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Where does the actual data come from? | ||
===================================== | ||
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Equistore manages the metadata, where does the data come from. How does | ||
equistore deal with it and how to register new data origins in the python | ||
wrapper | ||
TBD | ||
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(Python automagically transforms data to numpy.ndarray or a torch.tensor) | ||
Equistore has no idea about the data itself (only knows the pointer to data | ||
and operations you can perform on it - create, destroy move and reshape data) | ||
.. Equistore manages the metadata, where does the data come from. How does | ||
.. equistore deal with it and how to register new data origins in the python | ||
.. wrapper | ||
.. (Python automagically transforms data to numpy.ndarray or a torch.tensor) | ||
.. Equistore has no idea about the data itself (only knows the pointer to data | ||
.. and operations you can perform on it - create, destroy move and reshape data) |
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Gradients and how we manage them | ||
================================ | ||
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Gradient samples - "special" format | ||
TBD | ||
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first sample of gradients is "sample" that refers to the row in block.values | ||
that we are taking the gradient of. | ||
the other samples - what we are taking the gradient with respect to. | ||
Write what this entails -- block.gradients.sample (i A j) (pair feature i j A k) | ||
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Cell gradients - Sample (i) | ||
components [[x y z ] [x y z]] (displacement matrix) | ||
.. Gradient samples - "special" format | ||
Gradient wrt hypers | ||
.. first sample of gradients is "sample" that refers to the row in block.values | ||
.. that we are taking the gradient of. | ||
.. the other samples - what we are taking the gradient with respect to. | ||
.. Write what this entails -- block.gradients.sample (i A j) (pair feature i j A k) | ||
.. Cell gradients - Sample (i) | ||
.. components [[x y z ] [x y z]] (displacement matrix) | ||
.. Gradient wrt hypers |
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Overview of Equistore's Documentation | ||
===================================== | ||
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This documentation covers everything you need to know about equistore. | ||
It comprises of the following five broad sections: | ||
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- :ref:`userdoc-get-started` | ||
- :ref:`userdoc-how-to` | ||
- :ref:`userdoc-references` | ||
- :ref:`userdoc-explanations` | ||
- :ref:`devdoc` | ||
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If you are new to equistore we recommend starting with the | ||
:ref:`userdoc-get-started` section. If you want to contribute to the development | ||
of the library please have a look at our :ref:`developer documentation | ||
<devdoc>`. | ||
Equistore: data storage for atomistic machine learning | ||
====================================================== | ||
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Equistore is a specialized data storage format suited to all your atomistic | ||
machine learning needs and more. You can think of it like ``numpy.ndarray`` or | ||
``torch.Tensor``, but carrying extra metadata together with the data. | ||
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Getting started | ||
--------------- | ||
This metadata can be about the nature of the **objects** being described, about | ||
**how** this object is being described, about **symmetry** properties of the | ||
data (this is especially relevant for equivariant machine learning), different | ||
**sparsity** linked to one-hot encoding of species or **components** of | ||
gradients of the above with respect to various parameters. | ||
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If you are an absolute beginner, we recommend you to start with the get started | ||
pages to familiarize yourself with equistore and the equistore ecosystem. | ||
For example, the object being described could be "one atom in a structure", or | ||
"a pair of atoms", while the how could be "using SOAP power spectrum features" | ||
or "Hamiltonian matrix elements". | ||
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How-to guides | ||
------------- | ||
Equistore main concern is about representing and manipulating this metadata, | ||
while using other well established library handle the data itself. We currently | ||
support using arbitrary CPU arrays created by any language (including numpy | ||
arrays), as well as PyTorch Tensor --- including full support for GPU and | ||
automatic differentiation. | ||
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This section comprises of guides that will take you through series of steps | ||
involved in addressing key problems and use-cases in equistore. It requires | ||
intermediate to advanced knowledge of how equistore works. If you are an | ||
absolute beginner, it is recommended you start from the | ||
:ref:`userdoc-get-started` section before going to the How to Guides. | ||
.. TODO: the end goal is to create an ecosystem of inter-operable libraries for atomistic ML | ||
.. TODO: equistore does not create data, other libraries do | ||
.. TODO: add a figure | ||
Reference guides | ||
---------------- | ||
-------------------------------------------------------------------------------- | ||
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The Reference Guide contains technical references for equistore's APIs. | ||
It describes the various functionalities | ||
provided by equistore. You can always refer to this section to learn more about | ||
classes, functions, modules, and other aspects of equistore's machinery you come | ||
across. | ||
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Explanations | ||
------------ | ||
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The explanation section discusses key topics and concepts at a fairly high level | ||
and provides useful explanations to expand your knowledge of equistore. It | ||
requires at least basic to intermediate knowledge of equistore If you are an | ||
absolute beginner, we recommend you start from the :ref:`userdoc-get-started` | ||
section of the documentation. | ||
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Developer documentation | ||
----------------------- | ||
This documentation covers everything you need to know about equistore. | ||
It comprises of the following five broad sections: | ||
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The developer guide introduces the aspects of how contributing to the code base | ||
or the documentation of equistore. | ||
- :ref:`userdoc-get-started`: familiarize yourself with equistore and it's | ||
ecosystem; | ||
- :ref:`userdoc-tutorials`: step-by-step tutorials addressing key problems and | ||
use-cases for equistore; | ||
- :ref:`userdoc-references`: technical description of all the functionalities | ||
provided by equistore; | ||
- :ref:`userdoc-explanations`: high-level explanation of more advanced | ||
functionalities; | ||
- :ref:`devdoc`: how to contribute to the code or the documentation of equistore. | ||
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.. toctree:: | ||
:hidden: | ||
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get-started/index | ||
how-to/index | ||
tutorials/index | ||
reference/index | ||
explanations/index | ||
devdoc/index |
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Creating TensorMap manually | ||
=========================== | ||
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.. container:: sphx-glr-footer sphx-glr-footer-example | ||
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.. container:: sphx-glr-download sphx-glr-download-python | ||
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:download:`Download Python source code for this example: first-tensormap.py <../examples/first-tensormap.py>` | ||
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.. container:: sphx-glr-download sphx-glr-download-jupyter | ||
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:download:`Download Jupyter notebook for this example: first-tensormap.ipynb <../examples/first-tensormap.ipynb>` | ||
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.. include:: ../examples/first-tensormap.rst | ||
:start-after: start-body | ||
:end-before: end-body |
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.. _userdoc-tutorials: | ||
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Tutorials | ||
========= | ||
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This section comprises of guides that will take you through series of steps | ||
involved in addressing key problems and use-cases in equistore. It requires | ||
intermediate to advanced knowledge of how equistore works. If you are an | ||
absolute beginner, it is recommended you start from the | ||
:ref:`userdoc-get-started` section before going to the How to Guides. | ||
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.. toctree:: | ||
:maxdepth: 2 | ||
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first-tensormap | ||
linear-model | ||
operations | ||
torch |
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Operating on equistore data | ||
--------------------------- | ||
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TBD |
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Integration with PyTorch | ||
------------------------ | ||
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TBD | ||
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.. Using torch tensors as data (gpus, autograd) |
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