XNeur
XNeur is an open-source neural network framework for building and training deep learning models. It provides a simple API for constructing neural networks and running them on CPUs or GPUs.
deep-learning
neural-networks
gpu-acceleration
XNeur: Open-Source Neural Network Framework
An open-source framework for building and training deep learning models, XNeur provides a simple API for constructing neural networks and running them on CPUs or GPUs.
What is XNeur?
XNeur is an open-source neural network framework designed for efficiency and flexibility. It allows researchers and developers to quickly build and train deep learning models without worrying about the underlying infrastructure.
Some key features of XNeur include:
- Simple and consistent API for constructing neural networks
- Support for all major neural network layer types including convolutional, recurrent, and transformer layers
- Automatic differentiation for fast model training
- GPU acceleration with CUDA and cuDNN support
- Distributed training across multiple machines
- Export models to ONNX format for inference
- Integration with popular data science libraries like Numpy and Pandas
XNeur makes it easy to take a model from idea to implementation quickly. It abstracts away redundant boilerplate code and provides building blocks for creating optimized neural networks. Models built in XNeur can be trained on laptops, cloud VMs, servers with multiple GPUs, or large clusters. The framework is focused on flexibility and putting the user in control.
XNeur Features
Features
- Modular and extensible architecture
- Support for common neural network layers and activation functions
- Automatic differentiation for computing gradients
- GPU acceleration using CUDA
- Helper functions for training, evaluation and prediction
- Model exporting to ONNX format
- Integration with popular Python data science libraries like NumPy and Pandas
Pricing
- Open Source
- Free
Pros
Easy to use API for building neural networks
Fast performance with GPU acceleration
Open source with permissive license
Active development and community support
Cons
Limited documentation and examples compared to larger frameworks
Less flexibility than lower-level frameworks like PyTorch and TensorFlow
Lacks some advanced features like distributed training
Official Links
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