[doc] improve readability (#18675)

Signed-off-by: reidliu41 <reid201711@gmail.com> Co-authored-by: reidliu41 <reid201711@gmail.com>
2025-05-25 16:40:31 +08:00
parent 624b77a2b3
commit 279f854519
20 changed files with 206 additions and 59 deletions
--- a/docs/getting_started/installation/ai_accelerator/hpu-gaudi.inc.md
+++ b/docs/getting_started/installation/ai_accelerator/hpu-gaudi.inc.md
@@ -45,7 +45,15 @@ Use the following commands to run a Docker image:

 ```console
 docker pull vault.habana.ai/gaudi-docker/1.18.0/ubuntu22.04/habanalabs/pytorch-installer-2.4.0:latest
-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 --ipc=host vault.habana.ai/gaudi-docker/1.18.0/ubuntu22.04/habanalabs/pytorch-installer-2.4.0:latest
+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 \
+  --ipc=host \
+  vault.habana.ai/gaudi-docker/1.18.0/ubuntu22.04/habanalabs/pytorch-installer-2.4.0:latest
 ```

 # --8<-- [end:requirements]
@@ -91,7 +99,14 @@ Currently, there are no pre-built Intel Gaudi images.

 ```console
 docker build -f docker/Dockerfile.hpu -t vllm-hpu-env  .
-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
+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
 ```

 !!! tip
--- a/docs/getting_started/installation/ai_accelerator/neuron.inc.md
+++ b/docs/getting_started/installation/ai_accelerator/neuron.inc.md
@@ -38,7 +38,8 @@ The installation of drivers and tools wouldn't be necessary, if [Deep Learning A
 sudo tee /etc/apt/sources.list.d/neuron.list > /dev/null <<EOF
 deb https://apt.repos.neuron.amazonaws.com ${VERSION_CODENAME} main
 EOF
-wget -qO - https://apt.repos.neuron.amazonaws.com/GPG-PUB-KEY-AMAZON-AWS-NEURON.PUB | sudo apt-key add -
+wget -qO - https://apt.repos.neuron.amazonaws.com/GPG-PUB-KEY-AMAZON-AWS-NEURON.PUB \
+    | sudo apt-key add -

 # Update OS packages
 sudo apt-get update -y
@@ -96,12 +97,17 @@ source aws_neuron_venv_pytorch/bin/activate

 # Install Jupyter notebook kernel
 pip install ipykernel
-python3.10 -m ipykernel install --user --name aws_neuron_venv_pytorch --display-name "Python (torch-neuronx)"
+python3.10 -m ipykernel install \
+    --user \
+    --name aws_neuron_venv_pytorch \
+    --display-name "Python (torch-neuronx)"
 pip install jupyter notebook
 pip install environment_kernels

 # Set pip repository pointing to the Neuron repository
-python -m pip config set global.extra-index-url https://pip.repos.neuron.amazonaws.com
+python -m pip config set \
+    global.extra-index-url \
+    https://pip.repos.neuron.amazonaws.com

 # Install wget, awscli
 python -m pip install wget
--- a/docs/getting_started/installation/gpu/cuda.inc.md
+++ b/docs/getting_started/installation/gpu/cuda.inc.md
@@ -55,7 +55,9 @@ LLM inference is a fast-evolving field, and the latest code may contain bug fixe
 ##### Install the latest code using `pip`

 ```console
-pip install -U vllm --pre --extra-index-url https://wheels.vllm.ai/nightly
+pip install -U vllm \
+    --pre \
+    --extra-index-url https://wheels.vllm.ai/nightly
 ```

 `--pre` is required for `pip` to consider pre-released versions.
@@ -63,7 +65,9 @@ pip install -U vllm --pre --extra-index-url https://wheels.vllm.ai/nightly
 Another way to install the latest code is to use `uv`:

 ```console
-uv pip install -U vllm --torch-backend=auto --extra-index-url https://wheels.vllm.ai/nightly
+uv pip install -U vllm \
+    --torch-backend=auto \
+    --extra-index-url https://wheels.vllm.ai/nightly
 ```

