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 NVMe-over-Fabrics storage-target work on a BlueField DPU or ConnectX NIC with DOCA STA — standing up a doca_sta DOCA Core context that accelerates the target-side NVMe-oF data path over RDMA, defining doca_sta_subsystem targets (NQN + namespaces) backed by local NVMe-PCI backend disks (doca_sta_be), checking device support via doca_sta_cap_is_supported, sizing the per-connection I/O queues, or debugging DOCA_ERROR_* from a STA call. Trigger even when the user does not say "DOCA STA" — typical implicit phrasings include "my NVMe-oF Connect never completes", "Identify Controller times out over RoCE", "16 I/O queues at depth 1024 — does this BlueField support that", "offload the nvmf target onto the DPU", or "DOCA_ERROR_IO_FAILED on an NVMe read". Refuse and route elsewhere for DOCA install, raw RDMA data movement, raw packet I/O, flow-rule programming, or initiator-side / host NVMe stack work — 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_sta + subsystem/namespaces; do not
rewrite the initiator stack here.doca_sta_cap_is_supported and queue sizing before
changing backend disks.Follow Loading order below. Open the tasks companion for configure / target / debug. Open the capabilities companion for queue limits and NVMe-oF caps. Do not open every companion file.
Where to start: This skill assumes DOCA is already installed and
the user is doing hands-on NVMe-over-Fabrics storage-target work on
a BlueField-class device with DOCA. Open the tasks companion if
the user wants to do something (configure / modify / build / run
/ test / debug); open the capabilities companion when the
question is what can DOCA STA express on this version. If the
user has not installed DOCA yet, route to
doca-setup/SKILL.md first. If the user is
asking "is this an NVMe-oF initiator/host transport?", the
answer is no — doca-sta accelerates the target side: it presents
NVMe-oF doca_sta_subsystem targets backed by local NVMe-PCI
disks; the model lives in
the capabilities companion.
The CLASSES of DOCA STA 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_sta_subsystem (NQN) with one namespace backed by a local
NVMe-PCI disk (doca_sta_be) and accept NVMe-over-RDMA
connections from a remote initiator". Answered by the
target-model-and-lifecycle
workflow in the tasks companion +
the capabilities companion
target-object table.doca_sta_cap_is_supported against a
doca_devinfo) in
the capabilities companion
doca_sta_get_max_* query (e.g.
doca_sta_get_max_qps, doca_sta_get_max_io_queue_size).doca-flow/SKILL.md and the RDMA substrate
to doca-rdma/SKILL.md.doca-version/SKILL.md and adds the
STA-specific cap-query rule (pkg-config --modversion doca-sta
is the build-time anchor; the runtime doca_sta_cap_is_supported
query is the truth).DOCA_ERROR_* from a STA call mean and which
layer caused it?" — worked example: "DOCA_ERROR_NOT_PERMITTED
when opening the doca_dev".
Answered by the STA overlay on the cross-library taxonomy in
the capabilities companion
doca-debug/SKILL.md.A Connect handshake that never completes is usually not an STA bug. It is the single most common first-app failure, and the answer is to walk the three preconditions in the capabilities companion before any code change: the RDMA substrate resolves on the chosen device, the
doca_devwas opened with sufficient permission, and — most often the culprit — a DOCA Flow rule actually steers the matching NVMe-oF 5-tuples to this instance's queues. The steering side belongs todoca-flow/SKILL.md; route it rather than inventing adoca_sta_*steering call, and do not start by auditing STA lifecycle or target-side code. The same applies to an Identify Controller that times out over RoCE.
DOCA_ERROR_IO_FAILEDon a per-I/O completion is a transport error, not a target-side STA bug — a link drop, RDMA peer disconnect, firmware fault, or peer controller reset. Being able to ping the target does not exclude any of these. Do not retry blindly and do not change STA code first: capturedmesgand route todoca-rdma/SKILL.md, per the tasks companion.
This skill serves external developers building NVMe-over-Fabrics
storage targets that consume DOCA STA on BlueField — i.e., users
whose code calls doca_sta_* (directly in C/C++, or through
FFI/bindings from another language) to accelerate the target-side
data path of an NVMe-oF target on the BlueField hardware: presenting
doca_sta_subsystem targets (NQN + namespaces) backed by local
NVMe-PCI disks (doca_sta_be) to remote initiators over RDMA. The
skill is not for NVIDIA developers contributing to DOCA STA
itself, and it is not for initiator/host-side NVMe stacks.
Language scope. DOCA STA ships as a C library with
pkg-config module name doca-sta. DOCA STA ships no public
samples — it is absent from the DOCA libraries /
extension_libraries sample profiles — so the worked examples in
the tasks companion build against the public headers directly rather than
modify a shipped sample. C and C++ consumers are the canonical
case. Other-language
consumers (Rust, Go, Python, …) consume the same *.so through
FFI or language-specific bindings; the skill's contribution in
that case is to keep the target-model, lifecycle,
capability-discovery, queue-pair shape, substrate-dependency,
and error-taxonomy guidance language-neutral, and to route the
agent to the public C ABI as the authoritative surface that any
wrapper will eventually call.
