diff options
Diffstat (limited to 'src/python/doc/installation.rst')
| -rw-r--r-- | src/python/doc/installation.rst | 335 |
1 files changed, 253 insertions, 82 deletions
diff --git a/src/python/doc/installation.rst b/src/python/doc/installation.rst index 77d9e8b3..5491542f 100644 --- a/src/python/doc/installation.rst +++ b/src/python/doc/installation.rst @@ -5,41 +5,78 @@ Installation ############ -Conda -***** -The easiest way to install the Python version of GUDHI is using -`conda <https://gudhi.inria.fr/licensing/>`_. +Packages +******** +The easiest way to install the Python version of GUDHI is using pre-built packages. +We recommend `conda <https://gudhi.inria.fr/conda/>`_ + +.. code-block:: bash + + conda install -c conda-forge gudhi + +Gudhi is also available on `PyPI <https://pypi.org/project/gudhi/>`_ + +.. code-block:: bash + + pip install gudhi + +Third party packages are also available, for instance on Debian or Ubuntu + +.. code-block:: bash + + apt install python3-gudhi + +In all cases, you may still want to install some of the optional `run time dependencies`_. Compiling ********* -The library uses c++14 and requires `Boost <https://www.boost.org/>`_ ≥ 1.56.0, -`CMake <https://www.cmake.org/>`_ ≥ 3.1 to generate makefiles, -`NumPy <http://numpy.org>`_ and `Cython <https://www.cython.org/>`_ to compile -the GUDHI Python module. -It is a multi-platform library and compiles on Linux, Mac OSX and Visual -Studio 2015. - -On `Windows <https://wiki.python.org/moin/WindowsCompilers>`_ , only Python -≥ 3.5 are available because of the required Visual Studio version. - -On other systems, if you have several Python/python installed, the version 2.X -will be used by default, but you can force it by adding +These instructions are for people who want to compile gudhi from source, they are +unnecessary if you installed a binary package of Gudhi as above. They assume that +you have downloaded a `release <https://github.com/GUDHI/gudhi-devel/releases>`_, +with a name like `gudhi.3.X.Y.tar.gz`, then run `tar xf gudhi.3.X.Y.tar.gz`, which +created a directory `gudhi.3.X.Y`, hereinafter referred to as `/path-to-gudhi/`. +If you are instead using a git checkout, beware that the paths are a bit +different, and in particular the `python/` subdirectory is actually `src/python/` +there. + +The library uses c++17 and requires `Boost <https://www.boost.org/>`_ :math:`\geq` 1.66.0, +`CMake <https://www.cmake.org/>`_ :math:`\geq` 3.5 to generate makefiles, +Python :math:`\geq` 3.5, `NumPy <http://numpy.org>`_ :math:`\geq` 1.15.0, `Cython <https://www.cython.org/>`_ +:math:`\geq` 0.27 and `pybind11 <https://github.com/pybind/pybind11>`_ to compile the GUDHI Python module. +It is a multi-platform library and compiles on Linux, Mac OSX and Visual Studio 2017 or later. + +If you have several Python/python installed, the version 2.X may be used by default, but you can force it by adding :code:`-DPython_ADDITIONAL_VERSIONS=3` to the cmake command. GUDHI Python module compilation =============================== -To build the GUDHI Python module, run the following commands in a terminal: +After making sure that the `Compilation dependencies`_ are properly installed, +one can build the GUDHI Python module, by running the following commands in a terminal: .. code-block:: bash cd /path-to-gudhi/ mkdir build cd build/ - cmake .. + cmake -DCMAKE_BUILD_TYPE=Release .. cd python make +.. note:: + + :code:`make python` (or :code:`make` in python directory) is only a + `CMake custom targets <https://cmake.org/cmake/help/latest/command/add_custom_target.html>`_ + to shortcut :code:`python setup.py build_ext --inplace` command. + No specific other options (:code:`-j8` for parallel, or even :code:`make clean`, ...) are + available. + But one can use :code:`python setup.py ...