DeepStream Import Vision Model

When this skill is active, read the relevant reference document before starting each phase. Do not rely on memory — reference documents contain exact script paths, bash variable conventions, log filename contracts, and critical parsing rules.

Current scope: Object detection models only. Fail fast on classification, segmentation, or other architectures detected in config.json.

Pipeline Overview

| Step | Phase | Reference | What it does | |------|-------|-----------|--------------| | 1–3 | Model Acquire | references/model-acquire.md | Browse HF/NGC, detect format, download ONNX or export SafeTensors | | 4–5 | Engine Build | references/engine-build.md | Build dynamic TRT engine, run trtexec BS=1 and BS=MAX_BS | | 6–7 | DS Pipeline | references/pipeline-run.md | Custom bbox parser, nvinfer config, single-stream + multi-stream benchmarks | | 8 | Report | references/report-generation.md | 5 charts, HTML, PDF benchmark report |

Run the full pipeline autonomously without pausing for confirmation at each step.

Pre-flight Checks

Run before starting:

bash
# 1. GPU and drivers
nvidia-smi

# 2. TensorRT version match (must match between builder and DS runtime)
trtexec 2>&1 | head -3
dpkg -l | grep libnvinfer-bin

# 3. Shared Python venv — create once, reuse across all models
mkdir -p build
VENV=build/.venv_optimum
if [ ! -x "$VENV/bin/python3" ]; then
  python3 -m venv "$VENV"
  "$VENV/bin/pip" install --upgrade pip -q
  "$VENV/bin/pip" install "optimum[exporters]>=1.20,<2.0" "torch<2.12" \
    transformers onnxruntime matplotlib numpy markdown -q
fi

# 4. System tools
which wkhtmltopdf || apt-get install -y wkhtmltopdf
which mediainfo    || apt-get install -y mediainfo
which deepstream-app  # required for KITTI dump (Step 6g) and benchmark perf-measurement (Step 7c); shipped with DeepStream SDK

# 5. Sample video — only check default path when user has not provided a custom DS_VIDEO
if [ -z "$DS_VIDEO" ]; then
  [ -f /opt/nvidia/deepstream/deepstream/samples/streams/sample_720p.mp4 ] || \
    echo "WARNING: sample_720p.mp4 not found. Install DeepStream samples or set DS_VIDEO=/path/to/your.mp4"
fi

Mandatory Output Structure

Create once MODEL_NAME is known (Step 1). Never dump files flat.

models/{model_name}/
  model/           <- ONNX file(s)
  parser/          <- .cpp, Makefile, .so
  config/          <- nvinfer config, ds-app config, labels.txt
  scripts/         <- run helper scripts
  benchmarks/
    engines/       <- _dynamic_b{MAX_BS}.engine, timing.cache, build logs
    b1/            <- trtexec BS=1 log
    b{MAX_BS}/     <- trtexec BS=MAX_BS log
    ds/            <- DS benchmark logs
  reports/         <- benchmark_report.md, .html, .pdf, benchmark_data.json
    charts/        <- chart_*.png (5 charts)
  samples/         <- output .mp4 or .ogv (theoraenc fallback), test frames
    kitti_output/  <- KITTI detection .txt files

bash
mkdir -p models/$MODEL_NAME/{model,parser,config,scripts,benchmarks/engines,benchmarks/ds,reports/charts,samples/kitti_output}

Critical Rules

1. Engine naming — always {model}_dynamic_b{MAX_BS}.engine. Never bare model_dynamic.engine.
2. batch_size == num_streams — in DS runs, batch-size and stream count are always equal.
3. Log filenames are fixed — trtexec_b1.log, trtexec_b${MAX_BS}.log, ds_s${N}_run1.log, ds_s${N}_run2.log. No timestamps. Report generation reads exact paths.
4. Parser zero-init — always NvDsInferObjectDetectionInfo obj = {};. Required for DeepStream OBB support; bare obj; leaves rotation_angle uninitialized, causing tilted bounding boxes.
5. KITTI validation gate — do NOT proceed to Step 7 if KITTI frame count is zero or detection rate < 90%.
6. Shared venv — build/.venv_optimum reused across all models. Never create per-model venvs.
7. trtexec --noDataTransfers — GPU-only compute matches DeepStream's GPU-to-GPU data flow.
8. Report HTML+PDF — always use skills/deepstream-import-vision-model/scripts/report/md-to-html-pdf.py. Never write a custom HTML generator or call wkhtmltopdf directly.
9. Object detection only — reject non-detection architectures from config.json before building anything.
10. Encoder fallback (MANDATORY) — x264enc and openh264enc are prohibited. On NVENC-unavailable systems, use theoraenc + oggmux (LGPL; ships in gst-plugins-base; output is .ogv). If theoraenc/oggmux are absent, skip video creation (DS_SINGLE_STREAM_MODE=skipped). Report which mode was used: nvv4l2h264enc / theoraenc-fallback / skipped.
11. Video source (MANDATORY) — default is always sample_720p.mp4 (1280×720). Never autonomously substitute sample_1080p_h264.mp4 or any other file. Only use a different video when the user explicitly provides a path (via DS_VIDEO env var or script argument).

