tnsaai · Thishyaketh · Jul 21, 2025 · Apr 20, 2025 · Apr 20, 2025 · Apr 20, 2025
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@@ -1,3 +1,154 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:965f067efc69a37c32b7ead9f3b6e39e98be4230e055718e7fccc254bcf13fe1
-size 3715
+![OpenArchX Logo](https://raw.githubusercontent.com/TnsaAi/OpenArchX/refs/heads/main/oax_logo.png)  <!-- Replace with your logo URL -->
+
+--------------------------------------------------------------------------------
+
+OpenArchX is a deep learning framework designed for efficient model training and inference, with support for CUDA operations using CuPy. This framework is built to be independent of PyTorch while providing similar functionalities.
+
+You can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend OpenArchX when needed.
+
+Our trunk health (Continuous Integration signals) can be found at [your-ci-url](https://tnsaai.com/).
+
+<!-- toc -->
+
+- [More About OpenArchX](#more-about-openarchx)
+  - [A GPU-Ready Tensor Library](#a-gpu-ready-tensor-library)
+  - [Dynamic Neural Networks: Tape-Based Autograd](#dynamic-neural-networks-tape-based-autograd)
+  - [Python First](#python-first)
+  - [Imperative Experiences](#imperative-experiences)
+  - [Fast and Lean](#fast-and-lean)
+  - [Extensions Without Pain](#extensions-without-pain)
+- [Installation](#installation)
+  - [Binaries](#binaries)
+  - [From Source](#from-source)
+    - [Prerequisites](#prerequisites)
+      - [NVIDIA CUDA Support](#nvidia-cuda-support)
+      - [AMD ROCm Support](#amd-rocm-support)
+      - [Intel GPU Support](#intel-gpu-support)
+    - [Get the OpenArchX Source](#get-the-openarchx-source)
+    - [Install Dependencies](#install-dependencies)
+    - [Install OpenArchX](#install-openarchx)
+  - [Docker Image](#docker-image)
+- [Getting Started](#getting-started)
+- [Resources](#resources)
+- [Communication](#communication)
+- [Releases and Contributing](#releases-and-contributing)
+- [The Team](#the-team)
+- [License](#license)
+
+<!-- tocstop -->
+
+## More About OpenArchX
+
+[Learn the basics of OpenArchX](https://oax.tnsaai.com/#examples)
+
+At a granular level, OpenArchX is a library that consists of the following components:
+
+| Component | Description |
+| ---- | --- |
+| [**Tensor**](https://oax.tnsaai.com/) | A Tensor library like NumPy, with strong GPU support |
+| [**Autograd**](https://oax.tnsaai.com/) | A tape-based automatic differentiation library that supports all differentiable Tensor operations |
+| [**Neural Networks**](oax.tnsaai.com/) | A neural networks library deeply integrated with autograd designed for maximum flexibility |
+
+Usually, OpenArchX is used either as:
+
+- A replacement for NumPy to use the power of GPUs.
+- A deep learning research platform that provides maximum flexibility and speed.
+
+### A GPU-Ready Tensor Library
+
+OpenArchX provides Tensors that can live either on the CPU or the GPU and accelerates the computation significantly.
+
+We provide a wide variety of tensor routines to accelerate and fit your scientific computation needs such as slicing, indexing, mathematical operations, and linear algebra.
+
+### Dynamic Neural Networks: Tape-Based Autograd
+
+OpenArchX has a unique way of building neural networks using a tape-based autograd system, allowing for dynamic changes in network behavior without overhead.
+
+### Python First
+
+OpenArchX is built to be deeply integrated into Python, allowing you to use it naturally like you would use NumPy or SciPy.
+
+### Imperative Experiences
+
+OpenArchX is designed to be intuitive and easy to use, providing straightforward error messages and stack traces.
+
+### Fast and Lean
+
+OpenArchX has minimal framework overhead and integrates acceleration libraries to maximize speed.
+
+### Extensions Without Pain
+
+Writing new neural network modules or interfacing with OpenArchX's Tensor API is designed to be straightforward and with minimal abstractions.
+
+## Installation
+
+### Binaries
+
+Commands to install binaries via Conda or pip wheels are on our website: [https://example.com/install](https://example.com/install)
+
+### From Source
+
+#### Prerequisites
+
+If you are installing from source, you will need:
+- Python 3.8 or later
+- A compiler that fully supports C++17
+
+#### Get the OpenArchX Source
+
+```bash
+git clone https://github.com/yourusername/OpenArchX.git
+cd OpenArchX
+```
+
+#### Install Dependencies
+
+```bash
+pip install -r requirements.txt
+```
+
+#### Install OpenArchX
+
+```bash
+python setup.py develop
+```
+
+### Docker Image
+
+You can also pull a pre-built docker image from Docker Hub and run with docker v19.03+:
+
+```bash
+docker run --gpus all --rm -ti --ipc=host yourusername/openarchx:latest
+```
+
+## Getting Started
+
+Three pointers to get you started:
+- [Tutorials: get you started with understanding and using OpenArchX](https://oax.tnsaai.com/#examples)
+- [Examples: easy to understand OpenArchX code across all domains](https://oax.tnsaai.com/#examples)
+- [The API Reference]()
+
+## Resources
+
+* [OpenArchX Documentation](https://oax.tnsaai.com/)
+* [OpenArchX Tutorials](https://oax.tnsaai.com/)
+* [OpenArchX Examples](https://oax.tnsaai.com/)
+
+## Communication
+
+* Forums: Discuss implementations, research, etc. [OpenArchX Forum](https://github.com/TnsaAi/OpenArchX/issues)
+* GitHub Issues: Bug reports, feature requests, etc. [OpenArchX Issues](https://github.com/TnsaAi/OpenArchX/issues)
+
+## Releases and Contributing
+
+We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion.
+
+If you plan to contribute new features, please first open an issue and discuss the feature with us.
+
+## The Team
+
+OpenArchX is maintained by a community of contributors. 
+
+## License
+
+OpenArchX is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
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