Jetson Video Benchmark
NVIDIAOfficialCreated by Vinit BansalDistributed by NVIDIA
- Skill
- Workflow
- Software development
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The jetson-video-benchmark skill provides a structured workflow for measuring the encode and decode throughput of the Jetson Video Codec SDK and PyNvVideoCodec. It supports live performance measurements using authenticated samples, preset comparisons, and codec-worker capacity sweeps. For scenarios where representative media is unavailable, the skill generates clearly labeled planning estimates based on official documentation. It enforces strict validation of inputs, environment readiness, and output reporting to ensure results are reproducible and verifiable.
What it does
- Measures encode and decode FPS and megapixels per second for specific workloads.
- Compares performance across different presets and surfaces.
- Tests codec-worker capacity through systematic sweeps.
- Generates performance estimates based on documented SDK references when media is absent.
- Validates benchmark results against strict output contracts and sample markers.
- Reports detailed metrics including mean, minimum, and maximum performance values.
When to use it
- When you need to measure actual encode or decode throughput on a Jetson device.
- When comparing the performance impact of different codec presets or surfaces.
- When determining the capacity of codec workers for a specific workload.
- When you require a performance estimate based on official documentation for planning purposes.
- When you need to verify performance metrics using authenticated SDK samples.
Advantages
- Ensures consistent and verifiable benchmark results through strict output contracts.
- Provides clear separation between live measurements and documentation-based estimates.
- Automates the preflight checks for environment and dependency readiness.
- Supports both native C++ SDK samples and Python-based PyNvVideoCodec workflows.
- Prevents invalid comparisons by enforcing strict input and configuration validation.
Usage examples
- “Measure the decode throughput for this H.264 elementary stream file: /data/input.h264”
- The agent validates the input file, checks the environment, runs the AppDecPerf sample, and reports the measured FPS and MP/s.
- “What is the expected encode performance for 1080p60 H.265 on Jetson Orin?”
- The agent provides a documented performance estimate based on the SDK reference, clearly labeling it as a non-measured planning value.
- “Compare P4 and P5 preset performance for this raw video file: /data/raw_frames.yuv”
- The agent runs the encode benchmark for both presets using the provided raw input and reports the throughput difference.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/jetson-video-benchmark/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip jetson-video-benchmark.zip -d .agents/skills
Documentation by Vinit Bansal
Jetson Video Benchmark
Purpose
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.
Terminal gates
For a request solely for PSNR or SSIM, state that objective quality is outside this skill and needs a separately authorized workflow, then stop. Return only that scope response. Do not append an alternative benchmark or next step, name another tool, request media, probe, or install anything.
Resolve codec direction before probing or calculating. Explicit encode or decode wording wins. Otherwise, quality, preset, bitrate, rate control, recording, or compressed-output wording implies encode; explicitly compressed input, ingest, playback, or IP/RTSP input implies decode. Treat generic camera-count wording such as "connect" or "handle", and a codec name on a camera or device, as direction-neutral. Set neutral wording aside and resolve direction on the remaining cues; a neutral cue never creates a conflict. For an encode-led camera estimate, state that NVDEC applies only when cameras already emit the named compressed codec, keep encode primary, and ask the user to confirm direction after giving the planning scenarios. If non-neutral cues conflict or are absent, present both interpretations, state that a unique encode estimate also needs an exact preset and measured validation needs representative input, then ask which direction applies and stop at that gate. Transcoding consumes separate decode and encode budgets: measure it as the supported decode and encode routes and report each budget separately, never as one combined FPS.
An explicit live/run/real/actual measurement requires representative input for every requested direction: raw frames with format, geometry, and frame count for encode; a compressed elementary stream or container for decode. Each input must be one exact target-local path or user-supplied HTTP(S) URL. If any is absent, return only
input_requiredand 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.A no-media planning/expected/indicative question follows documented-performance-estimates.md. It requires the exact documented row and, for a scaled estimate, clock provenance; it sets
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.
Live preflight
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.
Measure
Follow that same reference for sample allowlists, builds, direct argument lists, frame accounting, option checks, marker grammar, comparisons, and worker sweeps.
Show the dry-run plan, then use a fresh mode-0700 workspace. Authenticate each launcher immediately before use and launch its literal argument list.
Run one excluded whole-process warmup and at least three new measured processes per variant and surface, each with a finite timeout and without
-loop.Accept the sample-reported FPS only when its positive marker and processed frame count match. Apply every identity, workload, marker, and statistics check in benchmark-output-contract.md.
