- Beginner who want to **try different AutoDL algorithms** for study
- Engineer who want to **try AutoDL** to investigate whether AutoDL works on your projects
- Researchers who want to **easily** implement and experiement **new** AutoDL algorithms.
## **Why should we use AutoDL-Projects**
- Simplest library dependencies: each examlpe is purely relied on PyTorch or Tensorflow (except for some basic libraries in Anaconda)
- All algorithms are in the same codebase. If you implement new algorithms, it is easy to fairly compare with many other baselines.
- I will actively support this project, because all my furture AutoDL research will be built upon this project.
## AutoDL-Projects Capabilities
At the moment, this project provides the following algorithms and scripts to run them. Please see the details in the link provided in the description column.
<table>
<tbody>
<tralign="center"valign="bottom">
<th>Type</th>
<th>Algorithms</th>
<th>Description</th>
</tr>
<tr><!-- (1-st row) -->
<tdrowspan="5"align="center"valign="middle"halign="middle"> NAS </td>
<tdalign="center"valign="middle"> Network Pruning via Transformable Architecture Search </td>
At first, this repo is `GDAS`, which is used to reproduce results in Searching for A Robust Neural Architecture in Four GPU Hours.
After that, more functions and more NAS algorithms are continuely added in this repo. After it supports more than five algorithms, it is upgraded from `GDAS` to `NAS-Project`.
Now, since both HPO and NAS are supported in this repo, it is upgraded from `NAS-Project` to `AutoDL-Projects`.
Some methods use knowledge distillation (KD), which require pre-trained models. Please download these models from [Google Drive](https://drive.google.com/open?id=1ANmiYEGX-IQZTfH8w0aSpj-Wypg-0DR-) (or train by yourself) and save into `.latent-data`.