🔥GoTorch🔥 is a high-performance, CPU-optimized implementation of PyTorch built using Golang at its core, and wrapped for Python use. Designed for highly-concurrent matrix operations and training deep learning models on CPUs, GoTorch leverages Go's concurrency model to achieve efficient computation.
- High-Performance CPU Training/Inference: Optimize for parallel computation of matrix operations using Go's concurrency
- Ease of Use: Provide a Python API that mimics PyTorch for seamless integration with existing ML workflows
- Extensibility: Allow future GPU support and integration with other ML tools
- Implement the core
Tensordata structure- Basic operations: addition, subtraction, matrix multiplication
- Broadcasting support
- Python wrapping using cgo or PyBind11
- Autograd engine for tracking and backpropagating gradients
- Support gradient computation for basic operations
- Implement common layers: Linear, Conv2D, etc
- Utility functions for model creation
- Loss functions: MSE, Cross-Entropy, etc
- Optimizers like SGD, Adam, RMSProp
- Design a training loop API
- Benchmark training speed against PyTorch on CPUs
- Optimize memory usage and concurrency further
- Support sparse tensors
- Explore GPU integration using CUDA or Vulkan
- Go 1 20+
- Python 3 8+
- PyBind11 or cgo for wrapping Golang in Python
1 Clone the repository:
git clone https://github com/brianreicher/gotorch git
cd gotorch2 Build the Golang library:
go build -o libgotorch.so ./3 Install the Python package:
pip install gotorch- Achieve faster training on CPUs compared to PyTorch for matrix-heavy workloads
- Minimize memory overhead using Go's efficient memory management
- Provide scalability for large models through parallel computation
GoTorch is licensed under the MIT License See the LICENSE file for details
Inspired by PyTorch, NumPy, and the Go programming language. Special thanks to the open-source community for their foundational work.
For questions or suggestions, feel free to reach out via GitHub Issues or email at [email protected]