The Machine Learning for Combinatorial Optimization (ML4CO) competition aims at improving a state-of-the-art mathematical solver by replacing key heuristic components with machine learning models trained on historical data. To that end participants will compete on the three following challenges, each corresponding to a distinct control task arising in a branch-and-bound solver: producing good solutions (primal task), proving optimality via branching (dual task), and choosing the best solver parameters (configuration task). Each task is exposed through an OpenAI-gym Python API build on top of the open-source solver SCIP, using the Ecole library. Participants can compete in any subset of the proposed challenges. While we encourage solutions derived from the reinforcement learning paradigm, any algorithmic solution respecting the competition's API is accepted.