Fundamental concepts
Here are a few definitions to help you understand the components and patterns used in Rendered.ai.
Platform as a Service (PaaS)
A Platform as a Service (PaaS) is a category of cloud computing services that allows users to provision, instantiate, run, and manage a modular bundle comprising a computing platform and one or more applications, without the complexity of building and maintaining the infrastructure typically associated with developing and launching the application(s).
Rendered.ai is a Platform as a Service for data scientists, data engineers, and developers who need to create and deploy unlimited and customized synthetic data for pipelines for machine learning and artificial intelligence workflows. The benefits of the platform are: reducing expense, closing gaps, overcoming bias and driving better labeling, security, and privacy outcomes when compared with the use of real-world data.
Organization
An organization is a billable entity and a way of segmenting off work for collaboration or from collaboration for security purposes. The Organization is fundamentally a collaboration tool. All subscriptions to Rendered.ai typically grant the customer access to one Organization.
Workspace
A Workspace is a container for organizing work related to one set of projects or applications. Workspaces may be used as a collaboration device in that Guest users can be invited to a Workspace who with then not have access to any other part of your Organization. Your Workspace shows recent Graphs, recent Jobs, and recent Datasets you have worked on.
Channel
A Channel is a container for Graphs, Packages (sensors and application specific requirements) and code that is used to define the universe of possible synthetic output for a particular application. For example, Channels may represent synthetic data generation as diverse as video of microscopy or satellite based SAR data acquisition. All of the components of a Channel together define the set of capabilities that solve a specific synthetic generation use-case.
Graph
A Graph is a visual representation of the elements (capabilities, objects, modifiers) and their relationships that compose an application. Jobs originate from Staged Graphs. Through both coding and visualization in the Rendered.ai user interface, Graphs are designed as Node-Edge diagrams.
Nodes
Nodes are either objects, capabilities or modifiers and will appear in a Graph as boxes with a name and properties that can be set in the Graph interface. Objects represent simulated physical components, while Modifiers affect image generation. Nodes may represent types of assets to be placed in a scene, a digital sensor model, a renderer, post-processing imagery filters, annotation generators, and much more.
Edges
Edges are the term we use to describe the connectors between Nodes in a Graph. Connectors demonstrate how one Node's parameter supplies or processes another Node's parameter.
Staged Graph
A Staged Graph is a Graph that has been queued to enable Members of the Organization to run Jobs that generate synthetic data.
Job
A Job is a processing effort that generates a specific quantity of synthetic images or video that will be run on the Rendered.ai high performance compute environment.
Dataset
A Dataset is a variable collection of output images or video, masks, annotation, and other metadata that has been created by execution of a Job. Channel-specific components vary based on application domain, with certain sensor models potentially unsuitable for straightforward mask generation.
Shared Data Libraries
Common functions useful across multiple channels.
Shared Data Volumes
Rendered.ai Volumes store static assets for use in synthetic data generation. Package Volumes, deployed with Rendered.ai channels, are maintained by channel developers and tested as ideal use cases. Workspace Volumes associate with user-managed spaces, created and maintained by Rendered.ai users for improved dataset fidelity.
The Rendered.ai Engine
The Engine is the underlying set of capabilities shared by all Channels and are accessible either through the SDK or the Rendered.ai web interface. The Engine manages cloud compute, configuration, and additional functions.
Microservices
Loosely coupled services that are available to be executed as part of a Channel: Preview, Annotations, Analytics and GAN are examples of the Platform's microservices.
CycleGAN Microservice
The service trains two neural nets, one that translates the synthetic data to real data, and the other one that translates the real data to synthetic and it attempts to make it self-consistent and allows it to converge into a matched style.
Developer concepts
Anatools SDK
anatools is Rendered.ai's SDK for connecting to the Platform API. It comprises the toolkit enabling third-party and Rendered.ai developers to create platform applications.
Package
A Channel uses a domain specific sensor and other capabilities for supporting the application.
Application/App Container
An application collects the Channel elements in executable code in a Container to produce synthetic data for a specific end-user use case.
Application Specific Libraries
Code that lives in a docker container. Defines nodes, node capabilities, and node procedures and how they interconnect. These libraries may incorporate packages describing sensors and channel components.