Installing NetKet is very easy, but it has several complex and optional dependencies. In general, we suggest to create a virtual-environment for every project you work on.

Installing NetKet#

Netket requires python>= 3.7 and can optionally benefit from a recent MPI install. GPUs are supported on linux.

Before attempting the installation, you should update pip to a recent version (>=20.3) to avoid getting a broken install. To install the basic version with no optional dependencies, run the following commands:

pip install --upgrade pip
pip install --upgrade netket

To query the installed netket version you can run the following command in your shell. If all went well, you should have at least version 3.3 installed. We recomend to always start a new project with the latest available version.

python -c "import netket; print(netket.__version__)"

Apple ARM (M1) processors

If you are on an Apple Arm (M1) processor you should follow the special instructions in this section.


If you are using a conda environment, please don’t. If you really want to use conda environments, see this section.

Install Errors?

If you experience an installation error under pip, please make sure you have upgraded pip first and that you are not inside of a conda environment. If the install succeds but you can’t load netket, you most likely need to update the dependencies. To get help, please open an issue pasting the output of python -m

GPU support#

If you want to run NetKet on a GPU, you must install a GPU-compatible jaxlib, which is only supported on Linux and requires CUDA>=11 and cuDNN>=8.2. We advise you to look at the instructions on jax repository because they change from time to time. At the time of writing, installing a GPU version of jaxlib is as simple as running the following command, assuming you have very recent versions of CUDA and cuDNN.

pip install --upgrade pip
pip install --upgrade "jax[cuda]" -f

Where the jaxlib version must correspond to the version of the existing CUDA installation you want to use. Refer to jax documentation to learn more about matching cuda versions with python wheels.


NetKet (due to Jax) only uses 1 or 2 CPU cores or 1 GPU by default at once unless you work on huge systems with mastodontic neural-networks. If you want to use all your CPU cores, multiple GPUs, or run your code among many computers you’ll need to use MPI. If you want to use MPI, make sure mpi is installed and can be used. To know if MPI is installed, try running the following command.

mpicc --showme:link

If the command fails, you need to install MPI using your favourite package manager. In general, for the love of yourself and in order to keep you sanity, we recomend not to use conda together with MPI.

  • On Mac, we reccomend to use homebrew:

brew install openmpi
  • On Linux, you can install it using your package manager:

# fedora
sudo dnf install mpich
# ubuntu/debian
sudo apt-get install mpich

You can install the dependencies necessary to run with MPI with the following command:

pip install --upgrade
pip install --upgrade "netket[mpi]"

Subsequently, NetKet will exploit MPI-level parallelism for the Monte-Carlo sampling. See this block to understand how NetKet behaves under MPI.


Conda is a great package manager as long as it works. But when it does not, it’s a pain.

To install NetKet using conda, simply run

conda install -c conda-forge netket

This will also install the conda MPI compilers and the MPI-related dependencies. This often creates problems if you also have a system MPI. Moreover, you should never use conda’s MPI on a supercomputing cluster.

In general, we advise against using conda or conda environments to install NetKet unless someone is pointing a gun at you. If you don’t want to die from that bullet, but would rather loose your mental sanity fighting conda, do expect weird setup errors.

Apple ARM Processors (M1)#

NetKet works natively on Apple M1 Arm computers, but Numba, one of its dependencies, is not easy to install on such platform as of February 2022 (If you are reading this in the future: hopefully this should not be an issue anymore. Probably from April/May 2022 you should be able to ignore those special instructions).

If you attempt to pip install netket, pip will first attempt to install [Numba], resulting in some hard-to-decipher LLVM compilation errors. The easiest solution is to install numba with conda, and everything else with pip. Conda is capable of installing numba without issues on Apple ARM processors, and pip will detect that Numba was already installed and won’t attempt modifying it.

conda install -c conda-forge numba
pip install --upgrade pip
pip install --upgrade netket

Alternatively you can use conda by running conda install -c conda-forge netket, but we advise against.


Netket is a numerical framework written in Python to simulate many-body quantum systems using variational methods. In general, netket allows the user to parametrize quantum states using arbitrary functions, be it simple mean-field ansatze, Jastrow, MPS ansatze or convolutional neural networks. Those states can be sampled efficiently in order to estimate observables or other quantities. Stochastic optimisation of the energy or a time-evolution are implemnented on top of those samplers.

Netket tries to follow the functional programming paradigm, and is built around jax. While it is possible to run the examples without knowledge of [jax], we strongly reccomend getting familiar with it if you wish to extend netket.

