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Move dockerfiles into their own directory (vllm-project#14549)
Signed-off-by: Harry Mellor <[email protected]>
1 parent f98a492 commit e6e3c55

34 files changed

+44
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lines changed

.buildkite/release-pipeline.yaml

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agents:
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queue: cpu_queue_postmerge
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commands:
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- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.4.0 --tag vllm-ci:build-image --target build --progress plain ."
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- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.4.0 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
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- "mkdir artifacts"
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- "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
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- "bash .buildkite/upload-wheels.sh"
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agents:
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queue: cpu_queue_postmerge
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commands:
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- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.1.0 --tag vllm-ci:build-image --target build --progress plain ."
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- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.1.0 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
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- "mkdir artifacts"
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- "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
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- "bash .buildkite/upload-wheels.sh"
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agents:
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queue: cpu_queue_postmerge
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commands:
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- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=11.8.0 --tag vllm-ci:build-image --target build --progress plain ."
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- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=11.8.0 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
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- "mkdir artifacts"
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- "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
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- "bash .buildkite/upload-wheels.sh"
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queue: cpu_queue_postmerge
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commands:
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- "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
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- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.4.0 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT --target vllm-openai --progress plain ."
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- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.4.0 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT --target vllm-openai --progress plain -f docker/Dockerfile ."
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- "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
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- label: "Build and publish TPU release image"
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agents:
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queue: tpu_queue_postmerge
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commands:
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- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --tag vllm/vllm-tpu:nightly --tag vllm/vllm-tpu:$BUILDKITE_COMMIT --progress plain -f Dockerfile.tpu ."
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- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --tag vllm/vllm-tpu:nightly --tag vllm/vllm-tpu:$BUILDKITE_COMMIT --progress plain -f docker/Dockerfile.tpu ."
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- "docker push vllm/vllm-tpu:nightly"
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- "docker push vllm/vllm-tpu:$BUILDKITE_COMMIT"
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plugins:
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queue: cpu_queue_postmerge
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commands:
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- "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
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- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:latest --progress plain --target vllm-openai -f Dockerfile.cpu ."
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- "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:latest --progress plain --target vllm-openai -f docker/Dockerfile.cpu ."
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- "docker push public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version)"
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env:
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DOCKER_BUILDKIT: "1"

.buildkite/run-cpu-test-ppc64le.sh

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remove_docker_container
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# Try building the docker image
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docker build -t cpu-test -f Dockerfile.ppc64le .
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docker build -t cpu-test -f docker/Dockerfile.ppc64le .
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.buildkite/run-cpu-test.sh

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remove_docker_container
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# Try building the docker image
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numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --tag cpu-test-"$BUILDKITE_BUILD_NUMBER" --target vllm-test -f Dockerfile.cpu .
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numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" --tag cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2 --target vllm-test -f Dockerfile.cpu .
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numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --tag cpu-test-"$BUILDKITE_BUILD_NUMBER" --target vllm-test -f docker/Dockerfile.cpu .
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numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" --tag cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2 --target vllm-test -f docker/Dockerfile.cpu .
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# Run the image, setting --shm-size=4g for tensor parallel.
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docker run -itd --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --cpuset-cpus="$CORE_RANGE" \

.buildkite/run-gh200-test.sh

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# Try building the docker image
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DOCKER_BUILDKIT=1 docker build . \
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--file docker/Dockerfile \
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--target vllm-openai \
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--platform "linux/arm64" \
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-t gh200-test \

.buildkite/run-hpu-test.sh

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set -ex
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# Try building the docker image
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docker build -t hpu-test-env -f Dockerfile.hpu .
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docker build -t hpu-test-env -f docker/Dockerfile.hpu .
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# Setup cleanup
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# certain versions of HPU software stack have a bug that can

.buildkite/run-neuron-test.sh

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date "+%s" > /tmp/neuron-docker-build-timestamp
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fi
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docker build -t "${image_name}" -f Dockerfile.neuron .
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docker build -t "${image_name}" -f docker/Dockerfile.neuron .
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# Setup cleanup
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remove_docker_container() {

