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README.md

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# GraphDL-Postprocess-USI-Project
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Project designed for the Graph Deep Learning course at Università della Svizzera italiana (USI), focusing on postprocessing wind NWP forecasts.
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# Graph Deep Learning Postprocessing Project @USI
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Project designed for the [Graph Deep Learning](https://search.usi.ch/en/courses/35270698/graph-deep-learning) course at Università della Svizzera italiana (USI), focusing on postprocessing wind NWP forecasts.
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<img src="./imgs/wind_stations.png" alt="Description" width="600">
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## Quickstart
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1. **Install the dependencies:**
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```sh
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poetry install
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```
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2. **Activate the environment:**
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```sh
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cd spatiotemporal_postprocessing
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poetry shell
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```
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3. **Train**
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Define the folder with the training data:
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```sh
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export DATA_BASE_FOLDER=<FOLDER>
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```
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Define the MLFlow tracking URI (defaults to a local folder called `mlruns`):
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```sh
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export MLFLOW_TRACKING_URI=<URI>
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```
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Train with default settings:
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```sh
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python train.py
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```
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Train with a different config:
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```sh
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python train.py --config-name <CFG>
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```
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Overwrite (if existing) or append (if not existing) a config value, such as the optimizer:
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```sh
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python train.py ++training.optim.algo=SDG
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```
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4. **Check the logs on MLflow:**
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```sh
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mlflow ui --port <PORT>
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```

imgs/wind_stations.png

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