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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 2.3.1 lets local users select an individual GPU for PA/DPD training and testing and run independent jobs on different GPUs concurrently. DPD Arena, introduced in 2.3.0, compares 23 DPD model entries on APA_200MHz_b 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.

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.

2.3.1 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==2.3.1" --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