[doc] improve readability (#18675)

Signed-off-by: reidliu41 <reid201711@gmail.com>
Co-authored-by: reidliu41 <reid201711@gmail.com>

docs/deployment/docker.md

@@ -11,7 +11,7 @@ vLLM offers an official Docker image for deployment.
 The image can be used to run OpenAI compatible server and is available on Docker Hub as [vllm/vllm-openai](https://hub.docker.com/r/vllm/vllm-openai/tags).
 
 ```console
-$ docker run --runtime nvidia --gpus all \
+docker run --runtime nvidia --gpus all \
     -v ~/.cache/huggingface:/root/.cache/huggingface \
     --env "HUGGING_FACE_HUB_TOKEN=<secret>" \
     -p 8000:8000 \
@@ -23,7 +23,7 @@ $ docker run --runtime nvidia --gpus all \
 This image can also be used with other container engines such as [Podman](https://podman.io/).
 
 ```console
-$ podman run --gpus all \
+podman run --gpus all \
   -v ~/.cache/huggingface:/root/.cache/huggingface \
   --env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
   -p 8000:8000 \
@@ -73,7 +73,10 @@ You can build and run vLLM from source via the provided <gh-file:docker/Dockerfi
 
 ```console
 # optionally specifies: --build-arg max_jobs=8 --build-arg nvcc_threads=2
-DOCKER_BUILDKIT=1 docker build . --target vllm-openai --tag vllm/vllm-openai --file docker/Dockerfile
+DOCKER_BUILDKIT=1 docker build . \
+    --target vllm-openai \
+    --tag vllm/vllm-openai \
+    --file docker/Dockerfile
 ```
 
 !!! note
@@ -96,8 +99,8 @@ of PyTorch Nightly and should be considered **experimental**. Using the flag `--
 
 ```console
 # Example of building on Nvidia GH200 server. (Memory usage: ~15GB, Build time: ~1475s / ~25 min, Image size: 6.93GB)
-$ python3 use_existing_torch.py
-$ DOCKER_BUILDKIT=1 docker build . \
+python3 use_existing_torch.py
+DOCKER_BUILDKIT=1 docker build . \
   --file docker/Dockerfile \
   --target vllm-openai \
   --platform "linux/arm64" \
@@ -113,7 +116,7 @@ $ DOCKER_BUILDKIT=1 docker build . \
 To run vLLM with the custom-built Docker image:
 
 ```console
-$ docker run --runtime nvidia --gpus all \
+docker run --runtime nvidia --gpus all \
     -v ~/.cache/huggingface:/root/.cache/huggingface \
     -p 8000:8000 \
     --env "HUGGING_FACE_HUB_TOKEN=<secret>" \

docs/deployment/frameworks/skypilot.md

@@ -82,7 +82,11 @@ Check the output of the command. There will be a shareable gradio link (like the
 **Optional**: Serve the 70B model instead of the default 8B and use more GPU:
 
 ```console
-HF_TOKEN="your-huggingface-token" sky launch serving.yaml --gpus A100:8 --env HF_TOKEN --env MODEL_NAME=meta-llama/Meta-Llama-3-70B-Instruct
+HF_TOKEN="your-huggingface-token" \
+  sky launch serving.yaml \
+  --gpus A100:8 \
+  --env HF_TOKEN \
+  --env MODEL_NAME=meta-llama/Meta-Llama-3-70B-Instruct
 ```
 
 ## Scale up to multiple replicas
@@ -155,7 +159,9 @@ run: |
 Start the serving the Llama-3 8B model on multiple replicas:
 
 ```console
-HF_TOKEN="your-huggingface-token" sky serve up -n vllm serving.yaml --env HF_TOKEN
+HF_TOKEN="your-huggingface-token" \
+  sky serve up -n vllm serving.yaml \
+  --env HF_TOKEN
 ```
 
 Wait until the service is ready:
@@ -318,7 +324,9 @@ run: |
 1. Start the chat web UI:
 
     ```console
-    sky launch -c gui ./gui.yaml --env ENDPOINT=$(sky serve status --endpoint vllm)
+    sky launch \
+      -c gui ./gui.yaml \
+      --env ENDPOINT=$(sky serve status --endpoint vllm)
     ```
 
 2. Then, we can access the GUI at the returned gradio link:

docs/deployment/frameworks/streamlit.md

@@ -33,7 +33,8 @@ pip install streamlit openai
 streamlit run streamlit_openai_chatbot_webserver.py
 
 # or specify the VLLM_API_BASE or VLLM_API_KEY
-VLLM_API_BASE="http://vllm-server-host:vllm-server-port/v1" streamlit run streamlit_openai_chatbot_webserver.py
+VLLM_API_BASE="http://vllm-server-host:vllm-server-port/v1" \
+    streamlit run streamlit_openai_chatbot_webserver.py
 
 # start with debug mode to view more details
 streamlit run streamlit_openai_chatbot_webserver.py --logger.level=debug

docs/deployment/nginx.md

@@ -77,7 +77,11 @@ If you are behind proxy, you can pass the proxy settings to the docker build com
 
 ```console
 cd $vllm_root
-docker build -f docker/Dockerfile . --tag vllm --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy
+docker build \
+    -f docker/Dockerfile . \
+    --tag vllm \
+    --build-arg http_proxy=$http_proxy \
+    --build-arg https_proxy=$https_proxy
 ```
 
 [](){ #nginxloadbalancer-nginx-docker-network }
@@ -102,8 +106,26 @@ Notes:
 ```console
 mkdir -p ~/.cache/huggingface/hub/
 hf_cache_dir=~/.cache/huggingface/
-docker run -itd --ipc host --network vllm_nginx --gpus device=0 --shm-size=10.24gb -v $hf_cache_dir:/root/.cache/huggingface/ -p 8081:8000 --name vllm0 vllm --model meta-llama/Llama-2-7b-chat-hf
-docker run -itd --ipc host --network vllm_nginx --gpus device=1 --shm-size=10.24gb -v $hf_cache_dir:/root/.cache/huggingface/ -p 8082:8000 --name vllm1 vllm --model meta-llama/Llama-2-7b-chat-hf
+docker run \
+    -itd \
+    --ipc host \
+    --network vllm_nginx \
+    --gpus device=0 \
+    --shm-size=10.24gb \
+    -v $hf_cache_dir:/root/.cache/huggingface/ \
+    -p 8081:8000 \
+    --name vllm0 vllm \
+    --model meta-llama/Llama-2-7b-chat-hf
+docker run \
+    -itd \
+    --ipc host \
+    --network vllm_nginx \
+    --gpus device=1 \
+    --shm-size=10.24gb \
+    -v $hf_cache_dir:/root/.cache/huggingface/ \
+    -p 8082:8000 \
+    --name vllm1 vllm \
+    --model meta-llama/Llama-2-7b-chat-hf
 ```
 
 !!! note
@@ -114,7 +136,12 @@ docker run -itd --ipc host --network vllm_nginx --gpus device=1 --shm-size=10.24
 ## Launch Nginx
 
 ```console
-docker run -itd -p 8000:80 --network vllm_nginx -v ./nginx_conf/:/etc/nginx/conf.d/ --name nginx-lb nginx-lb:latest
+docker run \
+    -itd \
+    -p 8000:80 \
+    --network vllm_nginx \
+    -v ./nginx_conf/:/etc/nginx/conf.d/ \
+    --name nginx-lb nginx-lb:latest
 ```
 
 [](){ #nginxloadbalancer-nginx-verify-nginx }