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 RDMI (RDMA Initiator) programming — picking doca-rdmi vs doca-rdma for an accelerator-initiated one-sided RDMA flow, standing up a doca_rdmi_connection or doca_rdmi_poster, attaching a doca_dpa_completion or doca_verbs_cq before doca_ctx_start(), retrieving the DPA-side handle for a DPA kernel, auditing whether a doca_rdmi_* symbol is EXPERIMENTAL on this DOCA, or debugging DOCA_ERROR_* returns from RDMI calls. Trigger even when the user does not say "DOCA RDMI" or "initiator" — implicit phrasings include "my DPA kernel needs to post RDMA writes to a remote responder", "DPA kernel sees no completions", "function not found on doca_rdmi_* at link time", "DOCA_ERROR_BAD_STATE from completion attach", or "the DPA posted but the work request never arrived". Refuse and route elsewhere for two-sided or host-CPU RDMA, the DPA programming model, GPU-side RDMA initiation, or general RDMA/IB/RoCE concepts — those belong to other skills.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
doca_rdmi_*; do not rewrite it as
host-CPU doca-rdma.Follow Loading order below. Open the tasks companion for configure / build / debug. Open the capabilities companion for RDMI vs RDMA and the experimental-symbol rule. Do not open every companion file.
Where to start: This skill assumes DOCA is already installed and
the user is doing hands-on RDMI work on a host or BlueField with
the DOCA package set that ships the doca-rdmi library. Open
the tasks companion if the user wants to do something
(install / configure / build / modify / run / test / debug / use);
open the capabilities companion when the question is
what can RDMI express on this version — the object model, the
DPA-side handle types, the relationship to doca-rdma, the
EXPERIMENTAL-tag policy, and the safety overlay.
End-to-end "walk me through doca-rdmi" questions are answerable entirely from this skill. Go straight to
which carries the self-contained install-check → device/cap
discovery → sample → pkg-config build → run → debug walkthrough
with the exact commands. You do not need to open doca-setup or
doca-programming-guide to answer an RDMI build/run/debug question.
Route to doca-setup/SKILL.md when the required
DOCA prerequisites are absent, partial, or version-mismatched.
The CLASSES of RDMI 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-rdmi or doca-rdma for this case?" —
worked example: "I have a DPA kernel that needs to fire 1 MB
RDMA writes at a remote responder; which library?". Answered by
the initiator-side vs general-purpose selection rule in
doca-rdma/SKILL.md when the use case is
two-sided or host-CPU initiated.doca_rdmi_connection, attach a DPA
completion context, hand the DPA-side handle to my kernel".
Answered by the connection-object lifecycle in
doca_rdmi_connection plus a
doca_rdmi_poster, or one of them?". Answered by the
two-object model in
doca_dpa_completion so my
kernel polls completions directly". Answered by the DPA-side
completion-attach pattern in
doca-dpa/SKILL.md for the DPA programming
surface itself.doca_rdmi_poster_post GA on
my installed DOCA, or still EXPERIMENTAL?". Answered by the
EXPERIMENTAL-tag policy in
pkg-config --modversion doca-rdmi) pinned inDOCA_ERROR_* from a doca_rdmi_* call
mean?" — worked example: "DOCA_ERROR_BAD_STATE from
doca_rdmi_connection_dpa_completion_attach". Answered by the
RDMI overlay on the cross-library taxonomy in
doca-debug/SKILL.md.This skill serves external developers building DPA-resident DOCA
applications that need to initiate one-sided RDMA operations
against a remote responder — i.e., users whose accelerator-side
code wants to post sends, writes, or reads directly from the
accelerator without round-tripping through the host CPU. The
canonical caller is a DPA kernel that has been compiled with
doca-dpacc-compiler and runs on the BlueField DPA datapath; a
GPU-side caller that drives the DPU's RDMA queues is the
sister case routed to doca-gpi/SKILL.md. This
skill is not for NVIDIA developers contributing to DOCA RDMI
itself, and it is not the right surface for general host-CPU
two-sided RDMA — that belongs to
doca-rdma/SKILL.md.
DOCA RDMI ships as a C library with the pkg-config module name
doca-rdmi. The library's host-side surface
(doca_rdmi_connection_*, doca_rdmi_poster_*) is C; the DPA-side
surface that the accelerator kernel uses is also C, compiled against
the DOCA DPA toolchain documented in
doca-dpa/SKILL.md. Other-language consumers (Rust,
Go, Python, …) consume the host-side *.so through FFI; the skill's
contribution in that case is to keep the connection / poster
lifecycle, the EXPERIMENTAL-tag policy, the DPA-side handoff rules,
and the safety overlay language-neutral, and to route the agent to
the public C ABI as the authoritative surface that any wrapper will
eventually call. The DPA-side surface is not wrappable in another
language — it is compiled and linked into the DPA binary itself.
