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https://github.com/tensorlayer/tensorlayerx
TensorLayerX: A Unified Deep Learning and Reinforcement Learning Framework for All Hardwares, Backends and OS.
https://github.com/tensorlayer/tensorlayerx
deep-learning jittor machine-learning mindspore neural-network oneflow paddlepaddle python pytorch tensorflow tensorlayer tensorlayerx
Last synced: 2 days ago
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TensorLayerX: A Unified Deep Learning and Reinforcement Learning Framework for All Hardwares, Backends and OS.
- Host: GitHub
- URL: https://github.com/tensorlayer/tensorlayerx
- Owner: tensorlayer
- License: other
- Created: 2021-12-03T07:41:20.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-09-08T05:45:53.000Z (19 days ago)
- Last Synced: 2024-09-25T01:02:43.656Z (2 days ago)
- Topics: deep-learning, jittor, machine-learning, mindspore, neural-network, oneflow, paddlepaddle, python, pytorch, tensorflow, tensorlayer, tensorlayerx
- Language: Python
- Homepage:
- Size: 5.46 MB
- Stars: 536
- Watchers: 16
- Forks: 44
- Open Issues: 15
-
Metadata Files:
- Readme: README.md
- License: LICENSE.rst
Awesome Lists containing this project
README
[]( https://tensorlayerx.readthedocs.io/en/latest/)
[](https://travis-ci.org/tensorlayer/tensorlayerx)
[](http://pepy.tech/project/tensorlayerx)
[](https://pepy.tech/project/tensorlayerx/week)
[](https://hub.docker.com/r/tensorlayer/tensorlayerx/)
[TensorLayerX](https://tensorlayerx.readthedocs.io) is a multi-backend AI framework, supports TensorFlow, Pytorch, MindSpore, PaddlePaddle, OneFlow and Jittor as the backends, allowing users to run the code on different hardware like Nvidia-GPU, Huawei-Ascend, Cambricon and more.
This project is maintained by researchers from Peking University, Peng Cheng Lab, HKUST, Imperial College London, Princeton, Oxford, Stanford, Tsinghua and Edinburgh.
- GitHub: https://github.com/tensorlayer/TensorLayerX
- OpenI: https://openi.pcl.ac.cn/OpenI/TensorLayerX
- Homepage: [English](http://www.tensorlayerx.com/index_en.html?chlang=&langid=2) [中文](http://tensorlayerx.com)
- Document: https://tensorlayerx.readthedocs.io
- Previous Project: https://github.com/tensorlayer/TensorLayer
# Deep Learning course
We have video courses for deep learning, with example codes based on TensorLayerX.
[Bilibili link](https://www.bilibili.com/video/BV1xB4y1h7V2?share_source=copy_web&vd_source=467c17f872fcde378494433520e19999) (chinese)
# Design Features
- ***Compatibility***: Support worldwide frameworks and AI chips, enabling one code runs on all platforms.
- ***Model Zoo***: Provide a series of applications containing classic and SOTA models, covering CV, NLP, RL and other fields.
- ***Deployment***: Support ONNX protocol, model export, import and deployment.
# Multi-backend Design
You can immediately use TensorLayerX to define a model via Pytorch-stype, and switch to any backends easily.
```python
import os
os.environ['TL_BACKEND'] = 'tensorflow' # modify this line, switch to any backends easily!
#os.environ['TL_BACKEND'] = 'mindspore'
#os.environ['TL_BACKEND'] = 'paddle'
#os.environ['TL_BACKEND'] = 'torch'
import tensorlayerx as tlx
from tensorlayerx.nn import Module
from tensorlayerx.nn import Linear
class CustomModel(Module):
def __init__(self):
super(CustomModel, self).__init__()
self.linear1 = Linear(out_features=800, act=tlx.ReLU, in_features=784)
self.linear2 = Linear(out_features=800, act=tlx.ReLU, in_features=800)
self.linear3 = Linear(out_features=10, act=None, in_features=800)
def forward(self, x, foo=False):
z = self.linear1(x)
z = self.linear2(z)
out = self.linear3(z)
if foo:
out = tlx.softmax(out)
return out
MLP = CustomModel()
MLP.set_eval()
```
# Quick Start
Get started with TensorLayerX quickly using the following examples:
- **MNIST Digit Recognition:** Train a simple multi-layer perceptron (MLP) model for digit recognition using the MNIST dataset. Choose between a simple training method or custom loops. See the examples: [mnist_mlp_simple_train.py](https://github.com/tensorlayer/TensorLayerX/blob/main/examples/basic_tutorials/mnist_mlp_simple_train.py) and [mnist_mlp_custom_train.py](https://github.com/tensorlayer/TensorLayerX/blob/main/examples/basic_tutorials/mnist_mlp_custom_train.py).
