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。
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.
中文简介正在准备中。当前显示原文说明。
查看安装方法安装前可以查看交给 AI 代理的指令内容。
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.还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
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。