.buildkite/run-tpu-v1-test.sh

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set -e
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# Build the docker image.
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docker build -f Dockerfile.tpu -t vllm-tpu .
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docker build -f docker/Dockerfile.tpu -t vllm-tpu .
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# Set up cleanup.
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remove_docker_container() { docker rm -f tpu-test || true; }

.buildkite/run-xpu-test.sh

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container_name="xpu_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
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# Try building the docker image
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docker build -t ${image_name} -f Dockerfile.xpu .
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docker build -t ${image_name} -f docker/Dockerfile.xpu .
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# Setup cleanup
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remove_docker_container() {

.github/mergify.yml

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- files~=\.buildkite/
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- files~=^cmake/
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- files=CMakeLists.txt
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- files~=^Dockerfile
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- files~=^docker/Dockerfile
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- files~=^requirements.*\.txt
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- files=setup.py
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actions:

.github/workflows/lint-and-deploy.yaml

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uses: helm/kind-action@a1b0e391336a6ee6713a0583f8c6240d70863de3 # v1.12.0
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- name: Build the Docker image vllm cpu
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run: docker buildx build -f Dockerfile.cpu -t vllm-cpu-env .
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run: docker buildx build -f docker/Dockerfile.cpu -t vllm-cpu-env .
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- name: Configuration of docker images, network and namespace for the kind cluster
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run: |

CMakeLists.txt

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#
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# Note: the CUDA torch version is derived from pyproject.toml and various
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# requirements.txt files and should be kept consistent. The ROCm torch
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# versions are derived from Dockerfile.rocm
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# versions are derived from docker/Dockerfile.rocm
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#
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set(TORCH_SUPPORTED_VERSION_CUDA "2.6.0")
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set(TORCH_SUPPORTED_VERSION_ROCM "2.6.0")
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docs/source/contributing/dockerfile/dockerfile.md

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# Dockerfile
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We provide a <gh-file:Dockerfile> to construct the image for running an OpenAI compatible server with vLLM.
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We provide a <gh-file:docker/Dockerfile> to construct the image for running an OpenAI compatible server with vLLM.
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More information about deploying with Docker can be found [here](#deployment-docker).
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Below is a visual representation of the multi-stage Dockerfile. The build graph contains the following nodes:
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> Commands to regenerate the build graph (make sure to run it **from the \`root\` directory of the vLLM repository** where the dockerfile is present):
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>
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> ```bash
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> dockerfilegraph -o png --legend --dpi 200 --max-label-length 50 --filename Dockerfile
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> dockerfilegraph -o png --legend --dpi 200 --max-label-length 50 --filename docker/Dockerfile
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> ```
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>
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> or in case you want to run it directly with the docker image:
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> --output png \
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> --dpi 200 \
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> --max-label-length 50 \
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> --filename Dockerfile \
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> --filename docker/Dockerfile \
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> --legend
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> ```
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>

docs/source/contributing/overview.md

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```
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:::{tip}
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Since the <gh-file:Dockerfile> ships with Python 3.12, all tests in CI (except `mypy`) are run with Python 3.12.
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Since the <gh-file:docker/Dockerfile> ships with Python 3.12, all tests in CI (except `mypy`) are run with Python 3.12.
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Therefore, we recommend developing with Python 3.12 to minimise the chance of your local environment clashing with our CI environment.
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:::

docs/source/deployment/docker.md

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## Building vLLM's Docker Image from Source
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You can build and run vLLM from source via the provided <gh-file:Dockerfile>. To build vLLM:
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You can build and run vLLM from source via the provided <gh-file:docker/Dockerfile>. To build vLLM:
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```console
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# optionally specifies: --build-arg max_jobs=8 --build-arg nvcc_threads=2
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DOCKER_BUILDKIT=1 docker build . --target vllm-openai --tag vllm/vllm-openai
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DOCKER_BUILDKIT=1 docker build . --target vllm-openai --tag vllm/vllm-openai --file docker/Dockerfile
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:::{note}
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# Example of building on Nvidia GH200 server. (Memory usage: ~15GB, Build time: ~1475s / ~25 min, Image size: 6.93GB)
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$ python3 use_existing_torch.py
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$ DOCKER_BUILDKIT=1 docker build . \
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--file docker/Dockerfile \
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--target vllm-openai \
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-t vllm/vllm-gh200-openai:latest \

