datamint 2.4.1

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
• Key Components
• Command Line Tools
• Examples
• Support

🚀 Features

• Dataset Management: Download, upload, and manage medical imaging datasets
• Annotation Tools: Create, upload, and manage annotations (segmentations, labels, measurements)
• Experiment Tracking: Integrated MLflow support for experiment management
• PyTorch Lightning Integration: Streamlined ML workflows with Lightning DataModules and callbacks
• DICOM Support: Native handling of DICOM files with anonymization capabilities
• Multi-format Support: PNG, JPEG, NIfTI, and other medical imaging formats

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

To be released soon

From Source

pip install git+https://github.com/SonanceAI/datamint-python-api

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 git+https://github.com/SonanceAI/datamint-python-api
   

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 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
💡 Examples	Practical usage examples

🔗 Key Components

Dataset Management

from datamint import Dataset

# Load dataset with annotations
dataset = Dataset(
    project_name="medical-segmentation",
)

# Access data
for sample in dataset:
    image = sample['image']       # torch.Tensor
    mask = sample['segmentation'] # torch.Tensor (if available)
    metadata = sample['metainfo'] # dict

PyTorch Lightning Integration

import lightning as L
from datamint.lightning import DatamintDataModule
from datamint.mlflow.lightning.callbacks import MLFlowModelCheckpoint

# Data module
datamodule = DatamintDataModule(
    project_name="your-project",
    batch_size=16,
    train_split=0.8
)

# ML tracking callback
checkpoint_callback = MLFlowModelCheckpoint(
    monitor="val_loss",
    save_top_k=1,
    register_model_name="best-model"
)

# Trainer with MLflow logging
trainer = L.Trainer(
    max_epochs=100,
    callbacks=[checkpoint_callback],
    logger=L.pytorch.loggers.MLFlowLogger(
        experiment_name="medical-segmentation"
    )
)

Annotation Management

# Upload segmentation masks
api.upload_segmentations(
    resource_id="resource-123",
    file_path="segmentation.nii.gz",
    name="liver_segmentation",
    frame_index=0
)

# Add categorical annotations
api.add_image_category_annotation(
    resource_id="resource-123",
    identifier="diagnosis",
    value="positive"
)

# Add geometric annotations
api.add_line_annotation(
    point1=(10, 20),
    point2=(50, 80),
    resource_id="resource-123",
    identifier="measurement",
    frame_index=5
)

🛠️ Command Line Tools

Upload Resources

Upload DICOM files with anonymization:

datamint-upload \
    --path /path/to/dicoms \
    --recursive \
    --channel "training-data" \
    --anonymize \
    --publish

Upload with segmentation masks:

datamint-upload \
    --path /path/to/images \
    --segmentation_path /path/to/masks \
    --segmentation_names segmentation_config.yaml

Configuration Management

# Interactive setup
datamint-config

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

🔍 Examples

Medical Image Segmentation Pipeline

import torch
import lightning as L
from datamint.lightning import DatamintDataModule
from datamint.mlflow.lightning.callbacks import MLFlowModelCheckpoint

class SegmentationModel(L.LightningModule):
    def __init__(self):
        super().__init__()
        # Model definition...
    
    def training_step(self, batch, batch_idx):
        # Training logic...
        pass

# Setup data
datamodule = DatamintDataModule(
    project_name="liver-segmentation",
    batch_size=8,
    train_split=0.8
)

# Setup model with MLflow tracking
model = SegmentationModel()
checkpoint_cb = MLFlowModelCheckpoint(
    monitor="val_dice",
    mode="max",
    register_model_name="liver-segmentation-model"
)

# Train
trainer = L.Trainer(
    max_epochs=50,
    callbacks=[checkpoint_cb],
    logger=L.pytorch.loggers.MLFlowLogger()
)
trainer.fit(model, datamodule)

🆘 Support

Full Documentation GitHub Issues