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Refactor Getting Started and add Licensing (#1423)
* Add a licensing section Signed-off-by: paul.profizi <[email protected]> * Add a licensing section Signed-off-by: paul.profizi <[email protected]> * Add a licensing section Signed-off-by: paul.profizi <[email protected]> * Add a licensing section Signed-off-by: paul.profizi <[email protected]> * Allow for local build Signed-off-by: paul.profizi <[email protected]> * Fix doc Signed-off-by: paul.profizi <[email protected]> * Fix doc Signed-off-by: paul.profizi <[email protected]> * Fix link Signed-off-by: paul.profizi <[email protected]> * Update compatibility.rst Signed-off-by: paul.profizi <[email protected]> * Update description of licensed operators in licensing.rst Signed-off-by: paul.profizi <[email protected]> * Move and rename getting_started_with_dpf_server.rst to dpf_server.rst Signed-off-by: paul.profizi <[email protected]> * Update the getting started index.rst Signed-off-by: paul.profizi <[email protected]> * Refactor licensing.rst Signed-off-by: paul.profizi <[email protected]> * Refactor dpf_server.rst Signed-off-by: paul.profizi <[email protected]> * Fixes Signed-off-by: paul.profizi <[email protected]> * Fixes Signed-off-by: paul.profizi <[email protected]> * Fixes Signed-off-by: paul.profizi <[email protected]> * Update mentions to ansys_dpf_server Signed-off-by: paul.profizi <[email protected]> * Mark 'dyna' as a valid license increment Signed-off-by: paul.profizi <[email protected]> --------- Signed-off-by: paul.profizi <[email protected]>
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‎doc/source/concepts/waysofusing.rst

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@@ -32,7 +32,7 @@ These capabilities can be accessed through client APIs, as shown here:
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.. image:: ../images/drawings/apis_2.png
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1. DPF server application can be accessed using Ansys Inc product, or DPF Server package (see :ref:`ref_getting_started_with_dpf_server`) available on the Customer portal.
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1. DPF server application can be accessed using Ansys Inc product, or DPF Server package (see :ref:`ref_dpf_server`) available on the Customer portal.
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2. Several client APIs are available (CPython, IronPython, C++, and so on).
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‎doc/source/conf.py

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@@ -319,7 +319,8 @@ def verify_meilisearch_is_active(app):
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MEILISEARCH_PUBLIC_API_KEY = os.getenv("MEILISEARCH_PUBLIC_API_KEY", None)
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if not MEILISEARCH_PUBLIC_API_KEY:
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sys.stderr.write("Could not find MEILISEARCH_PUBLIC_API_KEY")
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sys.exit(1)
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# sys.exit(1)
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def setup(app):
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app.connect("builder-inited", verify_meilisearch_is_active)

‎doc/source/getting_started/compatibility.rst

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@@ -13,7 +13,7 @@ more information, see `Ansys Platform Support <https://www.ansys.com/solutions/s
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Client-server
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-------------
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The DPF server version depends on your installed Ansys version.
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The DPF server version depends on your installed Ansys version or your installed standalone DPF Server pre-release version.
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The following table shows client-server compatibility for supported
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Ansys versions. With Ansys 2022 R2 and later, you can use PyDPF-Core ``0.10`` and later.
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With Ansys 2021 R2 and 2022 R1, you can use PyDPF-Core
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- ``ansys.grpc.dpf`` Python module version
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- ``ansys.dpf.gatebin`` binaries Python module version
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- ``ansys.dpf.gate`` Python module version
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* - 8.0 (Ansys 2024 R2 pre0)
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- 0.11.0 and later
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- None
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- None
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- None
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* - 7.1 (Ansys 2024 R1)
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- 0.10.1 and later
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- None
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- None
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- None
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* - 7.0 (Ansys 2024 R1 pre0)
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- | 0.10.0 and later
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| 0.9.0
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.. code::
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pip install --force-reinstall ansys-dpf-core==0.7.0
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.. _target_environment_variable_with_dpf_section:
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Environment variable
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--------------------
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The ``start_local_server()`` method uses the ``Ans.Dpf.Grpc.bat`` file or
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``Ans.Dpf.Grpc.sh`` file to start the server. Ensure that the ``AWP_ROOT{VER}``
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environment variable is set to your installed Ansys version. For example, if Ansys
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2022 R2 is installed, ensure that the ``AWP_ROOT222`` environment
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variable is set to the path for this Ansys installation.
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‎doc/source/getting_started/dpf_server.rst

