Tutorials ========= This section lists the various tutorial notebooks available in the `datamint-python-api GitHub repository `_. You can run these Jupyter Notebooks locally to learn how to use the Datamint Python API in different scenarios. Data Management --------------- * `upload_data.ipynb `_: A comprehensive guide on uploading data to Datamint. * `dataset_loading.ipynb `_: Learn how to load datasets for model training and evaluation. * `exploring_data_tutorial.ipynb `_: Learn how to explore and query resources in Datamint. Annotations ----------- * `upload_annotations.ipynb `_: Guide on how to import and manage simple annotations like image or frame categories. * `geometry_annotations.ipynb `_: Covers integrating and uploading lines, bounding boxes, and other geometry annotations. Machine Learning & Deployment ----------------------------- * `mlflow_simple_training.ipynb `_: Getting started with training models using the Datamint MLflow integration. * `mlflow_manual_logging.ipynb `_: Explains how to log models and experiments manually to MLflow via Datamint. * `deploy_model_demo.ipynb `_: Basic demonstration on deploying a Datamint model. * `external_model_deployment_tutorial.ipynb `_: Tutorial for adapting and deploying an externally-trained model in Datamint. Use Cases & End-to-End Examples ------------------------------- These notebooks provide complete, end-to-end workflows located in the `use_cases directory `_: * `fracatlas_classification.ipynb `_: End-to-end classification pipeline for the FracAtlas dataset. * `segmentation_2d_trainer_BUSI_tutorial.ipynb `_: Train a 2D segmentation model on the BUSI dataset.