140 lines
7.1 KiB
Markdown
140 lines
7.1 KiB
Markdown
# Install SGLang
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You can install SGLang using any of the methods below. For running DeepSeek V3/R1 with SGLang, refer to [DeepSeek V3 Support](https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3). It is always recommended to use the [latest release version](https://pypi.org/project/sglang/#history) and deploy it with [Docker](https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3#using-docker-recommended) to avoid fixed issues and environment-related problems.
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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 sgl-kernel --force-reinstall --no-deps
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pip install "sglang[all]>=0.4.3" --find-links https://flashinfer.ai/whl/cu124/torch2.5/flashinfer-python
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```
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Note: SGLang currently uses torch 2.5, so you need to install the flashinfer version for torch 2.5. If you want to install flashinfer separately, please refer to [FlashInfer installation doc](https://docs.flashinfer.ai/installation.html). Please note that the package currently used by FlashInfer is named `flashinfer-python`, not `flashinfer`.
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If you experience an error like `OSError: CUDA_HOME environment variable is not set. Please set it to your CUDA install root`， please try either of the following solutions:
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- Use `export CUDA_HOME=/usr/local/cuda-<your-cuda-version>` to set the `CUDA_HOME` environment variable.
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- Follow the procedure described in [FlashInfer installation doc](https://docs.flashinfer.ai/installation.html) first, then install SGLang as described above.
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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 v0.4.3 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 sgl-kernel --force-reinstall --no-deps
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pip install -e "python[all]" --find-links https://flashinfer.ai/whl/cu124/torch2.5/flashinfer-python
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```
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Note: SGLang currently uses torch 2.5, so you need to install the flashinfer version for torch 2.5. If you want to install flashinfer separately, please refer to [FlashInfer installation doc](https://docs.flashinfer.ai/installation.html).
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If you want to work on development in SGLang, it is highly recommended that you use docker. Please refer to [setup docker container](https://github.com/sgl-project/sglang/blob/main/docs/developer/development_guide_using_docker.md#setup-docker-container) for guidance. The image used is `lmsysorg/sglang:dev`.
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Note: To AMD ROCm system with Instinct/MI GPUs, do following instead:
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```
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# Use the last release branch
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git clone -b v0.4.3 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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cd sgl-kernel
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python setup_rocm.py install
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cd ..
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pip install -e "python[all_hip]"
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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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--shm-size 32g \
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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/Llama-3.1-8B-Instruct --host 0.0.0.0 --port 30000
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```
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Note: To AMD ROCm system with Instinct/MI GPUs, it is recommended to use `docker/Dockerfile.rocm` to build images, example and usage as below:
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```bash
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docker build --build-arg SGL_BRANCH=v0.4.3 -t v0.4.3-rocm630 -f Dockerfile.rocm .
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alias drun='docker run -it --rm --network=host --device=/dev/kfd --device=/dev/dri --ipc=host \
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--shm-size 16G --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined \
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-v $HOME/dockerx:/dockerx -v /data:/data'
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drun -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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v0.4.3-rocm630 \
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python3 -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct --host 0.0.0.0 --port 30000
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# Till flashinfer backend available, --attention-backend triton --sampling-backend pytorch are set by default
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drun v0.4.3-rocm630 python3 -m sglang.bench_one_batch --batch-size 32 --input 1024 --output 128 --model amd/Meta-Llama-3.1-8B-Instruct-FP8-KV --tp 8 --quantization fp8
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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](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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</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/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 OpenAI models with the frontend language, you can avoid installing other dependencies by using `pip install "sglang[openai]"`.
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- The language frontend operates independently of the backend runtime. You can install the frontend locally without needing a GPU, while the backend can be set up on a GPU-enabled machine. To install the frontend, run `pip install sglang`, and for the backend, use `pip install sglang[srt]`. `srt` is the abbreviation of SGLang runtime.
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- To reinstall flashinfer locally, use the following command: `pip install "flashinfer-python>=0.2.1.post1" -i https://flashinfer.ai/whl/cu124/torch2.5 --force-reinstall --no-deps` and then delete the cache with `rm -rf ~/.cache/flashinfer`.
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