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.

• exploring_data_tutorial.ipynb: Learn how to explore and query resources in Datamint.

• project_scoped_splits_tutorial.ipynb: Assign project-scoped train/val/test splits, inspect split records, and replay historical split snapshots in datasets.

• volume_dataset_tutorial.ipynb: Tutorial on working with volume datasets 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_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 with UNetPPTrainer and see how to plug a custom external segmentation model,.