CP HiCache write-through keeps a host copy, but current disaggregation registers and addresses prefill GPU KV pages. The document records why CPU-source transfer needs a distinct source contract, host backup readiness, host-slot pinning, and CP logical-page mapping before it can safely release prefill GPU L1 pages early. Constraint: Current Mooncake KVArgs register GPU token_to_kv_pool buffers as the prefill transfer source Rejected: Reuse device page_indices as host page ids | host/L2 allocator uses separate physical slots and would corrupt decode destinations Confidence: medium Scope-risk: narrow Directive: Do not switch prefill-to-decode transfer to host source without explicit backup-ack and host-slot lifetime contracts Tested: Documentation-only change; inspected current prefill/decode/Mooncake/HiCache paths Not-tested: CPU-source transfer implementation (cherry picked from commit 01d08e3526ad95c0ab924e6ba859139615fc65c9)
SGLang Documentation
This is the documentation website for the SGLang project (https://github.com/sgl-project/sglang).
We recommend new contributors start from writing documentation, which helps you quickly understand SGLang codebase.
Most documentation files are located under the docs/ folder.
Docs Workflow
Install Dependency
Linux:
apt-get update && apt-get install -y pandoc parallel retry
pip install -r requirements.txt
macOS:
brew install pandoc parallel retry
pip install -r requirements.txt
Update Documentation
Update your Jupyter notebooks in the appropriate subdirectories under docs/. If you add new files, remember to update index.rst (or relevant .rst files) accordingly.
pre-commit run --all-filesmanually runs all configured checks, applying fixes if possible. If it fails the first time, re-run it to ensure lint errors are fully resolved. Make sure your code passes all checks before creating a Pull Request.
# 1) Compile all Jupyter notebooks
make compile # This step can take a long time (10+ mins). You can consider skipping this step if you can make sure your added files are correct.
make html
# 2) Compile and Preview documentation locally with auto-build
# This will automatically rebuild docs when files change
# Open your browser at the displayed port to view the docs
bash serve.sh
# 2a) Alternative ways to serve documentation
# Directly use make serve
make serve
# With custom port
PORT=8080 make serve
# 3) Clean notebook outputs
# nbstripout removes notebook outputs so your PR stays clean
pip install nbstripout
find . -name '*.ipynb' -exec nbstripout {} \;
# 4) Pre-commit checks and create a PR
# After these checks pass, push your changes and open a PR on your branch
pre-commit run --all-files
Documentation Style Guidelines
- For common functionalities, we prefer Jupyter Notebooks over Markdown so that all examples can be executed and validated by our docs CI pipeline. For complex features (e.g., distributed serving), Markdown is preferred.
- Keep in mind the documentation execution time when writing interactive Jupyter notebooks. Each interactive notebook will be run and compiled against every commit to ensure they are runnable, so it is important to apply some tips to reduce the documentation compilation time:
- Use small models (e.g.,
qwen/qwen2.5-0.5b-instruct) for most cases to reduce server launch time. - Reuse the launched server as much as possible to reduce server launch time.
- Use small models (e.g.,
- Do not use absolute links (e.g.,
https://docs.sglang.io/get_started/install.html). Always prefer relative links (e.g.,../get_started/install.md). - Follow the existing examples to learn how to launch a server, send a query and other common styles.
Documentation Build, Deployment, and CI
The SGLang documentation pipeline is based on Sphinx and supports rendering Jupyter notebooks (.ipynb) into HTML/Markdown for web display. Detailed logits can be found in the Makefile.
Notebook Execution (make compile)
The make compile target is responsible for executing notebooks before rendering:
- Finds all
.ipynbfiles underdocs/(excluding_build/) - Executes notebooks in parallel using GNU Parallel, with a relatively small
--mem-fraction-static - Wraps execution with
retryto reduce flaky failures - Executes notebooks via
jupyter nbconvert --execute --inplace - Records execution timing in
logs/timing.log
This step ensures notebooks contain up-to-date outputs with each commit in the main branch before rendering.
Web Rendering (make html)
After compilation, Sphinx builds the website:
- Reads Markdown, reStructuredText, and Jupyter notebooks
- Renders them into HTML pages
- Outputs the website into:
docs/_build/html/
This directory is the source for online documentation hosting.
Markdown Export (make markdown)
To support downstream consumers, we add a new Makefile target:
make markdown
This target:
- Does not modify
make compile - Scans all
.ipynbfiles (excluding_build/) - Converts notebooks directly to Markdown using
jupyter nbconvert --to markdown - Writes Markdown artifacts into the existing build directory:
docs/_build/html/markdown/<relative-path>.md
Example:
docs/advanced_features/lora.ipynb
→ docs/_build/html/markdown/advanced_features/lora.md
CI Execution
In our CI, the documentation pipeline first gets all the executed results and renders HTML and Markdown by:
make compile # execute notebooks (ensure outputs are up to date)
make html # build website as usual
make markdown # export markdown artifacts into _build/html/markdown
Then, the compiled results are forced pushed to sgl-project.io for rendering. In other words, sgl-project.io is push-only. All the changes of SGLang docs should be made directly in SGLang main repo, then push to the sgl-project.io.