Pipeline Timing

Wrap every step:

bash
STEP_START=$(date +%s.%N)
# ... step commands ...
STEP_END=$(date +%s.%N)
STEP_DURATION=$(echo "$STEP_END - $STEP_START" | bc)
echo "[Step N] completed in ${STEP_DURATION}s"

Track PIPELINE_START (before Step 1) and PIPELINE_END (after Step 8). Report all durations in the benchmark report.

Report Output (MANDATORY — all 3 formats)

1. benchmark_report.md — markdown source (12 mandatory sections)
2. benchmark_report.html — styled HTML (charts base64-inlined, no local file access)
3. benchmark_report_{model_name}.pdf — via md-to-html-pdf.py; verify charts are embedded by counting data:image/png occurrences in the HTML output: grep -o 'data:image/png' benchmark_report.html | wc -l should equal 5

Run charts and report scripts with the shared venv active: source build/.venv_optimum/bin/activate.

Reference Documents

IMPORTANT: Read the relevant reference before starting each phase. Do NOT generate code from memory.

| Document | Use When | |----------|----------| | references/model-acquire.md | Steps 1–3: HF/NGC URL parsing, format detection, ONNX download, SafeTensors export, label extraction | | references/engine-build.md | Steps 4–5: trtexec engine build, benchmarks, PEAK_GPU_STREAMS derivation, iterative scaling | | references/pipeline-run.md | Steps 6–7: custom bbox parser, nvinfer config, single-stream validation, KITTI dump, multi-stream benchmark | | references/report-generation.md | Step 8: benchmark_data.json, 5 charts, 12-section markdown report, HTML + PDF |

Scripts

Located in scripts/.

| Script | Phase | Purpose | |--------|-------|---------| | model/hf-list-files.sh | 1–3 | List HuggingFace repo files | | model/hf-download-config.sh | 1–3 | Download config.json from HF | | model/ngc-list-files.sh | 1–3 | List NGC model files | | model/ngc-download.sh | 1–3 | Download NGC model archive | | model/safetensors-to-onnx.sh | 1–3 | Export SafeTensors → ONNX via optimum-cli | | model/inspect-onnx.py | 1–5 | Inspect ONNX input/output shapes | | model/make-static-batch-onnx.py | 4–5 | Bake batch dim into ONNX | | model/cleanup.sh | Any | Remove staging dirs, preserve shared venv | | engine/benchmark-trtexec.sh | 4–5 | Run trtexec with standard flags | | deepstream/ds-single-stream.sh | 6–7 | Single-stream visual validation (NVENC primary; theoraenc+oggmux fallback; skip if neither) | | deepstream/ds-sweep.sh | 6–7 | 2-phase batch size sweep | | deepstream/benchmark-ds.sh | 6–7 | Fixed-stream DS benchmark | | deepstream/ds-kitti-dump.sh | 6–7 | KITTI detection dump via deepstream-app | | deepstream/ds-perf-run.sh | 7 | Step 7c two-run benchmark — wraps deepstream-app with enable-perf-measurement=1, writes fixed-name log for the report parser | | deepstream/extract-frame.sh | 6–7 | Extract sample frames from output video (.mp4 NVENC path or .ogv theoraenc fallback) | | report/generate-benchmark-charts.py | 8 | Generate 5 benchmark PNG charts | | report/md-to-html-pdf.py | 8 | Markdown → styled HTML → PDF (canonical benchmark report path) | | report/md-to-pdf.sh | Any | Markdown → PDF via pandoc/pdflatex — for design docs and references only, NOT for benchmark reports (use md-to-html-pdf.py for those) | | report/report-style.css | 8 | CSS for HTML report | | report/render-mermaid-for-pdf.py | 8 | Mermaid diagram → PNG | | report/mermaid-puppeteer.json | 8 | Vetted Puppeteer config for Mermaid (sandboxed; non-root) | | report/mermaid-puppeteer-root.json | 8 | Vetted Puppeteer config for Mermaid (used when running as root) |

Quick Error Reference

| Error | Fix | |-------|-----| | Tilted/diagonal bounding boxes | Parser struct not zero-initialized — use NvDsInferObjectDetectionInfo obj = {}; | | Zero KITTI files | gie-kitti-output-dir not read by nvinfer — use ds-kitti-dump.sh (wraps deepstream-app) | | Engine rebuilds every DS run | model-engine-file path wrong — check relative path from config/ dir | | setDimensions negative dims | Add infer-dims=3;H;W to nvinfer config for dynamic ONNX models | | --memPoolSize workspace 0.03 MiB | Use M suffix not MiB — e.g. --memPoolSize=workspace:32768M | | ForeignNode build failure (DETR) | Use dynamo export path or run onnxsim — see references/engine-build.md | | Zero detections | Wrong net-scale-factor — check model family table in references/pipeline-run.md | | No module named 'pyservicemaker' | Install into venv: pip install /opt/nvidia/deepstream/.../pyservicemaker*.whl |

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