An example machine learning workflow for signal modulation classification, built using RIA Hub Workflows
Go to file
2025-05-21 15:46:28 -04:00
.riahub/workflows fixed the workflow to detect the helpers folder 2025-05-20 10:50:55 -04:00
conf fixed the workflow, app.yaml now matches app_settings.py, configured dataset.py to always create a new data folder 2025-05-20 10:43:56 -04:00
helpers fixed the workflow, app.yaml now matches app_settings.py, configured dataset.py to always create a new data folder 2025-05-20 10:43:56 -04:00
results reorganized file struture 2025-05-21 15:46:28 -04:00
wheel reversed the changes, for now will install from the wheel folder 2025-05-16 14:17:08 -04:00
.gitattributes added LFS tracking to the .whl files 2025-05-16 13:23:13 -04:00
.gitignore Added git ignore for pycache, and data folder 2025-05-20 10:43:17 -04:00
README.md Add ReadME 2025-05-14 15:36:31 -04:00
requirements.txt Merge branch 'main' of https://riahub.qoherent.internal:3000/qoherent/modrec-workflow 2025-05-16 14:31:41 -04:00

RIA Hub Technical Demo

This repository demonstrates a full ML pipeline via Gitea Actions:

  • Recordings
    A collection of raw .npy radio recordings stored via Git LFS.

  • Workflows
    A CI pipeline that automatically:

    1. Builds a labeled dataset from raw recordings
    2. Trains a model on that dataset
    3. Optimizes the model and packages an inference application
  • Scripts

    • scripts/build_dataset.sh
      Reads through recordings/, applies preprocessing, and outputs training .npz or .csv files into data/.
    • scripts/train_model.sh
      Consumes data/, trains a PyTorch model, and writes checkpoints to checkpoints/.
    • scripts/build_app.sh
      Takes the best checkpoint and builds a small inference CLI or server in dist/.