datamint 2.16.0

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

Datamint Python API

A comprehensive Python SDK for interacting with the Datamint platform, providing seamless integration for medical imaging workflows, dataset management, and machine learning experiments.

📋 Table of Contents

• Features
• Installation
• Quick Setup
• Documentation
• Command Line Tools
• Support

🚀 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
• PyTorch Lightning Integration: Streamlined machine learning workflows featuring specialized LightningDataModules, built-in trainers (SegmentationTrainer), and automated MLflow checkpoint logging
• 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

See the full documentation at https://sonanceai.github.io/datamint-python-api/

📦 Installation

[!NOTE] We recommend using a virtual environment to avoid package conflicts.

From PyPI

pip install -U datamint

Virtual Environment Setup

Click to expand virtual environment setup instructions

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
   

⚙ Setup API key

To use the Datamint API, you need to setup your API key (ask your administrator if you don't have one). Use one of the following methods to setup your API key:

Method 1: Command-line tool (recommended)

Run datamint-config (or python -m datamint config if that doesn't work) in the terminal and follow the instructions. See command_line_tools for more details.

Method 2: Environment variable

Specify the API key as an environment variable.

Bash:

export DATAMINT_API_KEY="my_api_key"
# run your commands (e.g., `datamint-upload`, `python script.py`)

Python:

import os
os.environ["DATAMINT_API_KEY"] = "my_api_key"

📚 Documentation

Resource	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
💡 Examples	Practical usage examples

🛠️ Command Line Tools

Full documentation at command_line_tools.

Upload Resources

Upload DICOM files with anonymization:

datamint-upload /path/to/dicoms --recursive --channel "training-data" --publish --tag "my_data_tag"

It anonymizes by default.

Upload resources with segmentations and associate them with a deployed model:

datamint-upload /path/to/dicoms \
   --recursive \
   --segmentation_path /path/to/segmentations \
   --segmentation_names /path/to/segmentation_names.yaml \
   --ai-model "knee-segmentation-v2" \
   --publish

Use --ai-model when uploaded segmentation files should be linked to an existing deployed model by name. --segmentation_names accepts YAML mappings and ITK-SNAP label export CSV/TXT files.

Configuration Management

# Interactive setup
datamint-config

# Set API key
datamint-config --api-key "your-key"

🔒 SSL Certificate Troubleshooting

If you encounter SSL certificate verification errors like:

SSLCertVerificationError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate

Quick Fix

1. Upgrade certifi:

pip install --upgrade certifi

2. Set environment variables:

export SSL_CERT_FILE=$(python -m certifi)
export REQUESTS_CA_BUNDLE=$(python -m certifi)

3. Run your script:

python your_script.py

Alternative Solutions

Option 1: Use Custom CA Bundle

from datamint import Api

api = Api(verify_ssl="/path/to/your/ca-bundle.crt")

Option 2: Disable SSL Verification (Development Only)

from datamint import Api

# ⚠️ WARNING: Only use in development with self-signed certificates
api = Api(verify_ssl=False)

🆘 Support

Full Documentation
GitHub Issues