numpy-interop

What problem does it solve?

This Skill enables efficient data sharing between different numerical and tensor libraries (like NumPy, PyTorch, TensorFlow) by leveraging standardized protocols, avoiding costly data copying.

Core Features & Use Cases

• Cross-Library Data Exchange: Facilitates seamless transfer of data between Python libraries without redundant memory allocation.
• Custom Array Behavior: Allows custom array-like objects to integrate with NumPy's universal functions (ufuncs).
• Low-Level Memory Access: Provides access to raw memory pointers for interfacing with C/C++ extensions.
• Use Case: Transferring a large tensor from a GPU-accelerated PyTorch model to NumPy for CPU-based analysis without copying the entire tensor's data.

Quick Start

Use the numpy-interop skill to convert a PyTorch tensor to a NumPy array.