Report
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.
Limitations
Results apply only to the evidenced target, release, clocks, sample, content, frame range, and controls; they are not a portable product ceiling.
Do not interpolate undocumented presets or scale across format, bit depth, chroma, codec, rate control, or tuning. Resolution scaling is allowed only as the estimate reference's explicitly labeled pixel-area heuristic.
Keep documented estimates per engine; use an all-engine multiplier only under the explicit aggregate-planning rule in the estimate reference.
The PyNvVideoCodec 2.1 encode-performance helper caps each worker at 1,000 frames; follow the common-frame rule in the workflow.
Preserve
input_required,selection_required,dependency_required,blocked,partial, andfailed; do not promote a peer's success.
Files
- ATTRIBUTION.mdAdded by AutoGineer930 B
- BENCHMARK.md7.8 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md6.4 KiB
- evals/evals.json6.2 KiB
- references/benchmark-output-contract.md3.6 KiB
- references/benchmark-workflow.md10.1 KiB
- references/documented-performance-estimates.md12.3 KiB
- skill-card.md4.9 KiB
- skill.oms.sig5.2 KiB
references/benchmark-output-contract.md
Benchmark evidence
Keep the result small, readable, and independently checkable as a concise evidence handoff.
Minimal retained result
Before reporting a live benchmark complete, write one
benchmark-results.json in the run workspace and retain the raw output of each
launch in its own log file. The JSON needs only:
status, target/release, selected surface, GPU, and clocks when relevant;
input canonical path, bytes, SHA-256, format, geometry, FPS, and source frame count;
recipe canonical path, bytes, SHA-256, and compared knob, for encode;
authenticated launcher/sample path, bytes, SHA-256, exact argv, and timeout;
warmup outcome and, for every measured repetition, reported frame count, elapsed time, FPS, MP/s when supported, plus raw-log paths and hashes;
recomputed repetition count, mean, minimum, and maximum; and
branch status, limitations, and failed attempts.
Keep package listings, setup details, SDK sources, build trees, and media outside this file. Keep native and PyNvVideoCodec results separate.
Acceptance checklist
Apply every relevant check before accepting the result:
Reopen and rehash the input, recipe/config, and launcher immediately before the series and after it. Their paths must be canonical regular files, not symlinks, and their recorded byte counts and SHA-256 values must still match.
For raw encode input, match width, height, frame count, FPS, and pixel format to the recipe. Do not compare the raw input's codec with the recipe codec:
rawdescribes the source whileh264,hevc, orav1describes encoder output. Never relabel raw input to satisfy an output-codec field.Bind the launched native preset or Python
-jsonconfig to that recipe. In a comparison, recipes and launch arguments differ only in the requested knob; P4/P5 therefore differs only in preset.Use exactly the same checked argv for one excluded warmup and at least three sequential measured processes per variant. Apply the common 1,000-frame cap when a PyNv encode surface participates and report the effective aggregate frame count.
Each process exits zero without timeout or a failure marker, and its official sample marker reports the expected positive frame count and FPS. Wall time is not a substitute for sample-reported FPS.
Derive every retained metric from the raw logs. Require positive finite FPS; compute
MP/s = FPS * width * height / 1_000_000only when the sample binds the dimensions; recompute mean, minimum, and maximum from the measured runs.
If any check fails, name the check and offending values, reject that branch,
and retain independently completed peers as partial. A result whose metrics
cannot be recomputed from the retained raw logs is not complete.
Documentation estimates are not live benchmark evidence. They set
measurement_performed: false and follow
documented-performance-estimates.md.
A throughput result measures only the evidenced codec workload. It does not prove zero copy, shared-buffer compatibility, absence of copies, or a usable cross-stage synchronization primitive unless those properties were separately traced and authenticated.
Interpretation
Never pool native and Python repetitions or rank an unspecified
autorequest.Omit MP/s for the released Python decode sample because it does not report dimensions.
A preset comparison reports throughput only; it does not prove visual quality or compression ordering.
A worker sweep reports only the maximum passing tested codec-stage bound, not a camera or end-to-end capacity.
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Created by
- Vinit Bansal
- Publisher
- NVIDIA (Official)
- Distributed by
- NVIDIA
- Source
- NVIDIA/skills · skills/jetson-video-benchmark · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Vinit Bansal or NVIDIA.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download jetson-video-benchmark.zip