datamint 2.18.3

A library for interacting with the Datamint API, designed for efficient data management, processing and Deep Learning workflows.

Datamint Python API

Datamint turns medical imaging ML work. Dataset management, annotation, training, and deployment into a few lines of Python, with built-in support for DICOM/NIfTI/PNG, PyTorch Lightning trainers, and MLflow tracking.

Common use cases: 🩻 Segmentation · 🏷️ Classification · 📦 Detection

Datamint handles the full journey from raw files to a deployed model:

flowchart LR
    Files(["📁 Your Files"])

    subgraph s1 [1 · Ingest]
        Resource(["📦 Resource"])
    end

    subgraph s2 [2 · Organize & Annotate]
        direction TB
        Project(["🗂️ Project"])
        Annotations(["🏷️ Annotations"])
        Project -.->|annotate| Annotations
    end

    subgraph s3 [3 · Train]
        direction LR
        Dataset(["🧮 Dataset"])
        Trainer(["🧠 Trainer"])
        Model(["📈 Model"])
        Dataset -->|train| Trainer -->|register| Model
    end

    subgraph s4 [4 · Deploy & Predict]
        direction LR
        DeployJob(["🚀 Deploy Job"])
        Inference(["🔮 Inference"])
        DeployJob -->|predict| Inference
    end

    Files -->|upload| Resource
    Resource -->|organize| Project
    Project -->|load| Dataset
    Model -->|deploy| DeployJob

    classDef ingestNode fill:#ffffff,stroke:#1f6feb,stroke-width:2px,color:#0b2b4c
    classDef organizeNode fill:#ffffff,stroke:#1a7f37,stroke-width:2px,color:#0b3a1c
    classDef mlNode fill:#ffffff,stroke:#8250df,stroke-width:2px,color:#2c1a4d
    classDef deployNode fill:#ffffff,stroke:#d1720f,stroke-width:2px,color:#4d2b00
    classDef fileNode fill:#f6f8fa,stroke:#57606a,stroke-width:2px,color:#24292f

    class Files fileNode
    class Resource ingestNode
    class Project,Annotations organizeNode
    class Dataset,Trainer,Model mlNode
    class DeployJob,Inference deployNode

    style s1 fill:#dceeff,stroke:#1f6feb,stroke-width:2px,color:#0b2b4c
    style s2 fill:#dbf5df,stroke:#1a7f37,stroke-width:2px,color:#0b3a1c
    style s3 fill:#ecdcff,stroke:#8250df,stroke-width:2px,color:#2c1a4d
    style s4 fill:#ffe8c7,stroke:#d1720f,stroke-width:2px,color:#4d2b00

📋 Table of Contents

• See it in action
• Features
• Quick Start
• Documentation
• Support

🎬 See it in action

Create a project, split the data, train, and deploy, all through the API:

🚀 Features

• Dataset Management: Download, upload, and manage medical imaging datasets using intuitive object-based APIs or CLI tools
• Annotation Tools: Create, upload, and manage annotations (segmentations, labels, measurements) with ease
• Experiment Tracking: Seamless support for experiment management via MLflow integration
• One-line Trainers: Train segmentation, classification, and detection models with built-in PyTorch Lightning trainers, skipping the dataset class, training loop, and logging setup
• DICOM Support: Native handling of DICOM files, including powerful anonymization capabilities during upload to protect patient privacy
• Multi-format Support: Robust support for a wide range of medical imaging formats: PNG, JPEG, NIfTI (NIfTI/NRRD), DICOMs and more

⚡ Quick Start

1. Install

pip install -U datamint

Using a virtual environment (recommended)

We recommend that you install Datamint in a dedicated virtual environment, to avoid conflicting with your system packages. For instance, create the enviroment once with python3 -m venv datamint-env and then activate it whenever you need it with:

1. Create the environment (one-time setup):

   python3 -m venv datamint-env
   

2. Activate the environment (run whenever you need it):

   Platform	Command
   Linux/macOS	source datamint-env/bin/activate
   Windows CMD	datamint-env\Scripts\activate.bat
   Windows PowerShell	datamint-env\Scripts\Activate.ps1

3. Install the package:

   pip install datamint
   

2. Configure your API key

datamint config

Follow the prompts (ask your administrator if you don't have a key yet). Environment variable and programmatic options are in the Setup API Key guide.

3. Scaffold a project — the fastest way to start

datamint init

This is the recommended on-ramp: it asks for a project name and task type (segmentation, classification, or detection), then generates a ready-to-run, numbered set of scripts (01_upload_data.py → 06_deploy.py) — upload data, train, and deploy by running them in order.

4. ...or write it yourself

from datamint import Api
from datamint.lightning import UNetPPTrainer

api = Api()
api.projects.create(name="my-project", exists_ok=True)

trainer = UNetPPTrainer(project="my-project")
results = trainer.fit()

📚 Documentation

Difficulty levels:

• no ML knowledge needed
• assumes SDK familiarity, introduces ML/dataset concepts
• full training pipelines, custom models, 3D data, multi-step workflows.

Resource	Level	Description
🚀 Getting Started		Step-by-step setup and basic usage
📖 API Reference		Complete API documentation
🔥 PyTorch Integration		ML workflow integration
🧠 Trainer Guide		Built-in trainers, trainer lifecycle, and custom model integration
🛠️ Command Line Tools		Full reference for datamint upload, datamint init, and datamint config
🔒 SSL Troubleshooting	—	Fixing SSLCertVerificationError
📓 Notebooks		Numbered, runnable tutorials. Start at 01_getting_started and work through annotations, datasets, experiment tracking, deployment, and a full end-to-end example

🆘 Support

Full Documentation
GitHub Issues