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A template for the deep learning projects using PyTorch Lightning 2.0+.

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YOUR PROJECT NAME HERE

Publication Paper Conference
Environment Lightning CUDA Python
Metadata License: MIT Hatch project Ruff Checked with mypy
Table of Contents

👋 Overview

YOUR OVERVIEW HERE

⚙️ Prerequisites

🔥 REMOVE THIS BLOCK QUOTE IN YOUR PROJECT README
This template supports packaging using hatch. If you have published your project, please describe how to install and import your project through this and the next section: ✨ project/.

For example, suppose you published your project as a package named my-package,
(but the top-level namespace is still project)

If you want to use this project as a package in yours, install this via pip:

$ python -m pip install my-package

Now you can import this in your code:

# Import `MyModel` from `project/models/path/to/file.py`
from project.models.path.to.file import MyModel

# Import `MyDataset` from `project/datasets/path/to/file.py`
from project.datasets.path.to.file import MyDataset

First, clone this repository:

$ git clone https://github.com/YOUR/REPOSITORY project
$ cd project

Next, install the dependencies via pip:

$ pip install [-U] -r requirements.txt

# Using a virtual environment is recommended
$ python -m venv .venv
$ .venv/Scripts/activate
(.venv) $ pip install [-U] -r requirements.txt

If you want to modify or test the source codes, install the dev-dependencies:

$ pip install [-U] -r requirements-dev.txt

📁 Directories

project/

🔥 REMOVE THIS BLOCK QUOTE IN YOUR PROJECT README
If you rename this folder, you should rename project/ and change links as well.

project/ self-contains all main source codes, and consists of submodules such as models/ and datasets/.

To use these source codes inside your project, copy this folder inside your project, and see below:

# Import `MyModel` from `project/models/path/to/file.py`
from project.models.path.to.file import MyModel

# Import `MyDataset` from `project/datasets/path/to/file.py`
from project.datasets.path.to.file import MyDataset

🧪 tests/

tests/ contains every test case of the source codes of this project.
To run them, you should install the pytest included in the dev-dependencies.

In the VSCode environment, This extension may help you to run the test cases conveniently.

🖥️ scripts/

scripts/ contains self-runnable scripts for training, validation, testing, visualization, etc.

In most cases, you can run the scripts by choosing one of these two commands:

$ python ./scripts/SCRIPT_NAME.py [OPTION ... [--FLAG=VALUE ...]]

$ python -m scripts.SCRIPT_NAME [OPTION ... [--FLAG=VALUE ...]]

Some scripts may show you how to use them:

$ python ./scripts/SCRIPT_NAME.py --help

# the script may print its manual ...

📒 notebooks/

notebooks/ contains jupyter notebook files for training, validation, testing, visualization, etc.

In the VSCode environment, this extension may help you to run the notebooks conveniently.

💾 saves/

saves/ contains static files such as checkpoints, logs, and prediction results.

📜 Citation

YOUR CITATION HERE

🙏 Acknowledgements

YOUR ACKNOWLEDGEMENTS HERE

🔍 References

YOUR REFERENCES HERE

⚖️ License

This project is distributed under the terms of the YOUR LICENSE HERE license.

🔥 REMOVE THIS BLOCK QUOTE FROM YOUR PROJECT README
The lightning-project-template is under the MIT license.
Change the LICENSE file and license-badge for your project.

Even if your project is under the same license as the template,
Copyright (c) 2023 Jaewoo Park (line 3) must be modified.

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A template for the deep learning projects using PyTorch Lightning 2.0+.

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