_____
______ ___(_)________ _______ _____ __
_ __ `/_ /_ __ \_ | /| / / __ `/_ / / /
/ /_/ /_ / / /_/ /_ |/ |/ // /_/ /_ /_/ /
\__,_/ /_/ \____/____/|__/ \__,_/ _\__, /
/____/
An optimizing compiler for ML algorithms.
Aioway is an optimizing compiler for deep learning algorithms. It treats the machine learning models / algorithms as instructions and build the pipeline that way.
Most of these features are done but not polished yet! But will be in couple of weeks.
In the recent years, machine learning's entry barrier higher, rather than lower. People with expert training are expensive, as they need years of experience to be good.
However, current AutoML solutions are subpar. Each one of them have clear limitations: slow, inflexible, unreliable, or unable to handle modern data.
Drawing inspriation from opitmizing compilers (especially SQLs), aioway aims to solve that.
That's all for now!
If you have read this far, please consider giving me a star (⭐) or a fork (🍴).
This will keep me motivated!
Or if you have too much cash at hand:
We are most likely launching v0.1.0 before July 2026, but before that,
see the pre-release tracking project for more details.
Contributing is of course welcome. Please see the contributing guide and follow the code of conduct.
Aioway builds on top of the original koila (moved to a branch). The torch team built FakeTensor which overlaps a lot with koila's functionality, so it's no longer maintained. See the rationale in the koila branch.
Conceptually, aioway works in a similar way, but instead of Tensor ops, aioway focuses on a higher level, on algorithm building.
$ claude mcp add aioway \
-- python -m otcore.mcp_server <graph>