Installation#
This document describes how to install CapyMOA and its dependencies. CapyMOA is tested against Python 3.9, 3.10, and 3.11. Newer versions of Python will likely work but have yet to be tested.
Environment#
We recommend using a virtual environment to manage your Python environment. Miniconda is a good choice for managing Python environments. You can install Miniconda from here. Once you have Miniconda installed, you can create a new environment with:
conda create -n capymoa python=3.9
conda activate capymoa
When your environment is activated, you can install CapyMOA by following the instructions below.
Dependencies#
Most of the dependencies for CapyMOA are installed automatically with pip. However, there are a few dependencies that need to be installed manually.
Java (Required)#
CapyMOA requires Java to be installed and accessible in your environment. You can check if Java is installed by running the following command in your terminal:
java -version
If Java is not installed, you can download OpenJDK (Open Java Development Kit) from this link, or alternatively the Oracle JDK from this link. Linux users can also install OpenJDK using their distribution’s package manager.
Now that Java is installed, you should see an output similar to the following
when you run the command java -version
:
openjdk version "17.0.9" 2023-10-17
OpenJDK Runtime Environment (build 17.0.9+8)
OpenJDK 64-Bit Server VM (build 17.0.9+8, mixed mode)
PyTorch (Required)#
The CapyMOA algorithms using deep learning require PyTorch. It is not installed by default because different versions are required for different hardware. If you want to use these algorithms, follow the instructions here to get the correct version for your hardware.
For CPU only, you can install PyTorch with:
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
Install CapyMOA#
pip install capymoa
Install CapyMOA for Development#
If you want to contribute to CapyMOA, you should clone the repository, install development dependencies, and install CapyMOA in editable mode:
git clone https://github.com/adaptive-machine-learning/CapyMOA.git
# or clone via the SSH protocol (often preferred if you use SSH keys for git):
# ``git clone with git@github.com:adaptive-machine-learning/CapyMOA.git``
cd CapyMOA
pip install --editable ".[dev]"
If you are intending to contribute to CapyMOA, consider making a fork of the repository and cloning your fork instead of the main repository. This way, you can push changes to your fork and create pull requests to the main repository.