Modulation Recognition Demo

RIA Hub Workflows is an automation platform built into RIA Hub. This project contains an example machine learning workflow for the problem of signal modulation classification. It also serves as an excellent introduction to RIA Hub Workflows.

📡 The machine learning development workflow

The development of intelligent radio solutions involves multiple steps:

1. First, we need to prepare a machine learning-ready dataset. This involves signal synthesis or capture, followed by dataset curation to extract and qualify training examples. Finally, we need to perform any required data preprocessing —such as augmentation—and split the dataset into training and test sets.

2. Secondly, we need to design and train a model. This is often an iterative process and can leverage techniques like Neural Architecture Search (NAS) and hyperparameter optimization to automate finding a suitable model structure and optimal hyperparameter configuration, respectively.

3. Once a machine learning model has been trained and validated, the next step is to build an inference application. This step transforms the model from a research artifact into a practical tool capable of making predictions in real-world conditions. Building an inference application typically involves several substeps including model optimization, packaging and integration, and monitoring and logging.

This is a lot of work, and much of it involves tedious software development and repetitive tasks like setting up and configuring infrastructure. What's more? There is a shortage of domain expertize in ML and MLOps for radio. That's where we come in. RIA Hub offers a no- and low-code solution for the end-to-end development of intelligent radio systems, allowing for a sharper focus on innovation.

▶️ RIA Hub Workflows

One of the core principles of RIA Hub is Workflows, which allow users to run jobs in isolated Docker containers.

You can create workflows in one of two ways:

• Writing YAML and placing it in the special .riahub/workflows/ directory in your repository.
• Using RIA Hub's built-in tools for Dataset Management, Model Building, and Application Development, which will automatically generate the YAML workflow file(s) for you.

Workflows can be configured to run automatically on push and pull request events. You can monitor and manage running workflows in the 'Workflows' tab in your repository.

⚙️ Qoherent-hosted runners

Qoherent-hosted runners are job containers that Qoherent provides and manages to run your workflows and jobs in RIA Hub Workflows.

Why use GitHub-hosted runners?

• Easy to set up and start running workflows quickly, without the need to set up your own infrastructure.
• Qoherent maintains runners equipped with access to common hardware and tools for radio ML development, including SDR testbeds and common embedded targets.

If you want to learn more about the runners we have available, please feel free to reach out. We can also provide custom runners equipped with specific radio hardware and RAN software upon request.

Want to register your own runner? No problem! Please refer to the RIA Hub documentation for more details.

🔍 Modulation Recognition

🚀 Getting started

1. Fork the project repo, using the button in the upper right hand corner.

2. Enable Workflows (Settings → Advanced Settings → Enable Repository Actions).

3. Check for available runners. The runner management tab can found at the top of the 'Workflows' tab. If no runners are available, you'll need to register one before proceeding.

4. Clone down the project. For example:

git clone https://git.riahub.ai/user/modrec-workflow.git
cd modrec-workflow

5. Set the workflow runner in .riahub/workflows/workflow.yaml. The runner is set on line 13:

runs-on: ubuntu-latest

Note: We recommend running this demo on a GPU-enabled runner. If a GPU runner is not available, you can still run the workflow, but we suggest reducing the number of training epochs to keep runtime reasonable.

6. (Optional) Configure the workflow. All parameters—including file paths, model architecture, and training settings—are set in conf/app.yaml. Want to jump right in? The default configuration is suitable for getting started.

7. Push changes. This will start the workflow automatically.

8. Inspect the workflow output. You can expand and collapse individual steps to view their terminal output. A check mark indicates that the step completed successfully.

9. Inspect the workflow artifacts. Additional information on workflow artifacts can be found in the next section.

Workflow artifacts

The example generates several workflow artifacts, including:

• dataset: The training and validation datasets: train.h5 and val.h5, respectively.

• checkpoints: Saved model checkpoints. Each checkpoint contains the model’s learned weights at various stages of training.

• onnx-file: The trained model as an ONNX graph.

• ort-file: Model in .ORT format, recommended for edge deployments. (.ORT files are optimized and serialized by ONNX Runtime for more efficient loading and execution.)

• profile-data: Model execution traces, in JSON format.

• recordings: Folder of synthesised signal recordings.

🤝 Contribution

We welcome contributions from the community! Whether it's an enhancement, bug fix, or new how-to guide, your input is valuable. To get started, please contact us directly, we're looking forward to collaborating with you. 🚀

If you encounter any issues or to report a security vulnerability, please submit a bug report.

Qoherent is dedicated to fostering a friendly, safe, and inclusive environment for everyone. For more information on our commitment to diversity, please refer to our Diversity Statement.

We kindly insist that all contributors review and adhere to our Code of Conduct and that all code contributors review our Coding Guidelines.

🖊️ Authorship

This demonstration was developed by Liyu Xiao during his summer co-op term at Qoherent.

If you like this project, don’t forget to give it a star! ⭐

📄 License

This example is free and open-source, released under AGPLv3.

Alternative licensing options are available. Alternative licensing options are available. Please contact us for further details.

Configure GitHub Secrets

Before running the pipeline, add the following repository secrets in GitHub (Settings → Secrets and variables → Actions):

• RIAHUB_USER: Your RIA Hub username.
• RIAHUB_TOKEN: RIA Hub access token with read:packages scope (from your RIA Hub account Settings → Access Tokens).
• CLONER_TOKEN: Personal access token for stark_cloner_bot with read_repository scope (from your on-prem Git server user settings).

Once secrets are configured, you can run the pipeline:

How to View the JSON Trace File

• Captures a full trace of model training and inference performance for profiling and debugging
• Useful for identifying performance bottlenecks, optimizing resource usage, and tracking metadata

Access this link Click on Open Trace File -> Select your specific JSON trace file Explore detailed visualizations of performance metrics, timelines, and resource usage to diagnose bottlenecks and optimize your workflow.

Submiting Issues

Found a bug or have a feature request? Please submit an issue via the GitHub Issues page. When reporting bugs, include: Steps to reproduce

• Error logs and screenshots (if applicable)
• Your app.yaml configuration (if relevant)

Developer Details

Coding Guidelines: Follow PEP 8 for Python code style. Include type annotations for all public functions and methods. Write clear docstrings for modules, classes, and functions. Use descriptive commit messages and reference issue numbers when relevant. Contributing All contributions must be reviewed via pull requests. Run all tests and ensure code passes lint checks before submission.