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Get Started Free →nvidia/deepstream-import-vision-model
Use this skill to bring any vision model from HuggingFace or NVIDIA NGC into an NVIDIA DeepStream pipeline with end-to-end automation: ONNX download, SafeTensors export, TRT engine build, custom nvinfer bbox parser, multi-stream benchmark, and PDF report. Object detection models only.
Passed 14 of 21 test cases with the skill, and 4 of 21 without it.
and 8 more in the full report
11 fixed4 passed either way7 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
Which sample video file should be selected as the baseline input when DS_VIDEO environment variable is not defined by the user?
| Checked | Without skill | With skill |
|---|---|---|
| Selects sample_720p.mp4 as the standard default video file path. | Missed | Passed |
The instructions your model reads when the skill loads.
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.
| 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.
Run before starting:
# 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
test cases passed, vs 4 / 21 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.