AutoRA Differentiable Architecture Search

autora-theorist-darts is a Python module for fitting data using differentiable architecture search, built on AutoRA.

User Guide

You will need:

python 3.8 or greater: https://www.python.org/downloads/
graphviz (optional, required for computation graph visualizations): https://graphviz.org/download/

Install DARTS as part of the autora package:

pip install -U "autora[theorist-darts]"

It is recommended to use a python environment manager like virtualenv.

Check your installation by running:

python -c "from autora.theorist.darts import DARTSRegressor; DARTSRegressor()"

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution for autora-theorist-darts 1.1.0
File	Size	Uploaded
autora_theorist_darts-1.1.0.tar.gz	532.6 kB	Jul 28, 2024	Details

Table of built distributions (wheels) for autora-theorist-darts 1.1.0
File	Interpreter	ABI	Platform	Reset
autora_theorist_darts-1.1.0-py3-none-any.whl 32.6 kB Jul 28, 2024	Python 3	none	any	Details

Total release size: 565.1 kB

Download URL	autora_theorist_darts-1.1.0.tar.gz
Size	532.6 kB
Tags	Source
SHA-256 checksum How to use checksums	`79b74f9b295cc6e0face31c999f17883f27d46e53cdc8fa4aeb363007914c7ad`
BLAKE2b-256 checksum How to use checksums	`fee559f47019aab5582a9b7104db8aca9a21d1e1f2f2428d39d8ec759299b26a`
Upload date	Jul 28, 2024
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/5.1.1 CPython/3.9.19`

Download URL	autora_theorist_darts-1.1.0-py3-none-any.whl
Size	32.6 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`7fb7fde7cdabc2ae7a5409a303a5766e6f3d73677c50c97586d232bd10090590`
BLAKE2b-256 checksum How to use checksums	`562d99a95a8576739b88eea4ca9e7981121ec08425a33b8b9e50dc6b6d316814`
Upload date	Jul 28, 2024
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/5.1.1 CPython/3.9.19`