NetKet is an open-source project delivering cutting-edge methods for the study of many-body quantum systems with artificial neural networks and machine learning techniques. It is a Python library built on JAX.
NetKet is an affiliated project to numFOCUS.
- Homepage: https://www.netket.org
- Citing: https://www.netket.org/cite/
- Documentation: https://netket.readthedocs.io/en/latest/index.html
- Tutorials: https://netket.readthedocs.io/en/latest/tutorials/gs-ising.html
- Examples: https://github.com/netket/netket/tree/master/Examples
- Source code: https://github.com/netket/netket
NetKet runs on MacOS and Linux. We recommend to install NetKet using pip, but it can also be installed with conda.
It is often necessary to first update pip to a recent release (>=20.3) in order for upper compatibility bounds to be considered and avoid a broken installation.
For instructions on how to install the latest stable/beta release of NetKet see the Get Started page of our website or run the following command (Apple M1 users, follow that link for more instructions):
pip install --upgrade pip
pip install --upgrade netketIf you wish to install the current development version of NetKet, which is the master branch of this GitHub repository, together with the additional dependencies, you can run the following command:
pip install --upgrade pip
pip install 'git+https://github.com/netket/netket.git#egg=netket[all]'We recommend to install NetKet with all it's extra dependencies, which are documented below.
The latest release of NetKet is always available on PyPi and can be installed with pip.
When installing netket with pip, you can pass the following extra variants as square brakets. You can install several of them by separating them with a comma.
"[dev]": installs development-related dependencies such as black, pytest and testing dependencies"[extra]": Installstensorboardxto enable logging to tensorboard, and openfermion to convert the QubitOperators."[all]": Installs all extra dependencies
To get started with NetKet, we recommend you give a look at our tutorials page, by running them on your computer or on Google Colaboratory. There are also many example scripts that you can download, run and edit that showcase some use-cases of NetKet, although they are not commented.
If you want to get in touch with us, feel free to open an issue or a discussion here on GitHub, or to join the MLQuantum slack group where several people involved with NetKet hang out. To join the slack channel just accept this invitation