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Doca Bare Metal Deployment

NVIDIAOfficialCreated by NVIDIA

  • Skill
  • Workflow
  • Cloud and DevOps
  • Apache-2.0
  • Medium risk

From the author

Running a DOCA-linked binary directly on the hardware, with no container or kubelet: on host x86 with a BlueField NIC over PCIe, or bare-metal on BlueField Arm. Use this skill for launching and supervising it (direct, tmux, systemd), and when it "exits 1 right after launch", "systemd keeps restarting it", reports "no matching device on the BF", or the BlueField itself is unhealthy: "bfb-install exited 0 but DPU is dead", "ping 192.168.100.2 works but ssh fails", "host PFs aren't showing netdevs". Refuse service containers under kubelet (doca-container-deployment). Covers PCI/NUMA/CPU/IRQ binding, co-tenant isolation (cgroup-v2/netns/numactl), a seven-layer error taxonomy, platform lifecycle (BFB install, RShim/TMFIFO, host PF rebind, post-BFB recovery) and a six-state BlueField lifecycle classifier. Destructive firmware burn / mlxconfig set requires explicit confirmation via doca-hardware-safety; library APIs, env prep, and build use other skills.
— the description by NVIDIA

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured approach for running DOCA-linked application binaries directly on host x86 systems with BlueField NICs or on BlueField Arm cores. It covers the full lifecycle of bare-metal deployment, including launch modes, hardware resource binding, per-tenant isolation, and troubleshooting. It is designed for operators who need to manage binaries outside of containerized environments, focusing on stability, performance, and correct hardware interaction.

What it does

  • Guides the configuration and launch of DOCA-linked binaries on bare-metal hardware.
  • Provides methods for process supervision using direct execution, tmux, or systemd.
  • Details hardware-resource binding for PCIe, NUMA, CPU, and IRQ affinity.
  • Implements per-tenant isolation using cgroup-v2, network namespaces, and numactl.
  • Offers a seven-layer error taxonomy for diagnosing launch and runtime failures.
  • Manages BlueField platform lifecycle including BFB installation and recovery.

When to use it

  • When deploying a DOCA-linked binary directly on hardware without containers or Kubernetes.
  • When diagnosing launch failures, immediate exits, or device-access issues on BlueField.
  • When setting up process isolation for multiple co-tenant DOCA applications.
  • When determining the appropriate supervision strategy for long-running DOCA services.
  • When recovering a BlueField DPU from an unhealthy state or failed BFB installation.

Advantages

  • Ensures optimal performance through precise hardware and NUMA binding.
  • Prevents resource contention between multiple DOCA processes on the same hardware.
  • Provides a systematic, layered approach to debugging complex hardware-level failures.
  • Standardizes deployment procedures across different host and DPU environments.
  • Promotes safe operational practices for high-stakes bare-metal environments.

Usage examples

“My DOCA-linked binary exits with status 1 immediately after launch. How do I debug this?”
The agent will guide you through the seven-layer error taxonomy to isolate whether the failure is due to device binding, resource constraints, or environment configuration.
“Should I run my DOCA application in tmux or as a systemd unit?”
The agent will provide a decision framework based on your requirements for persistence, logging, and automatic recovery after reboots.
“How do I pin my DOCA process to a specific NUMA node to improve performance?”
The agent will provide the correct commands for CPU and NUMA binding using numactl and taskset to ensure the process aligns with the BlueField PCIe topology.
“My BlueField DPU is unresponsive after a BFB install. What are the recovery steps?”
The agent will walk you through the BlueField lifecycle classifier and the post-BFB recovery sequence to restore the DPU to a healthy state.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/nvidia/doca-bare-metal-deployment/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip doca-bare-metal-deployment.zip -d .agents/skills

Documentation by NVIDIA

DOCA bare-metal deployment

Examples

  • "The binary exits 1 right after launch." — this is in scope. Treat it as a launch / device-bind / isolation failure before rewriting application code.

  • "systemd keeps restarting my DOCA app." — a restart loop on hardware is high-stakes. Debug the first exit before enabling Restart=always.

Instructions

Follow Loading order below. Open the tasks companion for launch / supervise / recover, and the capabilities companion for the runtime contract. Do not open every companion file.