` specific options in the python directory: + +.. code-block:: bash + + python setup.py clean --all # Clean former compilation + python setup.py build_ext -j 8 --inplace # Build in parallel + GUDHI Python module installation ================================ @@ -56,22 +93,37 @@ Or install it definitely in your Python packages folder: .. code-block:: bash cd /path-to-gudhi/build/python - # May require sudo or administrator privileges - make install + python setup.py install # add --user to the command if you do not have the permission + # Or 'pip install .' + +.. note:: + It does not take into account :code:`CMAKE_INSTALL_PREFIX`. + But one can use + `alternate location installation <https://docs.python.org/3/install/#alternate-installation>`_. Test suites =========== -To test your build, `py.test <http://doc.pytest.org>`_ is optional. Run the -following command in a terminal: +To test your build, `py.test <http://doc.pytest.org>`_ is required. Run the +following `Ctest <https://cmake.org/cmake/help/latest/manual/ctest.1.html>`_ +(CMake test driver program) command in a terminal: .. code-block:: bash cd /path-to-gudhi/build/python # For windows, you have to set PYTHONPATH environment variable export PYTHONPATH='$PYTHONPATH:/path-to-gudhi/build/python' - make test + ctest + +.. note:: + + One can use :code:`ctest` specific options in the python directory: + +.. code-block:: bash + + # Launch tests in parallel on 8 cores and set failing tests in verbose mode + ctest -j 8 --output-on-failure Debugging issues ================ @@ -84,63 +136,74 @@ If :code:`import gudhi` succeeds, please have a look to debug information: .. code-block:: python - import gudhi - print(gudhi.__debug_info__) + import gudhi as gd + print(gd.__debug_info__) + print("+ Installed modules are: " + gd.__available_modules) + print("+ Missing modules are: " + gd.__missing_modules) You shall have something like: .. code-block:: none - Python version 2.7.15 - Cython version 0.26.1 - Numpy version 1.14.1 - Eigen3 version 3.1.1 - Installed modules are: off_reader;simplex_tree;rips_complex; - cubical_complex;periodic_cubical_complex;reader_utils;witness_complex; - strong_witness_complex;alpha_complex; - Missing modules are: bottleneck_distance;nerve_gic;subsampling; - tangential_complex;persistence_graphical_tools; - euclidean_witness_complex;euclidean_strong_witness_complex; - CGAL version 4.7.1000 - GMP_LIBRARIES = /usr/lib/x86_64-linux-gnu/libgmp.so - GMPXX_LIBRARIES = /usr/lib/x86_64-linux-gnu/libgmpxx.so - TBB version 9107 found and used + Pybind11 version 2.8.1 + Python version 3.7.12 + Cython version 0.29.25 + Numpy version 1.21.4 + Boost version 1.77.0 + + Installed modules are: off_utils;simplex_tree;rips_complex;cubical_complex;periodic_cubical_complex; + persistence_graphical_tools;reader_utils;witness_complex;strong_witness_complex; + + Missing modules are: bottleneck;nerve_gic;subsampling;tangential_complex;alpha_complex;euclidean_witness_complex; + euclidean_strong_witness_complex; -Here, you can see that bottleneck_distance, nerve_gic, subsampling and -tangential_complex are missing because of the CGAL version. -persistence_graphical_tools is not available as matplotlib is not -available. +Here, you can see that the modules that need CGAL are missing, because CGAL is not installed. +:code:`persistence_graphical_tools` is installed, but +`its functions <https://gudhi.inria.fr/python/latest/persistence_graphical_tools_ref.html>`_ will produce an error as +matplotlib is not available. Unitary tests cannot be run as pytest is missing. A complete configuration would be : .. code-block:: none - Python version 3.6.5 - Cython version 0.28.2 - Pytest version 3.3.2 - Matplotlib version 2.2.2 - Numpy version 1.14.5 - Eigen3 version 3.3.4 - Installed modules are: off_reader;simplex_tree;rips_complex; - cubical_complex;periodic_cubical_complex;persistence_graphical_tools; - reader_utils;witness_complex;strong_witness_complex; - persistence_graphical_tools;bottleneck_distance;nerve_gic;subsampling; - tangential_complex;alpha_complex;euclidean_witness_complex; - euclidean_strong_witness_complex; - CGAL header only version 4.11.0 + Pybind11 version 2.8.1 + Python version 3.9.7 + Cython version 0.29.24 + Pytest version 6.2.5 + Matplotlib version 3.5.0 + Numpy version 1.21.4 + Scipy version 1.7.3 + Scikit-learn version 1.0.1 + POT version 0.8.0 + HNSWlib found + PyKeOps version [pyKeOps]: 2.1 + EagerPy version 0.30.0 + TensorFlow version 2.7.0 + Sphinx version 4.3.0 + Sphinx-paramlinks version 0.5.2 + python_docs_theme found + Eigen3 version 3.4.0 + Boost version 1.74.0 + CGAL version 5.3 GMP_LIBRARIES = /usr/lib/x86_64-linux-gnu/libgmp.so GMPXX_LIBRARIES = /usr/lib/x86_64-linux-gnu/libgmpxx.so + MPFR_LIBRARIES = /usr/lib/x86_64-linux-gnu/libmpfr.so TBB version 9107 found and used + + Installed modules are: