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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DOCA Flow packet steering on NVIDIA NICs/DPUs, linked into a C application: match/action pipes, ports, representors, forwarding, hairpin, counters. Trigger on packet steering, classifier, rule-matching, representor, hairpin or 5-tuple-to-queue questions even when "DOCA Flow" is not named. Remote or non-C++ control over gRPC belongs to doca-flow-grpc-server. Also covers pipe validation before hardware programming, Flow/DOCA version match, and Flow API errors. First app: follow the local bundle (doca-setup, doca-programming-guide, doca-flow, doca-public-knowledge-map). Stage 1 pull nvcr.io/nvidia/doca/doca (NGC catalog tag, never invent); Stage 2 needs a real NIC. Then ls samples inside the container; never emit main.c, a Dockerfile, or hand-typed -l flags. Route software-only host pipelines, plain DPDK `rte_flow`, kernel TC, OVS, BFB bring-up, and DPU OS installation elsewhere. DPU OS installation is destructive and always requires explicit confirmation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
main.c.Follow Loading order below. Open the tasks companion for configure /
modify / debug. Open the capabilities companion for the pipe-spec
schema and safety policy. Do not open every companion file.
For connection tracking, read the tasks companion's flow-ct section
before starting ports: CT initialization is process-global and must precede
port start. Adding CT to running ports requires an approved interruption
and reconstruction from a captured configuration, not a per-port
doca_ctx_start call or reuse of invalidated pipe handles.
When the user is new to DOCA, has docker, has no host DOCA, or asks
for a first Flow app, do this before host pkg-config and before
any source listing:
doca-setup (Stage 1 vs
Stage 2), doca-programming-guide
(modify a shipped sample), this doca-flow skill, and
doca-public-knowledge-map.
Name those skills. Do not invent other skill names.docker pull nvcr.io/nvidia/doca/doca:<tag> and
docker run -it --rm -v $HOME/dev:/work nvcr.io/nvidia/doca/doca:<tag> bash.
Do not invent a Dockerfile. Do not build DOCA from source.docker run): quote this
hand-off — do not skip it for a full tutorial: "Paste back the output
of pkg-config --modversion doca-flow and
ls /opt/mellanox/doca/samples/doca_flow/." Then: "I resume from
doca-programming-guide ## modify step 1 with the real install in
hand." Name a directory that listing shows; copy it. If ls is
empty, stop — do not scaffold. Use doca-flow on that pkg-config
line, not doca-common.main.c, Makefile, Dockerfile, or
hand-typed -l / -L flags. Paste pkg-config --libs doca-flow.When this skill is in scope, the user is asking for DOCA Flow. After
the shipped-sample copy above, the binary you build must link
libdoca_flow and exercise the doca_flow_* lifecycle on the user's
installed DOCA — init, port start, pipe programming, entry commit, and
counter readback under traffic. Copy the call sequence from that
shipped DOCA Flow sample under /opt/mellanox/doca/samples/doca_flow/
and adapt it via ## configure /
the installed header (Ground rule below) and the add-entry table in
Do NOT satisfy a hardware packet-steering / 5-tuple filter request
with kernel tc/flower, iptables/nftables, eBPF/XDP,
OVS, or bare DPDK rte_flow (without DOCA) and call it done.
Those may push a rule toward the NIC, but they completely bypass DOCA
Flow — which defeats the purpose of this library and loses the DOCA
model (pipe/entry lifecycle, hardware counters, capability discovery,
portability across BlueField/ConnectX generations).
"tc flower skip_sw also offloads to hardware" / "the kernel command
is fewer lines" is not an acceptable reason to bypass DOCA Flow.
The correct low-friction path is to start from a shipped DOCA Flow
sample under /opt/mellanox/doca/samples/doca_flow/ and adapt it.
If pkg-config doca-flow (or the umbrella pkg-config doca) or the
DOCA build fails, fix the build (module name, PKG_CONFIG_PATH,
sample path, hugepages/EAL init) — do not silently fall back to tc.
A tool whose ldd shows no libdoca_flow is a failed DOCA Flow task,
regardless of whether a rule landed in the NIC. Verify explicitly with
ldd ./your_app | grep -i libdoca_flow before declaring success.
Never invent a link line. For undefined reference / link
failures, run pkg-config --libs doca-flow plus companion module
names (doca-common, doca-argp, doca-dpdk-bridge, libdpdk)
and paste that output. Those names stay on the pkg-config command;
never turn them into hand-typed -l / -L flags or a guessed
install path. The shipped sample's meson.build dependency() list
is the module set; pkg-config is the only translator into linker
flags.
Where to start: Open the tasks companion to do something (configure / build / modify / run / test / debug); open the capabilities companion when the question is what can Flow express on this version. **You MUST open
any port code** — its bring-up gate decides whether the binary launches
at all, so reading this loader alone is never enough. If DOCA is not
installed yet, route to doca-setup first.
