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Merge pull request #1234 from fastmachinelearning/docsv110
Update docs for v1.1.0
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docs/advanced/extension.rst

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@@ -18,6 +18,9 @@ To implement a custom layer in ``hls4ml`` with the extension API, the required c
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* Function config template
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* Registration of layer, source code, and templates
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.. note::
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currently, then extension API supports keras models. Support for pytorch models is in development.
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Complete example
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================
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docs/intro/setup.rst

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@@ -20,14 +20,8 @@ If you want to use our :doc:`profiling <../advanced/profiling>` toolbox, you mig
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pip install hls4ml[profiling]
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``hls4ml`` is also available as a ``conda`` package in the ``conda-forge`` repository. To install, run:
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.. warning::
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Version of hls4ml available on ``conda-forge`` is outdated, we recommend installing with ``pip`` to get the latest version.
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.. code-block::
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conda install -c conda-forge hls4ml
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Previously, versions of hls4ml were made available on ``conda-forge``. These are outdated and should NOT be used. Installing with ``pip`` is currently the only supported method.
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Development version
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-------------------

docs/intro/status.rst

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Other feature notes:
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* ``hls4ml`` is tested on Linux, and supports
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* ``hls4ml`` is tested on the following platforms. Newer versions might work just fine, but try at your own risk.
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* Vivado HLS versions 2018.2 to 2020.1
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* Intel HLS versions 20.1 to 21.4
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* Vitis HLS versions 2022.2 to 2024.1
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* Intel HLS versions 20.1 to 21.4, versions \> 21.4 have not been tested.
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* Vitis HLS versions 2022.2 to 2024.1. Versions \<= 2022.1 are known not to work.
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* Catapult HLS versions 2024.1_1 to 2024.2
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* oneAPI versions 2024.1 to 2025.0
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* Windows and macOS are not supported
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* ``hls4ml`` supports Linux and requires python \>=3.10. hlsml does not require a specific Linux distribution version and we recommended to follow the requirements of the HLS tool you are using.
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* Windows and macOS are not supported. Setting up ``hls4ml`` on these platforms, for example using the Windows Subsystem for Linux (WSL) should be possible, but we do not provide support for such use cases.
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* BDT support has moved to the `Conifer <https://github.com/thesps/conifer>`__ package
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Example Models

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