DatamintAPI

For Developers

  • Quick Start
  • Installation
  • Scaffold your first project
  • Logging to a file
  • Your first API call
  • Command-line tools
  • Client Python API
  • PyTorch & Lightning Integration
  • Training your Model
  • Evaluating your Model
  • Bringing an External Model into Datamint
  • Tutorials
    • Getting Started
    • Annotations
    • Datasets
    • Experiment Tracking
    • Deployment
      • Model Deployment API Demo
      • Deploying an Externally Trained Model with Datamint
      • Validating a Model Before Deployment
      • Running Predictions on Images, Volumes, and Videos
      • Building and Running a Model Locally with Podman
    • End-to-End Examples
    • Evaluating
  • Datamint vs Raw PyTorch
  • Agent Skills
  • SSL Certificate Troubleshooting

For Non-Developers

  • Upload Images & Segmentations (No Coding Required)

Python Modules Reference

  • Client API
  • Base API Classes
  • Dataset Classes
  • datamint.importers
  • Entities
  • Lightning API
  • MLflow Integration
  • datamint.exceptions
DatamintAPI
  • Tutorials
  • Deployment
  • View page source

Deployment

Deploy registered or external models, validate them, and run predictions.

  • Model Deployment API Demo
  • Deploying an Externally Trained Model with Datamint
  • Validating a Model Before Deployment
  • Running Predictions on Images, Volumes, and Videos
  • Building and Running a Model Locally with Podman
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