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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Use when measuring Jetson Video Codec SDK or PyNvVideoCodec encode/decode throughput, comparing presets or surfaces, testing codec-worker capacity with authenticated samples and user media, or producing a clearly labeled documentation-derived planning estimate when representative media is absent. Also use for a video request limited to PSNR or SSIM, to apply the terminal scope response.
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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。
Measure codec-stage FPS and megapixels/second for an evidenced target and exact workload. Supported live routes are encode, decode, P4/P5 comparison, and a strictly increasing worker-capacity sweep. A separate no-media path calculates clearly labeled SDK-documentation estimates; it never claims a measurement.
input_required and ask for the missing
item(s), including a separate exact path or URL for every requested
direction, before reading workflow references or sibling skills, probing,
authentication, recipe work, or workspace creation. Never benchmark the
setup smoke fixture or substitute catalog media.measurement_performed: false. Preserve that reference
byte-for-byte. Omitted preset, per-stream FPS, or stream mix does not block
planning: enumerate the reference's bounded documented candidates and
scenarios, disclose every assumption, and ask for the omitted values.Read benchmark-workflow.md completely once,
then apply its preflight section. Preserve explicit
native, pynvc, and both; map delegated selection to auto. Obtain fresh
read-only readiness from jetson-video-setup, passing any exact interpreter
already supplied or established in this conversation. Setup otherwise checks
the conventional profile path. For auto, zero eligible surfaces blocks, one
runs, and two returns selection_required. Python encode and comparison need a
full-samples environment; decode performance may use pynvc-smoke.
Encode, compare, and encode-capacity also require a validated schema-2 recipe
from jetson-video-recipe. A missing dependency stops that branch with
dependency_required; an eligible peer may continue as partial.
-loop.Retain each launch's unedited output and write the compact result described by benchmark-output-contract.md. Report every repetition, recomputed mean/minimum/maximum, MP/s only when dimensions are sample-bound, partial branches, limitations, and retry reason. Label worker sweeps as codec-stage capacity bounds and preset comparisons as throughput-only.
input_required, selection_required, dependency_required,
blocked, partial, and failed; do not promote a peer's success.还没有评价。欢迎分享你的使用感受。
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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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