Where to start: This skill is the bundle's home for operating a DOCA-linked application binary directly on hardware — no container, no kubelet, no static-pod manifest. It is the parallel of doca-container-deployment/SKILL.md for the non-container path. If the user has a DOCA-linked binary they built (per the canonical workflow in doca-programming-guide/SKILL.md) and they want to know how to actually run it on the host or on the BlueField Arm cores correctly, open the tasks companion and start at ## configure. If the question is what shape does the bare-metal runtime even have and what is the deployment contract, start at the capabilities companion. If the user is not yet sure whether their target system shape is the container path or the bare-metal path, route the recognition step to doca-setup/SKILL.md first; only return here once bare-metal is the confirmed shape.

Audience

This skill serves external DOCA developers and operators who have a DOCA-linked application binary they built and want to run it directly on hardware — i.e., people who already have:

  • a DOCA-linked application binary they built per doca-programming-guide ## build,

  • a real BlueField NIC and a host that talks to it (the host x86 path — DOCA host install on the host talks to the BlueField NIC over PCIe), OR a BlueField with a console or SSH to the Arm side (the BlueField Arm bare-metal path — DOCA installed on the DPU Arm cores; the binary runs there directly), and

  • a desire to RUN that binary directly on the hardware, not inside a kubelet-standalone-managed container.

It is not for:

  • kernel-driver developers contributing to mlx5_* or the BlueField OS,

  • DOCA library contributors (library changes are outside the scope of a bare-metal deployment),

  • full-Kubernetes-cluster operators managing a fleet of BlueFields (the bundle covers doca-container-deployment/SKILL.md for the single-host kubelet-standalone shape; fleet/production-scale deployment is fleet-orchestration scope — route to the orchestration entry-point in doca-public-knowledge-map ## Deploying DOCA services at scale (DPF / Network Operator / Launch Kit), not hand-rolled static-pod loops),

  • fresh-laptop-no-hardware users with no DOCA install yet — those belong on doca-setup ## no-install.

The skill teaches the agent the bare-metal-deployment procedure and the rules for quoting documented commands from the public DOCA Programming Guide and the public BlueField / DPU User Manual via doca-public-knowledge-map/SKILL.md; it does not invent flag names, PCI BDFs, NUMA numbers, devlink paths, representor strings, or systemd Restart= mode names from memory.

When to load this skill

Load this skill when the user is doing hands-on bare-metal deployment of a DOCA-linked application binary on either of the two supported host modes (host x86 or BlueField Arm), or asking a cross-cutting bare-metal question that is not specific to one library's API. Concretely:

  • Launching a DOCA-linked binary for the first time on a host with a BlueField NIC in a PCIe slot, with DOCA installed on the host.

  • Launching a DOCA-linked binary on the BlueField Arm cores directly (BlueField Arm bare-metal mode), with DOCA installed on the Arm side per the BlueField OS image.

  • Deciding which launch mode to use (direct foreground for interactive debug; tmux/screen for long-running with manual reattach; systemd-supervised for restart-after-reboot, journald-integrated logs, and Restart= policy).

  • Binding the DOCA process to the right PCIe function, the right representor, the right NUMA node, and the right CPU set — and pinning IRQs to match — without inventing the addresses or the flag names.

  • Setting up per-tenant isolation (cgroup-v2 cpu / memory / io controllers, network namespaces for multi-tenant deployments, numactl / taskset for CPU + NUMA binding) so multiple DOCA processes co-tenant on the same BlueField without crushing each other.

  • Diagnosing a bare-metal launch that is misbehaving — won't start, starts and exits immediately, runs but can't find the device, attaches to the device but the workload errors, OOMs or is signal-killed, is in a restart loop under a supervisor, or is being interfered with by a co-tenant.

  • Cross-cutting questions: "should I run this in tmux or as a systemd unit", "what is the smoke-before-bulk loop for a binary on bare metal", "my binary works in a container on the BlueField but not when I run it directly on the Arm — what changed".

Do not load this skill for the container-path equivalent (those questions go to doca-container-deployment/SKILL.md); for full-Kubernetes-cluster operations (out of scope per the bundle's non-goals); for library-API questions (route to the matching libs/<library> skill); for env-preparation questions including hugepages, IOMMU, pkg-config, and devlink mode flips (use doca-setup/SKILL.md); for any hardware-state-changing operation including mlxconfig writes and BFB reflashes (route to doca-hardware-safety/SKILL.md for the cross-cutting meta-policy); or for cross-library programming questions (use doca-programming-guide/SKILL.md).

