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No installation. Explore example data or upload your own CSV, train PA/DPD models on shared CUDA compute, and download your checkpoints. During the trial, 2 hours without user activity clears that IP’s temporary workspaces; all data is deleted within 12 hours.

OpenDPD

Model a power amplifier. Train a digital predistorter. Understand the result.

OpenDPD is a PyTorch framework for power amplifier (PA) modeling and digital predistortion (DPD), developed by the Efficient Machine Intelligence Lab at TU Delft. Use OpenDPD Studio in the browser or locally for a guided workflow, or automate experiments with the CLI and Python API. All three use the original OpenDPD training core.

Documentation · Studio walkthrough · Examples & Colab · Papers & citation

What's new

OpenDPD 2.3.0 adds DPD Arena on APA_200MHz_b: compare 23 DPD model entries across parameter budgets using test EVM, output ACLR, arithmetic cost and Pareto fronts. Results are predictions through one frozen PA model, not new hardware measurements. It includes the import, session and GPU security improvements from 2.2.18.

Signal Generator → PA Library → PA training → DPD training/testing. Generate a PA input, simulate its output with one of nine Virtual PAs, or use existing input/output data. Standard presets are uncoded engineering stimuli; each capture keeps its own sample rate and length.

2.3.0 release notes · Signal Generator · Signal Analyzer. During the hosted trial, 2 hours of inactivity clears that IP’s temporary workspaces; the top bar shows the expiry time.

Feature history · Verified platform status

Get started with Studio

Use Studio on the web, or install locally:

1. Install uv (then open a new terminal).

macOS / Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows PowerShell:

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

2. Create an environment, install, and launch (same commands on all three platforms):

mkdir opendpd-lab
cd opendpd-lab
uv venv --python 3.12
uv pip install --python .venv "opendpd==2.3.0" --torch-backend=auto
uv run --no-project --python .venv opendpd gui

PyTorch, Studio and pywebview install together; no Node.js is needed. uv selects a PyTorch backend for the detected platform/drivers. Studio prefers available CUDA or Apple MPS, then CPU. Keep the terminal running. A 127.0.0.1 link opens on the computer running OpenDPD; for SSH, use the port-forwarding instructions.

Click Get Started → Signal Generator, or Use an existing dataset. See Installation for drivers, Linux system libraries, native-window troubleshooting and pip; Studio walkthrough for your first experiment.

Studio 2.3 adds DPD Arena with Rank, Submit and Rules pages. The benchmark uses only APA_200MHz_b. Compare individual DPD configurations through one frozen TRes-GRU PA, using complete-symbol EVM, output ACLR and fixed-reference complexity FoM across a 250–2,000-parameter sweep. Inspect PA validation/test accuracy, actual parameter and operation counts, and four Pareto plots, or evaluate a validated custom backbone in your workspace. PA and DPD training, validation and test inputs come only from original measured captures; cascade outputs are PA-model predictions.

git clone https://github.com/lab-emi/OpenDPD.git
cd OpenDPD
uv venv --python 3.12
uv pip install --python .venv -e ".[dev]" --torch-backend=auto
npm --prefix frontend ci
npm --prefix frontend run build
uv run --no-project --python .venv opendpd gui

The PA → DPD workflow

A DPD result evaluated through a PA model is a simulation. Exported u = DPD(x) is the PA input; a physical PA measurement is needed to establish measured linearization performance. See the Studio walkthrough and measured DPD guide.

PA Library guide · Reading signal-chain PSD plots

Choose your next step

Contribute & cite

Contributions of models, tests and documentation are welcome. Start with CONTRIBUTING.md; see testing and how we maintain the docs.

If you use OpenDPD in research, cite the OpenDPD paper. BibTeX and related papers · CITATION.cff

Chang Gao — Project Leader · Yizhuo Wu — Leading Developer. Meet the team · EMI Lab