BiaPy

Bioimage analysis pipelines in Python

Latest release notes

🔥NEWS🔥: Check out our last bioRxiv preprint!

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    Please install Docker to use the GUI following these instructions.
    Find a instructions on how to use the GUI in this video.

  • For each workflow we have both 2D and 3D versions:

    Semantic segmentation
    (2D)
    Instance segmentation
    (2D)
    Detection
    (2D)
    Denoising
    (2D)
    Super-resolution
    (2D)
    Self supervised learning
    (2D)
    Classification
    (2D)
    Semantic segmentation
    (3D)
    Instance segmentation
    (3D)
    Detection
    (3D)
    Denoising
    (3D)
    Super-resolution
    (3D)
    Self supervised learning
    (3D)
    Classification
    (3D)

  • We have two container prepared to run BiaPy, one for the actual NVIDIA driver versions and another container for old drivers:

    latest-11.8
    Pytorch 2.1
    CUDA 11.8
    Ubuntu 22.04
    latest-10.2
    Pytorch 1.12.1
    CUDA 10.2
    Ubuntu 20.04

    You need to check the CUDA version that you NVIDIA driver can handle. You can do that with nvidia-smi command in Linux/macOS or by running NVIDIA Control Panel in Windows. The driver information will tell you the maximum CUDA version it can handle. Select one of the above containers depending on your GPU driver. For instance, if the CUDA version it can handle is 12.0 you can use biapyx/biapy:latest-11.8 container.

    Docker Engine is available for Windows, macOS, and Linux, through Docker Desktop. For instructions on how to install Docker Desktop, see:

  • To use BiaPy via the command line, you will need to set up a conda environment. To do this, you will first need to install Anaconda/Miniconda. For detailed installation instructions based on your operating system, please see the following links: Windows, macOS and Linux. Then you need to create a conda environment through a terminal:

    conda create -n BiaPy_env python=3.10
conda activate BiaPy_env

    • Then you will need to install BiaPy package:

      pip install biapy

    • To clone the repository you will need to install git, a free and open source distributed version control system. Git will allow you to easily download the code with a single command. You can download and install it here. For detailed installation instructions based on your operating system, please see the following links: Windows, macOS and Linux.

      Once you have installed Anaconda and git, you will need to open a terminal to complete the following steps. Then, you are prepared to download BiaPy repository by running this command in the terminal:

      git clone https://github.com/BiaPyX/BiaPy.git

      This will create a folder called BiaPy that contains all the files of the library’s official repository. Then you will need to install BiaPy dependencies and for that you need to check the CUDA version that your NVIDIA driver can handle. You can do that with nvidia-smi command in Linux/macOS or by running NVIDIA Control Panel in Windows. The driver information will tell you the maximum CUDA version it can handle. We here provide two stable installations, one based in CUDA 11.8 and another one with an older version of Pytorch and with CUDA 10.2 (BiaPy will work anyway). Once you have checked it, proceed with the installation depending on the CUDA version:

      • # Install Pytorch and GPU dependencies
conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
cd BiaPy
pip install --editable .

      • # Install Pytorch and GPU dependencies
conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=10.2 -c pytorch
cd BiaPy
pip install --editable .

      Verify installation:

      python -c 'import torch; print(torch.__version__)'
>>> 2.1.0
python -c 'import torch; print(torch.cuda.is_available())'
>>> True

      From now on, to run BiaPy you will need to just activate the environment:

      conda activate BiaPy_env