This repository is currently inactive and serves only as a supplement some of our papers. We have transitioned to using individual repositories for new projects. For our current work, see the Magenta website and Magenta GitHub Organization.

Magenta is a research project exploring the role of machine learning in the process of creating art and music. Primarily this involves developing new deep learning and reinforcement learning algorithms for generating songs, images, drawings, and other materials. But it's also an exploration in building smart tools and interfaces that allow artists and musicians to extend (not replace!) their processes using these models. Magenta was started by some researchers and engineers from the Google Brain team, but many others have contributed significantly to the project. We use TensorFlow and release our models and tools in open source on this GitHub. If you’d like to learn more about Magenta, check out our blog, where we post technical details. You can also join our discussion group.

This is the home for our Python TensorFlow library. To use our models in the browser with TensorFlow.js, head to the Magenta.js repository.

Take a look at our colab notebooks for various models, including one on getting started. Magenta.js is also a good resource for models and demos that run in the browser. This and more, including blog posts and Ableton Live plugins, can be found at https://magenta.tensorflow.org.

• Installation
• Using Magenta
• Development Environment (Advanced)

Magenta maintains a pip package for easy installation. We recommend using Anaconda to install it, but it can work in any standard Python environment. We support Python 3 (>= 3.5). These instructions will assume you are using Anaconda.

If you are running Mac OS X or Ubuntu, you can try using our automated installation script. Just paste the following command into your terminal.

curl https://raw.githubusercontent.com/tensorflow/magenta/main/magenta/tools/magenta-install.sh > /tmp/magenta-install.sh
bash /tmp/magenta-install.sh

After the script completes, open a new terminal window so the environment variable changes take effect.

The Magenta libraries are now available for use within Python programs and Jupyter notebooks, and the Magenta scripts are installed in your path!

Note that you will need to run source activate magenta to use Magenta every time you open a new terminal window.

If the automated script fails for any reason, or you'd prefer to install by hand, do the following steps.

Install the Magenta pip package:

pip install magenta

NOTE: In order to install the rtmidi package that we depend on, you may need to install headers for some sound libraries. On Ubuntu Linux, this command should install the necessary packages:

sudo apt-get install build-essential libasound2-dev libjack-dev portaudio19-dev

On Fedora Linux, use

sudo dnf group install "C Development Tools and Libraries"
sudo dnf install SAASound-devel jack-audio-connection-kit-devel portaudio-devel

The Magenta libraries are now available for use within Python programs and Jupyter notebooks, and the Magenta scripts are installed in your path!

You can now train our various models and use them to generate music, audio, and images. You can find instructions for each of the models by exploring the models directory.

If you want to develop on Magenta, you'll need to set up the full Development Environment.

First, clone this repository:

git clone https://github.com/tensorflow/magenta.git

Next, install the dependencies by changing to the base directory and executing the setup command:

pip install -e .

You can now edit the files and run scripts by calling Python as usual. For example, this is how you would run the melody_rnn_generate script from the base directory:

python magenta/models/melody_rnn/melody_rnn_generate --config=...

You can also install the (potentially modified) package with:

pip install .

Before creating a pull request, please also test your changes with:

pip install pytest-pylint
pytest

To build a new version for pip, bump the version and then run:

python setup.py test
python setup.py bdist_wheel --universal
twine upload dist/magenta-N.N.N-py2.py3-none-any.whl