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BiaPy

Accessible deep learning on bioimages

Latest release notes

🔥NEWS🔥: BiaPy's paper is finally out in Nature Methods!
[Preprint in bioRxiv]

{% tabs installation %}

{% tab installation GUI %}

Download

Get Additional Installers |

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

You can also install previous versions of BiaPy's graphical user interface.

{% include carousel.html height="50" unit="%" duration="7" number="1" %}

{% endtab %}

{% tab installation Colab Notebooks %}

{% include notebook-carousel.html %}

{% endtab %}

{% tab installation Docker %}

We have a container prepared to run BiaPy:

latest-11.8

Pytorch	2.9.1
CUDA	11.8
Ubuntu	22.04

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

{% endtab %}

{% tab installation Command line %}

You have three different options to install BiaPy. Choose one or another depending on your preferences:

{% tabs command_line_installation %}

{% tab command_line_installation Option 1: Conda %}

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 Conda. Then choose one of the following options based on your machine capabilities:

A. GPU-capable machine (NVIDIA GPU)

conda config --set channel_priority strict
conda create -n BiaPy_env -c conda-forge python=3.11 biapy pytorch-gpu
conda activate BiaPy_env

Verify GPU at runtime:

python -c 'import torch; print(torch.__version__)'
>>> 2.9.1
python -c 'import torch; print(torch.cuda.is_available())'
>>> True

B. CPU-only machine

conda config --set channel_priority strict
conda create -n BiaPy_env -c conda-forge python=3.11 biapy
conda activate BiaPy_env

{% endtab %}

{% tab command_line_installation Option 2: Mamba %}

Before you begin, ensure you have Mamba installed. Mamba is a faster alternative to Conda and can be used to manage your conda environments.Once you have mamba installed you will to choose one of the following options based on your machine capabilities:

A. GPU-capable machine (NVIDIA GPU)

mamba create -n BiaPy_env -c conda-forge python=3.11 biapy pytorch-gpu
mamba activate BiaPy_env

Verify GPU at runtime:

python -c 'import torch; print(torch.__version__)'
>>> 2.9.1
python -c 'import torch; print(torch.cuda.is_available())'
>>> True

B. CPU-only machine

mamba create -n BiaPy_env -c conda-forge python=3.11 biapy
mamba activate BiaPy_env

{% endtab %}

{% tab command_line_installation Option 3: Developer %}

Set up a conda/mamba environment:

mamba create -n BiaPy_env -c conda-forge python=3.11
mamba activate BiaPy_env

Clone BiaPy repository:

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

Install PyTorch first, choosing GPU if available. Use the official PyTorch selector for your platform (CUDA / ROCm / CPU). Example (CUDA, just as an example-use the selector’s exact command):

pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118

Install BiaPy in editable mode:

cd BiaPy
pip install --editable .

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{% endtabs %}

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Docs

Find BiaPy step-by-step guides, video tutorials and more on ReadTheDocs
Discuss

Join other BiaPy users and search discussions on image.sc
Develop

Check out BiaPy's source code on GitHub
Troubleshooting

Check out usage tips and documented errors in our FAQ & Troubleshooting section