MLOS is a project to enable autotuning for systems.
MLOS currently focuses on an offline tuning approach, though we intend to add online tuning in the future.
To accomplish this, the general flow involves
Source: LlamaTune: VLDB 2022
For a brief overview of some of the features and capabilities of MLOS, please see the following video:
To do this this repo provides two Python modules, which can be used independently or in combination:
mlos-bench
provides a framework to help automate running benchmarks as described above.
mlos-viz
provides some simple APIs to help automate visualizing the results of benchmark experiments and their trials.
It provides a simple plot(experiment_data)
API, where experiment_data
is obtained from the mlos_bench.storage
module.
mlos-core
provides an abstraction around existing optimization frameworks (e.g., FLAML, SMAC, etc.)
It is intended to provide a simple, easy to consume (e.g. via pip
), with low dependencies abstraction to
register()
and suggest()
) so we can easily swap out different implementations methods of searching (e.g. random, BO, LLM, etc.)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 systems over top.
By providing wrappers we aim to also allow more easily experimenting with replacing underlying optimizer components as new techniques become available or seem to be a better match for certain systems.
See CONTRIBUTING.md for details on development environment and contributing.
The development environment for MLOS uses conda
and devcontainers
to ease dependency management, but not all these libraries are required for deployment.
For instructions on setting up the development environment please try one of the following options:
conda
activationCreate the mlos
Conda environment.
conda env create -f conda-envs/mlos.yml
See the
conda-envs/
directory for additional conda environment files, including those used for Windows (e.g.mlos-windows.yml
).
or
# This will also ensure the environment is update to date using "conda env update -f conda-envs/mlos.yml"
make conda-env
Note: the latter expects a *nix environment.
Initialize the shell environment.
conda activate mlos
mlos-core
For an example of using the mlos_core
optimizer APIs run the BayesianOptimization.ipynb
notebook.
mlos-bench
For an example of using the mlos_bench
tool to run an experiment, see the mlos_bench
Quickstart README.
Here's a quick summary:
./scripts/generate-azure-credentials-config > global_config_azure.jsonc
# run a simple experiment
mlos_bench --config ./mlos_bench/mlos_bench/config/cli/azure-redis-1shot.jsonc
See Also:
sqlite
.mlos-viz
For a simple example of using the mlos_viz
module to visualize the results of an experiment, see the sqlite-autotuning
repository, especially the mlos_demo_sqlite_teachers.ipynb notebook.
The MLOS modules are published to pypi when new releases are tagged:
To install the latest release, simply run:
# this will install just the optimizer component with SMAC support:
pip install -U mlos-core[smac]
# this will install just the optimizer component with flaml support:
pip install -U "mlos-core[flaml]"
# this will install just the optimizer component with smac and flaml support:
pip install -U "mlos-core[smac,flaml]"
# this will install both the flaml optimizer and the experiment runner with azure support:
pip install -U "mlos-bench[flaml,azure]"
# this will install both the smac optimizer and the experiment runner with ssh support:
pip install -U "mlos-bench[smac,ssh]"
# this will install the postgres storage backend for mlos-bench
# and mlos-viz for visualizing results:
pip install -U "mlos-bench[postgres]" mlos-viz
Details on using a local version from git are available in CONTRIBUTING.md.
Working example of tuning sqlite
with MLOS.
These can be used as starting points for new autotuning projects.