Follow these steps to start your journey by interfacing with the Rendered.ai web interface to create an account, configure a graph, run a job, and download the resulting dataset. Start by registering.
Landing Page Upon Registration
Setting up your Organization
New accounts with Rendered.ai must first set up their organization. This is where all workspaces will be housed, and where all members from your business will be able to share their work, contribute to existing channels, and generate new datasets.
Initially the organization’s name is set to “default”, edit this by clicking the user icon to see the menu of account settings.
Account Settings Menu
Edit the Organization Name
Example Workspace and Channel
Each new organization comes with a workspace called “Example”. A workspace is a place to stage graphs and hold datasets which may be shared within or outside your organization. You can create a new workspace by clicking the green “New Workspace” icon in the upper right of the Rendered.ai engine page. From there, you can name your workspace and choose which channels to include within it.
Organization View Showing Existing Workspaces
The “Example” workspace contains the Rendered.ai Example channel by default. Channels define the architecture (3D objects, backgrounds, sensors, sensor platforms, etc.) required to generate synthetic data. The Rendered.ai Example channel serves as a generic channel (toys generated in a box) that allows you to experiment and learn the platform. This channel corresponds to the public code base on Github: https://github.com/Rendered-ai/ana.
New organizations will have only the example channel to start. Rendered.ai maintains several other starter channels for various applications that are available on request. Channel customizations can be made directly to a cloned version of the Ana code base (see Ana Software Architecture), or provided as a service by Rendered.ai.
Graphs within a Workspace
A graph is a visual diagram that is based on a channel’s codebase, allowing you to view the objects and connections that exist in that channel, modify them within the diagram view, and stage the modified channel to produce simulation sets. Within the workspace view, create a new graph by clicking “New Graph” in the left-hand side of the screen, and provide a name, choice of channel, and brief description.
Creating a Graph
Clicking “Preview” in the top right of the screen renders a sample image of the provided graph configuration, allowing you to ensure your requirements are met before staging the graph for production.
Result of Preview
Staging Graphs within the Jobs Manager
Once the graph is providing a satisfactory output, it can be staged by clicking the “Stage” button. Staging a graph adds an item to the Staged Graphs section of the job manager.
Jobs Manager View of Staged Graphs
When configuring a job, a name and description can be given to the output dataset, and you can specify the number of images to generate, as well as a specific seed to initialize the random number generators in order to match results across multiple runs if desired. You can also designate priority for the job if multiple jobs are being run at once.
Configuring a Job from a Staged Graph
Once the job has started its run, you can view the status of the job by pressing the downward-facing arrow on the job to display progress details. This shows average time per image, and gives an estimated end time for the run. Once the run is complete, you can download the dataset and begin using the outputs to train your AI.
Now that you have set up a workspace, staged a graph, and run a job to create a dataset, you invite others to your workspace to collaborate on your project.
Inviting Others to a Workspace