DatamintAPI

User Guide

  • Getting Started with Datamint Python API
  • Prerequisites
  • Installation
    • Verify your installation
      • Setup API Key
    • Obtaining an API key
    • Configuring your API key
    • Troubleshooting
    • Next Steps
  • Command-line tools
    • Configuring the Datamint settings
      • Local data management
    • Uploading DICOMs/resources to Datamint server
      • Example using include and exclude extensions options:
      • Uploading segmentations along with the resources
      • Associating uploaded segmentations with a deployed model
      • JSON metadata support for NIfTI files
      • Checking uploaded segmentations
      • All available options
  • Client Python API
    • Getting Started with the API Client
    • Working with Resources
      • Upload resource files
      • List and filter resources
      • Upload with options
      • Download resources
      • Publishing resources
      • Deleting resources
    • Working with Annotations
      • Inspect annotations from a resource
      • Upload segmentations
      • Multi-class segmentations
      • Volume segmentations
      • Upload geometry annotations (bounding boxes, lines)
      • Upload classification annotations
      • Inspect annotation entities
    • Working with Projects
      • Create and manage projects
      • Project helper methods
      • Project-scoped dataset splits
    • Working with Channels
      • Organize resources with channels
      • Deploy a registered model
    • Working with Users
  • PyTorch & Lightning Integration
    • Overview
    • PyTorch Dataset Integration
      • Basic PyTorch Usage
      • Dataset Transforms
      • Split Reproducibility
    • Trainer API
  • Trainer API
    • Available Trainers
    • Quick Start
    • Inputs, Splits, and Outputs
    • Passing Lightning Trainer Options
    • Using an External Model Inside a Datamint Trainer
      • Preferred: Subclass a Datamint Lightning Module
      • Fully Custom LightningModule
    • Related Examples
  • Tutorials
    • Data Management
    • Annotations
    • Machine Learning & Deployment
    • Use Cases & End-to-End Examples
  • Datamint vs Raw PyTorch
    • Workflow comparison at a glance
    • Example 1 – Dataset and split setup
    • Example 2 – Full training loop
    • Example 3 – Inference & deployment
      • Key Observations
    • Further reading

Python Modules Reference

  • Client API
    • Base API Classes
      • Base API
      • Entity Base API
    • Main API Module
      • Api
    • datamint.api.dto
      • CreateAnnotationDto
    • API Endpoints
      • Projects API
      • Resources API
      • Annotations API
      • Channels API
      • Users API
      • Annotation Sets API
      • Models API
      • Deploy Model API
      • Inference API
    • Entities
      • Entity-first Workflows
      • Entity Reference
      • Annotations Subpackage
    • Annotations Subpackage
      • Annotation
      • AnnotationType
      • BoxAnnotation
      • BoxGeometry
      • Geometry
      • ImageClassification
      • ImageSegmentation
      • LineAnnotation
      • LineGeometry
      • VolumeSegmentation
      • annotation_from_dict()
      • Annotation Types
      • Base Annotation
      • Annotation Specification
      • Segmentation Annotations
      • Geometry Annotations
      • Classification Annotations
      • Annotation Types
      • Factory Function
    • datamint.exceptions
      • DatamintException
      • EntityAlreadyExistsError
      • ItemNotFoundError
      • ResourceNotFoundError
  • Base API Classes
    • Base API
      • ApiConfig
      • BaseApi
    • Entity Base API
      • CRUDEntityApi
      • CreatableEntityApi
      • DeletableEntityApi
      • EntityBaseApi
      • UpdatableEntityApi
  • Dataset Classes
    • Dataset Classes Overview
    • Split Modes
    • Base Classes
      • DatamintBaseDataset
      • DatamintDatasetException
      • MultiFrameDataset
    • Specialised Datasets
      • ImageDataset
      • VolumeDataset
      • VideoDataset
    • Sliced Datasets
      • SlicedVolumeDataset
      • SlicedVideoDataset
    • Annotation Processing
      • Annotation
      • AnnotationProcessor
    • Legacy Classes (Deprecated)
      • DatamintDataset
      • DatamintBaseDataset
      • DatamintDatasetException
  • Entities
    • Entity-first Workflows
      • Project objects
      • Resource objects
      • Annotation objects
    • Entity Reference
      • Base Classes
      • Resource Entities
      • Project Entity
      • Channel Entity
      • User Entity
      • Dataset Info Entity
      • Split Entity
      • Job Entities
    • Annotations Subpackage
  • Lightning API
    • DatamintDataModule
    • Trainers
    • Lightning Modules
    • DatamintDataModule
      • Constructor Parameters
      • Key Methods
    • Trainers
      • Available Trainers
      • BaseTrainer
      • Segmentation Trainers
      • Classification Trainers
      • Specialized Trainers
    • Lightning Modules
      • DatamintLightningModule
      • SegmentationModule
      • ClassificationModule
  • MLflow Integration
    • Model Flavors
    • Datamint Dataset
    • Checkpointing
    • Overview
    • Automatic Configuration
    • Environment Setup
      • ensure_mlflow_configured()
      • setup_mlflow_environment()
      • EnvVars
    • MLflow Dataset
      • DatamintMLflowDataset
    • Model Flavors
      • BaseDatamintModel
      • DatamintModel
      • TaskType
      • ModeSpec
      • PredictionRouter
      • prediction_mode()
      • PredictionMode
      • load_model()
      • log_model()
      • save_model()
    • Checkpointing
    • MLflow Tracking
      • set_project()
      • DatamintExperimentProvider
      • DatamintStore
    • Artifact Repository
      • DatamintArtifactsRepository
    • Usage Example
  • datamint.exceptions
    • DatamintException
    • EntityAlreadyExistsError
    • ItemNotFoundError
      • ItemNotFoundError.resource_type
      • ItemNotFoundError.set_params()
    • ResourceNotFoundError
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