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 Anaconda (preferred) or Pip:
-
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.
-
Pip: Install Pytorch to ensure GPU available. Then:
pip install -r requirements.txt pip install -e .
-
Usage
The project is separated into "experiments," which are just different main
functions. Use the experiment
group parameter to change which experiment is running. For example:
python main.py experiment=mnist
The results are then neatly sorted into the newly-created results
directory (ignored by default). This is important for reproduceability, utilizing Hydra's automatic logging and config storage.
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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