One-line command to generate a deep learning folder structure and code template!
Project description
<h1 align="center">DLCreator</h1>
<p align="center">One-line command to generate a deep learning folder structure and code template!</p>
<p align="center">
<a href="https://github.com/nghuyong/DLCreator/stargazers">
<img src="https://img.shields.io/github/stars/nghuyong/DLCreator.svg?colorA=orange&colorB=orange&logo=github"
alt="GitHub stars">
</a>
<a href="https://github.com/nghuyong/DLCreator/issues">
<img src="https://img.shields.io/github/issues/nghuyong/DLCreator.svg"
alt="GitHub issues">
</a>
<a href="https://github.com/nghuyong/DLCreator/">
<img src="https://img.shields.io/github/last-commit/nghuyong/DLCreator.svg">
</a>
<a href="https://github.com/nghuyong/DLCreator/blob/master/LICENSE">
<img src="https://img.shields.io/github/license/nghuyong/DLCreator.svg"
alt="GitHub license">
</a>
</p>
<h2 align="center">What is it</h2>
When you start a new deep learning project, are you still worrying about how to organize a project structure and writing many duplicate codes every time ?
**DLCreator** is made ! It is a one-line command tool, which will automatically generate the entire folder structure and code template including data loading; model training; configuration; logs; visualization; etc.
**So, All YOU NEED TO DO is just design your model and write some code snippet**.
<h2 align="center">Install</h2>
Install it via `pip`.
```bash
pip install DLCreator
```
:point_up: The command can be running on both Python 2 and 3.
<h2 align="center">Getting Started</h2>
Start a new deep learning project, just from this:
```bash
DLCreator <tensorflow|pytorch|keras> <project-name>
```
Take `DLCreator pytorch test` as an example, The same directory will generate a `test` directory, the structure is as follows:
```
test/
│
├── train.py - main script to start training
├── test.py - evaluation of trained model
├── config.json - config file
│
├── base/ - abstract base classes
│ ├── base_data_loader.py - abstract base class for data loaders
│ ├── base_model.py - abstract base class for models
│ └── base_trainer.py - abstract base class for trainers
│
├── data_loader/ - anything about data loading goes here
│ └── data_loaders.py
│
├── data/ - default directory for storing input data
│
├── model/ - models, losses, and metrics
│ ├── loss.py
│ ├── metric.py
│ └── model.py
│
├── saved/ - default checkpoints folder
│ └── runs/ - default logdir for tensorboardX
│
├── trainer/ - trainers
│ └── trainer.py
│
└── utils/
├── util.py
├── logger.py - class for train logging
├── visualization.py - class for tensorboardX visualization support
└── ...
```
<h2 align="center">TODOs</h2>
- [ ] Support tensorflow
- [ ] Support pytorch
- [ ] Support keras
- [ ] Release a version to pypi
<h2 align="center">Acknowledgments</h2>
This project is inspired these projects:
- [Tensorflow-Project-Template](https://github.com/MrGemy95/Tensorflow-Project-Template)
- [pytorch-template](https://github.com/victoresque/pytorch-template)
- [Keras-Project-Template](https://github.com/Ahmkel/Keras-Project-Template)
<p align="center">One-line command to generate a deep learning folder structure and code template!</p>
<p align="center">
<a href="https://github.com/nghuyong/DLCreator/stargazers">
<img src="https://img.shields.io/github/stars/nghuyong/DLCreator.svg?colorA=orange&colorB=orange&logo=github"
alt="GitHub stars">
</a>
<a href="https://github.com/nghuyong/DLCreator/issues">
<img src="https://img.shields.io/github/issues/nghuyong/DLCreator.svg"
alt="GitHub issues">
</a>
<a href="https://github.com/nghuyong/DLCreator/">
<img src="https://img.shields.io/github/last-commit/nghuyong/DLCreator.svg">
</a>
<a href="https://github.com/nghuyong/DLCreator/blob/master/LICENSE">
<img src="https://img.shields.io/github/license/nghuyong/DLCreator.svg"
alt="GitHub license">
</a>
</p>
<h2 align="center">What is it</h2>
When you start a new deep learning project, are you still worrying about how to organize a project structure and writing many duplicate codes every time ?
**DLCreator** is made ! It is a one-line command tool, which will automatically generate the entire folder structure and code template including data loading; model training; configuration; logs; visualization; etc.
**So, All YOU NEED TO DO is just design your model and write some code snippet**.
<h2 align="center">Install</h2>
Install it via `pip`.
```bash
pip install DLCreator
```
:point_up: The command can be running on both Python 2 and 3.
<h2 align="center">Getting Started</h2>
Start a new deep learning project, just from this:
```bash
DLCreator <tensorflow|pytorch|keras> <project-name>
```
Take `DLCreator pytorch test` as an example, The same directory will generate a `test` directory, the structure is as follows:
```
test/
│
├── train.py - main script to start training
├── test.py - evaluation of trained model
├── config.json - config file
│
├── base/ - abstract base classes
│ ├── base_data_loader.py - abstract base class for data loaders
│ ├── base_model.py - abstract base class for models
│ └── base_trainer.py - abstract base class for trainers
│
├── data_loader/ - anything about data loading goes here
│ └── data_loaders.py
│
├── data/ - default directory for storing input data
│
├── model/ - models, losses, and metrics
│ ├── loss.py
│ ├── metric.py
│ └── model.py
│
├── saved/ - default checkpoints folder
│ └── runs/ - default logdir for tensorboardX
│
├── trainer/ - trainers
│ └── trainer.py
│
└── utils/
├── util.py
├── logger.py - class for train logging
├── visualization.py - class for tensorboardX visualization support
└── ...
```
<h2 align="center">TODOs</h2>
- [ ] Support tensorflow
- [ ] Support pytorch
- [ ] Support keras
- [ ] Release a version to pypi
<h2 align="center">Acknowledgments</h2>
This project is inspired these projects:
- [Tensorflow-Project-Template](https://github.com/MrGemy95/Tensorflow-Project-Template)
- [pytorch-template](https://github.com/victoresque/pytorch-template)
- [Keras-Project-Template](https://github.com/Ahmkel/Keras-Project-Template)
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