arraybridge

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

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 Start

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'

Declarative Decorators

from arraybridge import numpy, torch, cupy

@torch(input_type='numpy', output_type='torch', oom_recovery=True)
def my_gpu_function(data):
    """Automatically converts input from NumPy to PyTorch."""
    return data * 2

# Use with NumPy input
result = my_gpu_function(np.array([1, 2, 3]))  # Returns PyTorch tensor

Installation

# Base installation (NumPy only)
pip install arraybridge

# With specific frameworks
pip install arraybridge[torch]
pip install arraybridge[cupy]
pip install arraybridge[tensorflow]
pip install arraybridge[jax]
pip install arraybridge[pyclesperanto]

# With all frameworks
pip install arraybridge[all]

Supported Frameworks

Why arraybridge?

Before (Manual conversion hell):

import numpy as np
import torch
import cupy as cp

def process_data(data, target='torch'):
    if target == 'torch':
        if isinstance(data, np.ndarray):
            return torch.from_numpy(data).cuda()
        elif isinstance(data, cp.ndarray):
            return torch.as_tensor(data, device='cuda')
    elif target == 'cupy':
        if isinstance(data, np.ndarray):
            return cp.asarray(data)
        elif hasattr(data, '__cuda_array_interface__'):
            return cp.asarray(data)
    # ... 30 more lines of if/elif ...

After (arraybridge):

from arraybridge import convert_memory, detect_memory_type

def process_data(data, target='torch'):
    source = detect_memory_type(data)
    return convert_memory(data, source_type=source, target_type=target, gpu_id=0)

Advanced Features

Thread-Local GPU Streams

from arraybridge import torch

@torch(oom_recovery=True)
def parallel_processing(data):
    # Automatically uses thread-local CUDA stream
    # Enables true parallelization across threads
    return data * 2

OOM Recovery

from arraybridge import cupy

@cupy(oom_recovery=True)
def memory_intensive_operation(data):
    # Automatically catches OOM errors
    # Clears GPU cache and retries
    return data @ data.T

Stack Utilities

from arraybridge import stack_slices, unstack_slices

# Stack 2D slices into 3D array
slices_2d = [np.random.rand(100, 100) for _ in range(50)]
volume_3d = stack_slices(slices_2d, memory_type='torch', gpu_id=0)

# Unstack 3D array into 2D slices
slices_back = unstack_slices(volume_3d, memory_type='torch', gpu_id=0)

Documentation

Full documentation available at arraybridge.readthedocs.io

Performance

arraybridge uses DLPack for zero-copy conversions when possible:

License

MIT License - see LICENSE file for details

Contributing

Contributions welcome! Please see CONTRIBUTING.md for guidelines.

Credits

Developed by Tristan Simas as part of the OpenHCS project.