Load this skill when the user is doing hands-on DOCA RDMI work on a host or BlueField with DOCA installed. Concretely:
doca-rdmi and doca-rdma for a new
one-sided RDMA workload that runs from the DPA datapath.doca_rdmi_connection or doca_rdmi_poster,
attaching a doca_dpa_completion or a doca_verbs_cq, and
starting the context on the DPA datapath.doca_rdmi_connection_get_dpa_handle /
doca_rdmi_poster_get_dpa_handle into a DPA kernel that calls
the matching device-side header
(doca_rdmi_dev_connection.h, doca_rdmi_dev_poster.h,
doca_rdmi_dev_cqe.h).doca_rdmi_connection_recv_ack after the DPA kernel consumed
them.DOCA_ERROR_* returned by a doca_rdmi_* call and
deciding whether the cause is a configuration mistake, a
lifecycle ordering bug, an unsupported capability on this device,
or a layer below DOCA.Do not load this skill for general DOCA orientation, install of
DOCA itself, or two-sided host-CPU RDMA questions. For those, use
doca-public-knowledge-map/SKILL.md,
doca-setup/SKILL.md, and
doca-rdma/SKILL.md respectively.
This is a thin loader. The body keeps only the orientation needed to pick the right next file. The substantive RDMI-specific material lives in two companion files:
doca_rdmi_connection and doca_rdmi_poster object model, the
DPA-side completion-attach pattern, the relationship to
doca-verbs (RDMI builds on a doca_verbs_context) and to
doca-dpa (the accelerator-side datapath), the EXPERIMENTAL-tag
rule for version handling, the RDMI overlay on the cross-library
DOCA_ERROR_* taxonomy, the observability surface (completion
events on the PE / DPA-side completions), and the safety policy
that gates posting work from an accelerator kernel into a remote
responder's memory.install, configure, build, modify, run, test,
debug, use. Plus a ## rollback overlay (RDMI-specific
five-step teardown for the verbs / connection / DPA-attach /
MR stack) and the 5-phase universal debug-loop instantiation
appended to ## debug. Plus a Deferred task verbs block
that points out-of-scope questions at the right next skill.The skill assumes a host or BlueField where DOCA is already
installed at the standard location and the user has the privileges
their public install profile expects. It does not cover installing
DOCA itself — that path goes through
doca-setup/SKILL.md.
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:
doca-programming-guide/SKILL.md,
layered with the RDMI-specific overrides in
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-rdmi
is the source of truth.doca-dpa/SKILL.md; RDMI's DPA-side headers are
consumed by the DPA programming model documented there. This
skill names the RDMI-specific handoff (the DPA handle type, the
completion-attach call) but does not author DPA kernels.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.Both companion files cross-link to each other and to
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 "RDMI-specific guidance".
doca-rdma/SKILL.md — the higher-level RDMA
library covering two-sided and host-CPU-initiated RDMA. RDMI is
the focused initiator-side surface; doca-rdma is the right
answer for the majority of RDMA use cases. The selection table
in ## Capabilities and modes
is the load-bearing decision aid.doca-dpa/SKILL.md — the DOCA DPA programming
surface. RDMI returns DPA-side handles
(doca_dpa_dev_rdmi_connection_t, doca_rdmi_dev_poster_t)
that the DPA kernel uses through the device-side headers
(doca_rdmi_dev_connection.h, doca_rdmi_dev_poster.h,
doca_rdmi_dev_cqe.h); the DPA toolchain, kernel build, and
execution model are owned by that skill.doca-gpi/SKILL.md — the GPU-side sister of
this skill. GPI is the channel/queue surface a CUDA kernel
uses to initiate RDMA; RDMI is the DPA-side surface. Both
layer on the same DOCA RDMA / verbs substrate; either may
apply depending on whether the initiator is on the DPA or on
the GPU.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.doca-setup/SKILL.md — env preparation,
install verification, and the I have no install yet path with
the public NGC DOCA container.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
Core-context lifecycle, the cross-library DOCA_ERROR_*
taxonomy. This skill layers RDMI specifics on top.doca-debug/SKILL.md — the cross-cutting
debug ladder (install / version / build / link / runtime /
program / driver). RDMI-specific debug overlays on top of it.doca-hardware-safety/SKILL.md —
the bundle-wide hardware-safety meta-policy. The ## Safety policy overlay in the capabilities companion cross-links it.doca-version/SKILL.md — the version
detection / four-way match rule every per-artifact ## Version compatibility anchor builds on. This skill quotes
the RDMI-specific overlay only.doca-structured-tools-contract/SKILL.md —
the JSON-schema contracts for the agent-preferred structured
helpers (env probe, capability snapshot, version-matrix
lookup); the ## Command appendix in the tasks companion defers to
them before falling back to the manual chain.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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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.
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.