@@ -11,7 +11,7 @@ It is recommended to use uv to install the dependencies for faster installation:
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``` bash
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pip install --upgrade pip
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pip install uv
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- uv pip install " sglang[all]>=0.4.9.post2 "
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+ uv pip install " sglang[all]>=0.4.9.post3 "
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```
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** Quick Fixes to Common Problems**
@@ -27,7 +27,7 @@ uv pip install "sglang[all]>=0.4.9.post2"
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``` bash
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# Use the last release branch
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- git clone -b v0.4.9.post2 https://github.com/sgl-project/sglang.git
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+ git clone -b v0.4.9.post3 https://github.com/sgl-project/sglang.git
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cd sglang
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pip install --upgrade pip
@@ -42,7 +42,7 @@ Note: For AMD ROCm system with Instinct/MI GPUs, do following instead:
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``` bash
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# Use the last release branch
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- git clone -b v0.4.9.post2 https://github.com/sgl-project/sglang.git
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+ git clone -b v0.4.9.post3 https://github.com/sgl-project/sglang.git
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cd sglang
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pip install --upgrade pip
@@ -71,7 +71,7 @@ docker run --gpus all \
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Note: For 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=v0.4.9.post2 -t v0.4.9.post2 -rocm630 -f Dockerfile.rocm .
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+ docker build --build-arg SGL_BRANCH=v0.4.9.post3 -t v0.4.9.post3 -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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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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- v0.4.9.post2 -rocm630 \
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+ v0.4.9.post3 -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 v0.4.9.post2 -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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+ drun v0.4.9.post3 -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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