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.
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Use this skill to read DOCA hardware-counter events from a `doca_dev` through the per-domain Telemetry reader libraries: `doca_telemetry_pcc`, `_dpa`, `_diag`, `_adp_retx`, `_phy`, and `_pci`. It covers capability checks, context creation, startup, and per-domain reads or samples. Trigger for implicit requests such as "read PCC counters from my BlueField app", "sample DPA counter exports", or "expose PHY, PCI, or DIAG counters from this doca_dev". This is the counter-reader surface, not a NetFlow, IPFIX, or local-socket collector. Route publishing and export to `doca-telemetry-exporter`; route the deployed DOCA Telemetry Service to `doca-dts`, and collectors and plain stdout logging elsewhere.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
"Build a second program that RECEIVES the events my exporter app
publishes." No DOCA library consumes exporter events, and this skill
is not that library: doca-telemetry reads a device's hardware
counters. The app links doca-telemetry-exporter to publish; the
receiver is the DOCA Telemetry Service over IPC (doca-dts), or the
exporter's file output for a local smoke. Do not invent a consume or
collector API. Then: the publishing app owns the schema; check the IPC
path with doca_telemetry_exporter_check_ipc_status; smoke one event
with field values intact (file output) before bulk; when reporting
cannot keep up, drop or aggregate on the hot path, never block.
"Read PCC counters from my BlueField app." — this is in
scope. Use the per-domain Telemetry reader; do not stand up
DTS or an exporter here. Sequence: doca_telemetry_pcc_cap_is_supported
on the device, create the PCC context on the open doca_dev
(doca_telemetry_pcc_rep_create for a representor), start it, read
once with doca_telemetry_pcc_get_counters, then stop and destroy.
Reads before start return DOCA_ERROR_BAD_STATE.
"Sample DPA counter exports." — stay on
doca_telemetry_dpa reads. Publishing those samples belongs
to the exporter skill.
Follow Loading order below. Open the tasks companion for configure / read / debug. Open the capabilities companion for the per-domain readers. Do not open every companion file.
Where to start: This skill assumes DOCA is already installed
and the user is doing hands-on hardware-counter-reader work —
opening a per-domain doca_telemetry_<domain> context against a
doca_dev and reading the latest hardware-counter snapshot for
that domain. The library is the counter-READER half of DOCA
telemetry; it is NOT a NetFlow / IPFIX collector and NOT a
generic schema-event consumer (the bundle previously framed it
that way and that framing was wrong — there is no NetFlow /
IPFIX / local-socket transport surface in the public header).
Open the tasks companion if the user wants to do something
(configure / build / modify / run / test / debug); open
the capabilities companion when the question is which
hardware-counter domains can this device read on this install
(PCC, DPA, DIAG, ADP_RETX, PHY, PCI). 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 (HW-counter
reader on a doca_dev) or
doca-telemetry-exporter/SKILL.md
(the publisher / export side, which is a separate library and
publishes structured telemetry / labeled metrics / OTLP logs),
read the reader-vs-exporter role split in
the capabilities companion
BEFORE configuring anything; mixing the two is the load-bearing
first-app failure for this skill. If the user is asking about
DOCA Telemetry Service (DTS) as deployed, route to doca-dts.
This skill serves external developers building applications
that READ DOCA hardware counters from a doca_dev through one
or more of the six per-domain DOCA Telemetry reader libraries
(doca_telemetry_pcc / _dpa / _diag / _adp_retx / _phy /
_pci) — i.e., users whose application code calls
doca_telemetry_<domain>_* (directly in C/C++, or through FFI /
bindings from another language) to open a per-domain context on
a doca_dev, configure the per-domain sample window, and read
the hardware-counter snapshot for that domain. It is not for
NVIDIA developers contributing to DOCA Telemetry itself, and it
is not for users writing the publishing / export side —
that is
doca-telemetry-exporter/SKILL.md,
a separate library and a separate skill.
Language scope. DOCA Telemetry's per-domain reader libraries
ship as a C surface with pkg-config module name
doca-telemetry. The shipped samples are written in C. C and
C++ readers are the canonical case; the worked examples in
the tasks companion assume that path. Other-language readers (Rust, Go,
Python, …) consume the same *.so through FFI or
language-specific bindings; the skill's contribution in that
case is to keep the reader-vs-exporter distinction, the per-
domain cap-query-first discipline, the per-domain DOCA Core
lifecycle on the doca_dev, the sample-window discipline, and
the error-taxonomy guidance language-neutral, and to route the
agent to the public per-domain C ABI as the authoritative
surface that any wrapper will eventually call.
Load this skill when the user is doing hands-on DOCA Telemetry hardware-counter-reader work, in any language. Concretely:
doca_telemetry_pcc.h for Programmable Congestion
Control counters, _dpa.h for DPA counters, _diag.h for
generic device diagnostic counters, _adp_retx.h for ADP
retransmit counters, _phy.h for physical-layer counters,
_pci.h for PCI / PCIe counters) and confirming the device
supports it via the per-domain _cap_is_supported(devinfo)
query — except _pci, which has no single _cap_is_supported
and instead exposes per-feature caps like
doca_telemetry_pci_cap_management_info_is_supported /
_cap_perf_counters_1_is_supported.doca_telemetry_<domain> context on a
doca_dev, walking the per-domain lifecycle
(doca_telemetry_<domain>_create(dev) → per-domain setters →
doca_telemetry_<domain>_start), configuring the per-domain
sample window, and reading the hardware-counter snapshot for
that domain. Note this is a per-domain _create/_start
surface, not the generic doca_ctx_* progress-engine
lifecycle.doca_telemetry_<domain>_cap_is_supported only for pcc,
dpa, diag, adp_retx, and phy; use the matching
per-feature doca_telemetry_pci_cap_*_is_supported query for
PCI.DOCA_ERROR_* returns from a counter
read (lifecycle vs. device-doesn't-support-this-domain vs.
