Adding Documentation for installation (#1300)
Co-authored-by: zhaochen20 <zhaochenyang20@gmail.com>
This commit is contained in:
Chayenne
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Welcome to SGLang's tutorials!
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Welcome to SGLang!
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====================================
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.. figure:: ./_static/image/logo.png
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@@ -27,9 +27,22 @@ SGLang has the following core features:
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* **Flexible Frontend Language**: Enables easy programming of LLM applications with chained generation calls, advanced prompting, control flow, multiple modalities, parallelism, and external interactions.
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* **Extensive Model Support**: SGLang supports a wide range of generative models including the Llama series (up to Llama 3.1), Mistral, Gemma, Qwen, DeepSeek, LLaVA, Yi-VL, StableLM, Command-R, DBRX, Grok, ChatGLM, InternLM 2 and Exaone 3. It also supports embedding models such as e5-mistral and gte-Qwen2. Easily extensible to support new models.
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* **Open Source Community**: SGLang is an open source project with a vibrant community of contributors. We welcome contributions from anyone interested in advancing the state of the art in LLM and VLM serving.
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Documentation
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-------------
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.. In this documentation, we'll dive into these following areas to help you get the most out of SGLang.
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.. _installation:
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.. toctree::
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:maxdepth: 1
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:caption: Installation
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install.md
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.. _hyperparameter_tuning:
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.. toctree::
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:maxdepth: 1
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@@ -58,7 +71,10 @@ Documentation
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sampling_params.md
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Search Bar
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==================
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* :ref:`search`
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.. _benchmark_and_profilling:
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.. toctree::
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:maxdepth: 1
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:caption: Benchmark and Profilling
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benchmark_and_profiling.md
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116
docs/en/install.md
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116
docs/en/install.md
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# SGLang Installation Guide
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SGLang consists of a frontend language (Structured Generation Language, SGLang) and a backend runtime (SGLang Runtime, SRT). The frontend can be used separately from the backend, allowing for a detached frontend-backend setup.
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## Quick Installation Options
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### 1. Frontend Installation (Client-side, any platform)
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```bash
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pip install --upgrade pip
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pip install sglang
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```
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**Note: You can check [these examples](https://github.com/sgl-project/sglang/tree/main/examples/frontend_language/usage) for how to use frontend and backend separately.**
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### 2. Backend Installation (Server-side, Linux only)
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```bash
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pip install --upgrade pip
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pip install "sglang[all]"
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pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/
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```
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**Note: The backend (SRT) is only needed on the server side and is only available for Linux right now.**
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**Important: Please check the [flashinfer installation guidance](https://docs.flashinfer.ai/installation.html) to install the proper version according to your PyTorch and CUDA versions.**
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### 3. From Source (Latest version, Linux only for full installation)
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```bash
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# Use the latest release branch
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# As of this documentation, it's v0.2.15, but newer versions may be available
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# Do not clone the main branch directly; always use a specific release version
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# The main branch may contain unresolved bugs before a new release
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git clone -b v0.2.15 https://github.com/sgl-project/sglang.git
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cd sglang
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pip install -e "python[all]"
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pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/
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```
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### 4. OpenAI Backend Only (Client-side, any platform)
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If you only need to use the OpenAI backend, you can avoid installing other dependencies by using:
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```bash
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pip install "sglang[openai]"
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```
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## Advanced Installation Options
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### 1. Using Docker (Server-side, Linux only)
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The docker images are available on Docker Hub as [lmsysorg/sglang](https://hub.docker.com/r/lmsysorg/sglang/tags), built from [Dockerfile](https://github.com/sgl-project/sglang/blob/main/docker). Replace `<secret>` below with your huggingface hub [token](https://huggingface.co/docs/hub/en/security-tokens).
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```bash
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docker run --gpus all -p 30000:30000 \
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-v ~/.cache/huggingface:/root/.cache/huggingface \
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--env "HF_TOKEN=<secret>" --ipc=host \
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lmsysorg/sglang:latest \
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python3 -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-8B-Instruct --host 0.0.0.0 --port 30000
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```
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### 2.Using docker compose
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This method is recommended if you plan to serve it as a service. A better approach is to use the [k8s-sglang-service.yaml](https://github.com/sgl-project/sglang/blob/main/docker/k8s-sglang-service.yaml).
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1. Copy the [compose.yml](https://github.com/sgl-project/sglang/blob/main/docker/compose.yaml) to your local machine
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2. Execute the command `docker compose up -d` in your terminal.
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### 3.Run on Kubernetes or Clouds with SkyPilot
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<details>
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<summary>More</summary>
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To deploy on Kubernetes or 12+ clouds, you can use [SkyPilot](https://github.com/skypilot-org/skypilot).
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1. Install SkyPilot and set up Kubernetes cluster or cloud access: see [SkyPilot's documentation](https://skypilot.readthedocs.io/en/latest/getting-started/installation.html).
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2. Deploy on your own infra with a single command and get the HTTP API endpoint:
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<details>
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<summary>SkyPilot YAML: <code>sglang.yaml</code></summary>
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```yaml
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# sglang.yaml
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envs:
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HF_TOKEN: null
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resources:
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image_id: docker:lmsysorg/sglang:latest
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accelerators: A100
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ports: 30000
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run: |
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conda deactivate
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python3 -m sglang.launch_server \
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--model-path meta-llama/Meta-Llama-3.1-8B-Instruct \
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--host 0.0.0.0 \
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--port 30000
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```
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</details>
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```bash
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# Deploy on any cloud or Kubernetes cluster. Use --cloud <cloud> to select a specific cloud provider.
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HF_TOKEN=<secret> sky launch -c sglang --env HF_TOKEN sglang.yaml
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# Get the HTTP API endpoint
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sky status --endpoint 30000 sglang
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```
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3. To further scale up your deployment with autoscaling and failure recovery, check out the [SkyServe + SGLang guide](https://github.com/skypilot-org/skypilot/tree/master/llm/sglang#serving-llama-2-with-sglang-for-more-traffic-using-skyserve).
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</details>
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## Troubleshooting
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- For FlashInfer issues on newer GPUs, use `--disable-flashinfer --disable-flashinfer-sampling` when launching the server.
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- For out-of-memory errors, try `--mem-fraction-static 0.7` when launching the server.
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For more details and advanced usage, visit the [SGLang GitHub repository](https://github.com/sgl-project/sglang).
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@@ -7,6 +7,4 @@ sphinx-tabs
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sphinxcontrib-mermaid
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pillow
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pydantic
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torch
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transformers
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urllib3<2.0.0
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