arraybridge Documentation

Unified API for NumPy, CuPy, PyTorch, TensorFlow, JAX, and pyclesperanto

arraybridge provides a unified interface for working with multiple array/tensor frameworks, featuring automatic memory type conversion, declarative decorators, and zero-copy operations when possible.

Getting Started

User Documentation

API Documentation

Examples

Development

Features

  • Unified API: Single interface for 6 array/tensor frameworks

  • Automatic Conversion: DLPack + NumPy fallback with automatic path selection

  • Declarative Decorators: @numpy, @torch, @cupy for memory type declarations

  • Device Management: Thread-local GPU contexts and automatic stream management

  • OOM Recovery: Automatic out-of-memory detection and cache clearing

  • Dtype Preservation: Automatic dtype preservation across conversions

  • Zero Dependencies: Only requires NumPy (framework dependencies are optional)

Quick Example

from arraybridge import convert_memory, detect_memory_type
import numpy as np

# Create NumPy array
data = np.array([[1, 2], [3, 4]])

# Convert to PyTorch (if installed)
torch_data = convert_memory(data, source_type='numpy', target_type='torch', gpu_id=0)

# Detect memory type
mem_type = detect_memory_type(torch_data)  # 'torch'

Installation

# Base installation (NumPy only)
pip install arraybridge

# With specific frameworks
pip install arraybridge[torch]
pip install arraybridge[cupy]
pip install arraybridge[all]

Indices and tables