Traccc as-a-Service

Main objective of this repo: run traccc as-a-Service. Getting this working includes creating three main components:

1. a shared library of traccc and writing a standalone version with the essential pieces of the code included
2. a custom backend using the standalone version above to launch the trition server
3. a client to send data to the server

A minimal description of how to build a working version is detailed below. In each subdirectory of this project, a README containing more information can be found.

Previous work

A large portion of this work is based on the CPU version included here developed by Haoran Zhao. The original repo can be found here. This CPU version has been incorporated into the workflow here such that both a CPU and GPU version are available.

Running out of the box

First, clone the repository with

git clone --recurse-submodules git@github.com:milescb/traccc-aaS.git

Docker

A docker built for the triton server can be found at docexoty/tritonserver:latest. To run this do

shifter --module=gpu --image=docexoty/tritonserver:latest

or use your favorite docker application and mount the appropriate directories.

Shared Library

To run out of the box, an installation of traccc and the the backend can be found at /global/cfs/projectdirs/m3443/data/traccc-aaS/prod/ver_07032024/install. To set up the environment, run the docker then set the following environment variables

export DATADIR=/global/cfs/projectdirs/m3443/data/traccc-aaS/data
export INSTALLDIR=/global/cfs/projectdirs/m3443/data/traccc-aaS/prod/ver_07032024/install
export PATH=$INSTALLDIR/bin:$PATH
export LD_LIBRARY_PATH=$INSTALLDIR/lib:$LD_LIBRARY_PATH

Then the server can be launched with

tritonserver --model-repository=$INSTALLDIR/models

Once the server is launched, run the model via:

cd client && python TracccTritionClient.py 

The --architecture tag can be used to toggle the cpu and gpu version via -a cpu, for instance. More info in the client directory.

Building the backend

First, enter the docker and set environment variables as documented above. Then run

cd backend/traccc-gpu && mkdir build install && cd build
cmake -B . -S ../ \
    -DCMAKE_INSTALL_PREFIX=../install/ \
    -DCMAKE_INSTALL_PREFIX=../install/

cmake --build . --target install -- -j20

Both the CPU and GPU versions must be built separately. Then, the server can be launched as above:

tritonserver --model-repository=../../models