What this skill provides

This is a thin loader. Substantive material lives in two companion files:

  • the capabilities companion — the bare-metal deployment runtime contract for a DOCA-linked binary: the two host modes (host x86 vs BlueField Arm bare-metal), the three launch modes (direct, tmux/screen, systemd-supervised), the hardware-resource-binding surface (PF / VF / representor enumeration; NUMA topology discovery; CPU pinning rationale; IRQ affinity rules), the per-tenant isolation surface (cgroup-v2 cpu / memory / io, network namespaces, numactl / taskset), the restart and recovery semantics (documented systemd Restart= modes vs crash-and-investigate vs supervisor-driven restart), the bare-metal-specific version overlay on the four-way version match owned by doca-version/SKILL.md, the cross-cutting error taxonomy (seven layers, walked in order), the observability surface (stdout/stderr discipline by launch mode; device-state introspection via devlink / sysfs / mlxconfig query; per-tenant resource visibility), and the safety policy (overlay on doca-hardware-safety/SKILL.md: smoke-before-bulk for binaries; failed bare-metal process is HIGH-STAKES; do not invent PCI addresses, NUMA numbers, representor names, devlink paths, or systemd Restart= mode names; confirm tenant-isolation primitives BEFORE the workload starts).

  • the tasks companion — step-by-step workflows for the in-scope bare-metal verbs: configure, build, modify, run (with an explicit ### isolation sub-anchor covering cgroup-v2 / namespaces / numactl per-tenant primitives), test, debug, bluefield-lifecycle (the BFB-install → RShim/TMFIFO → post-BFB-recovery operational sequencing ladder, with the six-state bluefield-state-classifier sub-anchor), the Command appendix (documented commands the agent may quote, each cross-linked to its public-doc source — no invented commands), and the Deferred task verbs block routing container-path / cluster / library-API / env-prep / hardware-state-change / cross-library questions out to their owning skills. (The change-application discipline for any mutating burn invoked from ## bluefield-lifecycle is still meta-policy owned by doca-hardware-safety/SKILL.md, loaded alongside.)

The skill assumes a host or BlueField target where:

  • DOCA is already installed and healthy (per doca-setup ## test),

  • the user has a DOCA-linked application binary they built (per doca-programming-guide ## build),

  • the user has the host-OS permissions to enumerate devices, reserve hugepages, write systemd units (if they choose that launch mode), and bind processes to NUMA nodes.

It does not cover installing DOCA — that path goes through doca-setup/SKILL.md — and it does not cover building the binary — that path goes through doca-programming-guide/SKILL.md.

Loading order

  1. Read this skill first to confirm the user's question is in scope (bare-metal launch of a DOCA-linked binary on host x86 or BlueField Arm; NOT the container path, NOT a full cluster, NOT a library-API question).

  2. For the runtime contract (two host modes, three launch modes, hardware-binding surface, per-tenant isolation, version overlay, seven-layer error taxonomy, observability surface, bare-metal safety overlay), see the capabilities companion.

  3. For step-by-step workflows — configure, build (routing stub), modify (routing stub), run (with ### isolation sub-anchor), test, debug, bluefield-lifecycle (BFB install + RShim/TMFIFO + post-BFB recovery + the six-state bluefield-state-classifier), plus the Command appendix and the Deferred task verbs block — see the tasks companion.

Example questions this skill answers well

What this skill deliberately does not ship

Files

BENCHMARK.md

Skill Benchmark: doca-bare-metal-deployment

✅ Overall verdict: PASS — Recommended for publication

Publication Recommendation

Recommended for publication based on the completed evaluation evidence in this report.

Evaluation Metadata

  • Skill: doca-bare-metal-deployment

  • Validation status: passed

  • Evaluation date: 2026-10-28

  • Evaluator version: 1.6.2

  • Agents: Claude Code (aws/anthropic/bedrock-claude-opus-5-5), Codex (openai/openai/gpt-6.1-sol)

  • Tasks: 4 evaluation tasks (3 positive, 1 negative)

  • Dataset digest: sha256:39e7c229748cb1f865ca636d41514792e647c3c5b0bd6c055f1811615a582b6a (skill-evaluator-dataset-snapshot/1)

  • Attempts per task: 1

  • Environment: k8s-sandbox

  • Tier 2 evidence: required for publication

  • Tier 3 evidence: required for publication

Each task attempt ran in its own isolated sandbox pod.

