A lightweight, efficient, and pure-Rust implementation of n-dimensional tensors, suitable for numerical computations, machine learning, and more.
- Tensor data structure with:
- Data storage
- Shape and stride information
- Element-wise operations:
- Addition
- Subtraction
- Multiplication
- Division
- Basic tensor manipulation:
- Reshaping
- Squeezing
- Expanding dimensions
- Tensor indexing and slicing.
- Comprehensive tests for the above functionalities.
- Broadcasting support for operations between tensors of different shapes.
- Reduction operations:
- Sum
- Mean
- Max
- Min along specified dimensions
- More advanced tensor operations:
- Matrix multiplication
- Dot product
- Transpose
- Computation graph structure:
- Nodes for operations
- Edges for tensor data
- Basic autograd operations:
- Implement forward and backward methods for basic operations (element-wise ops, matrix multiplication)
- Backpropagation:
- Given a final tensor and its gradient, propagate gradients back through the computation graph.
- Advanced autograd:
- Handle operations like reductions, advanced indexing, etc.
- Basic optimizers:
- Gradient descent
- Stochastic gradient descent
- Extend the Tensor structure to hold intermediate gradients and computation history.
- Basic GPU tensor representation.
- GPU kernels for core tensor operations.
- GPU support for advanced operations and autograd.
- Efficient CPU-GPU memory transfer mechanisms.
- Serialization and deserialization of tensors.
- Custom operations: API for users to define their own tensor operations.
- Random number generation for tensors.
- Import/export compatibility with popular tensor libraries or model formats.
- Memory optimization:
- In-place operations
- Memory pooling
- Parallelism and concurrency:
- Multi-threaded operations for large tensors
- Algorithmic optimizations for specific tensor operations.
- Comprehensive API documentation.
- Tutorials and guides.
- Sample projects or applications using the library.
- Comprehensive test suite, including stress tests and edge cases.
- Benchmarking against established tensor libraries.
- Interoperability tools:
- C/C++ bindings
- ONNX compatibility, etc.
- Error handling and reporting mechanisms.