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Remove beta warning lazy signal #3282

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merged 2 commits into from Dec 19, 2023
Merged

Remove beta warning lazy signal #3282

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3f87a45
Make warning of "beta" state of lazy signals more specific
ericpre Dec 17, 2023
e848200
Add changelog entry
ericpre Dec 18, 2023
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21 changes: 11 additions & 10 deletions doc/user_guide/big_data.rst
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Expand Up @@ -3,16 +3,6 @@
Working with big data
*********************

.. warning:: All the features described in this chapter are in beta state.

Although most of them work as described, their operation may not always
be optimal, well-documented and/or consistent with their in-memory counterparts.

Therefore, although efforts will be taken to minimise major disruptions,
the syntax and features described here may change in patch and minor
HyperSpy releases. If you experience issues with HyperSpy's lazy features
please report them to the developers.

.. versionadded:: 1.2

HyperSpy makes it possible to analyse data larger than the available memory by
Expand Down Expand Up @@ -123,6 +113,11 @@ operations are only performed lazily, use the
Machine learning
----------------

.. warning:: The machine learning features are in beta state.

Although most of them work as described, their operation may not always
be optimal, well-documented and/or consistent with their in-memory counterparts.

:ref:`mva.decomposition` algorithms for machine learning often perform
large matrix manipulations, requiring significantly more memory than the data size.
To perform decomposition operation lazily, HyperSpy provides access to several "online"
Expand Down Expand Up @@ -498,6 +493,12 @@ Dask has two schedulers available for single machines.
Distributed Scheduler
^^^^^^^^^^^^^^^^^^^^^

.. warning:: Distributed computing is not supported for all file formats.

Distributed computing is limited to a few file formats, see the list of
:external+rsciio:ref:`supported file format <supported-formats>` in
RosettaSciIO documentation.

The recommended way to use dask is with the distributed scheduler. This allows you to scale your computations
to a cluster of machines. The distributed scheduler can be used on a single machine as well. ``dask-distributed``
also gives you access to the dask dashboard which allows you to monitor your computations.
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1 change: 1 addition & 0 deletions upcoming_changes/3282.doc.rst
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Update warning of "beta" state in big data section to be more specific.