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.
Hardware compression offload with DOCA Compress on a BlueField DPU, ConnectX NIC, or host: DEFLATE compress and decompress, plus LZ4 decompress (stream and block; LZ4 encode is NOT supported), instead of CPU zlib / zstd. Trigger on "offload this gzip", "decompress incoming network data on the NIC", "compress task returns INVALID_VALUE on alloc_init", "submitted a task but no completion arrives", or "decompress LZ4 on the BlueField", even without naming DOCA Compress. Covers enabling compress-deflate, decompress-deflate, decompress-lz4-stream and decompress-lz4-block tasks on a doca_compress context, sizing source / destination doca_buf against the per-task cap query, mmap permissions, offload vs CPU decisions, a round-trip smoke, and DOCA_ERROR_* from Compress calls. Refuse and route elsewhere for zstd / Snappy / brotli, LZ4 encode (a CPU LZ4 library), pure mmap-to-mmap copies (doca-dma), or DOCA Core lifecycle internals.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
doca_buf against the per-task cap query before changing
the algorithm.Follow Loading order below. Open the tasks companion for configure / test / debug. Open the capabilities companion for codec support and buffer-size policy. Do not open every companion file.
Where to start: This skill assumes DOCA is already installed and
the user is doing hands-on Compress work (bulk DEFLATE
compression or decompression) on a BlueField / ConnectX / host
with DOCA. Open the tasks companion if the user wants to do
something (configure / build / modify / run / test / debug); open
the capabilities companion when the question is what can
DOCA Compress express on this version. If the user has not
installed DOCA yet, route to
doca-setup/SKILL.md first. If the user is
asking "should I even offload this compression to the
accelerator?", the size-threshold path-selection rule in
is the first stop — bulk compress is the canonical fit, tiny one-shot is not.
The CLASSES of DOCA Compress 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_compress_task_compress_deflate on
this BlueField, and what is the max source size per task?".
Answered by the per-task capability-query rule
(doca_compress_cap_task_compress_deflate_is_supported,
_decompress_deflate_is_supported, the matching
_get_max_buf_size queries) in
doca_compress_task_compress_deflate
returns DOCA_ERROR_NOT_PERMITTED". Answered by the permission
matrix in
doca_compress_task_decompress_deflate in the DOCA I have
installed?". Answered by the version-compatibility overlay in
doca-version/SKILL.md and adds the
Compress-specific "discover per-task support, do not assume"
bullets.DOCA_ERROR_* from a Compress call mean and
which layer caused it?" — worked example: "DOCA_ERROR_INVALID_VALUE
on doca_compress_task_compress_deflate_alloc_init". Answered
by the Compress overlay on the cross-library taxonomy in
doca-debug/SKILL.md.This skill serves external developers building applications that
consume the DOCA Compress library — i.e., users whose code calls
doca_compress_* (directly in C/C++, or through FFI/bindings from
another language) to offload bulk DEFLATE compression or
decompression onto a BlueField DPU or ConnectX accelerator. It is
not for NVIDIA developers contributing to DOCA Compress itself.
Language scope. DOCA Compress ships as a C library with
pkg-config module name doca-compress. The shipped samples are
written in C. C and C++ consumers are the canonical case and the
worked examples in the tasks companion assume that path. 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 lifecycle, capability-discovery,
permission, error-taxonomy, and compress-vs-decompress 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 Compress work, in any language. Concretely:
doca_compress context on a doca_dev and
configuring at least one task type
(doca_compress_task_compress_deflate and/or
doca_compress_task_decompress_deflate) before
doca_ctx_start().doca_mmap correctly for the source
buffer (DOCA_ACCESS_FLAG_LOCAL_READ_ONLY at minimum) and the
destination buffer (DOCA_ACCESS_FLAG_LOCAL_READ_WRITE).doca_compress_cap_task_*_get_max_buf_size(devinfo) ceiling
and the destination buffer against the worst-case output size
the algorithm can produce on this input.doca_compress_cap_task_compress_deflate_is_supported and
doca_compress_cap_task_decompress_deflate_is_supported
against the active doca_devinfo.DOCA_ERROR_* returned from a Compress call
(lifecycle vs. buffer-sizing vs. permission vs.
unsupported-task) and the task-completion event on the progress
engine.Do not load this skill for general DOCA orientation, install
of DOCA itself, non-DEFLATE compression libraries on CPU (use
zlib / zstd / similar), or other DOCA libraries. For those, 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 Compress-specific material lives in two companion files:
doca_compress_cap_* for
task support and per-submission max buffer size), the Compress
error taxonomy (mapped onto the cross-library DOCA_ERROR_*
set), the observability surface (per-task completion events on
the progress engine), the safety policy that gates source /
destination mmap permission decisions, and the size-threshold
path-selection rule (when to use doca-compress versus CPU
compression or doca-dma for the no-compression copy case).configure, build, modify, run, test,
debug. Plus a ## rollback overlay (Compress-specific
five-step teardown that drains in-flight tasks before
doca_ctx_stop, unregisters mmap regions in reverse-register
order, and re-verifies the device path with the round-trip
smoke) 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 — 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:
/opt/mellanox/doca/samples/doca_compress/, plus
the File Compression reference application linked from the
public DOCA Compress guide. The agent's job is to route the
user to those files and prescribe a minimum-diff modification
on them via the universal modify-a-sample workflow in
doca-programming-guide/SKILL.md,
layered with the Compress-specific overrides in
zlib) as the known-good smoke; it does not ship
a fixture bank of its own.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-compress 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, 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 "Compress-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 DOCA
Compress page lives at
docs.nvidia.com/doca/sdk/DOCA-Compress/; the File Compression
reference application is the canonical worked example.doca-setup/SKILL.md — env preparation,
install verification, and the I have no install yet path
with the public NGC DOCA container. 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
Compress-specific "discover per-task support + per-task max
buffer size via cap query" overlay.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 Compress specifics
on top.doca-dma/SKILL.md — the right library when
the data flow is a pure mmap-to-mmap copy with no compression
required. This skill's path-selection rule routes to DMA when
Compress is not the answer (e.g. the user only wants to move
bytes, not encode them).doca-debug/SKILL.md — the cross-cutting
debug ladder (install / version / build / link / runtime /
program / driver). Compress-specific debug (task-not-supported,
source-buffer-too-large, destination-buffer-too-small) 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.
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.