development_guide_using_docker.md

Development Guide Using Docker

Setup VSCode

Download code from Https://code.visualstudio.com/docs/?dv=linux64cli

wget https://vscode.download.prss.microsoft.com/dbazure/download/stable/fabdb6a30b49f79a7aba0f2ad9df9b399473380f/vscode_cli_alpine_x64_cli.tar.gz
tar xf vscode_cli_alpine_x64_cli.tar.gz

# https://code.visualstudio.com/docs/remote/tunnels
./code tunnel

Setup Docker Container

The following startup command is an example for internal development by the SGLang team. You can modify or add directory mappings as needed, especially for model weight downloads, to prevent repeated downloads by different Docker containers.

H100

# Change the name to yours
docker run -itd --shm-size 32g --gpus all -v /opt/dlami/nvme/.cache:/root/.cache --ipc=host --name sglang_zhyncs lmsysorg/sglang:dev /bin/zsh
docker exec -it sglang_zhyncs /bin/zsh

H200

docker run -itd --shm-size 32g --gpus all -v /mnt/co-research/shared-models:/root/.cache/huggingface --ipc=host --name sglang_zhyncs lmsysorg/sglang:dev /bin/zsh
docker exec -it sglang_zhyncs /bin/zsh

Profile

# Change batch size, input, output and add `disable-cuda-graph` (for easier analysis)
# e.g. DeepSeek V3
nsys profile -o deepseek_v3 python3 -m sglang.bench_one_batch --batch-size 1 --input 128 --output 256 --model deepseek-ai/DeepSeek-V3 --trust-remote-code --tp 8 --disable-cuda-graph

Evaluation

# e.g. gsm8k 8 shot
python3 benchmark/gsm8k/bench_sglang.py --num-questions 2000 --parallel 2000 --num-shots 8