Cellan: Analyze cell images.

Installation

Cellan works for Windows, Mac and Linux systems. Installation steps can vary for different systems. But in general, you need to:

1. Install Python3 (>=3.9)
2. If using an NVIDIA GPU, set up CUDA (v11.8) and install PyTorch with cu118 support.
3. Install Cellan with pip

Below is the guide for Windows.

1. Install Python3, for example, Python 3.12.

   Scroll down to the bottom and click the Windows installer (64-bit) option. Run the installer and select 'Add python to path' and 'Disable long path limit'.

2. If you're using an NVIDIA GPU, install CUDA Toolkit 11.8 and cuDNN, and install PyTorch with cu118 support.

   First, Install CUDA Toolkit 11.8. Select your version of Windows, select "exe (local)," then click "Download."

   Next, install cuDNN. You will need to register an NVIDIA Developer account, which you can do for free. You can choose cuDNN v8.9.7 that supports CUDA toolkit v11.8. Choose 'Local Installer for Windows (Zip)', download and extract it. And then copy the three folders 'bin', 'lib', and 'include' into where the CUDA toolkit is installed (typcially, 'C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8'), and replace all the three folders with the same names. After that, you may need to add the 'C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8' to path via environmental variables.

   Finally, install PyTorch with cu118 support.

   py -3.12 -m pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu118
   

3. Upgrade pip, wheel, setuptools.

   py -3.12 -m pip install --upgrade pip wheel setuptools
   

4. Install Cellan via pip.

   py -3.12 -m pip install Cellan
   

Launch Cellan:

Cellan

If this doesn't work, which typically is because the python3/script is not in your environment path. You can google 'add python3 script to path environmental variable in windows' to add it to path, or simply use the following commands to initiate Cellan:

py -3.12

from Cellan import __main__

__main__.main()