Graph Neural Network

The benchmark reference for Graph Neural Network can be found in this link, and here is the PR for the minified benchmark implementation: link.

Project setup

An important requirement is that you must have Docker installed.

# Create Python environment and install MLCube Docker runner 
virtualenv -p python3 ./env && source ./env/bin/activate && pip install pip==24.0 && pip install mlcube-docker
# Fetch the implementation from GitHub
git clone https://github.com/mlcommons/training && cd ./training
git fetch origin pull/762/head:feature/mlcube_graph_nn && git checkout feature/mlcube_graph_nn
cd ./graph_neural_network/mlcube

Inside the mlcube directory run the following command to check implemented tasks.

mlcube describe

MLCube tasks

Download dataset.

mlcube run --task=download_data -Pdocker.build_strategy=always

Process dataset.

mlcube run --task=process_data -Pdocker.build_strategy=always

Train GNN.

mlcube run --task=train -Pdocker.build_strategy=always

Execute the complete pipeline

You can execute the complete pipeline with one single command.

mlcube run --task=download_data,process_data,train -Pdocker.build_strategy=always