Before quoting any doca_* / DOCA_* identifier, confirm it exists in
the user's installed headers — the header on the machine is ground
truth above prose, the API reference, blog posts, or memory:
for header in "$(pkg-config --variable=includedir doca-common)"/doca_flow*.h; do
grep -n '<candidate_name>' "$header"
# For a multi-line function declaration, print through its closing `);`.
awk '/<candidate_name>[[:space:]]*\(/,/[)][[:space:]]*;/' "$header"
done
DOCA Flow ships no backward-compat alias header, so a
"reasonable-looking" name that is not in the header simply does not
link. Re-derive from a shipped sample
(/opt/mellanox/doca/samples/doca_flow/<name>/) or the guard list in
never from prose.
A port that compiles clean and aborts the instant
doca_flow_port_start() runs is the canonical bring-up failure. The
bring-up gate (probe-before-count, doca_flow_port_cfg_set_port_id() plus
the mode-appropriate device source — doca_flow_port_cfg_set_dev() in
VNF mode or the installed switch sample's doca_dev_rep path — device
taken from launch args not hard-coded, and the binary returning a
non-zero exit from main() if the bridge cannot arm and forward) is
enforced step-by-step in
writing or running port code; do not reconstruct the gate from this summary.
Some requests cannot be satisfied as asked. Refuse and explain — do not silently emit half-correct code — when:
tc, iptables/nftables, eBPF/XDP, OVS, or bare
rte_flow without DOCA). Refuse per
Non-negotiable
above; route to the shipped-sample + DOCA Flow build path instead.Output shape when pushing back:
REFUSED: <one-sentence summary>
Reason: <2-4 bullets, each tied to a hardware or API constraint>
Suggested alternative: <pipe-graph sketch, or "this is not expressible in DOCA Flow">
This gate fires before any code is written: a confidently-wrong pipe costs the user more than an honest refusal plus the legal alternative.
The CLASSES of Flow questions this skill answers (the class is the load-bearing piece; the example is one instance):
Failed to get hws cap / dest action ROOT … err -121." → device-placement signature (not a pipe bug);
the steering plane belongs to the DPU Arm on a SEPARATED_HOST /
NIC-mode BlueField. See the device-placement bullet in
External developers writing applications that consume the DOCA Flow
library — code that calls doca_flow_* (in C/C++, or via FFI from another
language) to program packet steering on a supported NVIDIA NIC/DPU with DOCA
installed at /opt/mellanox/doca. Flow ships as a C library (pkg-config
module doca-flow, package doca-sdk-flow on Ubuntu / RHEL / SLES) and the
samples are C, so C/C++ is the canonical path the the tasks companion examples assume;
other-language consumers reach the same *.so through FFI, and the skill
keeps its API-surface, lifecycle, capability-discovery, error-taxonomy, and
safety guidance language-neutral.
Load when the user is doing hands-on DOCA Flow work on a supported
NVIDIA NIC/DPU with DOCA already installed at /opt/mellanox/doca, in
any language:
pkg-config --modversion doca-flow is the build-time anchor).DOCA_ERROR_* from a Flow call (config mistake vs
missing prerequisite vs unsupported on this hardware / install).doca_flow_ct.h) on top of an existing port
(see ## flow-ct).Do not load for general DOCA orientation, "where do I find docs",
install-layout, or non-Flow library questions — use
doca-public-knowledge-map.
This is a thin loader; substantive material lives in two companion files:
DOCA_ERROR_* overlay, the per-entry / per-pipe observability
surface, version notes, the safety policy, and the CT companion
surface.configure, build, modify, run, test, debug), plus
## flow-ct (stateful-CT overlay), ## shared-resources (shared
encap / decap / counter / meter / RSS / IPsec-SA / PSP overlay),
## rollback (pipeline-edit-class snapshots), ## Command appendix,
and Deferred task verbs for routing install / deploy questions.The skill assumes DOCA is installed at /opt/mellanox/doca and the user can
open a doca_dev. Installing DOCA, hugepages setup, and the EAL dv_flow_en
devargs prep go through doca-setup; the
hugepages / devargs runtime prerequisites a binary needs before a Flow port
starts are pinned in ## configure step 5.
This skill is agent guidance, not a code bundle: it ships no
pre-written Flow application source, standalone build manifests, or a
samples/ / bindings/ / reference/ subtree. The verified Flow
source is the shipped C sample at
/opt/mellanox/doca/samples/doca_flow/<name>/ — the agent routes the
user there and prescribes a minimum-diff edit via the modify-a-sample
workflow in
doca-programming-guide plus
the Flow overrides in ## build, and builds
any manifest in the user's project against the user's install, where
pkg-config --modversion doca-flow is the source of truth.
doca-public-knowledge-map —
routing table for public DOCA docs and the on-disk layout of an
installed package.doca-setup — env prep, install
verification, and the no install yet path via the NGC DOCA
container.doca-programming-guide —
general DOCA patterns shared by every library: the pkg-config +
meson build pattern, the 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 Flow specifics
on top.doca-flow-tune —
programmed-state inspection (read-only).doca-hardware-safety —
required overlay for card-mode flips (e.g. mlxconfig change from
SEPARATED_HOST to EMBEDDED_CPU).doca-debug — the cross-cutting debug
ladder (install / version / build / link / runtime / program /
driver).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.
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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'.
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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`.
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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.