Tier 1 LLM Provenance

SkillSpector provider, model, analyzer revision, and determinism evidence retained for this scan.

SkillProviderAdapterAnalyzerModelRevisionDeterminism
doca-bare-metal-deploymentnv_inferenceopenaimcp_tool_poisoningaws/anthropic/bedrock-claude-opus-5-52.13.1nondeterministic / provider_defaults
doca-bare-metal-deploymentnv_inferenceopenaisemantic_developer_intentaws/anthropic/bedrock-claude-opus-5-52.13.1nondeterministic / provider_defaults
doca-bare-metal-deploymentnv_inferenceopenaisemantic_quality_policyaws/anthropic/bedrock-claude-opus-5-52.13.1nondeterministic / provider_defaults
doca-bare-metal-deploymentnv_inferenceopenaisemantic_security_discoveryaws/anthropic/bedrock-claude-opus-5-52.13.1nondeterministic / provider_defaults
doca-bare-metal-deploymentnv_inferenceopenaimeta_analyzeraws/anthropic/bedrock-claude-opus-5-52.13.1nondeterministic / provider_defaults
  • Controls for doca-bare-metal-deployment: temperature=not set (source=provider_default, forwarded=no); seed=not set (source=unset, forwarded=no); reasoning_effort=not set (source=provider_default, forwarded=no)

What This Report Answers

The three-tier evaluation checks whether the skill:

  • is safe to use;

  • produces correct answers;

  • is discovered and activated when needed;

  • helps the agent complete the user's goal and expected workflow; and

  • avoids wasted skill and tool usage.

Results at a Glance

MeasureClaude Code (Baseline → Skill Uplift)Codex (Baseline → Skill Uplift)
Overall94.5% — baseline ran, but no comparable score was available; uplift unavailable94.2% — baseline ran, but no comparable score was available; uplift unavailable
Security100.0% → 100.0% (±0.0 points)100.0% → 100.0% (±0.0 points)
Correctness60.0% → 95.0% (+35.0 points)70.0% → 95.0% (+25.0 points)
Discoverability100.0% — baseline ran, but no comparable score was available; uplift unavailable95.0% — baseline ran, but no comparable score was available; uplift unavailable
Effectiveness45.8% → 100.0% (+54.2 points)58.3% → 100.0% (+41.7 points)
Efficiency77.4% — baseline ran, but no comparable score was available; uplift unavailable81.0% — baseline ran, but no comparable score was available; uplift unavailable

How to read this table: baseline is the same task attempted without the target skill. Scores are rounded to one decimal; threshold-adjacent values use additional precision so their displayed band matches the verdict. Uplift is derived from those displayed scores and shown in percentage points.

Example: 47.0% → 92.0% (+45.0 points) means the skill-assisted run scored 92.0%, 45.0 percentage points above its 47.0% no-skill baseline.

A partial dimension was calculated from only the available configured signals; review the detailed report before relying on it.

Comparison details

Shared-source comparisons use the same signals and weights on both conditions. They do not replace the full dimension scores above or change the publication verdict.

AgentDimensionBasisBaseline → With skill (uplift)Explanation
claude-codeDiscoverabilityDimension comparisonN/ADiscoverability is not applicable to the no-skill baseline; there is no installed skill to discover.
claude-codeEfficiencyToken-only comparison91.2% → 94.8% (+3.6 points)Only Token Efficiency is comparable; the full dimension scores use different evaluator sources.
codexDiscoverabilityDimension comparisonN/ADiscoverability is not applicable to the no-skill baseline; there is no installed skill to discover.
codexEfficiencyToken-only comparison88.0% → 88.1% (+0.1 points)Only Token Efficiency is comparable; the full dimension scores use different evaluator sources.

Token Usage

Actual Tier 3 execution usage is reported for every observed agent/case pair and both conditions.

