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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Hardware-offloaded erasure coding with DOCA Erasure Coding (Reed-Solomon, doca_ec) on a BlueField DPU, ConnectX NIC, or host: generating parity for data blocks, rebuilding lost blocks from the survivors, and refreshing parity after a change. Trigger on "one data block changed, how do I refresh parity without re-encoding", "a disk failed and 2 parity blocks are gone, can I rebuild", "RAID-6 resilience across 12 disks", "my doca_ec_task_create returns NOT_PERMITTED", or "is this N+K layout still recoverable", even without naming DOCA Erasure Coding. Covers bringing up a doca_ec context, the create / recover / update tasks, matrix type / N / K / block size, doca_ec_cap_* queries before sizing, doca_mmap src/dst permissions, and DOCA_ERROR_* from doca_ec_task_*. Refuse and route elsewhere for non-Reed-Solomon codes (fountain / LDPC / raptor), pure-replication designs, network FEC, or other DOCA accelerator libraries (SHA / Compress / AES-GCM / DMA).
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
doca_ec_cap_*
before sizing recover.Before writing code, query the device: doca_ec_cap_task_create_is_supported,
doca_ec_cap_task_update_is_supported or doca_ec_cap_task_recover_is_supported
for the task you need, then doca_ec_cap_get_max_block_size and
doca_ec_cap_get_max_buf_list_len to size blocks. Start from the shipped sample
for that task under /opt/mellanox/doca/samples/doca_erasure_coding/ (copy it;
never edit the installed one). Use the update task to refresh parity for a
changed block instead of re-encoding everything.
Follow Loading order below. Open the tasks companion for configure / recover / debug. Open the capabilities companion for create / recover / update and matrix policy. Do not open every companion file.
Where to start: This skill assumes DOCA is already installed and
the user is doing hands-on erasure-coding work 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 Erasure Coding express on this version. If the
user has not installed DOCA yet, route to
doca-setup first. If the user is
asking "should I even use erasure coding here, or just replicate
the data?", the path-selection rule in
is the first stop — erasure coding is a storage-resilience primitive (RAID-6 / distributed file system parity / object-storage erasure-coded buckets), not a network primitive and not a replication substitute.
How to use this skill. Read this file, then search the companion file
for the ## <name> heading the question needs and read that whole section.
Answer completely from it, quoting only its symbols and commands. Do not
browse docs; use the entry points in doca-public-knowledge-map. Give
environment checks as commands for the user's DOCA host. For out-of-scope
requests, name the skill that owns them and stop.
The CLASSES of DOCA Erasure Coding 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_ec_task_create on this BlueField, what is the maximum
block size, what is the cap on N+K, and does the device
advertise a Vandermonde Reed-Solomon matrix?". Answered by
the per-task + matrix + size capability-query rule
(doca_ec_cap_task_create_is_supported,
_task_recover_is_supported, _task_update_is_supported,
_task_galois_mul_is_supported — the 4th public task on the
EC accelerator, doca_ec_cap_get_max_block_size,
doca_ec_cap_get_max_buf_list_len, and per-variant
doca_ec_matrix_create() constructor success — the public
header does NOT ship a doca_ec_cap_get_matrix_* family) in
doca_ec_task_update row in the
task-type table in
doca_ec_task_recover row in the task-type table in
doca_ec_task_create returns
DOCA_ERROR_NOT_PERMITTED". Answered by the permission matrix
in ## Safety policy
doca_ec_task_update in
the DOCA I have installed, or do I have to recompute parity from
scratch?". Answered by the version-compatibility overlay in
doca-version and adds the
EC-specific "discover per-task support + matrix type via cap
query" bullets.DOCA_ERROR_* from an EC call mean and which
layer caused it?" — worked example: "DOCA_ERROR_INVALID_VALUE
on doca_ec_task_create_allocate_init with block_size = 4 MiB".
Answered by the EC overlay on the cross-library taxonomy in
doca-debug.This skill serves external developers building applications that
consume the DOCA Erasure Coding library — i.e., users whose code
calls doca_ec_* (directly in C/C++, or through FFI/bindings from
another language) to offload Reed-Solomon erasure coding onto a
BlueField DPU or ConnectX accelerator. It is not for NVIDIA
developers contributing to DOCA Erasure Coding itself.
