deepstream-generate-pipeline

DeepStream Pipeline Builder

Generate ready-to-run gst-launch-1.0 pipelines for NVIDIA DeepStream SDK by collecting pipeline requirements through an interactive questionnaire, then assembling the pipeline using a standalone BM25 retrieval backend with structural metadata boosting (similarity search over 270+ verified pipelines, zero external dependencies).

Prerequisites

• Python: 3.8+ (stdlib only — no pip packages required)
• DeepStream SDK: Installed at /opt/nvidia/deepstream/deepstream/ (for gst-inspect-1.0 validation and element verification)
• GStreamer: gst-launch-1.0 and gst-inspect-1.0 on PATH (installed with DeepStream)
• Platform: x86 dGPU (T4, A100, L40, RTX, etc.) or aarch64 — Jetson (Orin, Xavier, Nano) / SBSA (Grace, GH200)

Usage Examples

# Fully specified — skips most questions
detect and track on 4 rtsp streams and display on jetson

# Partially specified — asks remaining questions
give me a pipeline to infer on an image

# Minimal — asks all 7 questions
build a pipeline

Supported Configurations

Parameter	Options
Input	Local video (.mp4/.h264/.h265), local image (.jpg/.png), RTSP stream, USB camera, test pattern
Inference	None, primary (nvinfer), primary+secondary, with preprocessor, Triton (nvinferserver)
Tracker	None, NvDCF, IOU, NvSORT, DeepSORT
Sink	Display (dGPU/Jetson), save (JPG/PNG/MP4/H264), RTSP out, fakesink
Platform	x86 dGPU (T4, A100, L40, RTX, etc.) or aarch64 — Jetson (Orin, Xavier, Nano) / SBSA (Grace, GH200)
Extras	Resize, rotate/flip, crop, color format conversion

Scripts

Script	Purpose
scripts/generate_pipeline.py	BM25 retrieval engine — scores and ranks pipelines from data/data.csv. Supports --format {json,compact,summary} (default json)
scripts/validate_pipeline.py	4-stage validator: syntax, elements, properties, live parse. Supports --format {json,summary} (default json)
scripts/lint_data.py	Data quality linter for the pipeline CSV (--fix to auto-repair)

Workflow

Step 1 — Collect Pipeline Requirements

You MUST Read references/requirement-extraction.md before doing this step. It contains the query-inference table, compound-extraction examples, the full AskUserQuestion question bank (with the default-first ordering contract), the automatic-OSD and extras/flip-method rules, and the dynamic question-reduction examples that this step depends on. Apply them exactly.

Order of operations:

1. Infer everything you can from the query using the inference table in references/requirement-extraction.md. The goal is to identify which of the 7 parameters (input source, num sources, inference, tracker, sink, platform, extras) the user has already specified.
2. Ask the user about the unknowns via AskUserQuestion in a single call. Do not silently default tracker/sink/platform/extras — these are real choices the user should make explicitly (display vs save, no tracker vs NvDCF, x86 dGPU vs aarch64 Jetson/SBSA, etc.). Skip only the questions whose answer is already clear from the query.
3. Quote the inferred parameters back to the user in the lead-in to the question call so they can see what you already extracted. Example: "From your query I have: 3 mp4 videos, primary inference. Just need a few more details:"

Follow the inference table, question bank, and OSD/extras rules in references/requirement-extraction.md to decide which questions to ask and how to place transform elements, then proceed to Step 2.

Step 2 — Build the Natural Language Query

From the user's answers, construct a single descriptive query string. Follow this pattern:

Please provide a GStreamer pipeline that [operation] on [num_sources] [input_type] [input_detail] [tracker_detail] and [output_action] [platform_detail]

Examples of constructed queries:

User Selections	Constructed Query
Local video, 1 source, Primary detector, No tracker, Display, dGPU	"Please provide a GStreamer pipeline that performs primary inference on a single mp4 video and displays the output"
RTSP, 4 sources, Primary+Secondary, NvDCF, Save MP4, dGPU	"Please provide a GStreamer pipeline that performs primary and secondary inference with NvDCF tracker on 4 RTSP streams and saves output to MP4 file"
Local video, 2 sources, Primary with preprocessor, IOU, Display, Jetson	"Please provide a GStreamer pipeline that performs preprocessing before primary inference with IOU tracker on 2 mp4 streams and displays the output on Jetson"
Local image, 1 source, None, No tracker, Save file, dGPU, Rotate 90° cw	"Please provide a GStreamer pipeline that rotates a single jpg image 90° clockwise before processing and saves it to a file"
Local video, 3 sources, Primary detector, NvDCF, Save MP4, dGPU, Rotate 180°	"Please provide a GStreamer pipeline that rotates 3 mp4 videos 180° before primary inference with NvDCF tracker and saves output to MP4 file"

