What is Gradsflow? 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. Image classification example 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()