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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doca_telemetry_utils command line on a host with DOCA: discover the diagnostic-counter schema, translate counter names to binary Data IDs and back, and check per-device counter support before committing a DOCA Telemetry exporter config. Start here when telemetry counters vanish between the exporter and the collector: "my exporter ships but the collector sees nothing", "this metric silently drops downstream", "which counters does this BlueField expose", "translate this 0x... back to a counter name", "what do node / pcie_index / depth mean here", or "is this counter supported on this device", even without naming the tool. Writing exporter or collector code belongs to doca-telemetry-exporter / doca-telemetry; DTS deployment to doca-dts; DOCA install or repair to doca-setup.
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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。
port_rx_bytes but nothing shows up
downstream." — translate name to Data ID and probe
per-device support before changing the exporter.doca_telemetry_utils get-counters: is the counter known to this release?doca_telemetry_utils <counter>: print its property options.doca_telemetry_utils <counter> <properties>: resolve the Data ID.doca_telemetry_utils <device PCI> <counter> <properties>: "not
supported" on this device means the exporter drops it silently; do not
commit that counter.doca_telemetry_utils <0x... Data ID>: reverse-resolve an ID captured on
the wire.doca-version.doca-telemetry ## debug.Quote the tool's output verbatim; never construct Data IDs by hand.
Follow Loading order below. Open the tasks companion for install / run / test. Open the capabilities companion for schema discovery and pairing with the telemetry libraries. Do not open every companion file.
Where to start: This is a tool skill for invoking
doca_telemetry_utils — the documented host-side CLI that
supports a DOCA Telemetry exporter / collector pipeline by
discovering the counter schema, translating counter names ↔
Data IDs, and probing per-device counter support. Open
the tasks companion and start at
## install for the host-side
prerequisites and ## run for the
three documented invocation classes
(enumerate / name→ID / ID→name). Open
the capabilities companion when the question is
what does this tool actually discover about the telemetry
schema, how does it pair with the developer-side
doca-telemetry and
exporter libraries, how do I confirm a device supports a
counter before committing an exporter config to it, or
why does my exporter pipeline silently drop a metric.
How to use this skill. Read this file, then search the companion file
for the ## <name> heading the question needs and read that whole section.
Answer completely from it, quoting only its symbols and commands. Do not
browse docs; use the entry points in doca-public-knowledge-map. Give
environment checks as commands for the user's DOCA host. For out-of-scope
requests, name the skill that owns them and stop.
This skill is the operator-side support tool for a
DOCA Telemetry deployment. It is NOT the developer-side
collector library (that is
doca-telemetry),
NOT the developer-side publisher library (that is
doca-telemetry-exporter — see
doca-telemetry ## Related skills),
and NOT a DOCA Telemetry Service (DTS) deployment guide
(route to doca-dts).
Three separate surfaces; conflating them is the most
common telemetry first-touch error.
The CLASSES of doca_telemetry_utils 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.
port_rx_bytes but
nothing shows up downstream — what did I get wrong?" —
worked example: "my exporter config has a counter name
string and my collector sees no events with that
name". Answered by the name ↔ Data ID translation
step in
the capabilities companion
doca_telemetry_utils get-counters lists every
counter name the diagnostic-data surface knows about;
pair with a per-device probe to confirm support).Data ID: 0x1160000600030201 — translate it
back so I can correlate against the public guide".
Answered by the reverse-resolve invocation class in
the capabilities companion
pcie_link_write_stalled_time_* — what
do node / pcie_index / depth mean and what
values does the device accept?". Answered by the
property-dimension table in
the capabilities companion
<name> invocation without arguments which
prints the documented property options + units +
unit-specific axes.port_rx_bytes with node=1 is
exposed on the BlueField at PCIe address X before I
write the exporter config". Answered by the
per-device-support probe (<device PCI> <name> [properties]) in
the capabilities companion
doca_telemetry_utils on my install, and is it
paired with the matching doca-telemetry library
version?" — worked example: "is the diagnostic-
data counter set my exporter targets on this DOCA
version?". Answered by the version-overlay in
the capabilities companion,
which redirects to the canonical
doca-version chain
and adds the tool ↔ doca-telemetry library
schema-version match rule.Load this skill when the user is — or the agent needs
to — invoke doca_telemetry_utils on a real host with
DOCA installed, alongside an exporter / collector
pipeline that needs schema discovery, per-device
support validation, or Data ID translation.
