Getting Started with Datamint Python API
This guide will help you set up and start using the Datamint Python API for your medical imaging projects.
Prerequisites
Python 3.10 or later (earlier versions are not supported)
pip or conda for package management
A Datamint account with an API key (get one at app.datamint.io)
Installation
Datamint requires Python 3.10+. Install/update Datamint and its dependencies using pip
pip install -U datamint
We recommend that you install Datamint in a dedicated virtualenv, to avoid conflicting with your system packages. You can do this by running:
python3 -m venv datamint-env
source datamint-env/bin/activate # In Windows, run datamint-env\Scripts\activate.bat
pip install -U datamint
Verify your installation
python -c "import datamint; print(datamint.__version__)"
datamint-config --help
Setup API Key
Obtaining an API key
Before using the Datamint API, you need to configure your API key. If you have the necessary permissions, you can obtain one from the Datamint platform:
In the left sidebar, select Teams.
Click Edit on your user profile.
Click Generate API key to create a new API key.
Note
If you don’t have the necessary permissions, ask your administrator.
Configuring your API key
Once you have your API key, choose one of the following options:
Option 1: Using the CLI tool (recommended)
datamint-config --api-key YOUR_API_KEY
Option 2: Setting an environment variable
export DATAMINT_API_KEY="your_api_key"
Option 3: Programmatically in Python
from datamint import Api
api = Api(api_key="your_api_key")
Troubleshooting
ImportError: No module named ‘datamint’
Make sure you activated your virtual environment before running your script:
source datamint-env/bin/activate # Linux/macOS
datamint-env\Scripts\activate # Windows
Or install globally (not recommended):
pip install --user -U datamint
API authentication errors
Verify your API key is set correctly:
datamint-config
Next Steps
Now that you have the basics set up, explore these advanced topics:
Master the command-line interface: Command-line tools
Check out our Python API documentation: Client Python API
Our PyTorch, Lightning and MLflow integration: PyTorch & Lightning Integration
Use the built-in Trainer API and custom model integration patterns: Trainer API
Browse tutorial notebooks: Tutorials