97 lines
3.3 KiB
Markdown
97 lines
3.3 KiB
Markdown
## Install SGLang
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You can install SGLang using any of the methods below.
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### Method 1: With pip
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```
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pip install --upgrade pip
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pip install "sglang[all]"
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# Install FlashInfer CUDA kernels
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pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/
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```
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### Method 2: From source
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```
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# Use the last release branch
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git clone -b v0.3.0 https://github.com/sgl-project/sglang.git
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cd sglang
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pip install --upgrade pip
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pip install -e "python[all]"
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# Install FlashInfer CUDA kernels
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pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/
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```
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### Method 3: Using docker
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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/tree/main/docker).
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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 \
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-p 30000:30000 \
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-v ~/.cache/huggingface:/root/.cache/huggingface \
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--env "HF_TOKEN=<secret>" \
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--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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### Method 4: Using docker compose
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<details>
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<summary>More</summary>
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> This method is recommended if you plan to serve it as a service.
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> A better approach is to use the [k8s-sglang-service.yaml](./docker/k8s-sglang-service.yaml).
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1. Copy the [compose.yml](./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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</details>
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### Method 5: 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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### Common Notes
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- [FlashInfer](https://github.com/flashinfer-ai/flashinfer) is the default attention kernel backend. It only supports sm75 and above. If you encounter any FlashInfer-related issues on sm75+ devices (e.g., T4, A10, A100, L4, L40S, H100), please switch to other kernels by adding `--attention-backend triton --sampling-backend pytorch` and open an issue on GitHub.
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- If you only need to use the OpenAI backend, you can avoid installing other dependencies by using `pip install "sglang[openai]"`.
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