per-domain AGAIN-means-snapshot-not-ready vs. permission /
driver) and the per-read status reported back to the
application.doca-telemetry-exporter/SKILL.md
when the user actually wants to PUBLISH / EXPORT the counter
values (OTLP / Prometheus / labeled metrics);
doca-log when plain structured stdout logging is enough; a
generic Prometheus / OpenTelemetry client library when the
counter source is a non-DOCA program; the DOCA Telemetry
Service (doca-dts) when the user wants a turnkey aggregator.doca_dev lifecycle, the sample-window
discipline, and the error rules the wrapper must honor.Do not load this skill for general DOCA orientation, install
of DOCA itself, the publishing / export side
(doca-telemetry-exporter/SKILL.md
has its own skill), the DOCA Telemetry Service (doca-dts), or
non-reader 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 hardware-counter-reader material lives in two companion files:
doca_telemetry_pcc / _dpa /
_diag / _adp_retx / _phy / _pci) and which counter
family each one exposes, the per-domain DOCA Core lifecycle
on a doca_dev, the domain-level capability query for PCC /
DPA / DIAG / ADP_RETX / PHY and the per-feature capability
queries for PCI, the
reader error taxonomy (mapped onto the cross-library
DOCA_ERROR_* set, with the NOT_SUPPORTED-means-domain-
not-exposed-on-this-device rule and the AGAIN-means-
snapshot-not-ready rule called out explicitly), the
observability surface (per-read status + per-domain cap-
query snapshot at configure time), the safety policy that
gates per-domain reads behind the cap-query result, and the
path-selection rule against
doca-telemetry-exporter/SKILL.md,
doca-log, and standalone Prometheus / OpenTelemetry / DTS.configure, build, modify, 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 and a target BlueField DPU or ConnectX NIC
is available. The ## run workflow
opens the corresponding doca_dev and requires the per-domain
cap-query to return DOCA_SUCCESS. It does not cover installing
DOCA — that path goes through
doca-setup/SKILL.md — and it does not
cover writing the publishing / export side, which is
doca-telemetry-exporter/SKILL.md.
doca_dev — not PUBLISH counters, which is
doca-telemetry-exporter/SKILL.md;
not deploy DTS, which is doca-dts; and not stand up a
NetFlow / IPFIX collector, which this library does not
expose a surface for).doca_dev, the per-domain capability query, the error
taxonomy (including the NOT_SUPPORTED-means-domain-
not-exposed-on-this-device rule and the AGAIN-means-
snapshot-not-ready rule), observability, the safety
policy, and the path-selection rule, 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 "reader-specific
guidance".
doca-telemetry-exporter/SKILL.md —
the publishing / export side. This skill (the per-domain
hardware-counter reader) and doca-telemetry-exporter (the
application-side labeled-metrics / OTLP-logs publisher) are
two separate libraries that solve two different problems —
they do NOT form a publisher/collector pair (there is no
collector transport surface here). An application that
wants to EMIT counters to its monitoring pipeline links the
exporter; an application that wants to READ hardware
counters off a doca_dev links this skill. Wiring the
wrong library into the user's app is the #1 first-app
failure for both skills, and the load-bearing rule the
agent must surface before any code-level 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
per-domain reader's public guide URL is
https://docs.nvidia.com/doca/sdk/DOCA-Telemetry/index.html
(distinct from the publisher's guide at
https://docs.nvidia.com/doca/sdk/DOCA-Telemetry-Exporter/index.html);
the on-disk samples live under
/opt/mellanox/doca/samples/doca_telemetry/. The DOCA
Telemetry Service (DTS) has its own skill, doca-dts.doca-setup/SKILL.md — env preparation,
install verification, and the I have no install yet path
with the public NGC DOCA container. This skill assumes its
preconditions are satisfied (in particular, a doca_dev is
open and doca_telemetry_<domain>_cap_is_supported(devinfo)
returns DOCA_SUCCESS for the per-domain library the user
wants to read).doca-version/SKILL.md — canonical
DOCA version-handling rules. This skill's Version
compatibility overlay cross-links the four-way match rule
plus detection chain and adds the per-domain reader overlay
(each per-domain library's counter set can grow across DOCA
releases; cap-query first, then read).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 per-domain reader specifics on top.doca-log — the right primitive
when the user actually wanted plain structured stdout
logging from their own program rather than a structured
hardware-counter snapshot. The reader's per-domain cap-
query discipline is overhead the user does not need for
plain logs; this skill's path-selection rule routes there
when log shipping is the actual requirement.doca-debug/SKILL.md — the cross-
cutting debug ladder (install / version / build / link /
runtime / program / driver). Per-domain reader-specific
debug (domain not supported on this device, snapshot not
yet ready, sample window too short, cap-query forgotten
before read) overlays on top of that ladder.还没有评价。欢迎分享你的使用感受。
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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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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'.
中文简介正在准备中。当前显示原文说明。
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`.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。