Concretely:
get-counters).Do not load this skill for general DOCA orientation,
collector / exporter library programming, DTS
deployment, or DOCA install. For those, route to
doca-public-knowledge-map,
doca-telemetry,
or doca-setup.
This is a thin loader. Substantive material lives in two companion files:
doca_telemetry_utils
discovers + how it pairs with the developer-side
surfaces: the three documented invocation classes
(enumerate / name→ID / ID→name), the property-
dimension model (counters carry property axes such
as node, pcie_index, depth, plus per-unit
axes), the optional per-device capability probe
(<device PCI> <name> runs the resolved counter
against the device), the operator-side support
role (this is NOT the developer-side library; it
exists to make exporter / collector setups
honest), the version overlay (tool ↔
doca-telemetry library schema version pairing),
the layered error taxonomy (install / parse /
unknown-counter / unknown-data-id / property-out-
of-range / device-not-supported / version /
cross-cutting), the observability surface
(stdout-only), and the safety policy (the tool is
read-only; mistakes appear downstream as silent
metric drops, not crashes).install (host-side DOCA +
telemetry component prerequisites), configure
(axis decisions: which invocation class, which
device, which counter), build (route to install
— the binary is shipped), modify (refuse — do
not patch the binary; modify the invocation and
the exporter / collector config that consumes the
resolved Data IDs), run (the three documented
invocations), test (round-trip a chosen
counter through name → Data ID → per-device
probe → exporter config → collector receipt),
debug (walk the error taxonomy), use (the
hand-off into the developer-side doca-telemetry
collector / exporter pipeline), plus a Deferred task verbs block.The skill assumes a host where DOCA is already
installed with the telemetry component, a BlueField
the operator is targeting is visible to DOCA, and the
exporter / collector pipeline that consumes the
resolved Data IDs is either being authored or has
already been authored against the matching
doca-telemetry
library version.
This skill is agent guidance, not a samples or scripts bundle. To keep the boundary clean, it deliberately does not contain — and pull requests should not add:
doca_telemetry_utils get-counters on the
installed binary is the authoritative source.doca-public-knowledge-map.
This skill resolves the counter names + IDs a
DTS pipeline consumes; it does not configure DTS.samples/ or reference/ subtree. This is
a thin loader for a shipped CLI; substantive
material lives on the public page, in --help,
and in doca-telemetry.install, configure, build,
modify, run, test, debug, use — see
the tasks companion.doca-telemetry
— the developer-side collector library whose
schema this tool helps the operator discover. Pair
them in every exporter-pipeline triage session.
The collector library skill teaches the
collector-vs-exporter rule, the schema-must-match
contract with the publisher, and the consumer-
queue-full back-pressure rule; this tool's role
is to make the schema half of that contract
inspectable from the operator side. Conflating
the library and the tool is the most common
telemetry first-touch error.doca-public-knowledge-map
— routing to the public DOCA Telemetry guide and
the DOCA Telemetry Service (DTS) page on
docs.nvidia.com, plus the on-disk install
layout for the tool.doca-version —
canonical DOCA version-handling rules. The
## Version compatibility section in
the capabilities companion is a concise
overlay that redirects here for the body and
adds the tool ↔ doca-telemetry library
schema-version pairing rule.doca-setup — 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 (DOCA installed
with the telemetry component, BlueField visible
to DOCA).doca-debug — the
cross-cutting debug ladder. Telemetry-utils
feeds the cross-cutting ladder by surfacing the
counter schema + per-device support truth at the
runtime layer; exporter-pipeline regressions
often resolve at the schema layer before
touching the collector / network path.doca-structured-tools-contract
— the bundle's detect → prefer → fall back →
report contract for structured helper tools. The
command appendix in the tasks companion
honors this contract.doca-programming-guide
— general DOCA programming patterns shared by
every library / tool surface, including the
cross-library DOCA_ERROR_* taxonomy this
tool's host-side error layer overlays on top of
when downstream collector / exporter code
surfaces a related error.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'.
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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。