Installation¶
quimb
is available on both pypi and
conda-forge. While quimb
is
pure python and has no direct dependencies itself, the recommended distribution
would be miniforge
for installing the various backend array libraries and their dependencies.
Installing with pip
:
pip install quimb
Installing with conda
:
conda install -c conda-forge quimb
Installing with mamba
:
mamba install quimb
Hint
Mamba is a faster version of conda
tool, and the -forge distribution comes
pre-configured with the community conda-forge
channel only, which further
simplifies and speeds up installing dependencies. mini-
refers to not
including the many default packages that come with Anaconda
.
Installing the latest version directly from github:
If you want to checkout the latest version of features and fixes, you can install directly from the github repository:
pip install -U git+https://github.com/jcmgray/quimb.git
Installing a local, editable development version:
If you want to make changes to the source code and test them out, you can install a local editable version of the package:
git clone https://github.com/jcmgray/quimb.git
pip install --no-deps -U -e quimb/
Required Dependencies¶
The core packages quimb
requires are:
For ease and performance (i.e. mkl compiled libraries), miniforge is the recommended distribution with which to install these.
Hint
This is mostly becuase MKL still (as of January 2025) provides a significant performance boost over OpenBLAS for decompositions such as svd
, qr
and eigh
. Using conda-forge you can specify MKL using blas=*=mkl
in either the install command or your environment.yml
file.
In addition, the tensor network library, quimb.tensor
, requires:
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.
cotengra
efficiently optimizes and performs 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. If you are performing advanced contractions you may want to see the cotengra installation docs.
Optional Dependencies¶
Plotting tensor networks as colored graphs with weighted edges requires:
pygraphviz (optional, for faster layouts)
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:
mpi4py (v2.1.0+)
An MPI implementation (OpenMPI recommended, the 1.10.x series seems most robust for spawning processes).
To install these from conda-forge, with complex dtype specified for example, use:
mamba install -c conda-forge mpi4py petsc=*=*complex* petsc4py slepc=*=*complex* slepc4py
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.