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Pytorch wrapper for fast prototyping

Project description

# tensorop
<img style="float: centre;" src="tensorop_logo.png">

[![PyPI version](https://badge.fury.io/py/tensorop.svg)](https://badge.fury.io/py/tensorop)

Tensorop is a Pytorch wrapper for fast prototyping for research or deployment purpose.

## Getting Started


### Prerequisites

Install `pytorch` and `torchvision` from [pytorch.org](pytorch.org)
- Pytorch >= 0.4
- Torchvision
- Pandas
- Numpy

### Installing
Installation via Pypi
```
$ pip3 install tensorop
```
Using with git
```
$ git clone https://github.com/prajjwal1/tensorop
$ cd tensorop
```

### To check installation
```
$ >>> import tensorop; print(tensorop.__version__)
```

### Components (Structure)
- Vision
- metrics
- transforms
- NLP
- Sequence2Sequence model suport
- metrics (different metrics)
- nlp utilities
- optimizations
- RL
- Dynamic Programming
- GANs
- DCGANs
- Models
- Datasets
- Layers
- Loss Functions
- Numpy utilities
- tensorop.torch
- Utilities (I/O)

These are frequently changing once `v0.1` is out

## Contributing
There is so much work which needs to be done as of now, PRs are always welcome. Look for [issues](https://github.com/prajjwal1/tensorop/issues) to get started.

Docs can be found [here](https://prajjwal1.github.io/tensorop/). These are not updated frequently since the framework is under constant development.

## Acknowledgements
Tensorop includes research work from various sources to include the best practices. Acknowledgements can be found [here](https://github.com/prajjwal1/tensorop/acknowledgements.md). If you think your work is not listed, please send a PR ,after all I'm a single person.

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