pandasで扱う表データ処理をcuDFでGPUへ移す作業を支援します。結合・集計・CSVやParquet入出力から、結果の照合、メモリ対策、複数GPU利用まで扱います。
- 既存pandas処理を少ない変更で加速
- 結合や集計をGPUへ移したいとき
- 欠損値や並び順をpandasと照合
NVSHMEMのノード間通信方式を、導入済みプラグインやネットワーク、処理内容から選びます。互換性の根拠と不明点を整理し、選択に必要な最小限の環境変数を提示します。
Select an NVSHMEM remote transport from target system and kernel evidence. Use for inter-node selection, compatibility checks, or configuration.
NVSHMEMのノード間通信を担うtransportを、システム情報と処理内容に基づいて選びます。導入済みプラグイン、ネットワークの種類、ドライバーや依存機能を確認し、利用条件を満たす候補から推奨構成を提示します。結果には、選択に必要な最小限の環境変数、判断の根拠、次点候補、除外理由、不明点を含めます。
計算環境に適合する通信方式を決めたい人や、選択したCUDAカーネルの通信特性に合う候補を検討したい人に向いています。システムの適合性だけを調べる方法も選べ、その場合は互換性を優先した構成を提案します。対象環境で収集した情報を渡して相談する使い方にも対応します。
選定には導入済みプラグインの一覧など、対象環境の情報が必要です。通常の調査にAPIキーや管理者権限は不要です。調査は読み取り専用で、分散ジョブやベンチマークは実行しません。性能や実行時の正しさを保証せず、収集結果もクラスタ全体の均一性までは証明しません。
この紹介文は、公開されている SKILL.md をもとに AI(OpenAI Codex)が作成しました。正確な仕様は下の原文を確認してください。
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Recommend one NVSHMEM transport configuration from static evidence. Separate hard eligibility from expected kernel fit, distinguish documented facts from inference.
Focus on read-only system probes and source inspection. Do not run NVSHMEM applications, correctness tests, or performance benchmarks.
NVSHMEM_REMOTE_TRANSPORT.$nvshmem-docs workflow, or its readable ../nvshmem-docs/SKILL.md, when the user requests current/exact official verification or an exact-version-sensitive decision cannot be resolved from the bundled matrix. Do not invoke it merely because the target patch differs from a source version reviewed by the matrix.NVSHMEM_PREFIX, CMake cache, module/container details, launcher command, environment, and NVSHMEM configuration files.Do not re-ask facts the user already supplied. Prefer one best-evidence recommendation over a list of conditional branches. State material assumptions and unknowns, but withhold the selection configuration only when no candidate passes a hard gate or the setting itself cannot be resolved safely.
First establish whether the agent runs on the target system; ask unless the user already answered or supplied a complete target-system probe. Never infer this from the shell, scheduler, hostname, hardware, or repository.
If yes, inspect the complete bundled collector script before its first invocation in the current checkout. Run only that inspected file; if it is unavailable or incomplete, do not execute it. Then announce the read-only probe and invoke the collector without a prefix:
run_script("scripts/collect-transport-facts.sh")
If it resolves an NVSHMEM prefix, tell the user which installation will be analyzed. Otherwise ask for an absolute prefix and rerun:
run_script("scripts/collect-transport-facts.sh", "--prefix", "/opt/nvshmem")
Replace the example prefix with the resolved absolute prefix. Preserve partial output when it exits with status 2.
If the collector emits manual_follow_up_command entries, stop automatic probing and ask the user to run those commands manually in the target host or exact allocation/container used to launch NVSHMEM. Resume after the user returns their complete output. Do not repeat a command when equivalent successful output was already supplied.
If no, request complete output from bash scripts/collect-transport-facts.sh on the target, or its GPU/driver, NIC/fabric/provider, RDMA/provider-stack, and job-scale facts. Do not request an NVSHMEM prefix in this branch. Use installation evidence already supplied; otherwise eligibility remains conditional.
Ask one question with exactly two choices: kernel/workload analysis or system eligibility only. Skip this when already answered.
Treat assessment as read-only. Identify the job scale and optimization goal, and determine whether relevant communication can cross nodes; local P2P/NVLink traffic does not use the remote transport.
When kernel/workload analysis was selected, read kernel-fit.md and record:
Do not infer a high message rate merely from many logical elements. Account for application aggregation, one-thread issuance, warp coalescing, and batching.
Read transport-matrix.md completely. Exclude a candidate when any required condition is disproven:
Treat a missing fact as conditional only when it can invalidate the proposed configuration. Otherwise recommend the documented or compatibility-first default and list the missing fact under confidence and unknowns. Keep build support, runtime loadability, and hardware suitability separate.
For this skill, a loaded gdrdrv kernel module or visible /dev/gdrdrv device is sufficient positive evidence that the GDRCopy prerequisite is present. Do not require pkg-config, ldconfig, or a discovered libgdrapi path in addition, and do not treat missing userspace-library hints as evidence that GDRCopy is absent.
For IBGDA, evaluate both the GPU data path and a separate host remote transport. For GPUNetIO GDAKI, require gpunetio as the remote transport. Never enable IBGDA and GPUNetIO GDAKI together.
