qute

Framework to support deep-learning based computer-vision research in microscopy image analysis. Leverages and extends several PyTorch-based framework and tools.

Installation

Install prerequisites

Install Miniconda from: https://docs.anaconda.com/miniconda/ (for Linux, macOS, and Windows)
Install CUDA:
- Linux: https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64
- Windows: https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64
- macOS does not support CUDA; PyTorch will use mps on M1 processors.

Install qute

$ git clone https://github.com/aarpon/qute
$ cd qute
$ conda create -n qute-env python  # Minimum support version is 3.11
$ conda activate qute-env
$ pip install -e .

On Windows, PyTorch with CUDA acceleration has to be explicitly installed:

$ python -m pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

Test if GPU acceleration is available

Linux and Windows:

$ python -c "import torch; print(torch.cuda.is_available())"
True

macOS M1:

$ python -c "import torch; print(torch.backends.mps.is_available())"
True

First steps

To get started, try:

$ python qute/examples_new/cell_segmentation_demo_unet.py

Configuration parameters are explained in config_samples/.

To follow the training progress in Tensorboard, run:

$ tensorboard --logdir ${HOME}/Documents/qute/

and then open TensorBoard on http://localhost:6006/.

For an example on how to use ray[tune] to optimize hyper-parameters, see qute/examples/cell_segmentation_hp_optim_demo_unet.py.