Step 3 — Run the Pipeline Generator Script

Execute the backend script with the constructed query and user parameters:

python3 <skill-path>/scripts/generate_pipeline.py \
  --query "<constructed_query>" \
  --source-type "<Local video file|Local image file|RTSP stream|USB camera|Test pattern>" \
  --num-sources <N> \
  --inference "<None|primary|primary+secondary|primary+preprocess|primary+secondary+preprocess|primary-triton|primary+secondary-triton>" \
  --tracker "<none|NvDCF|IOU|NvSORT|DeepSORT>" \
  --sink "<display|display-jetson|save-jpg|save-png|save-mp4|save-h264|rtsp-out|fakesink>" \
  --platform "<dGPU|Jetson|SBSA>" \
  --extras "<none|resize|rotate|crop|color-convert|osd>" \
  --format compact

Always pass --format compact. The compact mode returns only confidence + the top retrieved pipeline (~25 lines), instead of dumping all 10 retrievals as ~150 lines of JSON in the chat. The json mode (default for backward compat) is only useful when debugging the retriever directly. A summary mode (single human-readable line) also exists for non-Claude callers.

The script will (zero external dependencies — pure Python stdlib):

1. Load the pipeline dataset (270+ verified DeepStream pipelines)
2. Extract structural metadata from each pipeline (platform, source type, sink type, inference mode, tracker, stream count)
3. Score with BM25 (document-length-normalized) + domain-specific synonym expansion on both queries and documents
4. Apply structural boosting — results matching the user's platform/source/sink/inference get boosted, mismatches get penalized
5. Return the top-K results as JSON with a confidence field (high/medium/low) based on the top score
6. Claude uses these retrieved examples + the assembly rules below to construct the final pipeline

When confidence is low, rely more heavily on the assembly rules below rather than the retrieved examples.

Step 4 — Validate the Pipeline

Before presenting, run the validation script to catch syntax errors, unknown elements, and linking issues:

python3 <skill-path>/scripts/validate_pipeline.py "<assembled_pipeline>" --format summary

Always pass --format summary. Summary prints a single status line (e.g. valid · 11 elements · 0 warnings · live-parse skipped (multi-stream)), with errors/warnings indented underneath only if present. The default json mode emits ~40 lines of structured output and is only useful for programmatic callers.

The validator performs 4 checks:

1. Syntax check — unbalanced quotes, empty pipe segments, missing source/sink
2. Element check — verifies each element exists via gst-inspect-1.0
3. Property check — validates known properties for DeepStream elements
4. Live parse check — uses gst-launch-1.0 itself to construct the pipeline graph (with fakesrc/fakesink substituted), catching linking errors and pad mismatches. Automatically skipped for multi-stream pipelines (those with named pad refs like m.sink_0) since fakesrc cannot negotiate caps through named pads.

If validation fails ("valid": false), fix the errors and re-validate before presenting. Limit validation retries to a maximum of 2 attempts — if the pipeline still fails after 2 fixes, present it as-is (the remaining checks already cover syntax, element, property, and structural correctness). If there are only warnings, present the pipeline but mention the warnings to the user.

Step 5 — Present the Pipeline

5.1 — Output format (THE ONLY ACCEPTED FORM)

Your response must be exactly five blocks, in this order:

1. One-line status badge (validation + confidence)
2. Single bash code block containing the full gst-launch-1.0 -e … command with concrete absolute paths, on one line (no \ continuations, no shell variables, no shell wrapper)
3. Breakdown table grouped by stage
4. Suggestions bullet list
5. (only if pre-flight failed) a ⚠ line above the status badge stating which default path is missing

That is the ONLY accepted output shape for this step. The Section 5.3 template in references/output-format.md is the literal template — match it.

5.2 — Pre-flight check (run before composing the response)

Run one Bash ls over the default paths the pipeline will reference (sample video, PGIE config, tracker lib/config). The result tells you whether to mark the badge with ⚠ default path not found: <path> and bump the matching "Use your own …" suggestion to the top.

ls /opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h264.mp4 \
   /opt/nvidia/deepstream/deepstream/samples/configs/deepstream-app/config_infer_primary.txt \
   2>&1

5.3 / 5.4 — Worked example & forbidden anti-patterns

You MUST Read references/output-format.md before composing this response. It contains the literal Section 5.3 template your output must match exactly, and the Section 5.4 gallery of forbidden output shapes (heredoc wrappers, shell-var indirection, \ line-continuations, stray "Run it" lines, Write-to-script). Mirror Section 5.3; never emit any Section 5.4 form.

5.5 — Self-check before sending the response

Before you emit your reply, mentally tick each box. If any check fails, rewrite the response.