docs/source/deployment/nginx.md

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```console
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docker build -f Dockerfile . --tag vllm
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docker build -f docker/Dockerfile . --tag vllm
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```
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If you are behind proxy, you can pass the proxy settings to the docker build command as shown below:
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```console
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cd $vllm_root
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docker build -f Dockerfile . --tag vllm --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy
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docker build -f docker/Dockerfile . --tag vllm --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy
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(nginxloadbalancer-nginx-docker-network)=

docs/source/getting_started/installation/ai_accelerator/hpu-gaudi.inc.md

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### Build image from source
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```console
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docker build -f Dockerfile.hpu -t vllm-hpu-env .
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docker build -f docker/Dockerfile.hpu -t vllm-hpu-env .
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docker run -it --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --net=host --rm vllm-hpu-env
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docs/source/getting_started/installation/ai_accelerator/neuron.inc.md

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See <project:#deployment-docker-build-image-from-source> for instructions on building the Docker image.
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Make sure to use <gh-file:Dockerfile.neuron> in place of the default Dockerfile.
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Make sure to use <gh-file:docker/Dockerfile.neuron> in place of the default Dockerfile.
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## Extra information
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docs/source/getting_started/installation/ai_accelerator/tpu.inc.md

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### Build image from source
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You can use <gh-file:Dockerfile.tpu> to build a Docker image with TPU support.
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You can use <gh-file:docker/Dockerfile.tpu> to build a Docker image with TPU support.
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```console
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docker build -f Dockerfile.tpu -t vllm-tpu .
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docker build -f docker/Dockerfile.tpu -t vllm-tpu .
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Run the Docker image with the following command:

docs/source/getting_started/installation/cpu.md

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### Build image from source
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```console
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$ docker build -f Dockerfile.cpu --tag vllm-cpu-env --target vllm-openai .
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$ docker build -f docker/Dockerfile.cpu --tag vllm-cpu-env --target vllm-openai .
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# Launching OpenAI server
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$ docker run --rm \
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```
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::::{tip}
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For ARM or Apple silicon, use `Dockerfile.arm`
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For ARM or Apple silicon, use `docker/Dockerfile.arm`
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::::{tip}
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For IBM Z (s390x), use `Dockerfile.s390x` and in `docker run` use flag `--dtype float`
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For IBM Z (s390x), use `docker/Dockerfile.s390x` and in `docker run` use flag `--dtype float`
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## Supported features

