Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
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Connect the Newton physics engine to an Omniverse simulation or ovstage with ovnewton. Use when importing a populated ovstage into Newton, stepping its physics, synchronizing stage edits, or publishing simulation state for Omniverse rendering.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Use ovnewton for the Newton ↔ ovstage bridge. The application owns model finalization, the solver, controls, collision handling, and the simulation loop. ovnewton reads a populated ovstage; Newton does not parse USD in this workflow. Standalone Newton simulations and generic Omniverse authoring do not need this skill.
Install ovnewton in the application's environment. Let its package metadata
select compatible dependencies; do not replace the qualified ovstage dependency
with an arbitrary build.
pip install ovnewton
python -m ovnewton.examples.example_ovnewton_basic --device cpu --no-render --num-frames 8
For rendering or MuJoCo-Warp, install "ovnewton[examples]". The basic example
accepts --solver mujoco or --solver vbd; VBD also supports CPU simulation.
Rendering needs a supported NVIDIA GPU and its own device selection.
Read the getting-started guide and adapt the basic example. For multiple robots, use the robot example. Use documentation and examples corresponding to the installed ovnewton release; repository main may describe a newer API.
ovnewton.register_usd_schemas() before the first USD schema read.
Create an ovstage.Stage() and populate the application-supplied USD with
population.open_usd(stage, scene_path, ordinal=1, domains=PopulationDomain.ALL).
Seal population with stage.advance_write_floor(1).wait() before importing.
If the application already supplies a populated stage, reuse it and its sealed
ordinal; do not reload the scene or assume its ordinal is 1.newton.ModelBuilder(). Configure defaults and register any
solver-owned custom attributes before calling
ovnewton.add_ovstage(builder, stage, ordinal=population_ordinal).
Compose all scene assets into one ovstage first: this entry point currently
requires a builder without model entities and supports one stage per builder.SolverMuJoCo.register_custom_attributes(builder) before
calling ovnewton.add_ovstage(). For rigid-body VBD, register
SolverVBD.register_custom_attributes(builder) before ovnewton.add_ovstage()
and call builder.color() after that import, before finalization.
Use SolverVBD(model, rigid_compliant_alm=True) for compliant rigid-body
contacts, following the example's solver settings and contact-material tuning.builder.finalize(skip_validation_joints=import_result.has_orphan_joints),
where import_result is the result of add_ovstage. Construct the solver and
bind with ovnewton.attach_ovstage(stage, model=model, ordinal=population_ordinal).
attach_ovstage(stage) is the convenience path when no builder customization
is needed. When binding an existing model, body and joint labels must match
stage paths.Allocate two model states, controls, and contacts. Each substep clears forces, computes contacts, calls the solver, and swaps the states. Start from the example's collision pipeline and solver configuration rather than assuming every solver has identical preparation requirements.
After stepping, publish the current state with
binding.update_to_ovstage(state, ordinal=output_ordinal), then seal it with
stage.advance_write_floor(output_ordinal).wait(). Coordinate increasing write
ordinals with the application's other stage writers.
To consume application edits, seal them first and call
binding.update_from_ovstage(state, control) before stepping, using the same
control passed to the solver. This reads the latest sealed supported state and
drive-target fields. An explicit read ordinal is a consistency ceiling, not
historical playback. Selected Newton-native output can instead be read through
binding.query() and binding.read() without stage publication.
For rendering, publish and seal Newton output before the renderer consumes the stage. Newton's simulation device and OVRTX's rendering device are independent; follow the example's synchronization and device-selection options.
Check the support guide before promising support for authored physics. Unsupported physics may fail attachment; do not silently replace it with a different physical model. See the concepts guide and output-read guide for synchronization, diagnostics, read layouts, and lifetimes.
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Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
中文简介正在准备中。当前显示原文说明。
Customize NVIDIA Nemotron Voice Agent's Generic Pipecat example for healthcare appointment, five-field patient intake, or custom tool-calling workflows without a separate backend.
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Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.
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Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.
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Calibrates pre-recorded `cam_*.mp4` datasets through the AutoMagicCalib REST API. Use for user-supplied local MP4s; route live RTSP streams to `amc-run-rtsp-calibration`.
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Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.
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