 ##### Install specific revisions using `pip`
@@ -83,7 +87,9 @@ If you want to access the wheels for previous commits (e.g. to bisect the behavi

 ```console
 export VLLM_COMMIT=72d9c316d3f6ede485146fe5aabd4e61dbc59069 # use full commit hash from the main branch
-uv pip install vllm --torch-backend=auto --extra-index-url https://wheels.vllm.ai/${VLLM_COMMIT}
+uv pip install vllm \
+    --torch-backend=auto \
+    --extra-index-url https://wheels.vllm.ai/${VLLM_COMMIT}
 ```

 The `uv` approach works for vLLM `v0.6.6` and later and offers an easy-to-remember command. A unique feature of `uv` is that packages in `--extra-index-url` have [higher priority than the default index](https://docs.astral.sh/uv/pip/compatibility/#packages-that-exist-on-multiple-indexes). If the latest public release is `v0.6.6.post1`, `uv`'s behavior allows installing a commit before `v0.6.6.post1` by specifying the `--extra-index-url`. In contrast, `pip` combines packages from `--extra-index-url` and the default index, choosing only the latest version, which makes it difficult to install a development version prior to the released version.
@@ -192,7 +198,11 @@ Additionally, if you have trouble building vLLM, we recommend using the NVIDIA P

 ```console
 # Use `--ipc=host` to make sure the shared memory is large enough.
-docker run --gpus all -it --rm --ipc=host nvcr.io/nvidia/pytorch:23.10-py3
+docker run \
+    --gpus all \
+    -it \
+    --rm \
+    --ipc=host nvcr.io/nvidia/pytorch:23.10-py3
 ```

 If you don't want to use docker, it is recommended to have a full installation of CUDA Toolkit. You can download and install it from [the official website](https://developer.nvidia.com/cuda-toolkit-archive). After installation, set the environment variable `CUDA_HOME` to the installation path of CUDA Toolkit, and make sure that the `nvcc` compiler is in your `PATH`, e.g.:
--- a/docs/getting_started/installation/gpu/rocm.inc.md
+++ b/docs/getting_started/installation/gpu/rocm.inc.md
@@ -91,19 +91,22 @@ Currently, there are no pre-built ROCm wheels.
 4. Build vLLM. For example, vLLM on ROCM 6.3 can be built with the following steps:

    ```bash
-    $ pip install --upgrade pip
+    pip install --upgrade pip

    # Build & install AMD SMI
-    $ pip install /opt/rocm/share/amd_smi
+    pip install /opt/rocm/share/amd_smi

    # Install dependencies
-    $ pip install --upgrade numba scipy huggingface-hub[cli,hf_transfer] setuptools_scm
-    $ pip install "numpy<2"
-    $ pip install -r requirements/rocm.txt
+    pip install --upgrade numba \
+        scipy \
+        huggingface-hub[cli,hf_transfer] \
+        setuptools_scm
+    pip install "numpy<2"
+    pip install -r requirements/rocm.txt

    # Build vLLM for MI210/MI250/MI300.
-    $ export PYTORCH_ROCM_ARCH="gfx90a;gfx942"
-    $ python3 setup.py develop
+    export PYTORCH_ROCM_ARCH="gfx90a;gfx942"
+    python3 setup.py develop
    ```

    This may take 5-10 minutes. Currently, `pip install .` does not work for ROCm installation.
@@ -154,7 +157,9 @@ It is important that the user kicks off the docker build using buildkit. Either
 To build vllm on ROCm 6.3 for MI200 and MI300 series, you can use the default:

 ```console
-DOCKER_BUILDKIT=1 docker build -f docker/Dockerfile.rocm_base -t rocm/vllm-dev:base .
+DOCKER_BUILDKIT=1 docker build \
+    -f docker/Dockerfile.rocm_base \
+    -t rocm/vllm-dev:base .
 ```

 #### Build an image with vLLM
@@ -189,7 +194,11 @@ DOCKER_BUILDKIT=1 docker build -f docker/Dockerfile.rocm -t vllm-rocm .
 To build vllm on ROCm 6.3 for Radeon RX7900 series (gfx1100), you should pick the alternative base image:

 ```console
-DOCKER_BUILDKIT=1 docker build --build-arg BASE_IMAGE="rocm/vllm-dev:navi_base" -f docker/Dockerfile.rocm -t vllm-rocm .
+DOCKER_BUILDKIT=1 docker build \
+    --build-arg BASE_IMAGE="rocm/vllm-dev:navi_base" \
+    -f docker/Dockerfile.rocm \
+    -t vllm-rocm \
+    .
 ```

 To run the above docker image `vllm-rocm`, use the below command: