Installation Guide

This guide provides detailed instructions for installing the celldetection package. Depending on your environment and requirements, you can choose from several installation methods.

Docker and Apptainer Installation

For users who prefer using Docker or Apptainer, installation of PyTorch or setting up virtual environments is not required, as the Docker image comes with all necessary dependencies.

Docker Installation

Pull the latest celldetection Docker image:

docker pull ericup/celldetection:latest

Verification for Docker

After pulling the image, you can run a Docker container to verify the installation:

docker run --rm --gpus="device=0" ericup/celldetection:latest python -c "import celldetection; print(celldetection.__version__)"

You may remove --gpus="device=0" if you do not have GPUs on your system.

Apptainer Installation

In HPC environments where Apptainer is preferred:

apptainer pull --dir . --disable-cache docker://ericup/celldetection:latest

If your system allows caching, you may remove --disable-cache. On some systems you may need to specify a custom cache directory with sufficient disk space.

Verification for Apptainer

To verify the installation in Apptainer, run the following command using the downloaded .sif file:

apptainer exec --nv celldetection_latest.sif python -c "import celldetection; print(celldetection.__version__)"

You may remove --nv if you do not have GPUs on your system.

Installation in Python Environment

For users who wish to install celldetection directly in their Python environment, follow the steps below. Ensure that you have PyTorch installed as it is a critical dependency for the package. Visit the PyTorch Installation Guide for instructions.

Virtual Environment Setup

It’s recommended to install celldetection in a virtual environment.

Using venv:

1. Create a virtual environment:

   python -m venv celldetection_env
   

2. Activate the virtual environment:

   On Windows:

   celldetection_env\Scripts\activate
   

   On macOS and Linux:

   source celldetection_env/bin/activate
   

Using Conda:

1. Create a Conda environment:

   conda create -n celldetection_env python=3.x
   

   Replace 3.x with the specific Python version you want to use.

2. Activate the Conda environment:

   conda activate celldetection_env
   

PyPI Installation

Install the latest stable release from PyPI:

pip install -U celldetection

GitHub Installation

For the latest development version from GitHub:

pip install git+https://github.com/FZJ-INM1-BDA/celldetection.git

Post-Installation

After installation, you can start using the celldetection package for your image processing tasks. If installed in a Python environment, remember to keep your virtual environment active. To exit the virtual environment, use the deactivate command.