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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use this skill when the user is doing hands-on DOCA Telemetry Exporter programming on a host where DOCA is installed — defining a doca_telemetry_exporter_schema and event types, creating sources, picking a publish surface (typed events / opaque events / the metrics counter-gauge-histogram API / OTLP logs / NetFlow), walking the schema-then-source lifecycle, or debugging DOCA_ERROR_* failures from the exporter API. Trigger even when the user does not explicitly mention "DOCA Telemetry Exporter" or "doca_telemetry_exporter_*" — typical implicit phrasings include "publishing counters from my DOCA app", "BAD_STATE when I report an event", "consumer/DTS sees nothing but my report succeeded", "how do I export NetFlow/IPFIX records", or "should I link the exporter or the telemetry service". Refuse and route elsewhere for the receiving DOCA Telemetry Service (DTS), plain stdout logging via doca_log, or real-time event subscription back into the app via doca-comch — those belong to other skills.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Follow Loading order below. Open the tasks companion for configure / publish / debug. Open the capabilities companion for publish surfaces. Do not open every companion file.
Where to start: This skill assumes DOCA is already installed and
the user is doing hands-on telemetry-exporter work — emitting
structured application telemetry (counters / events) from a
DOCA-using program to an external consumer. Open
the tasks companion if the user wants to do something (configure
/ build / modify + rebuild / run / test / debug); open
the capabilities companion when the question is what can
the exporter express on this install. If the user has not installed
DOCA yet, route to doca-setup/SKILL.md first.
If the user is confused about whether they want this library or the
DOCA Telemetry Service (the receiver) — read the
exporter-vs-service rule in
the capabilities companion
before configuring anything.
This library is NOT a DOCA Core context. There is no
doca_ctx_start()for the exporter and no per-doca_devinfocapability-query family (itsdoca_capsdump is a stub). The lifecycle isschema_init→ configure exporters → register type(s) →schema_start→source_create→source_start→ report → flush → destroy.
The CLASSES of telemetry-exporter questions this skill is built to answer, each with one worked example. The agent should treat the class as the load-bearing piece — the worked example is a single instance.
doca-telemetry-exporter as the publisher the application links
and route the receiving / consuming side away from this skill.packets_processed event record from my
DOCA Flow application". Answered by the schema → source
lifecycle in
telemetry_export/ sample._metrics_add_counter /
_add_gauge — and the telemetry_export_metrics/ sample), plus
the sample map in ## modify.DOCA_ERROR_BAD_STATE — what did I
get wrong?" — worked example:
"doca_telemetry_exporter_source_report returns BAD_STATE on
the first call". Answered by the BAD_STATE row in
DOCA_ERROR_AGAIN and NO DOCA_ERROR_NOT_FOUND on this API.check_ipc_status steps inCONNECTED and the receiver
is up).doca-version/SKILL.md) plus the honest
introspection rule in
doca_telemetry_exporter_check_ipc_status, not a device
cap-query).This skill serves external developers building applications that
emit structured telemetry through DOCA Telemetry Exporter — i.e.,
users whose application code calls doca_telemetry_exporter_*
(directly in C/C++, or through FFI/bindings from another language)
to publish counters, gauges, and events from their DOCA-using
program to an external telemetry consumer. It is not for NVIDIA
developers contributing to DOCA Telemetry Exporter itself, and it
is not for users building the receiving / aggregating telemetry
service (the DOCA Telemetry Service is a separate DOCA service with
its own public guide, reached via
doca-public-knowledge-map/SKILL.md).
Language scope. DOCA Telemetry Exporter ships as a C library
with pkg-config module name doca-telemetry-exporter. The
shipped samples are written in C. C and C++ consumers are the
canonical case; the worked examples in the tasks companion assume that path.
Other-language consumers (Rust, Go, Python, …) consume the same
*.so through FFI or language-specific bindings; the skill's
contribution in that case is to keep the exporter-vs-service
distinction, the schema → source lifecycle, the transport-not-caps
discovery rule, the same-user-as-the-app permission rule, the
buffered flush-based delivery model, and the error-taxonomy
guidance language-neutral, and to route the agent to the public C
ABI as the authoritative surface that any wrapper will eventually
call.
Load this skill when the user is doing hands-on DOCA Telemetry Exporter work, in any language. Concretely:
doca_telemetry_exporter_schema for the events the
application will emit (field names + field types), and
registering it with the exporter BEFORE any event is published.doca_telemetry_exporter_source instances
to represent distinct logical sources of telemetry inside the
application (e.g. one source per worker thread / per pipeline
stage)._source_report), opaque events (_source_opaque_report), the
Metrics API (counter / gauge / histogram), OTLP logs, or the
NetFlow sibling API — for what the application reports.doca_devinfo cap-query family and NO doca_caps data for
this library): doca_telemetry_exporter_check_ipc_status for
IPC liveness, _source_get_opaque_report_max_data_size for the
opaque payload bound, and the _schema_get_* config getters.DOCA_ERROR_* returned from an exporter call
(BAD_STATE lifecycle-order vs. INVALID_VALUE type/label
mismatch vs. NO_MEMORY vs. INITIALIZATION vs. UNKNOWN
backend) and the per-call status returned to the application.doca_log when stdout / structured-log shipping is enough; a
Prometheus client library when the user needs a non-DOCA-aware
sink; doca-comch/SKILL.md when the user
needs a real-time event subscription back INTO the app — the
exporter is publish-only / one-way).Do not load this skill for general DOCA orientation, install
of DOCA itself, the receiving telemetry service (the DOCA
Telemetry Service has its own public guide reachable through
doca-public-knowledge-map/SKILL.md),
or non-exporter library questions. For those, use
doca-public-knowledge-map/SKILL.md.