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.. _ref_dpf_server:
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==========
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DPF Server
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==========
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DPF provides numerical simulation users and engineers with a toolbox for accessing and transforming
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simulation data. With DPF, you can perform complex preprocessing or postprocessing of large amounts of simulation data within a
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simulation workflow.
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The DPF Server is packaged within the **Ansys installer** in Ansys 2021 R1 and later.
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It is also available as a standalone package that contains all the necessary files to run, enabling DPF capabilities.
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The standalone DPF Server is available on the `DPF Pre-Release page <https://download.ansys.com/Others/DPF%20Pre-Release>`_ of the Ansys Customer Portal.
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The first standalone version of DPF Server available is 6.0 (2023 R2).
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The sections on this page describe how to install and use a standalone DPF Server.
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* For a quick start on using PyDPF, see :ref:`ref_getting_started`.
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* For more information on DPF and its use, see :ref:`ref_user_guide`.
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Install DPF Server
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------------------
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.. _target_installing_server:
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#. Download the ``ansys_dpf_server_win_v2024.2.pre0.zip`` or ``ansys_dpf_server_lin_v2024.2.pre0.zip`` file as appropriate.
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#. Unzip the package.
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#. Optional: download any other plugin ZIP file as appropriate and unzip the package. For example, to access the ``composites`` plugin for Linux,
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download ``ansys_dpf_composites_lin_v2024.2.pre0.zip`` and unzip the package in the same location as ``ansys_dpf_server_lin_v2024.2.pre0.zip``.
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#. Change to the root folder (``ansys_dpf_server_win_v2024.2.pre0``) of the unzipped package.
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#. In a Python environment, run this command:
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.. code::
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pip install -e .
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As detailed in :ref:`licensing`, a standalone DPF Server is protected using the license terms specified in the
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`DPFPreviewLicenseAgreement <https://download.ansys.com/-/media/dpf/dpfpreviewlicenseagreement.ashx?la=en&hash=CCFB07AE38C638F0D43E50D877B5BC87356006C9>`_
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file, which is available on the `DPF Pre-Release page <https://download.ansys.com/Others/DPF%20Pre-Release>`_
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of the Ansys Customer Portal.
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To accept these terms, you must set this environment variable:
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.. code::
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ANSYS_DPF_ACCEPT_LA=Y
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To use :ref:`licensed DPF capabilities <target_to_ansys_license_mechanism>` you must set the
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``ANSYSLMD_LICENSE_FILE`` environment variable to point to a valid local or remote license
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following indications in :ref:`configure_licensing`.
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Use DPF Server
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--------------
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DPF Server is protected using the license terms specified in the
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`DPFPreviewLicenseAgreement <https://download.ansys.com/-/media/dpf/dpfpreviewlicenseagreement.ashx?la=en&hash=CCFB07AE38C638F0D43E50D877B5BC87356006C9>`_
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file, which is available on the `DPF Pre-Release page <https://download.ansys.com/Others/DPF%20Pre-Release>`_
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of the Ansys Customer Portal.
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Run DPF Server with PyDPF
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~~~~~~~~~~~~~~~~~~~~~~~~~
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PyDPF-Core is a Python client API communicating with a **DPF Server**, either
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through the network using gRPC or directly in the same process. PyDPF-Post is a Python
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module for postprocessing based on PyDPF-Core.
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Both PyDPF-Core and PyDPF-Post can be used with DPF Server. Installation instructions
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for PyDPF-Core are available in the PyDPF-Core `Getting started <https://dpf.docs.pyansys.com/version/stable/getting_started/install.html>`_.
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Installation instructions for PyDPF-Post are available in the PyDPF-Post `Getting started <https://post.docs.pyansys.com/version/stable/getting_started/install.html>`_.
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With PyDPF-Core and PyDPF-Post, the first creation of most DPF entities starts a DPF Server with the current default configuration and context.
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For example, the following code automatically starts a DPF Server behind the scenes:
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.. code::
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from ansys.dpf import core as dpf
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data_sources = dpf.DataSources()
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With PyDPF-Core, you can also explicitly start a DPF Server using this code:
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.. code::
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from ansys.dpf import core as dpf
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server = dpf.start_local_server()
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To start a DPF Server from outside a Python environment, you can also use the execution script provided with your DPF Server package.
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On Windows, start the DPF Server by running the ``Ans.Dpf.Grpc.bat`` file in the unzipped package.
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On Linux, start the DPF Server by running the ``Ans.Dpf.Grpc.sh`` file in the unzipped package.
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Manage multiple DPF Server installations
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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PyDPF automatically starts a local instance of a DPF Server when you run a method requiring a
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connection to a server, or when you use the ``start_local_server()`` method.
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The ``start_local_server()`` method allows to choose, if necessary, which DPF Server installation
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to use thanks to its ``ansys_path`` argument.
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PyDPF otherwise follows the logic below to automatically detect and choose which locally installed
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version of DPF Server to run:
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- it uses the ``ANSYS_DPF_PATH`` environment variable in priority if set and targeting a valid path to a DPF Server installation.
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- it then checks the currently active Python environment for any installed standalone DPF Server, and uses the latest version available.
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- it then checks for ``AWP_ROOTXXX`` environment variables, which are set by the **Ansys installer**, and uses the latest version available.
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- if then raises an error if all of the steps above failed to return a valid path to a DPF Server installation.
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Run DPF Server in a Docker container
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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DPF Server can be run in a Docker container.
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#. Along with the ``ansys_dpf_server_lin_v2024.2.pre0.zip`` file mentioned earlier
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in :ref:`Install DPF Server <target_installing_server>`, download the ``Dockerfile`` file.
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#. Optional: download any other plugin ZIP file as appropriate. For example, to access the ``composites`` plugin for Linux,
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download ``ansys_dpf_composites_lin_v2024.2.pre0.zip``.
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#. Copy all the ZIP files and ``Dockerfile`` file in a folder and navigate into that folder.
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#. To build the DPF Docker container, run the following command:
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.. code::
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docker build . -t dpf-core:v2024.2.pre0 --build-arg DPF_VERSION=242
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5. To run the DPF Docker container, license it. For more information, see :ref:`DPF Preview License Agreement<target_to_license_terms>`.

