hyperparameter VS lance

the full tutorial: https://github.com/reiase/hyperparameter/tree/master/examples/optuna

When I was working on my own configuration framework (HyperParameter, previous post), I suddenly realize that what I want is not another configuration framework with some fancy API. All I want is to change my ML experiments without modifying the code and get rid of the configuration handling codes. The right way of configuration is not writing configurable code and wasting time on different frameworks. The best solution is a tool that makes your code configurable.

github: https://github.com/reiase/hyperparameter

HyperParameter is a configuration and parameter management library for Python. HyperParameter provides the following features:

you can take a look at https://github.com/reiase/hyperparameter, a scoped, thread-safe config object that is lightweight enough. There is no need to modify too much code:

I'm developing a Hyperparameter tuning toolbox for my machine learning projects. It maps keyword arguments to hyper-parameters, for example:

I'm developing [a toolbox for managing hyper-parameters](https://github.com/reiase/hyperparameter) in my data science and machine learning projects. It provides object-style API for nested dict( which is very common for config files):

you should look at lance(https://lancedb.github.io/lance/)

Parquet has been the lakehouse file format of choice for nearly half a decade. But we are starting to see other contenders that are optimized more for lower latency like lance https://github.com/lancedb/lance

getting bunch of 404s on the docs. for example https://eto-ai.github.io/lance/format.html (But this works: https://lancedb.github.io/lance/*)

Did you guys just pivot from eto-ai to lancedb?

For Feature Stores check out: https://github.com/eto-ai/lance