Installing SCICO

SCICO requires Python version 3.8 or later. (Version 3.10 is recommended as it is the version under which SCICO has been most thoroughly tested.) It is supported on both Linux and MacOS, but is not currently supported on Windows due to the limited support for jaxlib on Windows. However, Windows users can use SCICO via the Windows Subsystem for Linux (WSL). Guides exist for using WSL with CPU only and with GPU support.

While not required, installation of SCICO and its dependencies within a Conda environment is recommended. Scripts are provided for creating a miniconda installation and an environment including all primary SCICO dependencies as well as dependencies for usage example, testing, and building the documentation.

From PyPI

The simplest way to install the most recent release of SCICO from PyPI is

pip install scico

which will install SCICO and its primary dependencies. If the additional dependencies for the example scripts are also desired, it can instead be installed using

pip install scico[examples]

Note, however, that since the astra-toolbox package available from PyPI is not straightforward to install (it has numerous build requirements that are not specified as package dependencies), it is recommended to first install this package via conda

conda install astra-toolbox

From conda-forge

SCICO can also be installed from conda-forge

conda install -c conda-forge "scico>0.0.5"

where the version constraint is required to avoid installation of an old package with broken dependencies.

Note, however, that installation from conda forge is only possible on a Linux platform since there is no conda package for the secondary dependency tensorstore under MacOS. There are also complications on Linux platforms with Python versions 3.9 or earlier due to the automatic installation of a version of secondary dependency etils that does not support Python versions earlier than 3.10. This can be rectified by

conda install etils=1.5.1

The most recent SCICO conda forge package also includes dependencies for the example scripts, except for bm3d, bm4d, and colour_demosaicing, for which conda packages are not available. These can be installed from PyPI

pip install bm3d bm4d colour_demosaicing

From GitHub

SCICO can be downloaded from the GitHub repo. Note that, since the SCICO repo has a submodule, it should be cloned via the command

git clone --recurse-submodules

Install using the commands

cd scico
pip install -r requirements.txt
pip install -e .

GPU Support

The instructions above install a CPU-only version of SCICO. To install a version with GPU support:

  1. Follow the CPU-only instructions, above

  2. Install the version of jaxlib with GPU support, as described in the JAX installation instructions. In the simplest case, the appropriate command is

    pip install --upgrade "jax[cuda11]"

    for CUDA 11, or

    pip install --upgrade "jax[cuda12]"

    for CUDA 12, but it may be necessary to explicitly specify the jaxlib version if the most recent release is not yet supported by SCICO (as specified in the requirements.txt file), or if using a version of CUDA older than 11.4, or CuDNN older than 8.2, in which case the command would be of the form

    pip install --upgrade "jaxlib==0.4.2+cuda11.cudnn82" -f

    with appropriate substitution of jaxlib, CUDA, and CuDNN version numbers.

The script misc/ in the source distribution is provided as an aid to debugging GPU support issues.

Additional Dependencies

See Example Dependencies for instructions on installing dependencies related to the examples.

For Developers

See Contributing for instructions on installing a version of SCICO suitable for development.