Install immuneML with a package manager ========================================= .. meta:: :twitter:card: summary :twitter:site: @immuneml :twitter:title: immuneML installation through a package manager :twitter:description: See tutorials on how to install immuneML with Conda or PyPI :twitter:image: https://docs.immuneml.uio.no/_images/receptor_classification_overview.png This manual shows how to install immuneML using either conda or pip. Install immuneML with pip ------------------------------ 0. To install immuneML with pip, make sure to have Python version 3.7 or later installed. 1. 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): .. code-block:: console python3 -m venv ./immuneml_venv/ 2. To activate the virtual environment on Mac/Linux, run the following command (for Windows, see the documentation in the previous step): .. code-block:: console 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 :ref:`Install immuneML with conda` steps 0-3) and proceed to install immuneML with pip inside the conda environment. 3. If not already up-to-date, update pip: .. code-block:: console python3 -m pip install --upgrade pip 4. If not already installed, install the wheel package. If it is not installed, the installation of some of the dependencies might default to legacy 'setup.py install'. .. code-block:: console pip install wheel 5. To install `immuneML from PyPI `_ in this virtual environment, run the following: .. code-block:: console pip install immuneML Alternatively, if you want to use the :ref:`TCRdistClassifier` ML method and corresponding :ref:`TCRdistMotifDiscovery` report, include the optional extra :code:`TCRdist`: .. code-block:: console pip install immuneML[TCRdist] See also this question under 'Troubleshooting': :ref:`I get an error when installing PyTorch (could not find a version that satisfies the requirement torch)` Install immuneML with conda ------------------------------ 0. If a conda distribution is not already installed on the machine, see `the official conda installation documentation `_. 1. Once conda is working, create a directory for immuneML and navigate to the directory: .. code-block:: console mkdir immuneML/ cd immuneML/ 2. 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: .. code-block:: console conda create --prefix immuneml_env/ python=3.8 3. Activate the created environment: .. code-block:: console conda activate immuneml_env/ 4. To install immuneML using conda, run: .. code-block:: console conda install -c bioconda immuneml Installing optional dependencies ---------------------------------- Optionally, if you want to use the :ref:`DeepRC` ML method and and corresponding :ref:`DeepRCMotifDiscovery` report, you also have to install DeepRC dependencies using the :download:`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: .. code-block:: console pip install -r requirements_DeepRC.txt --no-dependencies If you want to use the :ref:`CompAIRRDistance` or :ref:`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 :code:`make` in the main folder: .. code-block:: console git clone https://github.com/uio-bmi/compairr.git 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 :ref:`CompAIRRDistance` or :ref:`CompAIRRSequenceAbundance` encoder. Testing immuneML ----------------- 1. To validate the installation, run: .. code-block:: console immune-ml -h The output should look like this: .. code-block:: console 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 2. To quickly test out whether immuneML is able to run, try running the quickstart command: .. code-block:: console 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 (:code:`synthetic_dataset`) and one for the results of the machine learning analysis (:code:`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.