1. Installation#

quimb itself is a pure python package and can now be found on PyPI:

pip install quimb

However it is recommended to first install the main dependencies using e.g. conda, as below. The code is hosted on github and if the dependencies are satisfied, a development version can be installed with pip directly from there:

pip install --no-deps -U git+https://github.com/jcmgray/quimb.git@develop

1.1. Required Dependencies#

The core packages quimb requires are:

For ease and performance (i.e. mkl compiled libraries), conda is the recommended distribution with which to install these.

In addition, the tensor network library, quimb.tensor, requires:

opt_einsum efficiently optimizes tensor contraction expressions. It can be installed with pip or from conda-forge and is a required dependency since various bits of the core quimb module now make use tensor-network functionality behind the scenes. autoray allows backend agnostic numeric code for various tensor network operations so that many libraries other than numpy can be used. It can be installed via pip from pypi or via conda from conda-forge.

1.2. Optional Dependencies#

Plotting tensor networks as colored graphs with weighted edges requires:

Fast, multi-threaded random number generation no longer (with numpy>1.17) requires randomgen though its bit generators can still be used.

Finally, fast and optionally distributed partial eigen-solving, SVD, exponentiation etc. can be accelerated with slepc4py and its dependencies:

For best performance of some routines, (e.g. shift invert eigen-solving), petsc must be configured with certain options. Pip can handle this compilation and installation, for example the following script installs everything necessary on Ubuntu:

#!/bin/bash

# install build tools, OpenMPI, and OpenBLAS
sudo apt install -y openmpi-bin libopenmpi-dev gfortran bison flex cmake valgrind curl autoconf libopenblas-base libopenblas-dev

# optimization flags, e.g. for intel you might want "-O3 -xHost"
export OPTFLAGS="-O3 -march=native -s -DNDEBUG"

# petsc options, here configured for real
export PETSC_CONFIGURE_OPTIONS="--with-scalar-type=complex --download-mumps --download-scalapack --download-parmetis --download-metis --COPTFLAGS='$OPTFLAGS' --CXXOPTFLAGS='$OPTFLAGS' --FOPTFLAGS='$OPTFLAGS'"

# make sure using all the same version
export PETSC_VERSION=3.14.0
pip install petsc==$PETSC_VERSION --no-binary :all:
pip install petsc4py==$PETSC_VERSION --no-binary :all:
pip install slepc==$PETSC_VERSION --no-binary :all:
pip install slepc4py==$PETSC_VERSION --no-binary :all:

Note

For the most control and best performance it is recommended to compile and install these (apart from MPI if you are e.g. on a cluster) manually - see the PETSc instructions. It is possible to compile several versions of PETSc/SLEPc side by side, for example a --with-scalar-type=complex and/or a --with-precision=single version, naming them with different values of PETSC_ARCH. When loading PETSc/SLEPc, quimb respects PETSC_ARCH if it is set, but it cannot dynamically switch between them.