Adding Documentation for installation (#1300)

Co-authored-by: zhaochen20 <zhaochenyang20@gmail.com>

docs/en/index.rst

-Welcome to SGLang's tutorials!
+Welcome to SGLang!
 ====================================
 
 .. figure:: ./_static/image/logo.png
@@ -27,9 +27,22 @@ SGLang has the following core features:
 
 * **Flexible Frontend Language**: Enables easy programming of LLM applications with chained generation calls, advanced prompting, control flow, multiple modalities, parallelism, and external interactions.
 
+* **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.
+
+* **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.
+
 Documentation
 -------------
 
+.. In this documentation, we'll dive into these following areas to help you get the most out of SGLang.
+
+.. _installation:
+.. toctree::
+   :maxdepth: 1
+   :caption: Installation
+
+   install.md
+
 .. _hyperparameter_tuning:
 .. toctree::
    :maxdepth: 1
@@ -58,7 +71,10 @@ Documentation
 
    sampling_params.md
 
-Search Bar
-==================
 
-* :ref:`search`
+.. _benchmark_and_profilling:
+.. toctree::
+   :maxdepth: 1
+   :caption: Benchmark and Profilling
+
+   benchmark_and_profiling.md
\ No newline at end of file

docs/en/install.md

+# SGLang Installation Guide
+
+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.
+
+## Quick Installation Options
+
+### 1. Frontend Installation (Client-side, any platform)
+
+```bash
+pip install --upgrade pip
+pip install sglang
+```
+
+**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.**
+
+### 2. Backend Installation (Server-side, Linux only)
+
+```bash
+pip install --upgrade pip
+pip install "sglang[all]"
+pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/
+```
+
+**Note: The backend (SRT) is only needed on the server side and is only available for Linux right now.**
+
+**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.**
+
+### 3. From Source (Latest version, Linux only for full installation)
+
+```bash
+# Use the latest release branch
+# As of this documentation, it's v0.2.15, but newer versions may be available
+# Do not clone the main branch directly; always use a specific release version
+# The main branch may contain unresolved bugs before a new release
+git clone -b v0.2.15 https://github.com/sgl-project/sglang.git
+cd sglang
+pip install -e "python[all]"
+pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/
+```
+
+### 4. OpenAI Backend Only (Client-side, any platform)
+
+If you only need to use the OpenAI backend, you can avoid installing other dependencies by using:
+
+```bash
+pip install "sglang[openai]"
+```
+
+## Advanced Installation Options
+
+### 1. Using Docker (Server-side, Linux only)
+
+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).
+
+```bash
+docker run --gpus all -p 30000:30000 \
+    -v ~/.cache/huggingface:/root/.cache/huggingface \
+    --env "HF_TOKEN=<secret>" --ipc=host \
+    lmsysorg/sglang:latest \
+    python3 -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-8B-Instruct --host 0.0.0.0 --port 30000
+```
+
+### 2.Using docker compose
+
+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).
+
+1. Copy the [compose.yml](https://github.com/sgl-project/sglang/blob/main/docker/compose.yaml) to your local machine
+2. Execute the command `docker compose up -d` in your terminal.
+
+### 3.Run on Kubernetes or Clouds with SkyPilot
+
+<details>
+<summary>More</summary>
+
+To deploy on Kubernetes or 12+ clouds, you can use [SkyPilot](https://github.com/skypilot-org/skypilot).
+
+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).
+2. Deploy on your own infra with a single command and get the HTTP API endpoint:
+<details>
+<summary>SkyPilot YAML: <code>sglang.yaml</code></summary>
+
+```yaml
+# sglang.yaml
+envs:
+  HF_TOKEN: null
+
+resources:
+  image_id: docker:lmsysorg/sglang:latest
+  accelerators: A100
+  ports: 30000
+
+run: |
+  conda deactivate
+  python3 -m sglang.launch_server \
+    --model-path meta-llama/Meta-Llama-3.1-8B-Instruct \
+    --host 0.0.0.0 \
+    --port 30000
+```
+</details>
+
+```bash
+# Deploy on any cloud or Kubernetes cluster. Use --cloud <cloud> to select a specific cloud provider.
+HF_TOKEN=<secret> sky launch -c sglang --env HF_TOKEN sglang.yaml
+
+# Get the HTTP API endpoint
+sky status --endpoint 30000 sglang
+```
+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).
+</details>
+
+## Troubleshooting
+
+- For FlashInfer issues on newer GPUs, use `--disable-flashinfer --disable-flashinfer-sampling` when launching the server.
+- For out-of-memory errors, try `--mem-fraction-static 0.7` when launching the server.
+
+For more details and advanced usage, visit the [SGLang GitHub repository](https://github.com/sgl-project/sglang).
\ No newline at end of file

docs/requirements.txt

@@ -7,6 +7,4 @@ sphinx-tabs
 sphinxcontrib-mermaid
 pillow
 pydantic
-torch
-transformers
 urllib3<2.0.0