Metadata-Version: 2.1
Name: SolarNet-Lib
Version: 0.1.0
Summary: Deep Learning for Solar Physics Prediction
Home-page: https://gitlab.com/jdonzallaz/solarnet
Author: Jonathan Donzallaz
Author-email: jonathan.donzallaz@hefr.ch
License: MIT
Description: # SolarNet
        
        > Deep Learning for Solar Physics Prediction
        
        The SolarNet library permits to use the different Pytorch models, datasets, preprocessing, and others utilities
        developed during the SolarNet project. It also makes straightforward the download, loading, and finetuning of the big
        pretrained SSL models. With SolarNet, anyone can access the datasets (at this time, only SDO-Benchmark is available
        through the library) and finetune the powerful models. The library is compatible with Pytorch-Lightning, but the models
        are also pure Pytorch Module and the training loop can be written from scratch.
        
        Find the docs on [jdonzallaz.gitlab.io/solarnet](https://jdonzallaz.gitlab.io/solarnet/).
        
        ## Installation
        
        Use pip to install:
        
        ```
        pip install solarnet-lib
        ```
        
        Python 3.6+ is required.
        
        ## Data
        
        Two datasets are supported: SDO-Dataset and SDO-Benchmark. SDO-Dataset needs to be downloaded from the Stanford's servers.
        The SDO-Benchmark dataset can be downloaded using the CLI:
        
        ```
        solarnet download sdo-benchmark data/
        ```
        
        ## Dataset
        
        ```python
        path = Path("data") / "sdo-benchmark" / "train"
        dataset = SDOBenchmarkDataset(path)
        ```
        
        Also available as a pytorch-lightning datamodule.
        
        ```py
        path = Path("data") / "sdo-benchmark"
        datamodule = SDOBenchmarkDatamodule(path)
        ```
        
        ## Model
        
        ```py
        model = ImageClassification.from_pretrained("solarnet-ssl-bz-ft-sdo-benchmark")
        ```
        
        ## Finetuning
        
        ```py
        trainer = pl.Trainer(
            max_epochs=10,
            gpus=0,
        )
        trainer.fit(model, datamodule=datamodule)
        ```
        
        ## Deployment
        
        ```bash
        python setup.py sdist bdist_wheel
        twine check dist/*
        twine upload dist/*
        ```
        
        ## Author
        
        SolarNet is a deep learning research toolbox for solar physics. It was developed during a Master thesis
        by [Jonathan Donzallaz](mailto:jonathan.donzallaz@hefr.ch).
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
