git clone --depth 1 https://github.com/NVIDIA/skills /tmp/doca-dpa-hl-tracer && cp -r /tmp/doca-dpa-hl-tracer/skills/doca-dpa-hl-tracer ~/.claude/skills/doca-dpa-hl-tracerSKILL.md
# DOCA DPA High-Level Tracer **Where to start:** This is a tool skill for invoking `doca_dpa_hl_tracer` — the documented host-side CLI that captures DPA-side execution traces in higher-level terms (DPA programming events: kernel entry / exit, sync points, comm primitive calls, RDMA WR submission, completions) rather than raw cycle counts. Open [`TASKS.md`](TASKS.md) and start at [`## configure`](TASKS.md#configure) for the mode-vs-overhead decision and the JSON config layout, then [`## run`](TASKS.md#run) for the capture → decode → render pipeline. Open [`CAPABILITIES.md`](CAPABILITIES.md) when the question is *what does this tool actually trace*, *which DPA programming events does it expose*, *what is the trace-overhead vs fidelity tradeoff*, or *how does it slot into a DPA debug loop alongside [`doca-dpa`](../../libs/doca-dpa/SKILL.md) and [`doca-debug`](../../doca-debug/SKILL.md)*. If DPA is not the right surface for the user's question (e.g. the bug is host-side, the bug is in the DPACC-produced image, the user wants raw cycle counts), the path-selection rule in [`CAPABILITIES.md ## Capabilities and modes`](CAPABILITIES.md#capabilities-and-modes) routes the agent before any capture is attempted. ## Example questions this skill answers well The CLASSES of `doca_dpa_hl_tracer` questions this skill is built to answer, each with one worked example. The class is the load-bearing piece; the worked example is one instance. - **"My DPA kernel is doing the wrong thing — where do I look?"** — worked example: *"my host-side `doca_dpa_kernel_launch_update_*` completes, but the kernel's reported result is wrong; no host-side `DOCA_ERROR_*`"*. Answered by the *when DPA-side high-level tracing is the right surface* gate in [`CAPABILITIES.md ## Capabilities and modes`](CAPABILITIES.md#capabilities-and-modes) + the capture → decode → render flow in [`TASKS.md ## run`](TASKS.md#run) + the *which DPA programming events to focus on* rule in [`TASKS.md ## debug`](TASKS.md#debug). - **"My DPA kernel is slow at a granularity that doesn't show up in cycle profiles — how do I see kernel-entry to first-comm-call latency?"** — worked example: *"my DPA kernel runs but the time between launch and the first RDMA WR submission is bigger than I expected"*. Answered by the event-taxonomy table in [`CAPABILITIES.md ## Capabilities and modes`](CAPABILITIES.md#capabilities-and-modes) + the iterative loop in [`TASKS.md ## test`](TASKS.md#test) which treats trace overhead, mode (`TRACE` vs `CRIT`), and capture window as axes to tune. - **"How do I capture a trace without burying the DPA in observation overhead?"** — worked example: *"`TRACE` mode is producing too much data and my measured DPA latency went up by 2x compared to without the tracer"*. Answered by the mode-vs-overhead tradeoff in [`CAPABILITIES.md ## Capabilities and modes`](CAPABILITIES.md#capabilities-and-modes) + the `CRIT`-first guidance in [`TASKS.md ## configure`](TASKS.md#configure) (start with critical-events-only; widen to `TRACE` only when the bug demands per-event detail). - **"My trace file got truncated mid-run — how should I configure the file-size limits?"** — worked example: *"binary trace file hit 5 GB and the capture stopped"*. Answered by the `log_file_max_size_in_bytes` / `bin_file_max_size_in_bytes` / `file_size_limit_policy` triple in [`CAPABILITIES.md ## Capabilities and modes`](CAPABILITIES.md#capabilities-and-modes) + the JSON config layout in [`TASKS.md ## configure`](TASKS.md#configure). - **"Is the tracer on my install, and is it paired with the matching `doca-dpa` library and DPACC compiler version?"** — worked example: *"is the tracer ABI on my install compatible with the DPA image my DPACC just produced?"*. Answered by the version-overlay in [`CAPABILITIES.md ## Version compatibility`](CAPABILITIES.md#version-compatibility), which redirects to the canonical [`doca-version`](../../doca-version/SKILL.md) chain and adds the *tracer ↔ `doca-dpa` library ↔ DPACC compiler* match rule. - **"The capture file looks empty / decode failed — is the install broken, no events fired, or am I tracing the wrong thing?"** — worked example: *"`doca_dpa_hl_tracer` ran, wrote a file, but the parser shows zero events"*. Answered by the layered error taxonomy in [`CAPABILITIES.md ## Error taxonomy`](CAPABILITIES.md#error-taxonomy) (install / device-binding / DPA-image-instrumented / capture-window / decode-vs-elf / overhead-saturated / version / cross-cutting) + the layered walk in [`TASKS.md ## debug`](TASKS.md#debug). ## Audience This skill serves **external developers, platform operators, and AI agents who have already brought up a DPA-side workload through [`doca-dpa`](../../libs/doca-dpa/SKILL.md) and now need higher-level visibility into what the DPA kernel is actually doing on the wire** — DPA programming events ordering, sync gaps, comm-call latencies, RDMA-WR / completion timing — without dropping all the way down to raw cycle counters. Concretely: - A DPA developer who can launch their kernel cleanly from the host side but whose kernel's *result* is wrong or whose *DPA-side performance* is below expectation, and who needs a DPA-side ground truth before triaging. - A platform operator running a DPA-using workload (RDMA offload from accelerator, custom CC algorithm via `doca-pcc`) and needs to localize a regression to the DPA side without instrumenting the application. - An AI agent producing a *DPA-side trace report* as evidence for the host-side [`doca-dpa TASKS.md ## debug`](../../libs/doca-dpa/TASKS.md#debug) ladder when the host side reports clean completions but the DPA-side behaviour is wrong. It is **not** for users debugging the tracer binary itself, **not** a substitute for the live public DOCA DPA Tools guide, **not** the right place for users learning how to write a DPA kernel (that audience b
>-
Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
|
|
Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.
Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.
Calibrate a new dataset from pre-recorded video files via the AutoMagicCalib REST API. Use when user has local MP4s and says 'calibrate my videos', 'run AMC on these videos', or similar. For RTSP/live streams, use amc-run-rtsp-calibration instead.
Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.