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.