For system eligibility only, do not apply workload-fit rules. After hard eligibility, select a compatibility-first system default: IBRC for Mellanox InfiniBand or verified RoCE, native libfabric for EFA or Slingshot/CXI, a site-required UCX path, or none for a proven local-only deployment. Emit the selection block and do not claim it is the fastest. For kernel/workload analysis, apply these rules in order without assigning artificial scores:
efa on EFA and cxi on Slingshot/CXI when no known release-specific operation or memory-mode limitation conflicts with the application.none only when all relevant PEs communicate within a peer-reachable NVLink domain and no remote path is required.If two candidates remain close, choose the compatibility-first option as the recommendation, report the other as runner-up, and state that static analysis cannot establish the performance winner.
Order the recommendation and candidate discussion by the target fabric. On Mellanox InfiniBand or verified RoCE, lead with IBRC, IBDevX, and IBGDA/GPUNetIO as applicable; do not lead with libfabric. On EFA or Slingshot/CXI, lead with the matching native libfabric provider.
Emit only the variables required to select the resolved configuration:
| Configuration | Minimal exports |
|---|---|
| IBRC | NVSHMEM_REMOTE_TRANSPORT=ibrc |
| IBDevX | NVSHMEM_REMOTE_TRANSPORT=ibdevx |
| IBGDA | NVSHMEM_REMOTE_TRANSPORT=<resolved-host-transport> and NVSHMEM_IB_ENABLE_IBGDA=1 |
| GPUNetIO CPU | NVSHMEM_REMOTE_TRANSPORT=gpunetio and NVSHMEM_GPUNETIO_ENABLE_GDAKI=0 |
| GPUNetIO GDAKI | NVSHMEM_REMOTE_TRANSPORT=gpunetio and NVSHMEM_GPUNETIO_ENABLE_GDAKI=1 |
| UCX | NVSHMEM_REMOTE_TRANSPORT=ucx |
| libfabric | NVSHMEM_REMOTE_TRANSPORT=libfabric and `NVSHMEM_LIBFABRIC_PROVIDER=<cxi |
| No remote | NVSHMEM_REMOTE_TRANSPORT=none |
Do not add HCA mapping, QP counts, batching, NIC handlers, memory-mode changes, or provider tuning to this block. Put mandatory compatibility preconditions outside the block and route mapping/tuning to the appropriate sibling skill.
Default to emitting the minimal selection block for the recommended best-evidence configuration. Withhold it only when no viable candidate passes the hard gates or a required value cannot be resolved, such as a missing plugin, fabric/provider mismatch, or mandatory incompatible memory mode. Do not withhold merely because the patch version, workload profile, optional capability, or performance winner is unknown.
Use this exact section order:
Recommendation: <transport or combined host/GPU configuration; conditional if necessary>
Selection configuration
<minimal export block, or why it is withheld>
System eligibility evidence
<version, plugins, fabric/provider, driver/module/dependency evidence>
Kernel-fit evidence
<API surface, operation shape, sizes, concurrency, fan-out, completion, scale>
Runner-up
<candidate and the fact or tradeoff that kept it second>
Excluded candidates
<candidate: hard gate or unresolved prerequisite>
Confidence and unknowns
<high|medium|low; documented facts versus inference; missing evidence>
Static follow-up checks
<read-only checks or exact facts still needed; never benchmarks>
State the documentation version consulted when live verification was used. Describe performance as expected fit, not an assured result.
$nvshmem-configure-nic-pe-mapping when available.$nvshmem-install when available.$nvshmem-troubleshoot-and-report-bugs when available.| Script | Purpose | Arguments |
|---|---|---|
scripts/collect-transport-facts.sh | Collect read-only NVSHMEM, GPU, kernel-module, RDMA, and provider evidence from a target compute node. | Optional --prefix PATH |
Use $nvshmem-select-remote-transport. Inspect this target compute node and src/exchange.cu, then recommend a throughput-oriented transport for 256 PEs with 8 local PEs per node.Use $nvshmem-select-remote-transport with the attached collector output and communication profile. Recommend a compatibility-first transport for 32 PEs with 8 local PEs per node, emit the minimal selection exports, and state any assumptions.nvshmem-info -n query.diagnostics.nvidia-smi, ibstatus, ibv_devinfo, or rdma link fails or cannot see devices while static sysfs/module evidence suggests hardware exists, treat device access as unresolved rather than treating the hardware as absent. Ask the user to run the failed commands manually in the target host or exact launch environment; do not retry with privileges or infer eligibility from host-visible sysfs alone.$nvshmem-troubleshoot-and-report-bugs.まだレビューはありません。使ってみた感想をお寄せください。
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pandasで扱う表データ処理をcuDFでGPUへ移す作業を支援します。結合・集計・CSVやParquet入出力から、結果の照合、メモリ対策、複数GPU利用まで扱います。
NVIDIA Nemotron Voice Agentのサンプルを変更し、別のバックエンドを設けずに予約受付や患者情報の聞き取りを行う音声エージェントを作るスキル。
ライブカメラのRTSP映像をVIOSで録画し、AutoMagicCalibでカメラ校正を進めます。設定や位置合わせ資料の取り込み、進捗確認、結果取得まで扱います。
同梱の倉庫カメラ映像でAMCのカメラ校正を一通り実行し、正解データとの誤差を確認します。導入後の動作検証や、実データ投入前の確認に使えます。
録画済みの複数カメラ映像をAutoMagicCalibに送り、カメラの位置や向きを調整する処理を、プロジェクト作成から進捗確認・結果評価まで進めます。
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.
日本語の概要は準備中です。原文の説明を表示しています。