DatamintAPI

Contents

  • Getting Started with Datamint Python API
  • Installation
  • Setup API key
    • Method 1: Command-line tool (recommended)
    • Method 2: Environment variable
    • Method 3: Api constructor
    • Next Steps
  • Command-line tools
    • Configuring the Datamint settings
    • 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
      • 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
    • Working with Annotations
      • Inspect annotations from a resource
      • Upload segmentations
      • Multi-class segmentations
      • Volume segmentations
      • Inspect annotation entities
    • Working with Projects
      • Create and manage projects
      • Project helper methods
    • Working with Channels
      • Organize resources with channels
    • Working with Models & Deployment
      • Running inference
  • PyTorch & Lightning Integration
    • Overview
    • PyTorch Dataset Integration
      • Basic PyTorch Usage
      • Dataset Transforms
  • Tutorials
    • Data Management
    • Annotations
    • Machine Learning & Deployment
    • Use Cases & End-to-End Examples

Python Modules Reference

  • Client API
    • Main API Module
      • Api
    • API Endpoints
      • Projects API
      • Resources API
      • Annotations API
      • Channels API
      • Users API
      • Annotation Sets API
      • Models API
      • Deploy Model API
      • Inference API
    • Base API Classes
      • Base API
      • Entity Base API
    • datamint.exceptions
      • DatamintException
      • EntityAlreadyExistsError
      • ItemNotFoundError
      • ResourceNotFoundError
    • datamint.api.dto
      • AnnotationType
      • BoxGeometry
      • CreateAnnotationDto
      • Geometry
      • LineGeometry
  • Dataset Classes
    • Base Classes
      • DatamintBaseDataset
      • DatamintDatasetException
      • MultiFrameDataset
    • Specialised Datasets
      • ImageDataset
      • VolumeDataset
      • VideoDataset
    • Sliced Datasets
      • SlicedVolumeDataset
      • SlicedVideoDataset
    • Legacy Classes (Deprecated)
      • DatamintDataset
      • DatamintBaseDataset
      • DatamintDatasetException
  • Entities
    • Entity-first Workflows
      • Project objects
      • Resource objects
      • Annotation objects
    • Reference
      • Annotation
      • AnnotationSpec
      • BaseEntity
      • CacheManager
      • Channel
      • ChannelResourceData
      • DatasetInfo
      • InferenceJob
      • Project
      • Resource
      • User
DatamintAPI
  • Client Python API
  • View page source

Client Python API

This chapter describes how to use the Api class in Python, to interact with the Datamint API. Before continuing, you may want to check the Setup API key section to easily set up your API key, if you haven’t done so yet.

  • Getting Started with the API Client
  • Working with Resources
    • Upload resource files
    • List and filter resources
    • Upload with options
  • Download resources
  • Publishing resources
  • Working with Annotations
    • Inspect annotations from a resource
    • Upload segmentations
    • Multi-class segmentations
    • Volume segmentations
    • Inspect annotation entities
  • Working with Projects
    • Create and manage projects
    • Project helper methods
  • Working with Channels
    • Organize resources with channels
  • Working with Models & Deployment
    • Running inference
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