Metadata-Version: 2.4
Name: nnrt
Version: 26.4.1
Summary: Neural Network Runtime
Author-email: Atanu Debnath <playatanu@gmail.com>
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: numpy
Provides-Extra: gpu
Requires-Dist: cupy-cuda12x; extra == "gpu"

# NNRT

NNRT (Neural Network Runtime) is a high-performance numerical computation library designed for modern machine learning workloads. It provides a flexible and efficient multi-dimensional array system with strong GPU acceleration, making it suitable for both research and production environments.

## Overview

NNRT is built as a lightweight yet powerful alternative to traditional numerical libraries. It focuses on:

Fast tensor-like computations (similar to NumPy)
Native GPU acceleration for high throughput
Clean and minimal API design
Scalability from small experiments to large models

## Features
- `Multi-dimensional Array Engine`: 
Efficient handling of N-dimensional data structures for numerical computing.
- `GPU Acceleration`: 
Optimized backend for leveraging GPU hardware to speed up computations.
- `NumPy-like API`: 
Familiar interface for easy adoption and quick development.
- `High Performance`
Designed for low-latency and high-throughput operations.
- `Extensible Core`:
Modular design for future expansion (autograd, neural networks, etc.)

## Installation
```python
pip install nnrt
```

## Quick Example

```python
import nnrt

x1 = nnrt.Tensor([1,2])
x2 = nnrt.Tensor([1,2])

x3 = x1*x2

print(x3)
```

## GPU Example
```python
import nnrt

x1 = nnrt.Tensor([1,2], device="cuda")
x2 = nnrt.Tensor([1,2], device="cuda")

x3 = x1*x2

print(x3)
```

If you find NNRT useful, consider giving it a star on GitHub!
