[CI/Build] Add markdown linter

.github/workflows/sphinx-lint.yml → .github/workflows/doc-lint.yml

@@ -13,7 +13,7 @@ on:
       - "docs/**"
 
 jobs:
-  sphinx-lint:
+  doc-lint:
     runs-on: ubuntu-latest
     strategy:
       matrix:
@@ -29,4 +29,4 @@ jobs:
           python -m pip install --upgrade pip
           pip install -r requirements-lint.txt
       - name: Linting docs
-        run: tools/sphinx-lint.sh
+        run: tools/doc-lint.sh

docs/README.md

@@ -16,4 +16,5 @@ make html
 ```bash
 python -m http.server -d build/html/
 ```
+
 Launch your browser and open localhost:8000.

docs/source/api/model/index.md

@@ -9,4 +9,3 @@ interfaces_base
 interfaces
 adapters
 ```
-

docs/source/community/sponsors.md

@@ -6,13 +6,15 @@ vLLM is a community project. Our compute resources for development and testing a
 <!-- Note: Please keep these consistent with README.md. -->
 
 Cash Donations:
+
 - a16z
 - Dropbox
 - Sequoia Capital
 - Skywork AI
 - ZhenFund
 
 Compute Resources:
+
 - AMD
 - Anyscale
 - AWS

docs/source/contributing/model/multimodal.md

@@ -200,6 +200,7 @@ def get_mm_max_tokens_per_item(self, seq_len: int) -> Mapping[str, int]:
 ```{note}
 Our [actual code](gh-file:vllm/model_executor/models/llava.py) is more abstracted to support vision encoders other than CLIP.
 ```
+
 :::
 ::::
 
@@ -248,6 +249,7 @@ def get_dummy_processor_inputs(
         mm_data=mm_data,
     )
 ```
+
 :::
 ::::
 
@@ -312,6 +314,7 @@ def _get_mm_fields_config(
 Our [actual code](gh-file:vllm/model_executor/models/llava.py) additionally supports
 pre-computed image embeddings, which can be passed to be model via the `image_embeds` argument.
 ```
+
 :::
 ::::
 
@@ -369,6 +372,7 @@ def _get_prompt_replacements(
         ),
     ]
 ```
+
 :::
 ::::
 

docs/source/contributing/overview.md

@@ -37,8 +37,6 @@ pytest tests/
 Currently, the repository is not fully checked by `mypy`.
 ```
 
-# Contribution Guidelines
-
 ## Issues
 
 If you encounter a bug or have a feature request, please [search existing issues](https://github.com/vllm-project/vllm/issues?q=is%3Aissue) first to see if it has already been reported. If not, please [file a new issue](https://github.com/vllm-project/vllm/issues/new/choose), providing as much relevant information as possible.

docs/source/deployment/docker.md

@@ -28,8 +28,8 @@ memory to share data between processes under the hood, particularly for tensor p
 You can build and run vLLM from source via the provided <gh-file:Dockerfile>. To build vLLM:
 
 ```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
+# optionally specifies: --build-arg max_jobs=8 --build-arg nvcc_threads=2
+DOCKER_BUILDKIT=1 docker build . --target vllm-openai --tag vllm/vllm-openai
 ```
 
 ```{note}

docs/source/deployment/frameworks/cerebrium.md

@@ -13,14 +13,14 @@ vLLM can be run on a cloud based GPU machine with [Cerebrium](https://www.cerebr
 To install the Cerebrium client, run:
 
 ```console
-$ pip install cerebrium
-$ cerebrium login
+pip install cerebrium
+cerebrium login
 ```
 
 Next, create your Cerebrium project, run:
 
 ```console
-$ cerebrium init vllm-project
+cerebrium init vllm-project
 ```
 
 Next, to install the required packages, add the following to your cerebrium.toml:
@@ -58,10 +58,10 @@ def run(prompts: list[str], temperature: float = 0.8, top_p: float = 0.95):
 Then, run the following code to deploy it to the cloud:
 
 ```console
-$ cerebrium deploy
+cerebrium deploy
 ```
 
-If successful, you should be returned a CURL command that you can call inference against. Just remember to end the url with the function name you are calling (in our case` /run`)
+If successful, you should be returned a CURL command that you can call inference against. Just remember to end the url with the function name you are calling (in our case`/run`)
 
 ```python
 curl -X POST https://api.cortex.cerebrium.ai/v4/p-xxxxxx/vllm/run \

docs/source/deployment/frameworks/dstack.md

@@ -13,16 +13,16 @@ vLLM can be run on a cloud based GPU machine with [dstack](https://dstack.ai/),
 To install dstack client, run:
 
 ```console
-$ pip install "dstack[all]
-$ dstack server
+pip install "dstack[all]
+dstack server
 ```
 
 Next, to configure your dstack project, run:
 
 ```console
-$ mkdir -p vllm-dstack
-$ cd vllm-dstack
-$ dstack init
+mkdir -p vllm-dstack
+cd vllm-dstack
+dstack init
 ```
 
 Next, to provision a VM instance with LLM of your choice (`NousResearch/Llama-2-7b-chat-hf` for this example), create the following `serve.dstack.yml` file for the dstack `Service`:

docs/source/deployment/frameworks/skypilot.md

@@ -334,12 +334,12 @@ run: |
 
 1. Start the chat web UI:
 
-```console
-sky launch -c gui ./gui.yaml --env ENDPOINT=$(sky serve status --endpoint vllm)
-```
+    ```console
+    sky launch -c gui ./gui.yaml --env ENDPOINT=$(sky serve status --endpoint vllm)
+    ```
 
 2. Then, we can access the GUI at the returned gradio link:
 
-```console
-| INFO | stdout | Running on public URL: https://6141e84201ce0bb4ed.gradio.live
-```
+    ```console
+    | INFO | stdout | Running on public URL: https://6141e84201ce0bb4ed.gradio.live
+    ```

docs/source/deployment/integrations/llamastack.md

@@ -7,7 +7,7 @@ vLLM is also available via [Llama Stack](https://github.com/meta-llama/llama-sta
 To install Llama Stack, run
 
 ```console
-$ pip install llama-stack -q
+pip install llama-stack -q
 ```
 
 ## Inference using OpenAI Compatible API