Introduction

This repo demonstrates how to run and benchmark inferencing for llama3.2 models, with 8 GPUs.

Pre-req

• This assumes that you are using the latest ML-In-a-Box running on A100x8 or H100x8. If you're not running on ML-In-a-Box, please refer to the setup section of https://github.com/gradient-ai/trition-vllm/tree/main for more details.

Setup tasks

• Spin up the docker for triton-vllm:

docker run -ti \
  -d \
  --privileged \
  --gpus all \
  --network=host \
  --shm-size=10.24g --ulimit memlock=-1 \
  -v ${HOME}/newmodels:/root/models \
  -v ${HOME}/.cache/huggingface:/root/.cache/huggingface \
  nvcr.io/nvidia/tritonserver:24.08-vllm-python-py3

• Open a separate session tab, and ssh into the machine.
• Run docker exec -it $(docker ps -q) sh to shell into the container.
• Run the following commands to install vllm libraries and login to huggingface accounts:

pip install git+https://github.com/triton-inference-server/triton_cli.git@0.0.11
huggingface-cli login --token <token> # replace <token> with your HuggingFace Token obtained from https://huggingface.co/settings/tokens
pip install vllm-flash-attn==2.6.2
pip install vllm==0.6.2

Choose the model to run

• Download the zip files in this repo (e.g. llama3.2-1b.zip) and unzip it under /root/models directory within the docker container.
• Remove the zip file after unzipping.
• Run triton start. For first time running, it will take a few minutes depending on the model size.
• If the triton server starts successfully, You should see something similar to the following lines at the end:

#I0603 11:11:10.717657 779 grpc_server.cc:2463] "Started GRPCInferenceService at 0.0.0.0:8001"
#I0603 11:11:10.717897 779 http_server.cc:4692] "Started HTTPService at 0.0.0.0:8000"
#I0603 11:11:10.782444 779 http_server.cc:362] "Started Metrics Service at 0.0.0.0:8002"

Benchmark the model

• Download input.json file and store it under /opt/tritonserver/input.json. Use wget or scp the file into the machine. Do not Copy and Paste the contents of the file as the unicode can get messed up.
• Using perf analyzer to benchmark llama3.2-1b model based on the input.json file

perf_analyzer -m llama3.2-1b --async --input-data /opt/tritonserver/input.json -i grpc --streaming --concurrency-range 512 --service-kind triton -u localhost:8001 --measurement-interval 15000 --stability-percentage 999 --profile-export-file /opt/tritonserver/artifacts/profile_export.json

• After every run, please remove the output file by running rm /opt/tritonserver/artifacts/profile_export.json