| ========================= |
| Installing NeuralOperator |
| ========================= |
|
|
| The ``neuraloperator`` Python package provides all necessary tools for implementing operator learning. |
| Once installed, you can import it as ``neuralop``:: |
| |
| import neuralop |
|
|
| ~~~~~~~~~~~~~~~~~~~~~~~~~~ |
|
|
| Basic Install |
| ------------- |
|
|
| Pre-requisites |
| ++++++++++++++ |
|
|
| You will need to have Python 3 installed, as well as NumPy, Scipy, PyTorch, TensorLy and TensorLy-Torch. |
| If you are starting with Python or generally want a pain-free experience, we recommend that you |
| install the `Anaconda distribution <https://www.anaconda.com/download/>`_. It comes ready to use with all prerequisite packages. |
|
|
| ~~~~~~~~~~~~~~~~~~~~~~~~~~ |
|
|
| Installing with pip |
| +++++++++++++++++++ |
|
|
| We periodically package `neuraloperator` for release on PyPI. This version is not guaranteed to be up-to-date with |
| the latest changes to our code. |
|
|
| To install via pip, simply run, in your terminal:: |
| |
| pip install -U neuraloperator |
|
|
| (the `-U` is optional, use it if you want to update the package). |
|
|
| ~~~~~~~~~~~~~~~~~~~~~~~~~~ |
|
|
| Building ``neuraloperator`` from source |
| +++++++++++++++++++++++++++++++++++++++ |
|
|
| First ensure that you are in an environment with Python 3, NumPy, SciPy, PyTorch, TensorLy and TensorLy-Torch. |
| Then clone the repository and cd there:: |
| |
| git clone https://github.com/neuraloperator/neuraloperator |
| cd neuraloperator |
|
|
|
|
| Then, install the requirements :: |
| |
| pip install -r requirements.txt |
|
|
|
|
| Then install the package (here in editable mode with `-e`, or equivalently `--editable`):: |
| |
| pip install -e . |
|
|
|
|
| .. raw:: html |
|
|
| <br/> |
|
|
| ~~~~~~~~~~~~~~~~~~~~ |
|
|
| Advanced dependencies |
| --------------------- |
|
|
| .. _open3d_dependency: |
|
|
| Fast 3D spatial computing with Open3D |
| +++++++++++++++++++++++++++++++++++++ |
|
|
| To accelerate spatial computing for 3D applications, we include |
| `Open3D <https://github.com/isl-org/Open3D>`_ as an optional dependency. Open3D includes |
| utilities for reading 3D mesh files and fast 3D neighbor search. To install:: |
| |
| pip install open3d |
|
|
| Note that Open3D is only |
| compatible with specific builds of PyTorch and CUDA. Check the sub-package |
| `Open3D-ML <https://github.com/isl-org/Open3D-ML>`_ documentation for more details. |
|
|
| ~~~~~~~~~~~~~~~~~~~~~ |
|
|
| .. _torch_scatter_dependency: |
|
|
| Sparse computations with PyTorch-Scatter |
| ++++++++++++++++++++++++++++++++++++++++ |
|
|
| We use the package ``torch_scatter`` (available on PyPI) to speed up neighborhood reductions during |
| the final stage of the ``IntegralTransform`` layer, linked here: :ref:`gno_api`. |
|
|
| ``torch_scatter`` uses compiled CUDA kernels |
| to perform this reduction, providing both memory efficiency and speed advantages over a native PyTorch implementation. |
|
|
| However, since these kernels are precompiled, it is crucial to install the version of ``torch_scatter`` that is built for |
| your specific combination of OS, PyTorch and CUDA. All available wheels can be found `here <https://data.pyg.org/whl/>`_. |
|
|
| .. note :: |
| The latest PyTorch version for which ``torch_scatter`` is built is 2.5.1, so you should downgrade if you are using |
| the latest (``torch>=2.6.0``) release: |
|
|
|
|
| To force-downgrade PyTorch, run |
|
|
| .. code :: |
|
|
| pip install -U torch==2.5.1 |
|
|
