8265:8265— Ray Dashboardray start --head— Head 节点模式--gpus all— GPU worker(可选)- 固定 tag — Ray 与 Python 版本 pin
Kubernetes
apiVersion: ray.io/v1
kind: RayCluster
metadata:
name: ray-cluster
spec:
headGroupSpec:
rayStartParams:
dashboard-host: 0.0.0.0
template:
spec:
containers:
- name: ray-head
image: docker.io/anyscale/ray:2.9.0-py310
ports:
- containerPort: 8265
workerGroupSpecs:
- replicas: 2
template:
spec:
containers:
- name: ray-worker
image: docker.io/anyscale/ray:2.9.0-py310
---
apiVersion: v1
kind: Service
metadata:
name: ray-dashboard
spec:
selector:
ray.io/node-type: head
ports:
- port: 8265
targetPort: 8265
推荐 KubeRay;GPU 节点 taint/toleration 调度。
Latest tags (20)
View all 9244 tags →| Tag | Arch | Pushed At | Size | Digest | Sync | Wish | Action |
|---|---|---|---|---|---|---|---|
docker.io/anyscale/ray :nightly-py311-cu130 | linux/amd64linux/arm64 | 2026/06/30 19:21 | 7.0 GB | sha256:e335ba406069… | |||
docker.io/anyscale/ray :nightly-py310-cu130 | linux/amd64linux/arm64 | 2026/06/30 19:20 | 6.9 GB | sha256:54d562ecd83c… | |||
docker.io/anyscale/ray :nightly-cu130 | linux/amd64linux/arm64 | 2026/06/30 19:20 | 6.9 GB | sha256:54d562ecd83c… | |||
docker.io/anyscale/ray :nightly-py313-cu130 | linux/amd64linux/arm64 | 2026/06/30 19:20 | 7.0 GB | sha256:71196b1583b1… | |||
docker.io/anyscale/ray :nightly-py312-cu130 | linux/amd64linux/arm64 | 2026/06/30 19:20 | 6.9 GB | sha256:415bff5e0fa1… | |||
docker.io/anyscale/ray :nightly-py311-cu117 | linux/amd64linux/arm64 | 2026/06/30 19:17 | 5.2 GB | sha256:08a570b4fa2a… | |||
docker.io/anyscale/ray :nightly-py312-cu118 | linux/amd64linux/arm64 | 2026/06/30 19:17 | 6.2 GB | sha256:7e81a7f4532b… | |||
docker.io/anyscale/ray :nightly-py310-cu117 | linux/amd64linux/arm64 | 2026/06/30 19:17 | 5.2 GB | sha256:07ffcf82d83c… | |||
docker.io/anyscale/ray :nightly-py310-cu118 | linux/amd64linux/arm64 | 2026/06/30 19:17 | 6.2 GB | sha256:29c0ddc4ab9e… | |||
docker.io/anyscale/ray :nightly-cu117 | linux/amd64linux/arm64 | 2026/06/30 19:17 | 5.2 GB | sha256:07ffcf82d83c… | |||
docker.io/anyscale/ray :nightly-cu118 | linux/amd64linux/arm64 | 2026/06/30 19:17 | 6.2 GB | sha256:29c0ddc4ab9e… | |||
docker.io/anyscale/ray :nightly-py311-cu118 | linux/amd64linux/arm64 | 2026/06/30 19:17 | 6.2 GB | sha256:ed2f7b102665… | |||
docker.io/anyscale/ray :nightly-py312-cu117 | linux/amd64linux/arm64 | 2026/06/30 19:17 | 5.2 GB | sha256:ab2b5da3d996… | |||
docker.io/anyscale/ray :nightly-py310-cu121 | linux/amd64linux/arm64 | 2026/06/30 19:17 | 6.3 GB | sha256:44abd808150d… | |||
docker.io/anyscale/ray :nightly-cu121 | linux/amd64linux/arm64 | 2026/06/30 19:17 | 6.3 GB | sha256:44abd808150d… | |||
docker.io/anyscale/ray :nightly-py311-cu121 | linux/amd64linux/arm64 | 2026/06/30 19:17 | 6.3 GB | sha256:c78cc6bcfea5… | |||
docker.io/anyscale/ray :nightly-py313-cu121 | linux/amd64linux/arm64 | 2026/06/30 19:17 | 6.3 GB | sha256:a22a355615d3… | |||
docker.io/anyscale/ray :nightly-py313-cu118 | linux/amd64linux/arm64 | 2026/06/30 19:16 | 6.3 GB | sha256:fb129ce4a7d0… | |||
docker.io/anyscale/ray :nightly-py313-cu123 | linux/amd64linux/arm64 | 2026/06/30 19:16 | 5.6 GB | sha256:ae6b15add5de… | |||
docker.io/anyscale/ray :nightly-py311-cu124 | linux/amd64linux/arm64 | 2026/06/30 19:16 | 5.8 GB | sha256:88b50daf961b… |