mdmitry1/python311-dev-mac repository overview

⁠About SMLP

SMLP is a tool for optimization, synthesis and design space exploration. It is based on statistical and machine learning techniques combined with formal verification approaches that allows selection of optimal configurations with respect to given constraints on the inputs and outputs of the system under consideration.

SMLP has been applied at Intel for hardware design optimization. It is a general purpose optimization and verification tool applicable to any domain where ML models can be trained on data. The supported modes in SMLP include processing data and training models (neural networks, tree-based, and polynomial models), querying data and models, certifying and verifying assertions, synthesis and Pareto-optimization of configurations, design of experiments (to simulate systems and produce data), feature selection, rule learning/subgroup discovery, root-cause analysis, and more.

If you want to try out SMLP on your optimization problems and require support, please contact the developers through the discussion page⁠.

⁠Docker support for Macs with Apple M series processor

docker run --platform linux/arm64 -it mdmitry1/python311-dev-mac:latest

Within docker container prepend SMLP Python script with xvfb-run. For example:

xvfb-run smlp/src/run_smlp.py -h

More details - see smlp/README.md