This is the adapted version of this template: https://github.com/ashleve/lightning-hydra-template/tree/main For more informations check the original repository.
This fork exists, because it allows to create more subprojects and integrate them.
Lightning-Hydra-Template is licensed under the MIT License, so is this.
MIT License
Copyright (c) 2023 konrad-karanowski
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
DELETE EVERYTHING ABOVE FOR YOUR PROJECT
What it does
# clone project
git clone https://github.com/YourGithubName/your-repo-name
cd your-repo-name
# [OPTIONAL] create conda environment
conda create -n myenv python=3.9
conda activate myenv
# install pytorch according to instructions
# https://pytorch.org/get-started/
# install requirements
pip install -r requirements.txt
# clone project
git clone https://github.com/YourGithubName/your-repo-name
cd your-repo-name
# create conda environment and install dependencies
conda env create -f environment.yaml -n myenv
# activate conda environment
conda activate myenv
Train model with default configuration
# train on CPU
python src/train.py trainer=cpu
# train on GPU
python src/train.py trainer=gpu
Train model with chosen experiment configuration from configs/experiment/
python src/train.py experiment=experiment_name.yaml
You can override any parameter from command line like this
python src/train.py trainer.max_epochs=20 data.batch_size=64