  We present our submission for the configuration task of the Machine Learning
for Combinatorial Optimization (ML4CO) NeurIPS 2021 competition. The
configuration task is to predict a good configuration of the open-source solver
SCIP to solve a mixed integer linear program (MILP) efficiently. We pose this
task as a supervised learning problem: First, we compile a large dataset of the
solver performance for various configurations and all provided MILP instances.
Second, we use this data to train a graph neural network that learns to predict
a good configuration for a specific instance. The submission was tested on the
three problem benchmarks of the competition and improved solver performance
over the default by 12% and 35% and 8% across the hidden test instances. We
ranked 3rd out of 15 on the global leaderboard and won the student leaderboard.
We make our code publicly available at
\url{https://github.com/RomeoV/ml4co-competition} .