• The pipeline is on exactly one line inside a single ```bash code block.
• The pipeline begins with gst-launch-1.0 -e and contains only literal absolute paths (e.g. /opt/nvidia/deepstream/...) — no $VAR, no ${VAR:-default}, no cat >, no EOF, no \ line continuations.
• The response does not contain any of: cat > /tmp/pipeline.sh, bash /tmp/pipeline.sh, <<'EOF', ${VAR:-.
• The response does not call the Write tool. (Save-to-file is offered as a suggestion bullet, not an action.)
• The breakdown table is grouped by stage (Source / Mux / Inference / Tracking / Composition / Render — adapt names to the pipeline's actual stages, e.g. add an Encode/Mux row for file sinks).
• The "Save it to a script?" line appears in the Suggestions list — never as a primary action.

5.6 — Pre-flight failure variant

If the Section 5.2 ls reported one or more missing default paths, prepend a ⚠ line above the status badge and bump the matching "Use your own …" suggestion to the top:

⚠ default path not found: `/opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h264.mp4` — substitute your own video path before running
✓ Validated · 11 elements · 0 warnings · confidence: HIGH

```bash
gst-launch-1.0 -e filesrc location=/opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h264.mp4 ! …
```

[breakdown + suggestions as in Section 5.3, with the "Use your own video" suggestion bumped to the top]

On length: 5–8 stream pipelines run long when on a single line. That is correct and intended — chat clients render bash code blocks faithfully and copy reproduces them correctly. Long ≠ split.

Step 6 — Offer Refinement

After presenting the pipeline, ask the user if they want to adjust anything:

Want me to modify anything? For example:

• Change the number of streams
• Add/remove tracker or secondary inference
• Switch between display and file output
• Change the platform (x86 dGPU / aarch64 Jetson / SBSA)

If the user requests changes, go back to Step 2 with updated parameters — do NOT re-ask all 7 questions. Only ask about the specific parameter that changed, or just apply the change directly if it's clear.

Step 6.5 — Optional: Save Pipeline to a Script

Only do this step when the user explicitly asks (e.g. "save it", "save to pipeline.sh", "write it to a file", "put it in ~/run.sh"). Do not create the file proactively — Step 5 always shows the concrete pipeline in chat for direct copy-paste; saving is a follow-up convenience.

1. Filename: Default to /tmp/pipeline.sh if the user just says "save it". Use the exact path the user named otherwise (e.g. ~/run.sh, scripts/demo.sh).

2. File contents: Two lines — shebang + the same single-line pipeline shown in chat (concrete absolute paths, no shell vars). Keep them in sync — what the user runs from the file is bit-for-bit identical to what they could have copy-pasted.

   #!/usr/bin/env bash
   gst-launch-1.0 -e filesrc location=/opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h264.mp4 ! qtdemux ! h264parse ! nvv4l2decoder ! m.sink_0 … ! nvdsosd ! nveglglessink
   

   Use the Write tool to create the file.

3. Confirm to user with the run command:

   Saved to <path>. Run it with:

   bash <path>
   

---

Pipeline Assembly Rules

When the script is not available or fails, assemble the pipeline using the rules in references/assembly-rules.md. These rules cover source elements, multi-stream patterns, inference chains, tracker configs, sink elements, and extra operations. They also serve as validation for script output.

---

Error Handling

Failure	Cause	Recovery
generate_pipeline.py returns confidence: low	Query doesn't match any pipeline in the dataset closely	Rely on the assembly rules in this skill instead of retrieved examples
validate_pipeline.py reports unknown element	GStreamer/DeepStream not installed or not on PATH	Install DeepStream SDK; confirm gst-inspect-1.0 nvinfer works
Validation fails after 2 retries	Unusual element combination or linking issue	Present the pipeline as-is with a warning — syntax/element/property checks still passed
Script not found at <skill-path>/scripts/	Skill not installed correctly or path misconfigured	Verify the skill directory is symlinked into .claude/skills/ or .cursor/skills/

Testing

Run the test suite to verify retrieval quality and validator correctness:

python3 -m unittest discover -s <skill-path>/tests -v

The suite includes:

• Unit tests for the BM25 retriever (tokenizer, synonym expansion, metadata extraction, scoring)
• Unit tests for the validator (syntax, structure, property, named-pad checks)
• Golden regression tests — 20+ query→expected-result pairs ensuring retrieval quality doesn't regress
• Data quality linter — checks the CSV for duplicates, syntax issues, and structural bugs:

python3 <skill-path>/scripts/lint_data.py          # report issues
python3 <skill-path>/scripts/lint_data.py --fix     # auto-fix and overwrite

---

Security, Limitations & Notes

Security posture, known limitations, and operational notes are documented in references/security-and-limitations.md. Read that file when you need details on subprocess safety, input validation, platform/SDK requirements, the multi-stream dry-run caveat, or sample-path/config-file reminders.