Add sglang dockerfile

README.md

@@ -164,7 +164,15 @@ If you're having trouble with dependencies, and you have [nvidia-container-toolk
 you can start your environment like this: 
 
 ```shell
-sudo docker run --gpus all -it --ipc=host --name functionary -v ${PWD}/functionary_workspace:/workspace -p 8000:8000 nvcr.io/nvidia/pytorch:23.10-py3
+cd <ROOT>
+
+# vLLM
+sudo docker build -t functionary-vllm -f dockerfiles/Dockerfile.vllm .
+sudo docker run --runtime nvidia --gpus all -p 8000:8000 functionary-vllm
+
+# SGLang
+sudo docker build -t functionary-sglang -f dockerfiles/Dockerfile.sgl .
+sudo docker run --runtime nvidia --gpus all -p 8000:8000 functionary-sglang
 ```
 
 ### OpenAI Compatible Usage

dockerfiles/Dockerfile.sgl

@@ -0,0 +1,25 @@
+# Use SGLang's image as the base
+FROM lmsysorg/sglang:v0.3.4.post1-cu124
+
+# Define a build argument for the working directory, defaulting to /workspace
+ARG WORKDIR_ARG=/workspace
+
+# Set the working directory
+WORKDIR ${WORKDIR_ARG}
+
+# Install necessary build dependencies for sentencepiece
+RUN apt-get update && apt-get install -y \
+    pkg-config \
+    cmake \
+    build-essential
+
+# Copy functionary code and requirements into workspace
+COPY . .
+
+# Install additional Python dependencies
+RUN --mount=type=cache,target=/root/.cache/pip \
+    python3 -m pip install .[sglang]
+
+# Override the SGLang entrypoint with the functionary server
+ENTRYPOINT ["python3", "server_sglang.py", "--model-path", "meetkai/functionary-small-v3.2", "--host", "0.0.0.0", "--port", "8000", "--context-length", "8192"]
+CMD []