NimbleNet is a lightweight, optimized Neural Network Framework written from scratch in C++
Explore the docs »
View Demo
·
Report Bug
·
Request Feature
Table of Contents
NimbleNet is a lightweight, optimized Neural Network Framework written from scratch in C++.
To get a local copy up and running follow these simple example steps.
This is an example of how to list things you need to use the software and how to install them.
C++
- If you run a modern operating system, odds are C++ is already installed on your machine. You can check for it by running the following command in a terminal:
g++ --version
- If you do not get a version output, follow the instructions for installing C++
Cmake
Cuda (Optional for Nvidia GPU acceleration)
OpenCL (Optional for Non-Nvidia GPU acceleration)
Clone the repo
git clone https://github.com/Neumann-Labs/NimbleNet.git
Build with CMake
cmake make
Coming Soon
For more examples, please refer to the Documentation
Core Components:
- Tensor Library:
- Implement multi-dimensional array class
- Implement element-wise operations
- Implement broadcasting
- Implement memory management
- Autograd Engine:
- Implement reverse-mode autodiff
- Implement dynamic computation graph
- Implement gradient accumulation
- Implement gradient zeroing
- Operator Library:
- Implement basic math operators (+, -, *, /, etc.)
- Implement matrix multiplication
- Implement convolution operator
- Implement pooling operators (max, average)
- Implement activation functions (ReLU, sigmoid, tanh)
- Implement loss functions (MSE, cross-entropy)
- Tensor Library:
Neural Network Layers:
- Implement fully connected layer
- Implement convolutional layer
- Implement recurrent layers (RNN, LSTM, GRU)
- Implement attention mechanism
- Implement normalization layers (Batch Norm, Layer Norm)
- Implement dropout regularization
Optimization and Training:
- Implement optimization algorithms:
- Stochastic Gradient Descent (SGD)
- Adam optimizer
- RMSprop optimizer
- Implement learning rate scheduling
- Implement momentum and Nesterov momentum
- Implement weight decay regularization
- Implement gradient clipping
- Implement data loading and batching
- Implement model checkpointing and saving
- Implement optimization algorithms:
Hardware Acceleration:
- Implement CUDA support for GPU acceleration
- Implement OpenCL support for GPU acceleration
- Implement optimized kernels for basic operations
- Implement parallel reduction for efficient gradient accumulation
- Implement memory pooling for efficient memory management
Serialization and Deployment:
- Implement model serialization and deserialization
- Implement support for saving and loading trained models
- Implement model deployment for inference in C++ applications
- Implement Python bindings for model deployment
Compiler Optimizations:
- Implement graph-level optimizations:
- Operator fusion
- Constant folding
- Dead code elimination
- Implement automatic code generation for optimized kernels
- Implement cost modeling for performance optimization
- Implement support for different hardware backends
- Implement graph-level optimizations:
Interoperability and Ecosystem:
- Implement Python bindings for easy integration with Python workflows
- Implement compatibility with popular data formats (NumPy, Pandas)
- Implement integration with popular data loading libraries (OpenCV, LibSVM)
- Implement support for distributed training using MPI or other frameworks
Testing and Documentation:
- Implement unit tests for core components
- Implement integration tests for end-to-end functionality
- Implement performance benchmarks and profiling
- Write comprehensive API documentation
- Provide tutorials and examples for common use cases
- Set up continuous integration and continuous deployment (CI/CD) pipeline
See the open issues for a full list of proposed features (and known issues).
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE.txt for more information.
Nicholi Caron - @nicholicaron - nicholi@neumann-labs.com
Website: https://www.neumann-labs.com/