pandasで扱う表データ処理をcuDFでGPUへ移す作業を支援します。結合・集計・CSVやParquet入出力から、結果の照合、メモリ対策、複数GPU利用まで扱います。
- 既存pandas処理を少ない変更で加速
- 結合や集計をGPUへ移したいとき
- 欠損値や並び順をpandasと照合
BlueField DPUやConnectX NICのテレメトリをローカルで監視し、条件に合うイベントの前後のサンプルをログに記録します。
Use this skill when the operator is driving the DOCA Telemetry Traceback application to monitor telemetry data locally on a BlueField DPU or ConnectX NIC, detect long-term trends and threshold breaches with user-defined rules, and capture pre-event history plus active-event data to a log file. Trigger even when the user does not explicitly mention "DOCA Telemetry Traceback" — typical implicit phrasings include "monitor telemetry trends on the DPU without streaming all raw data", "log the samples before and after a threshold breach", "set up a rule that triggers when rx bytes/s exceeds 1M", "differential greater than rule on a data ID", "circular buffer of telemetry samples", or "traceback event log". Refuse and route elsewhere: the DOCA Telemetry Diag programming API and raw counter reads to doca-telemetry, DOCA install/bring-up to doca-setup, and version questions to doca-version.
実行スコア 未検証
インストール方法を見るDOCA Telemetry Tracebackアプリケーションを使い、BlueField DPUやConnectX NICのテレメトリをローカルで監視する手順を扱います。設定JSONでデータIDやルールを指定し、値のしきい値超過や差分などの条件を評価できます。条件に合ったイベントについて、発生前の履歴とイベント中のデータをログファイルに記録します。アプリケーションのビルド、実行、イベントログが空の場合や未対応データIDがある場合の確認も対象です。
すべての生データを外部へ流さずに傾向を監視したい場合や、しきい値に達する前後のサンプルを残したい場合に向いています。差分ルールが動かない理由や、設定したデータIDがデバイスで使えるかを調べる場面にも使えます。
Linux上のDOCA SDK、BlueField DPUまたはConnectX NIC、ハードウェアテレメトリにアクセスするroot権限が必要です。アプリケーションはソースコードで配布され、実行前にコンパイルします。
この紹介文は、公開されている SKILL.md をもとに AI(OpenAI Codex)が作成しました。正確な仕様は下の原文を確認してください。
インストールする前に、エージェントに与えられる指示の中身を確認できます。
These requests are out of scope: say so, name the skill that owns the request, and stop.
doca-telemetry/SKILL.md.doca-setup/SKILL.md.doca-version/SKILL.md.doca-public-knowledge-map/SKILL.md.comparison_type and sample_history_depth; differential rules require depth samples before functioning.doca-telemetry's: the application uses Diag internally, but programming the Diag API is not this skill.
Name doca-telemetry for it and do not write Diag API code here; handle every other part of the request
as this skill describes (for example, --force-ownership still goes through doca-hardware-safety).The authoritative source for the binary name, flags, and paths is the public
DOCA Telemetry Traceback guide for the release; confirm them with the installed
tool's --help before quoting any of them. As that guide describes it:
diag_sample_period_ns, sample_history_depth,
data-ids array, and rules array. Rule types are
incremental_greater_than (inc_gt), differential_greater_than
(dt_gt), differential_less_than (dt_lt), greater_than (gt),
and less_than (lt).-d PCIe address and -r rules
JSON; optional --event-log and --force-ownership.--force-ownership changes diag domain ownership; route any use of it
through doca-hardware-safety/SKILL.md.Follow Loading order below. Open the tasks companion for configure / build / run / debug. Open the capabilities companion for what the application monitors and logs. Do not open every companion file.
Where to start: This is a tool skill for invoking the DOCA Telemetry
Traceback application — the local telemetry monitoring and traceback logging
service that processes telemetry data on the device and writes event logs
when user-defined rules trigger. Open the tasks companion and start at
## configure for the JSON configuration and rule definitions, then
## run for compile and execution. Open the capabilities companion when
the question is what data can be monitored, what rules evaluate, and what
the event log contains. If the user has not installed DOCA yet, route to
doca-setup/SKILL.md first. If the user asks about version, route to
doca-version/SKILL.md. If any step changes hardware or firmware state
(e.g., --force-ownership), route to
doca-hardware-safety/SKILL.md.
