Pulsar Segmentation and Analysis for Radio-Astronomy (HTW Berlin)

This plug-in is part of itwinai. Its purpose is to provide integration with AI pipelines developed at HTW Berlin for the purposes of pulsar analysis and detection (radio-astronomy). Please visit the original repository for more technical details on the application.

Integration Author: Alex Krochak, FZJ

Plug-in description

itwinai implements a plugin architecture, allowing the community to independently develop sub-packages for itwinai. These sub-packages can later be installed from PyPI and imported into Python code as if they were part ofitwinai. This is made possible by namespace packages.

Itwinai plugins are developed by assuming that the plugin logic, once installed alongside itwinai, will be accessible under the itwinai.plugins.* namespace. Example:

```python from itwinai.plugins.pulsar import data, trainer

Instantiate a class implemented by the plugin

mydataset = data.PulsarDataset(...) mytrainer = trainer.PulsarTrainer(...) ```

Plug-in installation

A straightforward way to install the plug-in on local machine is as follows:

1. Clone this repository on your local machine or server.
2. Create a new Python virtual environment: python -m venv .venv.
3. Activate this virtual environment source .venv/bin/activate.
4. (Recommmended) Install uv for accelerated package management: pip install uv. More information can be found here.
5. Then run from the top directory: (uv) pip install .. This will install the plug-in. NOTE: itwinai itself is also installed automatically, as it is a plug-in dependency (see pyproject.toml)
6. Done ! Now you can either run itwinai from CLI, i.e.: itwinai exec-pipeline +pipe_key=syndata_pipeline. Alternatively, you can unpack the exec.zip outside the plug-in directory and run exec.py. Make sure you are using the virtual environment installed at the plug-in, but operate outside the plug-in directory !.

Plug-in installation on Juwels-Booster

When installing on Juwels-Booster at FZJ, additional steps need to be taken.

1. Load the necessary modules for Python 3.11.3 (recommended): module --force purge ml Stages/2024 GCCcore/.12.3.0 Python/3.11.3 Verify the Python version by python --version.
2. Clone this repository in your personal project folder.
3. Create a new Python virtual environment: python -m venv .venv.
4. Activate this virtual environment source .venv/bin/activate. Verify correct Python and Pip path with: which pip and which python.
5. (Recommmended) Install uv for accelerated package management: pip install uv --no-cache-dir. The argument --no-cache-dir is necessary whenever installing with pip to prevent the ~/.cache folder to fill up your home quota. More information on UV can be found here.
6. Then run from the top directory: (uv) pip install . --no-cache-dir. This will install the plug-in. NOTE: itwinai itself is also installed automatically, as it is a plug-in dependency (see pyproject.toml)
7. Extract exec.tar.gz with tar -xvzf exec.tar.gz outside the plug-in folder. Navigate to the exec folder and test the plug-in execution: itwinai exec-pipeline +pipe_key=syndata_pipeline.

When running from exec folder, inspect the config.yaml and batch-jsc.sh files and provide necessary updates. Set the correct path to your virtual environment .venv within the batch-jsc.sh script and also make sure to choose the proper distributed strategy in config.yaml (ddp is recommended).

Running from a configuration file

You can run the full pipeline sequence by executing the following commands locally. Please note that it is recommended to run these commands outside the plug-in repository for organizational reasons. You just need to make sure that the correct Python virtual environment is activated.

itwinai will read these commands from the config.yaml file.

1. Generate the synthetic data - itwinai exec-pipeline +pipe_key=syndata_pipeline
2. Initialize and train a UNet model - itwinai exec-pipeline +pipe_key=unet_pipeline
3. Initialize and train a FilterCNN model - itwinai exec-pipeline +pipe_key=fcnn_pipeline
4. Initialize and train a CNN1D model - itwinai exec-pipeline +pipe_key=cnn1d_pipeline
5. Compile a full pipeline and test it - itwinai exec-pipeline +pipe_key=evaluate_pipeline

When running on HPC, you can use the batch-jsc.sh SLURM script to run these commands. Please make sure you select an appropriate strategy in config.yaml when running on HPC. The recommended strategy is ddp.

Logging with MLflow

By default, the config.yaml ensures that the MLflow logging is enabled during the training. During or after the run, you can launch an MLflow server by executing mlflow server --backend-store-uri mllogs/mlflow and connecting to http://127.0.0.1:5000/ in your browser.

Test suite

The test suite is located in the tests/ folder.

Before running the test suite, you should make sure that the pytorch fixture in: tests/test_pulsar.py:torch_env()
is correctly defined and corresponds to the virtual environment where itwinai is installed on your system.

It contains integration tests for each of the pipelines 1-5 mentioned above. The configuration and execution of the test suite is defined in: tests/test_pulsar.py and in the configuration file: tests/.config-test.yaml. If you are updating the test suite, make sure you update both of these files.