DataGym.ai
  • DataGym.ai
  • Getting Started
  • Project
    • What is a project?
    • Create a new project
    • Update a project
    • Delete a project
    • Connect a dataset
    • Export data
    • Import Data
  • Dataset
    • What is a dataset?
    • Create a new dataset
    • Update a dataset
    • Connect with AWS S3
    • Delete a dataset
    • Manage images
      • Upload to DataGym.ai
      • Add public links
      • Synchronize with AWS S3
    • Connect to a project
    • Use the review mode
  • Label configuration
    • What is a label configuration?
    • Configuration entry
    • Entry types
    • Creating an entry
    • Editing an entry
    • Duplicating an entry
  • Label mode
    • What is the label mode?
    • Entry-list
    • Value-list
    • Task control
    • Toolbar
    • Workspace
    • AI-assisted labeling
    • Video labeling
  • Tasks
    • What is a task?
    • Process a task
    • Manage Tasks
  • AI-Assistant
    • AI-assisted pre-labeling
    • Object Classes
  • API Token
    • API
    • Manage API Token
  • Account-Management
    • Account Settings
    • Organisation-Management
  • Python API
    • Getting Started
    • Projects
    • Labeled data
    • Datasets
    • Images
    • Label configuration
    • Uploading COCO
  • Changelog
Powered by GitBook
On this page

Was this helpful?

  1. Dataset

Connect with AWS S3

How to connect DataGym.ai with AWS S3

PreviousUpdate a datasetNextDelete a dataset

Last updated 4 years ago

Was this helpful?

DataGym.ai works well with AWS S3 buckets so you can store all your images there and synchronise with your AI annotation tool.

The AWS S3 support may be limited by your subscription plan.

To connect our tool with AWS S3 fill the form within the AWS S3 tab. DataGym.ai needs to know some information about the bucket including:

  • A unique name to identify that connection. This attribute is only used within DataGym.ai.

  • The bucket name from AWS S3.

  • The location path is optional and can be used to limit the access to that path within the bucket.

  • The region where the bucket is located.

  • Some API credentials (access key and private key) to read the files within the bucket.

While saving the form or updating the configuration DataGym.ai synchronises the dataset the first time and displays the response beside the form.

Please fill the form.