‎doc/source/getting_started/index.rst

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Install PyDPF-Core
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------------------
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To install PyDPF-Core, in a Python environment, run this command:
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.. code::
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pip install ansys-dpf-core
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For more installation options, see :ref:`Installation section <installation>`.
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Install DPF Server
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------------------
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To use PyDPF-Core, you need access to a DPF Server.
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* DPF Server is packaged within the **Ansys installer** in Ansys 2021 R1 and later.
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To use it, download the standard installation using your preferred distribution channel,
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and install Ansys following the installer instructions. If you experience problems,
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see :ref:`Environment variable <target_environment_variable_with_dpf_section>`. For information on getting
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a licensed copy of Ansys, visit the `Ansys website <https://www.ansys.com/>`_.
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To use it, download the standard installation using your preferred distribution channel,
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and install Ansys following the installer instructions.
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For information on getting a licensed copy of Ansys, visit the `Ansys website <https://www.ansys.com/>`_.
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* DPF Server is available as a **standalone** package (independent of the Ansys installer) on the
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* DPF Server pre-releases are also available as **standalone** packages (independent of the Ansys installer) on the
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`DPF Pre-Release page <https://download.ansys.com/Others/DPF%20Pre-Release>`_ of the Ansys Customer Portal.
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As explained in :ref:`Ansys licensing <target_to_ansys_license_mechanism>`,
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DPF Server is protected by an Ansys license mechanism. Once you have access to an
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Ansys license, install DPF Server:
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.. card::
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As explained in :ref:`ref_licensing`, the standalone DPF Server is still protected by an Ansys license mechanism
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and requires accepting the :ref:`DPF Preview License Agreement<target_to_license_terms>`.
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Once you have access to an Ansys license, follow the guidelines to :ref:`install a standalone DPF Server <target_installing_server>`.
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* Download the ``ansys_dpf_server_win_v2023.2.pre1.zip`` or ``ansys_dpf_server_lin_v2023.2.pre1.zip``
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file as appropriate.
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* Unzip the package and go to its root folder (``ansys_dpf_server_win_v2023.2.pre1`` or
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``ansys_dpf_server_lin_v2023.2.pre1``).
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* In a Python environment, run this command:
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For more information regarding installing, managing and running DPF servers, see :ref:`ref_dpf_server`.
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.. code::
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pip install -e .
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* DPF Server is protected using the license terms specified in the
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`DPFPreviewLicenseAgreement <https://download.ansys.com/-/media/dpf/dpfpreviewlicenseagreement.ashx?la=en&hash=CCFB07AE38C638F0D43E50D877B5BC87356006C9>`_
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file, which is available on the `DPF Pre-Release page <https://download.ansys.com/Others/DPF%20Pre-Release>`_
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of the Ansys Customer Portal. To accept these terms, you must set this
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environment variable:
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.. code::
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Install PyDPF-Core
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------------------
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ANSYS_DPF_ACCEPT_LA=Y
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To install PyDPF-Core, in a Python environment, run this command:
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For more information about the license terms, see :ref:`DPF Preview License Agreement<target_to_license_terms>`.
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.. code::
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To use a remote license, set the ``ANSYSLMD_LICENSE_FILE`` environment
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variable to point to the Ansys license server ``<license_server_to_use>``:
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pip install ansys-dpf-core
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.. code::
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Be sure to check the :ref:`compatibility guidelines <ref_compatibility>` to know if your
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DPF Server version is compatible with the latest version of PyDPF-Core.
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ANSYSLMD_LICENSE_FILE=1055@<license_server_to_use>
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For more installation options, see :ref:`Installation section <installation>`.
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For installation methods that do not use `pip <https://pypi.org/project/pip/>`_,
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such as using **Docker containers**, see :ref:`ref_getting_started_with_dpf_server`.
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Use PyDPF-Core
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--------------
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.. toctree::
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:hidden:
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compatibility
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install
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dpf_server
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compatibility
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licensing
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dependencies
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