ocdkit

A toolkit for array manipulation, GPU dispatch, image I/O, spatial operations, morphology, and plotting.

Install

pip install ocdkit             # core (numpy, scipy, scikit-image, tifffile, matplotlib)
pip install ocdkit[torch]      # + PyTorch GPU support
pip install ocdkit[plot]       # + ncolor, cmap, opt_einsum
pip install ocdkit[spatial]    # + numba, fastremap (contour extraction, skeletonization)
pip install ocdkit[all]        # everything

Modules

Quick start

from ocdkit.array import rescale
from ocdkit.gpu import resolve_device
from ocdkit.plot import figure, image_grid

device = resolve_device()  # auto-detect CUDA / MPS / CPU

Performance tips

Pin numba's JIT cache to local disk

If your project source lives on a network filesystem (SMB / NFS), set NUMBA_CACHE_DIR to a local-disk location. By default numba writes its JIT cache to __pycache__ next to the source file, which on a NAS-mounted tree means dozens of small SMB ops per fresh subprocess — several seconds of overhead on every cold import.

ocdkit auto-applies $HOME/.cache/numba as the default if you haven't set it (see src/ocdkit/__init__.py), but for shells, test runners, and non-ocdkit code, set it explicitly:

# Linux / macOS — add to ~/.zshrc, ~/.bashrc, or ~/.profile
export NUMBA_CACHE_DIR="$HOME/.cache/numba"

# Windows — add to $PROFILE
[Environment]::SetEnvironmentVariable('NUMBA_CACHE_DIR', "$env:USERPROFILE\.cache\numba", 'User')

Compiled artifacts are machine-local anyway (CPU- and Python-version specific), so they don't belong on shared NAS regardless of perf.

License

BSD-3-Clause