Metadata-Version: 2.1
Name: rainbowneko
Version: 0.3
Summary: Neural network training and inference framework
Home-page: https://github.com/IrisRainbowNeko/RainbowNekoEngine
Author: IrisRainbowNeko
Author-email: rainbow-neko@outlook.com
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: accelerate>=0.15.0
Requires-Dist: torchmetrics
Requires-Dist: safetensors
Requires-Dist: einops>=0.6.0
Requires-Dist: hydra-core
Requires-Dist: loguru
Requires-Dist: numpy
Requires-Dist: omegaconf
Requires-Dist: opencv_python
Requires-Dist: Pillow
Requires-Dist: PyYAML
Requires-Dist: scikit_learn
Requires-Dist: tqdm
Requires-Dist: addict
Requires-Dist: albumentations
Requires-Dist: bitsandbytes

# RainbowNeko Engine


## Introduction
RainbowNeko Engine is a toolbox for pytorch based neural network training and inference. Multiple tasks and training strategies are built-in and highly expandable.


## Install

1. Install [pytorch](https://pytorch.org/)

2. Install from source:
```bash
git clone https://github.com/IrisRainbowNeko/RainbowNekoEngine.git
cd RainbowNekoEngine
pip install -e .
# Modified based on this project or start a new project and make initialization
nekoinit
```

3. To use xFormers to reduce VRAM usage and accelerate training:
```bash
# use conda
conda install xformers -c xformers

# use pip
pip install xformers>=0.0.17
```

## User guidance

### Training

Training scripts based on 🤗 Accelerate or Colossal-AI are provided.
+ For 🤗 Accelerate, you may need to [configure the environment](https://github.com/huggingface/accelerate/tree/main#launching-script) before launching the scripts.
+ For Colossal-AI, you can use [torchrun](https://pytorch.org/docs/stable/elastic/run.html) to launch the scripts.

```yaml
# with Accelerate
neko_train --cfg cfgs/train/cfg_file.yaml
# with Accelerate and only one GPU
neko_train_1gpu --cfg cfgs/train/cfg_file.yaml
```

### Inference
TODO

### Tutorials
TODO

## Contributing

You are welcome to contribute more models and features to this toolbox!