The canonical fit is distributed storage: RAID-6-style block layouts, distributed file system parity, object-storage erasure-coded buckets, and any data-durability workload where the N data + K redundancy block model lets the system tolerate K simultaneous block losses without data loss. The skill keeps the agent oriented to that domain: erasure coding is operationally distinct from pure replication (which keeps M whole copies) and from RAID-6 the disk layout (which is one specific instance of the N=k, K=2 case); confusing them produces wrong recommendations.
Language scope. DOCA Erasure Coding ships as a C library with
pkg-config module name doca-erasure-coding. 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 create-vs-recover-vs-update
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 Erasure Coding work, in any language. Concretely:
doca_ec context on a doca_dev and configuring
at least one task type (doca_ec_task_create,
doca_ec_task_recover, and/or doca_ec_task_update) before
doca_ctx_start().doca_mmap correctly for the source
buffers — data blocks for create, data + parity for recover —
(DOCA_ACCESS_FLAG_LOCAL_READ_ONLY at minimum) and the
destination buffers — redundancy blocks for create, recovered
blocks for recover, updated parity for update —
(DOCA_ACCESS_FLAG_LOCAL_READ_WRITE).doca_ec_cap_get_max_block_size(devinfo) and N+K against
doca_ec_cap_get_max_buf_list_len(devinfo, &max_buf_list_len).doca_ec_cap_task_*_is_supported (including the 4th task
_task_galois_mul_is_supported) and per-variant
doca_ec_matrix_create() constructor success — the public
header does NOT ship a doca_ec_cap_get_matrix_*
families against the active doca_devinfo.doca_ec_task_update
is far cheaper than recomputing all K parity blocks via a fresh
doca_ec_task_create).DOCA_ERROR_* returned from an EC call (lifecycle
vs. block-size vs. N+K vs. matrix-type vs. permission vs.
queue pressure) and the task-completion event on the progress
engine.Do not load this skill for general DOCA orientation, install
of DOCA itself, non-Reed-Solomon erasure codes (fountain codes,
LDPC — those belong on a CPU library, not the DOCA accelerator),
pure replication / mirroring designs (erasure coding is the wrong
primitive when one extra copy is the right answer), or other
DOCA libraries. For those, use
doca-public-knowledge-map.
This is a thin loader. The body keeps only the orientation needed to pick the right next file. The substantive EC-specific material lives in two companion files:
doca_ec_cap_* for task support, max block size, max N+K,
supported matrix types), the EC 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 path-selection rule (when to use
doca-erasure-coding versus a CPU EC library versus pure
replication versus RAID-6 on disk).configure, build, modify, run, test, 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.
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_erasure_coding/.
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,
layered with the EC-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-erasure-coding 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 for the canonical
version-handling rules, and
doca-public-knowledge-map
whenever the right answer is "look it up in the public docs or
the installed package layout" rather than "EC-specific guidance".
doca-public-knowledge-map —
the routing table for every public DOCA documentation source
and the on-disk layout of an installed DOCA package. The DOCA
Erasure Coding page lives at
https://docs.nvidia.com/doca/sdk/DOCA-Erasure-Coding/index.html;
the install-tree layout (samples directory, header location)
belongs to the public knowledge map, not to this skill.doca-setup — 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 — canonical DOCA
version-handling rules. This skill's
## Version compatibility
cross-links the four-way match rule and adds only the
EC-specific "discover per-task support + supported matrix
types + max block size + max N+K via cap query" overlay.doca-structured-tools-contract —
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 —
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 EC specifics on
top.doca-debug — the cross-cutting
debug ladder (install / version / build / link / runtime /
program / driver). EC-specific debug (task-not-supported,
matrix-type-unsupported, block-size-over-max, N+K-over-max,
recover-with-too-many-missing) overlays on top of that ladder.还没有评价。欢迎分享你的使用感受。
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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.
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.
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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.
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