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
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
- Model Benchmarking: Compare several trainers against the same dataset and split, and get a ranked leaderboard of their performance
- 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:
-
Create the environment (one-time setup):
python3 -m venv datamint-env
-
Activate the environment (run whenever you need it):
Platform Command Linux/macOS source datamint-env/bin/activateWindows CMD datamint-env\Scripts\activate.batWindows PowerShell datamint-env\Scripts\Activate.ps1 -
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 |
| 🔍 Bringing an External Model into Datamint | |
Integrate, log, and deploy a model trained outside Datamint for inference through the UI |
| 🛠️ 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
1 maintainer
Release files for datamint 2.39.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| datamint-2.39.0.tar.gz | 342.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| datamint-2.39.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 792.8 kB
Release files / datamint-2.39.0.tar.gz
| Download URL | datamint-2.39.0.tar.gz |
|---|---|
| Size | 342.1 kB |
| Tags | Source |
|
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| Download URL | datamint-2.39.0-py3-none-any.whl |
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| Size | 450.8 kB |
| Tags | Python 3 |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
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PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
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