Here are a few definitions to help you understand the components and patterns used in Rendered.ai.

Fundamental concepts

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 are created from Staged Graphs to create synthetic data.

Through both coding and visualization in the Rendered.ai user interface, Graphs are designed as Node-Edge diagrams. These diagrams allow the user to engineer the linkages between objects, modifiers, and other Channel components to design synthetic datasets.

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.

Some Nodes may be described as ‘Objects,' indicating that they are simulated physical objects that will be used in synthetic images. Other nodes may be referred to as 'Modifiers,' indicating that they somehow change or impact image generation during processing of the Graph.

For example, 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. Node capabilities are channel dependent.

Edges

Edges are the term we use to describe the connectors between Nodes in a Graph. A connector is used in the visual interface to show that a particular parameter of one Node can be used to populate a parameter or for processing in another Node.

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. Different Channels may contain different components depending on the specific application and problem domain of the Channel. Some sensor models, for example, may not lend themselves to easy creation of masks (images with pixel values capturing the location of scene assets).

Shared Data Libraries

Common functions useful across multiple channel.

Shared data volumes

Volume store large data repository that can be made availabe to the docker container at runtime.

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 executes the cloud compute management, configuration management and various other 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

https://pypi.org/project/anatools/ is Rendered.ai’s SDK for connecting to the Platform API. It is the toolkit for third-party and Rendered.ai developers to use in producing applications on the Platform.

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 include packages that describe sensors and other components of the channel.