medmlx-core

Shared MLX building blocks for MedMLX medical imaging models on Apple Silicon.

• Metal runtime checks and memory reporting
• 3D convolutions, resampling and sliding-window inference
• Tensor layouts, precision helpers and checkpoint conversion

PyPI · Releases · Technical reference

Requirements

Apple Silicon Mac, macOS, Python 3.12 or 3.13, and Metal GPU access. NumPy and MLX install automatically. Inference raises an error when Metal is unavailable.

Install

pip install medmlx-core

For optional PyTorch checkpoint conversion:

pip install "medmlx-core[conversion]"

Python

Run a small synthetic volume through overlapping patches:

import numpy as np
from medmlx_core import sliding_window_inference

volume = np.ones((1, 1, 32, 32, 32), dtype=np.float32)
output = sliding_window_inference(
    volume,
    roi_size=(16, 16, 16),
    sw_batch_size=1,
    predictor=lambda patch: patch * 2,
)
print(output.shape)  # (1, 1, 32, 32, 32)

Arrays use batch, channel, depth, height and width axes. Replace the example predictor with your model; the result is a NumPy array.

MedMLX model packages provide the architectures, weights and image workflows. This library supplies their shared runtime operations.

Technical details

API contracts, numerical references and development commands are in the technical reference. See the decoder note for the Metal transpose-convolution fix.

License

Apache-2.0. Includes third-party notices and the upstream MONAI license.