Application Flow

Application Flow

When to Use

Use this skill when the user asks how to structure an ovrtx program, what the main steps are, or how the pieces fit together.

Inputs

Resolve inputs in this order: existing repository files and referenced snippets, explicit user request, then broader agent context.

• Target API surface: Python, C/C++, or both.
• Application lifecycle stage: renderer creation, scene loading, stepping, warmup, output readback, or cleanup.
• Repository source snippets referenced below. Treat these snippets as the API source of truth.

Prerequisites

• Use an ovrtx checkout that contains the referenced examples and docs tests.
• Read the relevant > **Source:** snippet before writing or explaining API usage.
• Validate ovrtx workflows only on a machine with an NVIDIA RTX-capable GPU, a supported NVIDIA driver listed in docs/driver_requirements.rst, and unsandboxed runtime execution.
• If NVIDIA driver detection fails, the driver is inaccessible, or the detected version is older than the relevant baseline in docs/driver_requirements.rst, rerun validation outside the sandbox. If the driver is still missing or incompatible, stop runtime validation and tell the user they need a supported NVIDIA driver.
• For the repository minimal examples, require internet access because the scene is loaded from S3.
• Python 3.10-3.13 are supported.
• For code changes, preserve renderer lifecycle ordering and cleanup semantics for the selected language.

Instructions

1. Identify the requested language and lifecycle stage before choosing an example.
2. Read the referenced snippet that matches the requested stage and language.
3. Preserve the normal ovrtx order: create or initialize the renderer, load or compose USD, step or wait for work, read outputs when needed, then release C resources explicitly.
4. Apply the async, status-query, error-handling, and warmup skills when the workflow crosses those concerns.
5. When changing code, run the narrow example or docs test that owns the snippet whenever practical.

Output Format

• For explanations, return an ordered lifecycle with the relevant follow-on skills.
• For code changes, summarize the lifecycle files touched and validation run.

Scripts

This skill has no scripts.

Limitations

• The referenced snippets remain the source of truth; update or add tested snippets before documenting new API usage.

Overview

Every ovrtx application follows the same core lifecycle, whether in Python or C. This skill gives the high-level sequence and points to the detailed skill for each step.

Runtime Verification

Run the minimal example first when validating an ovrtx environment:

Do not claim runtime validation from parse-only checks, docs-only checks, or execution on a host without a supported RTX GPU and driver.

Key Concepts

The USD stage contains three prim types that work together to produce rendered output:

• Camera (or sensor) prim -- defines the sensor itself: position, orientation, lens properties (focal length, aperture), exposure settings. This is a standard UsdGeomCamera prim in the scene hierarchy.
• RenderProduct prim -- the thing you actually point the renderer at. It ties together a Camera (via rel camera), an output resolution (int2 resolution), and a list of output variables (via rel orderedVars). When you call step(), you pass RenderProduct paths, not Camera paths.
• RenderVar prim -- declares a single output. Its full prim path identifies the output in step results; its sourceName (e.g., LdrColor, HdrColor, DepthSD) selects the renderer AOV.

/World/Camera          ← Camera prim (sensor definition)
/Render/Camera         ← RenderProduct prim (render configuration)
    /Render/Camera/LdrColor  ← RenderVar prim (output variable)
    /Render/Camera/HdrColor  ← RenderVar prim (output variable)

Key implications for agents:

• step() takes RenderProduct paths, not Camera paths. If you pass a Camera path, nothing will render.
• Render settings (e.g., omni:rtx:rtpt:maxBounces) are written as attributes on the RenderProduct prim, not on the Camera prim. See the render-settings skill.
• Scene properties (position, materials, visibility) are on scene prims under /World. Render configuration (resolution, outputs, render mode) is on RenderProduct prims, typically under /Render.
• A single Camera can be referenced by multiple RenderProducts with different resolutions, outputs, or settings.

Lifecycle

1. Create renderer         → renderer-creation
2. Load USD scene(s)       → loading-usd
3. [Optional] Clone prims  → cloning-prims
4. Render loop:
   a. Write attributes     → writing-transforms, writing-attributes
   b. Step renderer        → stepping-and-rendering
   c. Read output          → reading-render-output
5. Cleanup

Python

Source: examples/python/minimal/main.py snippet create-renderer

Source (continued): examples/python/minimal/main.py snippet add-usd

Source (continued): examples/python/minimal/main.py snippet step

Source (continued): examples/python/minimal/main.py snippet read-render-output

For the full lifecycle with attribute writes, bindings, and cloning, compose the relevant skill snippets.

C

Source: examples/c/minimal/main.cpp snippet create-renderer

Source (continued): examples/c/minimal/main.cpp snippet load-usd-and-wait

Source (continued): examples/c/minimal/main.cpp snippet step-renderer

Source (continued): examples/c/minimal/main.cpp snippet fetch-results

Source (continued): examples/c/minimal/main.cpp snippet map-rendered-output-cpu

Source (continued): examples/c/minimal/main.cpp snippet unmap-and-cleanup

Key Differences: Python vs C

Troubleshooting

• On Linux systems with no display, repeatedly creating and destroying renderers may result in a crash with the stack trace pointing into libEGL.so when shared graphics resources are torn down between renderers. This can happen if keep_system_alive is configured to false, or if ovrtx_initialize() is not called before the multi-renderer lifecycle. In the implicit-initialization pattern (when ovrtx_initialize() is not called), the keep_system_alive config setting is effectively ignored. Avoid this by both configuring keep_system_alive to true (RendererConfig(keep_system_alive=True) in Python, ovrtx_config_entry_keep_system_alive(true) in C) and calling ovrtx_initialize() before creating renderers. If this is not possible, or the crash persists, a further workaround is to set the environment variable VK_LOADER_DISABLE_DYNAMIC_LIBRARY_UNLOADING=1.
• In C, every async operation (ovrtx_open_usd_from_file, ovrtx_clone_usd, ovrtx_step) returns an ovrtx_enqueue_result_t. You must wait on the op_index before using the results.
• In C, always destroy results with ovrtx_destroy_results() after processing. ovrtx will log warnings if results are leaked.
• In Python, the synchronous API (open_usd, step, clone_usd) handles waiting internally. Use the _async variants for explicit control.
• For best performance in animation loops, use attribute bindings (see attribute-bindings skill) or mapping (see mapping-attributes skill) instead of per-frame write_attribute calls.
• See error-handling skill for robust error checking patterns in both languages.

In Attached Mode (ovrtx 0.4+)

The renderer-owned scene operations described for standalone mode are deprecated in 0.4. For current code, manage scene data with ovstage, advance its write floor, attach it to ovrtx, and call Python step(..., ordinal=...) (or the C attached update/step pair). Keep the standalone flow only for compatibility work.

See docs/core/ovstage_integration.rst, skills/update-0_3-0_4-c/SKILL.md and skills/update-0_3-0_4-python/SKILL.md.

References

• Use the > **Source:** directives in this skill to locate tested snippets before reusing API patterns.
• Keep related skills, docs, and snippets synchronized when changing the workflow.