Try OpenDPD Studio in your browser →

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 2.2.12 fixes DeltaGRU PA training failing when Triton cannot find a C compiler. The hosted GPU runtime includes the required toolchain; local installations fall back to PyTorch on the same device. Studio also includes: 1,186 compact matrix presets, multi-preset datasets, optional ideal input filtering, automatic Virtual PA dataset creation, and CSV/ZIP downloads with a standalone PA replay script. Signal Analyzer accepts real or complex CSV signals.

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.

2.2.12 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==2.2.12" --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.

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