semvec/pypi-packages huggingface dataset:
hpsearch 0.0.7
A description of your project
License: MIT License
Requires Python: >=3.6
Keywords: some keywords

# Welcome to HPSearch
> Experiment tracking framework.


`HPSearch` is a flexible experiment tracking framework that provides features such as a simple and powerful query mechanism for studying and visualizing the performance of past experiments meeting given criteria, the possibility to resume a previous experiment with or without modifying the original hyper-parameters (e.g., extending the number of epochs of a promising past experiment, or gradually changing the hyper-parameters to obtain an approximate curriculum learning type of approach, etc.), the capability of visualizing the learning evolution of multiple experiments based on different metrics, and many other features.

## Key features

`HPSearch` provides, among others, the following features:

- Query the performance of past experiments meeting desired criteria. This can be done either from command line with a simple command, or programmatically. Queried experiments are shown as a table sorted by performance, and visualized in graphical form, comparing the evolution of the metrics of the selected experiments during training.
- Visualize the evolution of current experiments and compare them against previous ones using multiple metrics
- Resume previous experiments with or without modifying their original hyper-parameters. This can be applied, for instance, when we start by performing a quick exploration of hyper-parameters by allocating a small time budget for each experiment. Once this is done, we may want to increase the exploration of  a subset of experiments that were promising, e.g., by extending their number of epochs, or using curriculum learning to gradually changing their hyper-parameters across epochs. We may also want to change only to explore hyper-parameters affecting the final part of a pipeline, where the first steps are dedicated to pre-processing and normalization techniques that might be computationally expensive and which we want to reuse. With `HPSearch`, it is easy to do that, and annotate the fact that this was done when characterizing the new experiments.
- Good support for default values. When introducing a new hyper-parameter, all previous experiments are automatically assigned a default value for such parameter. This makes it easy to avoid repeating previous experiments in the case when the default value is one of the possible values to be explored.
- High level of decoupling between the experiment tracking code and the model code.

## Installation

`HPSearch` can be installed using pip:

```bash
pip install hpsearch
```

## Documentation 

Documentation can be found [here](https://jaume-jci.github.io/hpsearch/)