I am the lead developer of Oríon, an open-source framework developed at Mila for distributed black-box optimization. Its purpose is to serve as a meta-optimizer for machine learning models and training, as well as a flexible experimentation platform for large-scale asynchronous optimization procedures. Core design value is the minimum disruption of a researcher’s workflow. It allows fast and efficient tuning, providing a minimum simple non-intrusive helper client interface for a user’s script. Oríon is agnostic to computation infrastructures and can be used seamlessly in both traditional HPC infrastructure (e.g. Slurm) or modern containerized environments (e.g. Kubernetes). Oríon is also agnostic to underlying deep learning frameworks and therefore natively supports all of them.