The opinionated deep learning template.
Project description
Description
dlproject
believes three things.
- All code should be documented.
- All experiments should be logged.
- Configs are better than constants.
Installation
These instructions assume you are using a linux machine with at least one GPU (CUDA 11.1).
-
Create a new repository using this template and change to the root directory. For example,
git clone git@github.com:benjamindkilleen/dlproject.git cd dlproject
-
Install dependencies using either Pip or Anaconda (preferred):
-
Pip: Install Pytorch to ensure GPU available. Then:
pip install -r requirements.txt pip install -e .
-
Anaconda: modify
environment.yml
to suit your needs. Then run:conda env create -f environment.yml conda activate dlproject
This will create a new environment with the project installed as an edit-able package.
-
Configure
TODO: locations that need attention.
Usage
The project is separated into "experiments," which are just different main
functions using the vertview
library. Everything should be run through hydra, specifying the experiment
group parameter. For example:
python main.py experiment=mnist
Documentation
Documentation and tutorials for dlproject
are available here. You should document your code as you go. If you use Visual Studio Code, this is an extension which will create Google style docstrings automatically.
To build the docstrings you write into a local static web-page, run
pip install -r docs/requirements.txt
sphinx-apidoc -f -o docs/source dlproject
cd docs
make html
And open /docs/build/html/index.html
in your browser.
Citation
@article{YourName,
title={Your Title},
author={Your team},
journal={Location},
year={Year}
}
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