lisaanalysistools-cuda11x 1.1.0

A package for LISA Data Analysis

LISA Analysis Tools

LISA Analysis Tools is a package for performing LISA Data Analysis tasks, including building the LISA Global Fit.

To install the latest version of lisaanalysistools using pip, simply run:

# For CPU-only version
pip install lisaanalysistools

# For GPU-enabled versions with CUDA 11.Y.Z
pip install lisaanalysistools-cuda11x

# For GPU-enabled versions with CUDA 12.Y.Z
pip install lisaanalysistools-cuda12x

To know your CUDA version, run the tool nvidia-smi in a terminal a check the CUDA version reported in the table header:

$ nvidia-smi
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.54.15              Driver Version: 550.54.15      CUDA Version: 12.4     |
|-----------------------------------------+------------------------+----------------------+
...

You may also install lisaanalysistools directly using conda (including on Windows) as well as its CUDA 12.x plugin (only on Linux). It is strongly advised to:

1. Ensure that your conda environment makes sole use of the conda-forge channel
2. Install lisaanalysistools directly when building your conda environment, not afterwards

# For CPU-only version, on either Linux, macOS or Windows:
conda create --name lisatools_cpu -c conda-forge --override-channels python=3.12 lisaanalysistools
conda activate lisatools_cpu

# For CUDA 12.x version, only on Linux
conda create --name lisatools_cuda -c conda-forge --override-channels python=3.12 lisaanalysistools-cuda12x
conda activate lisatools_cuda

Now, in a python file or notebook:

import lisatools

You may check the currently available backends:

>>> for backend in ["cpu", "cuda11x", "cuda12x", "cuda", "gpu"]:
...     print(f" - Backend '{backend}': {"available" if lisatools.has_backend(backend) else "unavailable"}")
 - Backend 'cpu': available
 - Backend 'cuda11x': unavailable
 - Backend 'cuda12x': unavailable
 - Backend 'cuda': unavailable
 - Backend 'gpu': unavailable

Note that the cuda backend is an alias for either cuda11x or cuda12x. If any is available, then the cuda backend is available. Similarly, the gpu backend is (for now) an alias for cuda.

If you expected a backend to be available but it is not, run the following command to obtain an error message which can guide you to fix this issue:

>>> import lisatools
>>> lisatools.get_backend("cuda12x")
ModuleNotFoundError: No module named 'lisatools_backend_cuda12x'

The above exception was the direct cause of the following exception:
...

lisatools.cutils.BackendNotInstalled: The 'cuda12x' backend is not installed.

The above exception was the direct cause of the following exception:
...

lisatools.cutils.MissingDependencies: LISAanalysistools CUDA plugin is missing.
    If you are using lisatools in an environment managed using pip, run:
        $ pip install lisaanalysistools-cuda12x

The above exception was the direct cause of the following exception:
...

lisatools.cutils.BackendAccessException: Backend 'cuda12x' is unavailable. See previous error messages.

Once LISA Analysis Tools is working and the expected backends are selected, check out the examples notebooks on how to start with this software.

Installing from sources

Prerequisites

To install this software from source, you will need:

• A C++ compiler (g++, clang++, ...)
• A Python version supported by scikit-build-core (>=3.7 as of Jan. 2025)

Some installation steps require the external library LAPACK along with its C-bindings provided by LAPACKE. If these libraries and their header files (in particular lapacke.h) are available on your system, they will be detected and used automatically. If they are available on a non-standard location, see below for some options to help detecting them. Note that by default, if LAPACKE is not available on your system, the installation step will attempt to download its sources and add them to the compilation tree. This makes the installation a bit longer but a lot easier.

If you want to enable GPU support in LISA Analysis Tools, you will also need the NVIDIA CUDA Compiler nvcc in your path as well as the CUDA toolkit (with, in particular, the libraries CUDA Runtime Library, cuBLAS and cuSPARSE).

Installation instructions using conda

We recommend to install LISA Analysis Tools using conda in order to have the compilers all within an environment. First clone the repo

git clone https://github.com/mikekatz04/LISAanalysistools.git
cd LISAanalysistools

Now create an environment (these instructions work for all platforms but some adjustements can be needed, refer to the detailed installation documentation for more information):

conda create -n lisatools_env -y -c conda-forge --override-channels |
    cxx-compiler pkgconfig conda-forge/label/lapack_rc::liblapacke

activate the environment

conda activate lisatools_env

Then we can install locally for development:

pip install -e '.[dev, testing]'

Installation instructions using conda on GPUs and linux

Below is a quick set of instructions to install the LISA Analysis Tools package on GPUs and linux.

conda create -n lisatools_env -c conda-forge lisaanalysistools-cuda12x python=3.12
conda activate lisatools_env

Test the installation device by running python

import lisatools
lisatools.get_backend("cuda12x")

Running the installation

To start the from-source installation, ensure the pre-requisite are met, clone the repository, and then simply run a pip install command:

# Clone the repository
git clone https://github.com/mikekatz04/LISAanalysistools.git
cd LISAanalysistools

# Run the install
pip install .

If the installation does not work, first check the detailed installation documentation. If it still does not work, please open an issue on the GitHub repository or contact the developers through other means.

Running the Tests

The tests require a few dependencies which are not installed by default. To install them, add the [testing] label to LISA Analysis Tools package name when installing it. E.g:

# For CPU-only version with testing enabled
pip install lisaanalysistools[testing]

# For GPU version with CUDA 12.Y and testing enabled
pip install lisaanalysistools-cuda12x[testing]

# For from-source install with testing enabled
git clone https://github.com/mikekatz04/LISAanalysistools.git
cd LISAanalysistools
pip install '.[testing]'

To run the tests, open a terminal in a directory containing the sources of LISA Analysis Tools and then run the unittest module in discover mode:

$ git clone https://github.com/mikekatz04/LISAanalysistools.git
$ cd LISAanalysistools
$ python -m lisatools.tests  # or "python -m unittest discover"
...
----------------------------------------------------------------------
Ran 20 tests in 71.514s
OK

Contributing

Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.

If you want to develop LISA Analysis Tools and produce documentation, install lisatools from source with the [dev] label and in editable mode:

$ git clone https://github.com/mikekatz04/LISAanalysistools.git
$ cd LISAanalysistools
pip install -e '.[dev, testing]'

This will install necessary packages for building the documentation (sphinx, pypandoc, sphinx_rtd_theme, nbsphinx) and to run the tests.

The documentation source files are in docs/source. To compile the documentation locally, change to the docs directory and run make html.

Versioning

We use SemVer for versioning. For the versions available, see the tags on this repository.

Contributors

A (non-exhaustive) list of contributors to the LISA Analysis Tools code can be found in CONTRIBUTORS.md.

License

This project is licensed under the Apache License - see the LICENSE file for details.

Citation

Please make sure to cite LISA Analysis Tools papers and the LISA Analysis Tools software on Zenodo. We provide a set of prepared references in PAPERS.bib. There are other papers that require citation based on the classes used. For most classes this applies to, you can find these by checking the citation attribute for that class. All references are detailed in the CITATION.cff file.