https://github.com/iperov/litenn

Lightweight machine learning library based on OpenCL 1.2
https://github.com/iperov/litenn

Last synced: 3 months ago
JSON representation

Lightweight machine learning library based on OpenCL 1.2

• Host: GitHub
• URL: https://github.com/iperov/litenn
• Owner: iperov
• License: mit
• Created: 2020-11-06T14:44:58.000Z (about 4 years ago)
• Default Branch: master
• Last Pushed: 2020-11-23T14:09:17.000Z (almost 4 years ago)
• Last Synced: 2024-07-07T03:25:28.199Z (4 months ago)
• Language: Python
• Size: 906 KB
• Stars: 70
• Watchers: 5
• Forks: 10
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        


# LiteNN

### lightweight Machine Learning library based on OpenCL 1.2 and written in pure python

suitable for most popular ML tasks such as regression, recognition, classification, autoencoders, GANs.





# Features

written in pure python

Nothing to build from source! No headache with cmake, bazel, compilers, environments, etc.

future-proof

unlike CUDA, OpenCL 1.2 does not break backward compatibility with new video cards, so your app will work on future devices.

Simplified and pytorch-like

PyTorch-like, but more lightweight architecture with simplified things.

Easy to experiment

Implement your own custom GPU-accelerated ops much more faster, using OpenCL C-language as text directly in python. You don't need to compile or build from source.

user namespace

litenn is namespace for users. 

You will not see internal classes or functions in your vscode intellisense hint.

All things in litenn namespace are ready to use, contain editor hint, and the source code can be directly explored from your IDE.

Minimal dependencies

numpy only

# Getting started

```python

pip install litenn

import litenn as nn

nn.test.all()

```

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/iperov/litenn/blob/master/doc_github/ipynb/litenn_test_all.ipynb)

# [User guide](doc_github/user_guide/user_guide.md)

# [Developer guide](doc_github/dev_guide/dev_guide.md)

# [ LiteNN-apps](https://github.com/iperov/litenn-apps)

#machinelearning #machine-learning #deep-learning #deeplearning #deep-neural-networks #neural-networks #neural-nets #opencl