MlosCore

This repository contains a stripped down implementation of essentially just the core optimizer and config space description APIs from the original MLOS as well as the mlos-bench module intended to help automate and manage running experiments for autotuning systems with mlos-core.

It is intended to provide a simplified, easier to consume (e.g. via pip), with lower dependencies abstraction to

• describe a space of context, parameters, their ranges, constraints, etc. and result objectives
• an "optimizer" service abstraction (e.g. register() and suggest()) so we can easily swap out different implementations methods of searching (e.g. random, BO, etc.)
• provide some helpers for automating optimization experiment runner loops and data collection

For these design requirements we intend to reuse as much from existing OSS libraries as possible and layer policies and optimizations specifically geared towards autotuning over top.

Getting Started

The development environment for MlosCore uses conda to ease dependency management.

Devcontainer

For a quick start, you can use the provided VSCode devcontainer configuration.

Simply open the project in VSCode and follow the prompts to build and open the devcontainer and the conda environment and additional tools will be installed automatically inside the container.

Manually

See Also: conda install instructions

Note: to support Windows we currently rely on some pre-compiled packages from conda-forge channels, which increases the conda solver time during environment create/update.

To work around this the (currently) experimental libmamba solver can be used.

See https://github.com/conda-incubator/conda-libmamba-solver#getting-started for more details.

0. Create the mlos_core Conda environment.

   conda env create -f conda-envs/mlos_core.yml
   

   See the conda-envs/ directory for additional conda environment files, including those used for Windows (e.g. mlos_core-windows.yml).

   or

   # This will also ensure the environment is update to date using "conda env update -f conda-envs/mlos_core.yml"
   make conda-env
   

   Note: the latter expects a *nix environment.

1. Initialize the shell environment.

   conda activate mlos_core
   

2. Run the BayesianOptimization.ipynb notebook.

Distributing

1. Build the wheel file(s)

   make dist
   

2. Install it (e.g. after copying it somewhere else).

   # this will install just the optimizer component with emukit support:
   pip install dist/mlos_core-0.0.4-py3-none-any.whl[emukit]
   
   # this will install just the optimizer component with skopt support:
   pip install dist/mlos_core-0.0.4-py3-none-any.whl[skopt]
   

   # this will install both the optimizer and the experiment runner:
   pip install dist/mlos_bench-0.0.4-py3-none-any.whl
   

See Also

• API and Examples Documentation: https://aka.ms/mlos-core/docs
• Source Code Repository: https://aka.ms/mlos-core/src