Install immuneML with a package manager

This manual shows how to install immuneML using either conda or pip.

Install immuneML with pip

  1. To install immuneML with pip, make sure to have Python version 3.7 or later installed.

  2. Create a virtual environment where immuneML will be installed. It is possible to install immuneML as a global package, but it is not recommended as there might be conflicting versions of different packages. For more details, see the official documentation on creating virtual environments with Python. To create an environment, run the following in the terminal (for Windows-specific commands, see the virtual environment documentation linked above):

python3 -m venv ./immuneml_venv/
  1. To activate the virtual environment on Mac/Linux, run the following command (for Windows, see the documentation in the previous step):

source ./immuneml_venv/bin/activate

Note: when creating a python virtual environment, it will automatically use the same Python version as the environment it was created in. To ensure that the preferred Python version (3.8) is used, it is possible to instead make a conda environment (see Install immuneML with conda steps 0-3) and proceed to install immuneML with pip inside the conda environment.

  1. If not already up-to-date, update pip:

python3 -m pip install --upgrade pip
  1. If not already installed, install the wheel package. If it is not installed, the installation of some of the dependencies might default to legacy ‘ install’.

pip install wheel
  1. To install immuneML from PyPI in this virtual environment, run the following:

pip install immuneML

Alternatively, if you want to use the TCRdistClassifier ML method and corresponding TCRdistMotifDiscovery report, include the optional extra TCRdist:

pip install immuneML[TCRdist]

See also this question under ‘Troubleshooting’: I get an error when installing PyTorch (could not find a version that satisfies the requirement torch)

Install immuneML with conda

  1. If a conda distribution is not already installed on the machine, see the official conda installation documentation.

  2. Once conda is working, create a directory for immuneML and navigate to the directory:

mkdir immuneML/
cd immuneML/
  1. Create a virtual environment using conda. immuneML has been tested extensively with Python versions 3.7, 3.8 and 3.11. To create a conda virtual environment with Python version 3.8, use:

conda create --prefix immuneml_env/ python=3.8
  1. Activate the created environment:

conda activate immuneml_env/
  1. To install immuneML using conda, run:

conda install -c bioconda immuneml

Installing optional dependencies

Optionally, if you want to use the DeepRC ML method and and corresponding DeepRCMotifDiscovery report, you also have to install DeepRC dependencies using the requirements_DeepRC.txt file. Important note: DeepRC uses PyTorch functionalities that depend on GPU. Therefore, DeepRC does not work on a CPU. To install the DeepRC dependencies, run:

pip install -r requirements_DeepRC.txt --no-dependencies

If you want to use the CompAIRRDistance or CompAIRRSequenceAbundance encoder, you have to install the C++ tool CompAIRR. The easiest way to do this is by cloning CompAIRR from GitHub and installing it using make in the main folder:

git clone
cd compairr
make install

If such installation is unsuccessful (for example if you do not have the rights to install CompAIRR via make), it is also possible to directly provide the path to a CompAIRR executable as a parameter to CompAIRRDistance or CompAIRRSequenceAbundance encoder.

Testing immuneML

  1. To validate the installation, run:

immune-ml -h

The output should look like this:

usage: immune-ml [-h] [--tool TOOL] specification_path result_path

immuneML command line tool

positional arguments:
  specification_path  Path to specification YAML file. Always used to define
                      the analysis.
  result_path         Output directory path.

optional arguments:
  -h, --help          show this help message and exit
  --tool TOOL         Name of the tool which calls immuneML. This name will be
                      used to invoke appropriate API call, which will then do
                      additional work in tool-dependent way before running
                      standard immuneML.
  --version           show program's version and exit
  1. To quickly test out whether immuneML is able to run, try running the quickstart command:

immune-ml-quickstart ./quickstart_results/

This will generate a synthetic dataset and run a simple machine machine learning analysis on the generated data. The results folder will contain two sub-folders: one for the generated dataset (synthetic_dataset) and one for the results of the machine learning analysis (machine_learning_analysis). The files named specs.yaml are the input files for immuneML that describe how to generate the dataset and how to do the machine learning analysis. The index.html files can be used to navigate through all the results that were produced.