- **CIFAR-10 Dataflow:** Learn how to create datasets, process images, and load data through DataLoader using the CIFAR-10 dataset. See the example: [cifar10_cnn.py](https://github.com/tensorlayer/TensorLayerX/blob/main/examples/basic_tutorials/cifar10_cnn.py).
- **MNIST GAN Training:** Train a generative adversarial network (GAN) on the MNIST dataset. See the example: [mnist_gan.py](https://github.com/tensorlayer/TensorLayerX/blob/main/examples/basic_tutorials/mnist_gan.py).
- **MNIST Mix Programming:** Mix TensorLayerX code with other deep learning libraries such as TensorFlow, PyTorch, Paddle, and MindSpore to run on the MNIST dataset. See the example: [mnist_mlp_mix_programming.py](https://github.com/tensorlayer/TensorLayerX/blob/main/examples/basic_tutorials/mnist_mlp_mix_programming.py).
# Resources
- [Examples](https://github.com/tensorlayer/TensorLayerX/tree/main/examples) for tutorials
- [GammaGL](https://github.com/BUPT-GAMMA/GammaGL) is series of graph learning algorithm
- [TLXZoo](https://github.com/tensorlayer/TLXZoo) a series of pretrained backbones
- [TLXCV](https://github.com/tensorlayer/TLXCV) a series of Computer Vision applications
- [TLXNLP](https://github.com/tensorlayer/TLXNLP) a series of Natural Language Processing applications
- [TLX2ONNX](https://github.com/tensorlayer/TLX2ONNX/) ONNX model exporter for TensorLayerX.
- [Paddle2TLX](https://github.com/tensorlayer/paddle2tlx) model code converter from PaddlePaddle to TensorLayerX.
More official resources can be found [here](https://github.com/tensorlayer)
# Installation
- The latest TensorLayerX compatible with the following backend version
| TensorLayerX | TensorFlow | MindSpore | PaddlePaddle | PyTorch | OneFlow | Jittor|
| :-----:| :----: | :----: |:-----:|:----:|:----:|:----:|
| v0.5.8 | v2.4.0 | v1.8.1 | v2.2.0 | v1.10.0 | -- | v1.3.8.5 |
| v0.5.7 | v2.0.0 | v1.6.1 | v2.0.2 | v1.10.0 | -- | -- |
- via pip for the stable version
```bash
# install from pypi
pip3 install tensorlayerx
```
- build from source for the latest version (for advanced users)
```bash
# install from Github
pip3 install git+https://github.com/tensorlayer/tensorlayerx.git
```
For more installation instructions, please refer to [Installtion](https://tensorlayerx.readthedocs.io/en/latest/user/installation.html)
- via docker
Docker is an open source application container engine. In the [TensorLayerX Docker Repository](https://hub.docker.com/repository/docker/tensorlayer/tensorlayerx),
different versions of TensorLayerX have been installed in docker images.
```bash
# pull from docker hub
docker pull tensorlayer/tensorlayerx:tagname
```
# Contributing
Join our community as a code contributor, find out more in our [Help wanted list](https://github.com/tensorlayer/TensorLayerX/issues/5) and [Contributing](https://tensorlayerx.readthedocs.io/en/latest/user/contributing.html) guide!
# Getting Involved
We suggest users to report bugs using Github issues. Users can also discuss how to use TensorLayerX in the following slack channel.
# Contact
- [email protected]
# Citation
If you find TensorLayerX useful for your project, please cite the following papers：
```
@inproceedings{tensorlayer2021,
title={TensorLayer 3.0: A Deep Learning Library Compatible With Multiple Backends},
author={Lai, Cheng and Han, Jiarong and Dong, Hao},
booktitle={2021 IEEE International Conference on Multimedia \& Expo Workshops (ICMEW)},
pages={1--3},
year={2021},
organization={IEEE}
}
@article{tensorlayer2017,
author = {Dong, Hao and Supratak, Akara and Mai, Luo and Liu, Fangde and Oehmichen, Axel and Yu, Simiao and Guo, Yike},
journal = {ACM Multimedia},
title = {{TensorLayer: A Versatile Library for Efficient Deep Learning Development}},
url = {http://tensorlayer.org},
year = {2017}
}
```