This is a thin loader. The body keeps only the orientation needed to pick the right next file. The substantive exporter-specific material lives in two companion files:
doca_telemetry_exporter_schema → _type/_field →
_source with the schema → source lifecycle), the four publish
surfaces (typed events / opaque events / Metrics API / OTLP
logs) plus the NetFlow sibling API, the transport-not-caps
introspection rule (check_ipc_status,
_get_opaque_report_max_data_size, _schema_get_* — NO
doca_caps data, NO device cap-query), the exporter error
taxonomy (mapped onto the cross-library DOCA_ERROR_* set, with
the note that there is NO AGAIN and NO NOT_FOUND on this
surface), the observability surface (per-call status + IPC
status + file-write inspection + the receiver side as the
end-to-end signal), the safety policy that gates the
same-user-as-the-app permission and the receiver-up-first
staging, and the path-selection rule against doca_log and
doca-comch.configure, build, modify (followed by a
rebuild), run, test, debug. Plus a Deferred task verbs block that points
out-of-scope questions at the right next skill.The skill assumes a host where DOCA is already installed at the
standard location, the application runs as a user that can write
to the telemetry transport the exporter is configured for, and a
receiving telemetry consumer is reachable and started before the
exporter. It does not cover installing DOCA — that path goes
through doca-setup/SKILL.md — and it does
not cover configuring / operating the receiving telemetry service,
which is a separate DOCA service with its own public guide.
This skill is agent guidance, not a samples or templates bundle. To keep the boundary clean, it deliberately does not contain — and pull requests should not add:
/opt/mellanox/doca/samples/doca_telemetry_exporter/. The
agent's job is to route the user to those files and prescribe a
minimum-diff modification on them via the universal
modify-a-sample workflow in
doca-programming-guide/SKILL.md,
layered with the exporter-specific overrides in
doca-public-knowledge-map/SKILL.md.
This skill is about the publisher side only.meson.build, CMakeLists.txt,
Cargo.toml, …) parked inside the skill. The agent constructs
the build manifest in the user's project directory against
the user's installed DOCA, where pkg-config --modversion doca-telemetry-exporter is the source of truth.samples/, bindings/, or reference/ subtree of any
kind. A mock or incomplete artifact in this skill's tree, even
one labeled "reference", is misleading: users will read it as
buildable.AGAIN, no NOT_FOUND), observability, the safety
policy, and the path-selection rule against doca_log / comch,
see the capabilities companion.Both companion files cross-link to each other,
doca-version/SKILL.md for the canonical
version-handling rules, and
doca-public-knowledge-map/SKILL.md
whenever the right answer is "look it up in the public docs or
the installed package layout" rather than "exporter-specific
guidance".
doca-public-knowledge-map/SKILL.md —
the routing table for every public DOCA documentation source
and the on-disk layout of an installed DOCA package. The
exporter's public guide URL is
https://docs.nvidia.com/doca/sdk/DOCA-Telemetry-Exporter/index.html;
the on-disk samples live under
/opt/mellanox/doca/samples/doca_telemetry_exporter/. The
DOCA Telemetry Service (the receiver, out of scope here) is
a separate guide reachable through that same routing table.doca-setup/SKILL.md — env preparation,
install verification, transport-side reachability checks, and
the I have no install yet path with the public NGC DOCA
container. This skill assumes its preconditions are satisfied
(in particular, the application user can write to the
telemetry transport).doca-version/SKILL.md — canonical DOCA
version-handling rules. This skill's ## Version compatibility
cross-links the four-way match rule + detection chain and adds
the exporter-specific overlay rules.doca-structured-tools-contract/SKILL.md —
the bundle's structured-tools precedence rule (detect / prefer
/ fall back / report). The Command appendix in
the tasks companion honors this contract.doca-programming-guide/SKILL.md —
general DOCA programming patterns shared by every library: the
canonical pkg-config + meson build pattern, the universal
modify-a-shipped-sample first-app workflow, the universal
lifecycle, the cross-library DOCA_ERROR_* taxonomy, and the
program-side debug order. This skill layers exporter specifics
on top.doca-comch/SKILL.md — the right primitive
when the user needs a real-time event subscription back INTO
the application (host ↔ DPU control-plane messaging). The
exporter is publish-only / one-way; this skill's
path-selection rule routes there when subscription is the
actual requirement.doca-debug/SKILL.md — the cross-cutting
debug ladder (install / version / build / link / runtime /
program / driver). Exporter-specific debug (receiver not up,
lifecycle-order BAD_STATE, type/label INVALID_VALUE,
opaque-path-not-enabled) overlays on top of that ladder.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Customize NVIDIA Nemotron Voice Agent's Generic Pipecat example for healthcare appointment, five-field patient intake, or custom tool-calling workflows without a separate backend.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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'.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Calibrates pre-recorded `cam_*.mp4` datasets through the AutoMagicCalib REST API. Use for user-supplied local MP4s; route live RTSP streams to `amc-run-rtsp-calibration`.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.