gradsflow/gradsflow: First stable Release

Gradsflow is based on Optuna and PyTorch Lightning ⚡️. It leverages PyTorch Lightning Flash so that you don't have to write any PyTorch or Optuna code for model building or hyperparameter tuning 🚀

Although you might want to train a custom model and search hyperparameters, You can easily integrate any PyTorch/Lightning Flash Model with Gradsflow AutoModel ✨

gradsflow.core: Core defines the building blocks of AutoML tasks.

gradsflow.taskauto: AutoTasks defines different ML/DL tasks which is provided by Gradsflow AutoML API.

    from flash.core.data.utils import download_data
    from flash.image import ImageClassificationData

    from gradsflow import AutoImageClassifier

    # 1. Create the DataModule
    download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip", "./data")

    datamodule = ImageClassificationData.from_folders(
        train_folder="data/hymenoptera_data/train/",
        val_folder="data/hymenoptera_data/val/",
    )

    suggested_conf = dict(
        optimizers=["adam", "sgd"],
        lr=(5e-4, 1e-3),
    )
    model = AutoImageClassifier(datamodule,
                                suggested_conf=suggested_conf,
                                max_epochs=10,
                                optimization_metric="val_accuracy",
                                timeout=300)
    model.hp_tune()