vllm - ✅(Solved) Fix [Installation]: Ray not present in Container Image [1 pull requests, 1 comments, 2 participants]

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vllm-project/vllm#38113Fetched 2026-04-08 01:32:14
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PR fix notes

PR #39400: [Doc] Switch K8S examples to default MP mode

Description (problem / solution / changelog)

<!-- markdownlint-disable -->

PLEASE FILL IN THE PR DESCRIPTION HERE ENSURING ALL CHECKLIST ITEMS (AT THE BOTTOM) HAVE BEEN CONSIDERED.

Purpose

Fix https://github.com/vllm-project/vllm/issues/38113

per @ed-pai, previous k8s yaml won't work since default docker images has removed ray. so switch the k8s yaml example to default mp(multiprocessing) as distributed backend.

Background:

This PR preserves the old ray code there as comments, in case some people still need it for reference or pip install ray by themselves.

Also add explicit --distributed-executor-backend ray for examples/online_serving/multi-node-serving.sh

Test Plan

apply the example LWS.md

Test Result

the actual vllm command as below , which aligns https://docs.vllm.ai/en/latest/serving/parallelism_scaling/#running-vllm-with-multiprocessing

cloudshell-worker-npvr4qhxq5:~# kubectl exec -it vllm-0 -- ps -lefww
....  /usr/bin/python3 /usr/local/bin/vllm serve meta-llama/Meta-Llama-3.1-405B-Instruct --tensor-parallel-size 8 --pipeline-parallel-size 2 --nnodes 2 --node-rank 0 --master-addr vllm-0.vllm.default --port 8080

cloudshell-worker-npvr4qhxq5:~# kubectl exec -it vllm-0-1 -- ps -lefww
....  /usr/bin/python3 /usr/local/bin/vllm serve meta-llama/Meta-Llama-3.1-405B-Instruct --tensor-parallel-size 8 --pipeline-parallel-size 2 --nnodes 2 --node-rank 1 --master-addr vllm-0.vllm.default --headless

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  • The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
  • The test plan, such as providing test command.
  • The test results, such as pasting the results comparison before and after, or e2e results
  • (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.
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</details>

BEFORE SUBMITTING, PLEASE READ https://docs.vllm.ai/en/latest/contributing (anything written below this line will be removed by GitHub Actions)

Changed files

  • docs/deployment/frameworks/lws.md (modified, +11/-6)
  • docs/deployment/integrations/kthena.md (modified, +26/-10)
  • examples/online_serving/multi-node-serving.sh (modified, +1/-1)
RAW_BUFFERClick to expand / collapse

Your current environment

Docker image vllm/vllm-openai:latest

How you are installing vllm

The latest release removed Ray as a dependency (#33445). While this is beneficial to python installations, it breaks Kubernetes deployments that use use the upstream image for Ray for multi-node inference.

Can this be added back into the Dockerfile, or was it intentionally removed from there as well?

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extent analysis

Fix Plan

To resolve the issue, we need to add Ray back as a dependency in the Dockerfile for the vllm/vllm-openai:latest image.

Steps to Fix

  • Modify the Dockerfile to include Ray installation:
    • Add ray to the requirements.txt file or install it directly in the Dockerfile using pip install ray
    • Alternatively, use a multi-stage build to install Ray in a separate stage if it's not needed in the final image
  • Example Dockerfile modification:
    # Install dependencies
    RUN pip install -r requirements.txt
    # or
    RUN pip install ray
  • If using a requirements.txt file, add the following line:
    ray
  • Rebuild the Docker image with the updated Dockerfile

Verification

  • Verify that the Ray dependency is installed correctly by checking the Docker image's layers or running a container from the image and checking the installed packages
  • Test the Kubernetes deployment to ensure multi-node inference is working as expected

Extra Tips

  • Consider pinning the Ray version to ensure consistency across deployments
  • If the removal of Ray was intentional, explore alternative solutions for multi-node inference that do not require Ray as a dependency

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