This documentation is divided into several modules, each explaining in-depth how a sub-module of netket works. You can select a module from the list on the left, or you can read the following example which contains links to all relevant parts of the documentation.

Jax/Flax extensions#

Netket v3 API is centered around flax, a jax library to simplify the definition and usage of Neural-Networks. If you want to define more complex custom models, you should read Flax documentation on how to define a Linen module. However, you can also use jax.example_libraries.stax or haiku.

Flax supports complex numbers but does not make it overly easy to work with them. As such, netket exports a module, netket.nn which re-exports the functionality in flax.nn, but with the additional support of complex numbers. Also netket.optim is a re-export of flax.optim with few added functionalities.

Lastly, in netket.jax there are a few functions, notably jax.grad and jax.vjp adapted to work with arbitrary real or complex functions, and/or with MPI.

Legacy API support (API before 2021)#

With the 3.0 official release in the beginning of 2021, we have drastically changed the API of Netket, which are no longer compatible with the old version.

Netket will ship a copy of the old API and functionalities under the legacy submodule. To keep using your old scripts you should change your import at the top from import netket as nk to import netket.legacy as nk.

While you can keep using the legacy module, we will remove it sometime soon with version 3.1, so we strongly advise to update your scripts to the new version. To aid you in updating your code, a lot of deprecation warning will be issued when you use the legacy api suggesting you how to update your code.

While it might be annoying, the new API allows us to have less code to maintain and grants more freedom to the user when defining models, so it will be a huge improvement.

Some documentation of the legacy module can be found in this section Legacy Random Generation, but please be advised that it is no longer-supported and documentation will probably be of poor quality.

For more information on new features and API changes, please consult Whats New.


If you were using the previous version of NetKet, we strongly advise you to read Whats New as it lists several changes that might otherwise pass unnoticed.

Commented Example#

import netket as nk
import numpy as np

The first thing to do is import NetKet. We usually shorten it to nk.

g = nk.graph.Hypercube(length=20, n_dim=1, pbc=True)

Then, one must define the system to be studied. To do so, the first thing to do is usually defining the lattice of the model. This is not always required, but it can sometimes avoid errors. Several types of Lattices (graphs) are defined in the Graph submodule.

In the example above we chose a 1-Dimensional chain with 20 sites and periodic boundary conditions.

hi = nk.hilbert.Spin(s=1 / 2, N=g.n_nodes)
ha = nk.operator.Ising(hilbert=hi, graph=g, h=1.0)

Then, one must define the hilbert space and the hamiltonian. Common options for the Hilbert spacee are Spin, Fock or QuBit, but it is also possible to define your own. Those classes are contained in the The Hilbert module submodule.

The hamiltonian sub-module contains several pre-built hamiltonian, such as Ising and Bose-Hubbard, but you can also build the operators yourself by summing all the local terms. See the Operators documentation for more informations.

ma = nk.models.RBM(alpha=1, dtype=float)

sa = nk.sampler.MetropolisLocal(hi, n_chains=16)

Then, one must chose the model to use as a Neural Quantum State. Netket provides a few pre-built models in the Models sub-module. Netket models are simply [Flax] modules: check out the define-your-model section for more informations on how to define or use custom models. We specify dtype=float (which is the default, but we want to show it to you) which means that weights will be stored as double-precision. We advise you that Jax (and therefore netket) does not follow completely the standard NumPy promotion rules, instead treating float as a weak double-precision type which can _loose_ precision in some cases. This can happen if you mix single and double precision in your models and the sampler and is described in Jax:Type promotion semantics.

Hilbert space samplers are defined in the The Sampler module submodule. In general you must provide the constructor of the hilbert space to be sampled and some options. In this case we ask for 16 markov chains. The default behaviour for samplers is to output states with double precision, but this can be configured by specifying the dtype argument when constructing the sampler. Samples don’t need double precision at all, so it makes sense to use the lower precision, but you have to be careful with the dtype of your model in order not to reduce the precision.

# Optimizer
op = nk.optimizer.Sgd(learning_rate=0.01)

You can then chose an optimizer from the optimizer submodule. You can also use an arbitrary flax optimiser, or define your own.

# Variational monte carlo driver
gs = nk.VMC(ha, op, sa, ma, n_samples=1000, n_discard_per_chain=100), out=None)

Once you have all the pieces together, you can construct a variational monte carlo optimisation driver by passing the constructor the hamiltonian and the optimizer (which must always be the first two arguments), and then the sampler, machine and various options.

Once that is done, you can run the simulation by calling the run method in the driver, specifying the output loggers and the number of iterations in the optimisation.