bottleneck;off_utils;simplex_tree;rips_complex;cubical_complex;periodic_cubical_complex; + persistence_graphical_tools;reader_utils;witness_complex;strong_witness_complex;nerve_gic;subsampling; + tangential_complex;alpha_complex;euclidean_witness_complex;euclidean_strong_witness_complex; + + Missing modules are: + Documentation ============= -To build the documentation, `sphinx-doc <http://www.sphinx-doc.org>`_ and -`sphinxcontrib-bibtex <https://sphinxcontrib-bibtex.readthedocs.io>`_ are +To build the documentation, `sphinx-doc <http://www.sphinx-doc.org>`_, +`sphinxcontrib-bibtex <https://sphinxcontrib-bibtex.readthedocs.io>`_, +`sphinxcontrib-paramlinks <https://github.com/sqlalchemyorg/sphinx-paramlinks>`_ and +`python-docs-theme <https://github.com/python/python-docs-theme>`_ are required. As the documentation is auto-tested, `CGAL`_, `Eigen`_, -`Matplotlib`_, `NumPy`_ and `SciPy`_ are also mandatory to build the -documentation. +`Matplotlib`_, `NumPy`_, `POT`_, `Scikit-learn`_ and `SciPy`_ are +also mandatory to build the documentation. Run the following commands in a terminal: @@ -152,19 +215,25 @@ Run the following commands in a terminal: Optional third-party library **************************** +Compilation dependencies +======================== + +These third party dependencies are detected by `CMake <https://www.cmake.org/>`_. +They have to be installed before performing the `GUDHI Python module compilation`_. + CGAL -==== +---- Some GUDHI modules (cf. :doc:`modules list </index>`), and few examples -require CGAL, a C++ library that provides easy access to efficient and -reliable geometric algorithms. +require `CGAL <https://www.cgal.org/>`_, a C++ library that provides easy +access to efficient and reliable geometric algorithms. The procedure to install this library according to your operating system is detailed `here <http://doc.cgal.org/latest/Manual/installation.html>`_. -The following examples requires CGAL version ≥ 4.11.0: +The following examples require CGAL version :math:`\geq` 4.11.0: .. only:: builder_html @@ -176,14 +245,14 @@ The following examples requires CGAL version ≥ 4.11.0: * :download:`euclidean_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py>` Eigen -===== +----- Some GUDHI modules (cf. :doc:`modules list </index>`), and few examples require `Eigen <http://eigen.tuxfamily.org/>`_, a C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms. -The following examples require `Eigen <http://eigen.tuxfamily.org/>`_ version ≥ 3.1.0: +The following examples require `Eigen <http://eigen.tuxfamily.org/>`_ version :math:`\geq` 3.1.0: .. only:: builder_html @@ -193,8 +262,46 @@ The following examples require `Eigen <http://eigen.tuxfamily.org/>`_ version ≠* :download:`euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py>` * :download:`euclidean_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py>` +Threading Building Blocks +------------------------- + +`Intel® TBB <https://www.threadingbuildingblocks.org/>`_ lets you easily write +parallel C++ programs that take full advantage of multicore performance, that +are portable and composable, and that have future-proof scalability. + +Having Intel® TBB installed is recommended to parallelize and accelerate some +GUDHI computations. + +Run time dependencies +===================== + +These third party dependencies are detected by Python `import` mechanism at run time. +They can be installed when required. + +EagerPy +------- + +Some Python functions can handle automatic differentiation (possibly only when +a flag `enable_autodiff=True` is used). In order to reduce code duplication, we +use `EagerPy <https://eagerpy.jonasrauber.de/>`_ which wraps arrays from +PyTorch, TensorFlow and JAX in a common interface. + +Joblib +------ + +`Joblib <https://joblib.readthedocs.io/>`_ is used both as a dependency of `Scikit-learn`_, +and directly for parallelism in some modules (:class:`~gudhi.point_cloud.knn.KNearestNeighbors`, +:func:`~gudhi.representations.metrics.pairwise_persistence_diagram_distances`). + +Hnswlib +------- + +:class:`~gudhi.point_cloud.knn.KNearestNeighbors` can use the Python package +`Hnswlib <https://github.com/nmslib/hnswlib>`_ as a backend if explicitly +requested, to speed-up queries. + Matplotlib -========== +---------- The :doc:`persistence graphical tools </persistence_graphical_tools_user>` module requires `Matplotlib <http://matplotlib.org>`_, a Python 2D plotting @@ -215,28 +322,92 @@ The following examples require the `Matplotlib <http://matplotlib.org>`_: * :download:`euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py>` * :download:`euclidean_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py>` +LaTeX +~~~~~ + +If a sufficiently complete LaTeX toolchain is available (including dvipng and ghostscript), the LaTeX option of +matplotlib is enabled for prettier captions (cf. +`matplotlib text rendering with LaTeX <https://matplotlib.org/3.3.0/tutorials/text/usetex.html>`_). +It also requires `type1cm` LaTeX package (not detected by matplotlib). + +If you are facing issues with LaTeX rendering, like this one: + +.. code-block:: none + + Traceback (most recent call last): + File "/usr/lib/python3/dist-packages/matplotlib/texmanager.py", line 302, in _run_checked_subprocess + report = subprocess.check_output(command, + ... + ! LaTeX Error: File `type1cm.sty' not found. + ... + +This is because the LaTeX package is not installed on your system. On Ubuntu systems you can install texlive-full +(for all LaTeX packages), or more specific packages like texlive-latex-extra, cm-super. + +You can still deactivate LaTeX rendering by saying: + +.. code-block:: python + + import gudhi as gd + gd.persistence_graphical_tools._gudhi_matplotlib_use_tex=False + +PyKeOps +------- + +:class:`~gudhi.point_cloud.knn.KNearestNeighbors` can use the Python package +`PyKeOps <https://www.kernel-operations.io/keops/python/>`_ as a backend if +explicitly requested, to speed-up queries using a GPU. + +Python Optimal Transport +------------------------ + +The :doc:`Wasserstein distance </wasserstein_distance_user>` +module requires `POT <https://pythonot.github.io/>`_, a library that provides +several solvers for optimization problems related to Optimal Transport. + +PyTorch +------- + +`PyTorch <https://pytorch.org/>`_ is currently only used as a dependency of +`PyKeOps`_, and in some tests. + +Scikit-learn +------------ + +The :doc:`persistence representations </representations>` module require +`scikit-learn <https://scikit-learn.org/>`_, a Python-based ecosystem of +open-source software for machine learning. + +:class:`~gudhi.point_cloud.knn.KNearestNeighbors` can use the Python package +`scikit-learn <https://scikit-learn.org/>`_ as a backend if explicitly +requested. + SciPy -===== +----- -The :doc:`persistence graphical tools </persistence_graphical_tools_user>` -module requires `SciPy <http://scipy.org>`_, a Python-based ecosystem of -open-source software for mathematics, science, and engineering. +The :doc:`persistence graphical tools </persistence_graphical_tools_user>` and +:doc:`Wasserstein distance </wasserstein_distance_user>` modules require `SciPy +<http://scipy.org>`_, a Python-based ecosystem of open-source software for +mathematics, science, and engineering. -Threading Building Blocks -========================= +:class:`~gudhi.point_cloud.knn.KNearestNeighbors` can use the Python package +`SciPy <http://scipy.org>`_ :math:`\geq` 1.6.0 as a backend if explicitly requested. -`Intel® TBB <https://www.threadingbuildingblocks.org/>`_ lets you easily write -parallel C++ programs that take full advantage of multicore performance, that -are portable and composable, and that have future-proof scalability. +TensorFlow +---------- -Having Intel® TBB installed is recommended to parallelize and accelerate some -GUDHI computations. +The :doc:`cubical complex </cubical_complex_tflow_itf_ref>`, :doc:`simplex tree </ls_simplex_tree_tflow_itf_ref>` +and :doc:`Rips complex </rips_complex_tflow_itf_ref>` modules require `TensorFlow <https://www.tensorflow.org>`_ +for incorporating them in neural nets. + +`TensorFlow <https://www.tensorflow.org>`_ is also used in some automatic differentiation tests. Bug reports and contributions ***************************** -Please help us improving the quality of the GUDHI library. You may report bugs or suggestions to: - - Contact: gudhi-users@lists.gforge.inria.fr +Please help us improving the quality of the GUDHI library. +You may `report bugs <https://github.com/GUDHI/gudhi-devel/issues>`_ or +`contact us <https://gudhi.inria.fr/contact/>`_ for any suggestions. -GUDHI is open to external contributions. If you want to join our development team, please contact us. +GUDHI is open to external contributions. If you want to join our development team, please take some time to read our +`contributing guide <https://github.com/GUDHI/gudhi-devel/blob/master/.github/CONTRIBUTING.md>`_. |