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.
Compile and run commands are documented in the tasks companion; the
authoritative source is the public guide. The application is distributed as
source and must be compiled before execution. The source is located at
/opt/mellanox/doca/applications/telemetry/traceback/.
The CLASSES of telemetry-traceback 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_0_rx_bytes on the DPU and log
history before and after it exceeds 1 MB/s”. Answered by the JSON
configuration and rule definitions in the tasks companion.differential_greater_than but the event log stays empty”.
Answered by the depth requirement and delta logic in the capabilities
companion.Load this skill when the user is — or the agent needs to — drive the DOCA Telemetry Traceback application on a real host or BlueField Arm with DOCA installed (or inside the public NGC DOCA container with the right device passthrough). Concretely:
Do not load this skill for general DOCA orientation, the DOCA Telemetry
Diag programming API, raw telemetry collection, DOCA install, or version
questions. For those, route to
doca-public-knowledge-map/SKILL.md,
doca-setup/SKILL.md, or
doca-version/SKILL.md.
This is a thin loader. Substantive material lives in two companion files:
doca-version/SKILL.md, and the safety policy for
--force-ownership.configure (JSON config, rule selection), build (Meson compile all or
only this app), run (execution syntax, root, PCIe address), debug
(empty log, unsupported IDs, sample mismatch), plus a Command appendix
that honors the bundle's
doca-structured-tools-contract/SKILL.md
preamble.The skill assumes a host or BlueField where DOCA is already installed (or the public NGC DOCA container is running with the right device passthrough) and the operator has root privileges as required by the public guide.
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:
--help on the
user's version are the joint source of truth; copying them here pins the
skill to one release and silently rots when the application evolves. The
skill routes the agent at those sources instead.samples/ or reference/ subtree. This is a thin loader for a
documented DOCA artifact; substantive material lives on the public page and
in --help.configure, build,
run, debug, plus the Command appendix — see the tasks companion.doca-hardware-safety/SKILL.md — the
bundle's hardware/firmware state-change gate. The Telemetry Traceback
application is read-only for telemetry data, but the --force-ownership
flag changes diag domain ownership; route any use of that flag through this
skill before execution.doca-version/SKILL.md — canonical DOCA
version-handling rules. The ## Version compatibility section in the
capabilities companion is a concise overlay that redirects here for the
body.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.doca-public-knowledge-map/SKILL.md
— routing to the public DOCA Telemetry Traceback guide and the rest of the
public DOCA documentation set. The canonical URL is reached via
doca-public-knowledge-map ## DOCA tools
(doca-public-knowledge-map/SKILL.md#doca-tools).doca-structured-tools-contract/SKILL.md
— the bundle's detect → prefer → fall back → report contract for structured
helper tools. The Command appendix in the tasks companion honors this
contract.まだレビューはありません。使ってみた感想をお寄せください。
ログインなしで書き込めます。使ってみた感想、質問、動かなかった環境などを気軽にどうぞ。
まだコメントはありません。最初のコメントを書いてみませんか。
pandasで扱う表データ処理をcuDFでGPUへ移す作業を支援します。結合・集計・CSVやParquet入出力から、結果の照合、メモリ対策、複数GPU利用まで扱います。
NVIDIA Nemotron Voice Agentのサンプルを変更し、別のバックエンドを設けずに予約受付や患者情報の聞き取りを行う音声エージェントを作るスキル。
ライブカメラのRTSP映像をVIOSで録画し、AutoMagicCalibでカメラ校正を進めます。設定や位置合わせ資料の取り込み、進捗確認、結果取得まで扱います。
同梱の倉庫カメラ映像でAMCのカメラ校正を一通り実行し、正解データとの誤差を確認します。導入後の動作検証や、実データ投入前の確認に使えます。
録画済みの複数カメラ映像をAutoMagicCalibに送り、カメラの位置や向きを調整する処理を、プロジェクト作成から進捗確認・結果評価まで進めます。
AutoMagicCalibの自動キャリブレーション環境をDocker Composeで構築し、NGC認証からポート設定、サービスとWeb UIの起動確認まで進めます。