Load this skill when the user is doing hands-on DOCA STA work, in any language. Concretely:
doca_sta instance on a doca_dev opened
against a BlueField PF / SF and configuring the NVMe-oF
target subsystems before doca_ctx_start().doca_sta_subsystem (NQN +
namespaces) and doca_sta_be backend controllers (local
NVMe-PCI disks) — and accepting NVMe-oF connections (admin
queue plus N I/O queues per connection) on the target side as
remote initiators connect over RDMA CM.doca_sta_set_*
family, checking device support via doca_sta_cap_is_supported,
and querying sizing limits via the doca_sta_get_max_* family
(max I/O queue depth, max number of queue pairs, max I/O size,
max subsystems, max namespaces per subsystem, max backends).doca-rdma substrate and uses RDMA CM for
connection establishment — for the target's I/O queues.doca-flow, not doca-sta.DOCA_ERROR_* returned from a STA call (lifecycle
vs. capability vs. transport-layer I/O failure vs.
driver-below) and the per-queue events on the DOCA Core
progress engine.Do not load this skill for general DOCA orientation, install
of DOCA itself, raw RDMA data movement (use
doca-rdma/SKILL.md), raw packet I/O on
Ethernet queues (use doca-eth/SKILL.md),
flow-rule programming (use doca-flow/SKILL.md),
or initiator/host-side NVMe stack development
(SPDK or kernel-nvme own that, not this skill). For DOCA
documentation orientation, use
doca-public-knowledge-map/SKILL.md.
This is a thin loader. The body keeps only the orientation needed to pick the right next file. The substantive STA-specific material lives in two companion files:
doca_sta_subsystem /
namespaces / doca_sta_be backend NVMe-PCI disks),
the NVMe queue-pair shape (admin queue + I/O queues over RDMA),
the RDMA-only transport,
the capability-query surface (doca_sta_cap_is_supported plus
the doca_sta_get_max_* sizing queries), the STA
error taxonomy (mapped onto the cross-library DOCA_ERROR_*
set), the observability surface (per-queue progress engine
events, capability snapshots), and the safety policy that
gates substrate-library, permission, and steering
preconditions.configure, modify, build, run, test,
debug. Plus a Deferred task verbs block that points
out-of-scope questions at the right next skill, and a
Command appendix of the recurring commands the agent
reaches for.The skill assumes a BlueField (with DOCA installed at the
standard location) plus a remote NVMe-oF initiator reachable on
the fabric to connect into the accelerated target, and one or
more local NVMe-PCI disks to back the target's namespaces. It
does not cover installing
DOCA — that path goes through
doca-setup/SKILL.md. It does not cover
initiator/host-side NVMe stacks (SPDK bdev_nvme, kernel nvme
host) or NVMe protocol semantics above the accelerated target
data path — those are out of scope.
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:
/opt/mellanox/doca/samples/doca_sta/ directory, and STA is
absent from the libraries / extension_libraries sample
profiles. The authoritative surface is the public headers under
$(pkg-config --variable=includedir doca-common) plus the public
DOCA STA guide; the agent builds against those directly rather
than modifying a shipped sample, per the
the tasks companion workflow.bdev_nvme,
the kernel nvme host, and any initiator-side NVMe stack are
upstream projects out of scope for this skill — DOCA STA is
target-side acceleration, not an initiator transport provider.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-sta 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.Both companion files cross-link to each other,
doca-version/SKILL.md for the canonical
version-handling rules,
doca-rdma/SKILL.md for the RDMA substrate
that NVMe-over-RDMA transport lands on,
doca-flow/SKILL.md for the steering rules
that direct NVMe traffic to STA-managed queues, and
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 "STA-specific
guidance".
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. The
STA URL slug is DOCA-STA.doca-setup/SKILL.md — env preparation,
install verification, BlueField mode checks, and the
permission / group-membership requirements for opening a
doca_dev. This skill assumes its preconditions are
satisfied.doca-version/SKILL.md — canonical
DOCA version-handling rules. This skill's ## Version compatibility cross-links the four-way match rule and adds
only the STA-specific overlay (STA target-acceleration
availability windows, NVMe-oF feature-set device-conditional
support).doca-structured-tools-contract/SKILL.md —
the bundle's structured-tools precedence rule (detect /
prefer / fall back / report). The Command appendix in
the tasks companion honors this contract.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 lifecycle, the cross-library DOCA_ERROR_*
taxonomy, and the program-side debug order. This skill
layers STA specifics on top.doca-rdma/SKILL.md — the RDMA substrate
that NVMe-over-RDMA transport lands on. STA hides most of
the RDMA queue-pair details from the consumer, but the user
still needs doca-rdma linked in and the device's RDMA
capabilities discoverable for the NVMe-over-RDMA path to
work.doca-eth/SKILL.md — the queue-pair
shape that STA's per-connection queue model echoes. Reach
here if the user is asking general questions about how
DOCA exposes queue-pairs that don't have an STA-specific
answer.doca-flow/SKILL.md — the steering
surface that decides which NVMe-oF packets land on which
STA-managed queue. DOCA STA does not program steering
itself; an NVMe-oF target whose connections never come up
is often a missing or wrong Flow rule, not a STA bug.doca-debug/SKILL.md — the
cross-cutting debug ladder (install / version / build /
link / runtime / program / driver). STA-specific debug
(transport-type mismatches, queue-depth oversize,
IO-failed transport errors) overlays on top of that
ladder.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
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.
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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.