Tests

Unit tests for scripts/capacity_report.py. They do not require a GPU or DeepStream — pure-Python checks of the parser, classifier, and capacity-derivation logic.

Run

See README.md in the skill root for the standard test command.

What's covered

19 tests grouped by TestCase class:

Class	Test	Behaviour
`TestParseMicrobench`	`test_current_schema`	Parses `B,fps_aggregate,fps_per_stream` rows correctly.
	`test_legacy_schema`	Falls back to the legacy `B,fps_per_batch,fps_aggregate` columns (treats `fps_per_batch` as the aggregate, since the legacy script emitted aggregate fps under that name).
	`test_skips_garbage_rows`	Ignores rows with non-numeric values without raising.
`TestParseDmon`	`test_extracts_maxes`	Extracts max(SM%), max(mem%), max(dec%) from a `nvidia-smi dmon -s mu` log.
	`test_filters_by_gpu_id`	When `gpu_id` is given, only rows for that GPU index are aggregated.
	`test_handles_empty`	Empty / header-only logs return zeros without raising.
`TestClassifyBound`	`test_decode_strong`	B=1→B=2 fps ratio < 0.55 + NVDEC peak ≥ 90% ⇒ DECODE_BOUND, high confidence.
	`test_compute_bound`	Per-batch FPS plateaus immediately + SM% high + DRAM% low ⇒ COMPUTE_BOUND.
	`test_memory_bw_bound`	DRAM% high with SM% low ⇒ MEMORY_BW_BOUND.
	`test_inconclusive`	No strong signals ⇒ INCONCLUSIVE.
	`test_zero_fps_no_division_error`	Microbench rows with `fps_aggregate=0` don't crash the classifier with `ZeroDivisionError`.
	`test_zero_ceiling_fps_no_division_error`	Highest-B row with fps=0 skips the plateau-flatness check rather than dividing by zero.
`TestComputeCapacity`	`test_measurement_is_authoritative`	`n_overall = peak_measured // target_fps` regardless of the theoretical NVDEC-table value; theoretical is reported alongside as a sanity check.
	`test_unknown_gpu_uses_safe_defaults`	When `nvidia-smi` is unavailable, fall back to NVDEC=2, ada_hopper_blackwell arch.
`TestArchBucketLookup`	`test_modern_caps`	Compute cap ≥ 8 → `ada_hopper_blackwell`.
	`test_old_caps`	Compute cap < 8 → `turing_or_older`.
	`test_unknown_defaults_to_modern`	Empty / non-numeric input → `ada_hopper_blackwell` (safe default).
`TestNvdecCountLookup`	`test_known_gpus`	Substring match on GPU name returns the correct NVDEC count for known cards (T4, A6000, A40, A100, V100, L4, L40S, H100, Orin). Specific names listed before generic ones (`L40S` before `L4`, `A100` before `A10`).
	`test_unknown_gpu_safe_default`	Unknown GPU name falls back to NVDEC=2.

When tests fail

The classifier returning the wrong bound type is usually the most informative failure — review references/boundedness-rules.md for whether the trigger thresholds need adjusting, or whether the test's input represents an edge case that the rules haven't yet codified.

Third-party checks

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The deepstream-profile-pipeline skill is a legitimate development tool for profiling and optimizing video analytics pipelines. It uses standard NVIDIA profiling utilities and follows best practices for hardware-aware configuration derivation without any detected malicious patterns or security risks.
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Capability

owner: NVIDIA CORPORATION
service: deepstream
version: 0.1.0

Other metadata

metadata: { "author": "NVIDIA CORPORATION", "tags": [ "deepstream", "profiling", "nsight-systems", "nvtx", "nvidia-smi", "benchmarking" ], "languages": [ "bash", "python", "yaml" ], "domain": "video-analytics", "team": "deepstream-sdk" }
reviewed: 2026-04-24
compatibility: DeepStream SDK 9.0 on Ubuntu 22.04 or 24.04, run from the `nvcr.io/nvidia/deepstream:9.0-triton-multiarch` container (the dev image; the slimmer `samples-multiarch` variant strips the nsys NVTX injector and produces empty per-plugin NVTX traces — do not use it for profiling). Requires `nsys` (Nsight Systems 2024+) and `nvidia-smi` on PATH. No GUI dependency — the skill runs fully headless and uses only `nsys profile` + `nsys stats`.
data_classification: internal

/deepstream-profile-pipeline

testsREADME.md