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 when the user is writing their first DOCA app or asking a library-agnostic programming question — picking a shipped sample to copy and modify, wiring pkg-config + meson (or FFI from Rust / Go / Python against the public C ABI), walking the Core lifecycle (create → configure → start → connect → use → stop → destroy), or decoding a DOCA_ERROR_*. Trigger on implicit phrasing: "write my first DOCA program", "meson line for doca_rdma_*", "DOCA_ERROR_BAD_STATE on first call", "call DOCA from Rust", "built clean but nothing on the wire". Refuse install / hugepages / pkg-config (doca-setup), docs or version lookup (doca-public-knowledge-map), and library-internal API work (matching library skill).
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安装前可以查看交给 AI 代理的指令内容。
Follow Loading order below. Open the tasks companion for how-to and the capabilities companion for coverage. Do not open every companion file.
Where to start: Read ## Audience to confirm the user
is consuming DOCA, not contributing to it. Then jump to the H2
that matches the verb (## modify for first-app derivation,
## build for the canonical build pattern, ## test for the test
loop, ## debug for the program-class debug ladder). Docker / NGC
is the build half, not the runtime half. The NGC container can
compile and link. Flow pipe creation, RDMA QP ops, and any call
that needs a doca_dev on a port need a real BlueField or
ConnectX. Do not promise the app will work entirely inside docker.
These are the CLASSES of program-class questions the skill is built to answer, each with one worked example. Library-specific overlays (Flow / DMS / Caps / …) live in the matching library skill; this skill answers the library-agnostic shape.
doca_rdma_*?"
Answered by the pkg-config doca-<library> pattern in
doca_flow_pipe_* calls in my
program?" Answered by the Core order create → configure → start →
connect → use → stop → destroy in ## Capabilities and modes
(cfg-create / init implement configure and finish before start).DOCA_ERROR_* came back — what does it mean and what do I do?"
— worked example: "My code got DOCA_ERROR_BAD_STATE." Answered
by the cross-library doca_error_get_descr() rule in
If the question is env-class (install / build env / hugepages /
devices), route to doca-setup. If it is
library-specific (Flow pipe topology, RDMA QP setup, DMS service
deploy), layer the matching library skill on top.
This skill serves external developers building applications that
consume DOCA libraries — i.e., users whose code calls one or more
doca_<library>_* symbols (directly in C/C++, or through FFI /
bindings from another language). It is programming with DOCA,
not programming of DOCA: it is not for NVIDIA developers
contributing to DOCA itself, and it does not assume access to the
DOCA source tree, internal NVIDIA tooling, or any non-public
information. The only inputs it ever points the agent at are the
ones any external user has: the public docs at
docs.nvidia.com/doca/sdk/,
the public catalog at
catalog.ngc.nvidia.com, the
public GitHub repos under
github.com/NVIDIA /
github.com/NVIDIA-DOCA, the
public developer forum, and the on-disk /opt/mellanox/doca tree
that the public DOCA install (or the public NGC DOCA container,
nvcr.io/nvidia/doca/doca) puts on the user's host. Where to find
and how to install questions are routed elsewhere — see Related
skills below.
Language scope. DOCA itself is a C library family; every shipped
sample in /opt/mellanox/doca/samples/ and every shipped reference
application in /opt/mellanox/doca/applications/ is C. C and C++
consumers are the canonical case for every prescriptive workflow in
this skill. Other-language consumers (Rust, Go, Python, …) consume the
same *.so libraries through FFI or language-specific bindings against
the public C ABI; the skill keeps the lifecycle, capability, error,
observability, and safety guidance language-neutral, and routes the
language-specific build / FFI work back to the consumer's own toolchain
without authoring wrappers.
Load this skill when the user has DOCA installed and the env-class
preconditions are already satisfied (i.e.,
doca-setup has produced a clean install
where pkg-config doca-<library> resolves, hugepages are mounted, and
devices are visible), and is now asking a question about how to
actually program against DOCA in a library-agnostic way:
pkg-config + meson build pattern any DOCA application
follows, regardless of which library it consumes.doca-setup because it
is a programming verb, not an env verb.create → configure (including permissions and task set_conf) → start → connect → use → stop → destroy) and how it manifests across libraries. Configuration MUST
finish before doca_ctx_start(). Connect is its own step after
start; do not write connect/use as one token.DOCA_ERROR_* pattern, doca_error_t, and
doca_error_get_descr() — the cross-library shape, not Flow- or
RDMA-specific overlays.Do not load this skill for:
pkg-config not find doca-flow?
How do I mount hugepages? I don't have DOCA installed yet — can I
use a container?" — env-class questions belong in
doca-setup, which owns install
verification, env preparation, env-class debugging, and the
no-install → NGC container fallback path for any user on macOS,
Windows, or Linux without DOCA.doca-public-knowledge-map.doca-flow).This is a thin loader. The body keeps only the orientation needed to pick the right next file. The substantive material lives in two companion files:
DOCA_ERROR_* taxonomy,
the program-side observability surface (DOCA logging, capability
snapshots), and the program-side safety policy that every library
skill inherits.configure, build, modify, run, test, debug. The
## modify verb owns the universal derive a custom first app from
a sample pattern that every DOCA library skill extends with
library-specific overrides; the ## build verb owns the canonical
pkg-config doca-<library> build pattern in two language tracks
(C/C++ direct, non-C via FFI).This skill assumes doca-setup has already
produced a clean install. It does not cover env preparation; that
is doca-setup's job, including the no-install → NGC container
(nvcr.io/nvidia/doca/doca) fallback for users on macOS, Windows, or
Linux without DOCA.
This skill is agent guidance, not a samples or templates bundle. It deliberately does not contain — and pull requests should not add:
meson.build, CMakeLists.txt,
Cargo.toml, setup.py, go.mod, …) 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-<library> 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.configure, build,
modify, run, test, debug — see the tasks companion.If the user is asking for a first app in a specific library, walk
through ## modify for the universal
copy-and-edit pattern, then hand off to the library skill (e.g.
doca-flow) for the library-specific values
to swap.
doca-public-knowledge-map
— public DOCA documentation routing and the on-disk layout of an
installed DOCA package. This skill defers all "where is X
documented", "where on disk is Y", and "how do I check the
installed version" questions to the knowledge-map.doca-setup — env preparation, install
verification, env-class debugging, and the I have no install yet
procedure with the NGC container (nvcr.io/nvidia/doca/doca) as
the universal Stage-1 fallback for any user on macOS, Windows, or
Linux without DOCA. This skill assumes doca-setup's preconditions
are already satisfied.doca-flow — DOCA Flow on BlueField.
Extends this skill's ## modify (universal first-app derivation)
with the Flow-specific list of fields to swap.doca-container-deployment
— shipping an application that already builds: container manifests,
rollout, and service-readiness verification. This skill ends once the
program compiles and runs locally; it does not cover deploying it.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。