aarpon / qute

Leverages and extends several PyTorch-based framework and tools.

qute

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

• PyTorch
• PyTorch Lightning
• MONAI

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; qute will use mps on M1 processors.

Install qute

$ git clone https://github.com/aarpon/qute
$ cd qute
$ conda create -n qute-env python
$ 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/cell_segmentation_demo_unet.py 

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

To follow the training progress in Tensorboard, run:

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

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