[CI/Build] Add markdown linter (#11857)

Signed-off-by: Rafael Vasquez <rafvasq21@gmail.com>

docs/source/serving/distributed_serving.md

@@ -35,16 +35,16 @@ output = llm.generate("San Franciso is a")
 To run multi-GPU serving, pass in the `--tensor-parallel-size` argument when starting the server. For example, to run API server on 4 GPUs:
 
 ```console
-$ vllm serve facebook/opt-13b \
-$     --tensor-parallel-size 4
+ vllm serve facebook/opt-13b \
+     --tensor-parallel-size 4
 ```
 
 You can also additionally specify `--pipeline-parallel-size` to enable pipeline parallelism. For example, to run API server on 8 GPUs with pipeline parallelism and tensor parallelism:
 
 ```console
-$ vllm serve gpt2 \
-$     --tensor-parallel-size 4 \
-$     --pipeline-parallel-size 2
+ vllm serve gpt2 \
+     --tensor-parallel-size 4 \
+     --pipeline-parallel-size 2
 ```
 
 ## Running vLLM on multiple nodes
@@ -56,21 +56,21 @@ The first step, is to start containers and organize them into a cluster. We have
 Pick a node as the head node, and run the following command:
 
 ```console
-$ bash run_cluster.sh \
-$                   vllm/vllm-openai \
-$                   ip_of_head_node \
-$                   --head \
-$                   /path/to/the/huggingface/home/in/this/node
+bash run_cluster.sh \
+                vllm/vllm-openai \
+                ip_of_head_node \
+                --head \
+                /path/to/the/huggingface/home/in/this/node
 ```
 
 On the rest of the worker nodes, run the following command:
 
 ```console
-$ bash run_cluster.sh \
-$                   vllm/vllm-openai \
-$                   ip_of_head_node \
-$                   --worker \
-$                   /path/to/the/huggingface/home/in/this/node
+bash run_cluster.sh \
+                vllm/vllm-openai \
+                ip_of_head_node \
+                --worker \
+                /path/to/the/huggingface/home/in/this/node
 ```
 
 Then you get a ray cluster of containers. Note that you need to keep the shells running these commands alive to hold the cluster. Any shell disconnect will terminate the cluster. In addition, please note that the argument `ip_of_head_node` should be the IP address of the head node, which is accessible by all the worker nodes. A common misunderstanding is to use the IP address of the worker node, which is not correct.
@@ -80,16 +80,16 @@ Then, on any node, use `docker exec -it node /bin/bash` to enter the container,
 After that, on any node, you can use vLLM as usual, just as you have all the GPUs on one node. The common practice is to set the tensor parallel size to the number of GPUs in each node, and the pipeline parallel size to the number of nodes. For example, if you have 16 GPUs in 2 nodes (8 GPUs per node), you can set the tensor parallel size to 8 and the pipeline parallel size to 2:
 
 ```console
-$ vllm serve /path/to/the/model/in/the/container \
-$     --tensor-parallel-size 8 \
-$     --pipeline-parallel-size 2
+ vllm serve /path/to/the/model/in/the/container \
+     --tensor-parallel-size 8 \
+     --pipeline-parallel-size 2
 ```
 
 You can also use tensor parallel without pipeline parallel, just set the tensor parallel size to the number of GPUs in the cluster. For example, if you have 16 GPUs in 2 nodes (8 GPUs per node), you can set the tensor parallel size to 16:
 
 ```console
-$ vllm serve /path/to/the/model/in/the/container \
-$     --tensor-parallel-size 16
+vllm serve /path/to/the/model/in/the/container \
+     --tensor-parallel-size 16
 ```
 
 To make tensor parallel performant, you should make sure the communication between nodes is efficient, e.g. using high-speed network cards like Infiniband. To correctly set up the cluster to use Infiniband, append additional arguments like `--privileged -e NCCL_IB_HCA=mlx5` to the `run_cluster.sh` script. Please contact your system administrator for more information on how to set up the flags. One way to confirm if the Infiniband is working is to run vLLM with `NCCL_DEBUG=TRACE` environment variable set, e.g. `NCCL_DEBUG=TRACE vllm serve ...` and check the logs for the NCCL version and the network used. If you find `[send] via NET/Socket` in the logs, it means NCCL uses raw TCP Socket, which is not efficient for cross-node tensor parallel. If you find `[send] via NET/IB/GDRDMA` in the logs, it means NCCL uses Infiniband with GPU-Direct RDMA, which is efficient.