DPF Server#

DPF provides numerical simulation users and engineers with a toolbox for accessing and transforming simulation data. With DPF, you can perform complex preprocessing or postprocessing of large amounts of simulation data within a simulation workflow.

The DPF Server is packaged within the Ansys installer in Ansys 2021 R1 and later.

It is also available as a standalone package that contains all the necessary files to run, enabling DPF capabilities. The standalone DPF Server is available on the DPF Pre-Release page of the Ansys Customer Portal. The first standalone version of DPF Server available is 6.0 (2023 R2).

The sections on this page describe how to install and use a standalone DPF Server.

Install DPF Server#

  1. Download the ansys_dpf_server_win_v2024.2.pre0.zip or ansys_dpf_server_lin_v2024.2.pre0.zip file as appropriate.

  2. Unzip the package.

  3. Optional: download any other plugin ZIP file as appropriate and unzip the package. For example, to access the composites plugin for Linux, 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.

  4. Change to the root folder (ansys_dpf_server_win_v2024.2.pre0) of the unzipped package.

  5. In a Python environment, run this command:

pip install -e .

As detailed in Licensing, a standalone DPF Server is protected using the license terms specified in the DPFPreviewLicenseAgreement file, which is available on the DPF Pre-Release page of the Ansys Customer Portal. To accept these terms, you must set this environment variable:

ANSYS_DPF_ACCEPT_LA=Y

To use licensed DPF capabilities you must set the ANSYSLMD_LICENSE_FILE environment variable to point to a valid local or remote license following indications in Configure licensing.

Use DPF Server#

DPF Server is protected using the license terms specified in the DPFPreviewLicenseAgreement file, which is available on the DPF Pre-Release page of the Ansys Customer Portal.

Run DPF Server with PyDPF#

PyDPF-Core is a Python client API communicating with a DPF Server, either through the network using gRPC or directly in the same process. PyDPF-Post is a Python module for postprocessing based on PyDPF-Core.

Both PyDPF-Core and PyDPF-Post can be used with DPF Server. Installation instructions for PyDPF-Core are available in the PyDPF-Core Getting started. Installation instructions for PyDPF-Post are available in the PyDPF-Post Getting started.

With PyDPF-Core and PyDPF-Post, the first creation of most DPF entities starts a DPF Server with the current default configuration and context. For example, the following code automatically starts a DPF Server behind the scenes:

from ansys.dpf import core as dpf
data_sources = dpf.DataSources()

With PyDPF-Core, you can also explicitly start a DPF Server using this code:

from ansys.dpf import core as dpf
server = dpf.start_local_server()

To start a DPF Server from outside a Python environment, you can also use the execution script provided with your DPF Server package. On Windows, start the DPF Server by running the Ans.Dpf.Grpc.bat file in the unzipped package. On Linux, start the DPF Server by running the Ans.Dpf.Grpc.sh file in the unzipped package.

Manage multiple DPF Server installations#

PyDPF automatically starts a local instance of a DPF Server when you run a method requiring a connection to a server, or when you use the start_local_server() method. The start_local_server() method allows you to choose, if necessary, which DPF Server installation to use thanks to its ansys_path argument. PyDPF otherwise follows the logic below to automatically detect and choose which locally installed version of DPF Server to run:

  • it uses the ANSYS_DPF_PATH environment variable in priority if set and targeting a valid path to a DPF Server installation.

  • it then checks the currently active Python environment for any installed standalone DPF Server, and uses the latest version available.

  • it then checks for AWP_ROOTXXX environment variables, which are set by the Ansys installer, and uses the latest version available.

  • if then raises an error if all of the steps above failed to return a valid path to a DPF Server installation.

Run DPF Server in a Docker container#

DPF Server can be run in a Docker container.

  1. Along with the ansys_dpf_server_lin_v2024.2.pre0.zip file mentioned earlier in Install DPF Server, download the Dockerfile file.

  2. Optional: download any other plugin ZIP file as appropriate. For example, to access the composites plugin for Linux, download ansys_dpf_composites_lin_v2024.2.pre0.zip.

  3. Copy all the ZIP files and Dockerfile file in a folder and navigate into that folder.

  4. To build the DPF Docker container, run the following command:

docker build . -t dpf-core:v2024.2.pre0 --build-arg DPF_VERSION=242

  1. To run the DPF Docker container, license it. For more information, see DPF Preview License Agreement.