docs/source/getting_started/installation/gpu/rocm.inc.md

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#### (Optional) Build an image with ROCm software stack
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Build a docker image from <gh-file:Dockerfile.rocm_base> which setup ROCm software stack needed by the vLLM.
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Build a docker image from <gh-file:docker/Dockerfile.rocm_base> which setup ROCm software stack needed by the vLLM.
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**This step is optional as this rocm_base image is usually prebuilt and store at [Docker Hub](https://hub.docker.com/r/rocm/vllm-dev) under tag `rocm/vllm-dev:base` to speed up user experience.**
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If you choose to build this rocm_base image yourself, the steps are as follows.
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@@ -140,12 +140,12 @@ It is important that the user kicks off the docker build using buildkit. Either
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To build vllm on ROCm 6.3 for MI200 and MI300 series, you can use the default:
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```console
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DOCKER_BUILDKIT=1 docker build -f Dockerfile.rocm_base -t rocm/vllm-dev:base .
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DOCKER_BUILDKIT=1 docker build -f docker/Dockerfile.rocm_base -t rocm/vllm-dev:base .
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```
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#### Build an image with vLLM
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First, build a docker image from <gh-file:Dockerfile.rocm> and launch a docker container from the image.
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First, build a docker image from <gh-file:docker/Dockerfile.rocm> and launch a docker container from the image.
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It is important that the user kicks off the docker build using buildkit. Either the user put `DOCKER_BUILDKIT=1` as environment variable when calling docker build command, or the user needs to setup buildkit in the docker daemon configuration /etc/docker/daemon.json as follows and restart the daemon:
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```console
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}
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```
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<gh-file:Dockerfile.rocm> uses ROCm 6.3 by default, but also supports ROCm 5.7, 6.0, 6.1, and 6.2, in older vLLM branches.
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<gh-file:docker/Dockerfile.rocm> uses ROCm 6.3 by default, but also supports ROCm 5.7, 6.0, 6.1, and 6.2, in older vLLM branches.
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It provides flexibility to customize the build of docker image using the following arguments:
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- `BASE_IMAGE`: specifies the base image used when running `docker build`. The default value `rocm/vllm-dev:base` is an image published and maintained by AMD. It is being built using <gh-file:Dockerfile.rocm_base>
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- `BASE_IMAGE`: specifies the base image used when running `docker build`. The default value `rocm/vllm-dev:base` is an image published and maintained by AMD. It is being built using <gh-file:docker/Dockerfile.rocm_base>
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- `USE_CYTHON`: An option to run cython compilation on a subset of python files upon docker build
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- `BUILD_RPD`: Include RocmProfileData profiling tool in the image
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- `ARG_PYTORCH_ROCM_ARCH`: Allows to override the gfx architecture values from the base docker image
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To build vllm on ROCm 6.3 for MI200 and MI300 series, you can use the default:
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```console
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DOCKER_BUILDKIT=1 docker build -f Dockerfile.rocm -t vllm-rocm .
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DOCKER_BUILDKIT=1 docker build -f docker/Dockerfile.rocm -t vllm-rocm .
173173
```
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To build vllm on ROCm 6.3 for Radeon RX7900 series (gfx1100), you should pick the alternative base image:
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```console
178-
DOCKER_BUILDKIT=1 docker build --build-arg BASE_IMAGE="rocm/vllm-dev:navi_base" -f Dockerfile.rocm -t vllm-rocm .
178+
DOCKER_BUILDKIT=1 docker build --build-arg BASE_IMAGE="rocm/vllm-dev:navi_base" -f docker/Dockerfile.rocm -t vllm-rocm .
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To run the above docker image `vllm-rocm`, use the below command:

docs/source/getting_started/installation/gpu/xpu.inc.md

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### Build image from source
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```console
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$ docker build -f Dockerfile.xpu -t vllm-xpu-env --shm-size=4g .
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$ docker build -f docker/Dockerfile.xpu -t vllm-xpu-env --shm-size=4g .
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$ docker run -it \
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--rm \
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--network=host \

docs/source/getting_started/quickstart.md

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If desired, you can also manually set the backend of your choice by configuring the environment variable `VLLM_ATTENTION_BACKEND` to one of the following options: `FLASH_ATTN`, `FLASHINFER` or `XFORMERS`.
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```{attention}
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There are no pre-built vllm wheels containing Flash Infer, so you must install it in your environment first. Refer to the [Flash Infer official docs](https://docs.flashinfer.ai/) or see [Dockerfile](https://github.com/vllm-project/vllm/blob/main/Dockerfile) for instructions on how to install it.
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There are no pre-built vllm wheels containing Flash Infer, so you must install it in your environment first. Refer to the [Flash Infer official docs](https://docs.flashinfer.ai/) or see <gh-file:docker/Dockerfile> for instructions on how to install it.
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```

vllm/config.py

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) and backend == "FLASHINFER" and find_spec("flashinfer") is None:
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raise ValueError(
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"VLLM_ATTENTION_BACKEND is set to FLASHINFER, but flashinfer "
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"module was not found."
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"See https://github.com/vllm-project/vllm/blob/main/Dockerfile "
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"module was not found. See "
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"https://github.com/vllm-project/vllm/blob/main/docker/Dockerfile " # noqa: E501
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"for instructions on how to install it.")
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# The tokenizer version is consistent with the model version by default.

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