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Opendpd

Opendpd is a Python and PyTorch framework for learning-based power-amplifier modeling and digital predistortion. It provides browser and local Studio interfaces, plus CLI and Python API workflows, for training and evaluating models against example, uploaded or measured signal data.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Opendpd — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Opendpd when a project involves learning-based PA characterization or DPD development and you want a guided Studio workflow or programmatic access to the same training core. Choose the browser trial to explore example data or upload CSVs without installation; use a local setup for local experiments and GPU selection.

Who it is for

  • RF and wireless researchers modeling power-amplifier behavior.
  • Developers training or comparing neural-network approaches to digital predistortion.
  • Teams working with measured PA input/output captures or building custom model backbones.

Documented uses

  • Train a behavioral model to predict a power amplifier’s response to input signals.
  • Train and evaluate a digital predistorter against a trained PA model.
  • Generate or select signal data, inspect data splits and compare model results.
  • Benchmark DPD configurations using EVM, output ACLR and computational-cost measures.
  • Export a predistorted I/Q signal for a separate physical measurement experiment.
Before you choose

Requirements & limits

The repository is licensed under Apache-2.0. Browser Studio is described as a trial using shared CUDA compute; its temporary workspaces are cleared after two hours without user activity, and all data is deleted within 12 hours. Local installation is also documented.

  • The framework is built on Python and PyTorch and supports browser Studio, local Studio, CLI and Python API workflows.
  • Local Studio selects available CUDA or Apple MPS before falling back to CPU; newer local releases document individual GPU selection and concurrent independent jobs on different GPUs.
  • Studio accepts example data or uploaded CSVs, while the wider workflow also supports existing input/output datasets and generated signals.
  • DPD Arena’s comparisons use APA_200MHz_b and evaluate cascades through a frozen PA model; those results are predictions rather than new hardware measurements.
  • Exported predistorted I/Q data is a PA input signal, not evidence by itself of measured linearization.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Use OpenDPD Studio in the browser with example or CSV data

Open the hosted Studio and start a PA/DPD modeling workflow using example data or an uploaded CSV.

Before you start

  • A web browser.
  • Example data in Studio or a CSV file to upload.

Steps

  1. Open https://opendpd.com/studio/.

  2. Explore the example data or upload your own CSV.

  3. Train PA/DPD models using the shared CUDA compute.

  4. Download the resulting checkpoints.

Expected outcome

Studio provides trained model checkpoints for download.

Guide limitations

  • Two hours without user activity clears that IP's temporary workspaces.
  • All data is deleted within 12 hours.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
8 October 2026
Original website title
lab-emi/OpenDPD · Official homepage title · retained in its source language
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