AgentDataset caseWith skillWithout skillDeltaChangeCoverage
claude-codeAll cases711,414396,169+315,245+79.57%skill 4/4; base 4/4
claude-codedoca-bare-metal-deployment.explicit-positive.v1198,770108,203+90,567+83.70%skill 1/1; base 1/1
claude-codedoca-bare-metal-deployment.implicit-positive.v1199,062107,688+91,374+84.85%skill 1/1; base 1/1
claude-codedoca-bare-metal-deployment.implicit-positive.v2204,73872,859+131,879+181.01%skill 1/1; base 1/1
claude-codedoca-bare-metal-deployment.negative.v1108,844107,419+1,425+1.33%skill 1/1; base 1/1
codexAll cases349,424110,340+239,084+216.68%skill 4/4; base 4/4
codexdoca-bare-metal-deployment.explicit-positive.v1121,68539,660+82,025+206.82%skill 1/1; base 1/1
codexdoca-bare-metal-deployment.implicit-positive.v194,82430,571+64,253+210.18%skill 1/1; base 1/1
codexdoca-bare-metal-deployment.implicit-positive.v2119,39226,871+92,521+344.32%skill 1/1; base 1/1
codexdoca-bare-metal-deployment.negative.v113,52313,238+285+2.15%skill 1/1; base 1/1
ALL AGENTSDataset aggregate1,060,838506,509+554,329+109.44%skill 8/8; base 8/8

Prompt tokens include cached reads, so total tokens are prompt + completion (cached is not added twice). The Efficiency score uses (prompt - cached) + completion. Change is 100 * (with - without) / without; negative means fewer tokens. N/A comparisons include their reason in the coverage column; coverage is never estimated.

Tier Status

TierPurposeStatusEvidence
Tier 1Static validationPASSED WITH OBSERVATIONS11 validator(s); 12 finding(s)
Tier 2Semantic deduplicationPASSED WITH OBSERVATIONS2 validator(s); 1 finding(s)
Tier 3Live agent evaluationPASS2 agent(s); 4 task(s)

Findings and Observations

<details> <summary>Show detailed findings and successful checks</summary>

  • HIGH DUPLICATE/duplicate: Duplicate content found across CAPABILITIES.md and TASKS.md: "## Error taxonomy" in CAPABILITIES.md (lines 395-497) vs "## debug" in TASKS.md (lines 431-523) (CAPABILITIES.md:395)

  • MEDIUM QUALITY/quality_efficiency: Deeply nested references in details.md (skills/doca-bare-metal-deployment/SKILL.md)

  • MEDIUM SCHEMA/metadata_key_style: Metadata key 'doca_version' is not kebab-case (skills/doca-bare-metal-deployment/SKILL.md)

  • MEDIUM SECURITY/External Transmission (E1): Data Exfiltration: curl to it is hitting the host, not the BlueField. The agent ALWAYS runs ip route get before trusting ping for TMFIFO recovery, and accepts either tmfifo_net0 or tm-br as the (TASKS.md:700)

  • LOW QUALITY/quality_discoverability: Description very long (961 chars, recommend 50-150) (skills/doca-bare-metal-deployment/SKILL.md)

  • 8 additional finding(s) are available in the full evaluation artifacts.

</details>

Scoring Methodology

<details> <summary>Show dimension definitions, source signals, and thresholds</summary>

DimensionQuestionScored signals
SecurityIs it safe to use?security (100%)
CorrectnessIs the answer correct?accuracy (100%)
DiscoverabilityWas the right skill loaded when needed?skill_execution (100%)
EffectivenessDid the skill help complete the task?goal_accuracy (50%) + behavior_check (50%)
EfficiencyDid it avoid wasted tool calls and token usage?skill_efficiency (50%) + token_efficiency (50%)
  • Dimension bands: PASS at 50% or above; NEUTRAL from 40% to below 50%; FAIL below 40%.

  • Overall Tier 3 lift: PASS at +5 points or more; FAIL at -10 points or less; values between those bands are NEUTRAL.

  • Overall verdict: PASS only when every configured dimension passes for at least one supported agent. Lift is reported as diagnostic evidence and does not override this gate.

  • The 50% attempt pass threshold is a separate per-task gate; it is not the dimension pass threshold.

  • Effectiveness is the equal-weight mean of goal completion (goal_accuracy) and expected workflow adherence (behavior_check).

  • Efficiency is 50% tool-call productivity (the backward-compatible skill_efficiency wire id) and 50% token_efficiency. Positive-case skill routing is scored under Discoverability, not Efficiency; a negative case without a routing target is N/A. N/A sources are omitted, remaining weights are renormalized, and the dimension is marked partial.

Signals present in this run:

  • security (Security): unsafe operations, secret leakage, and unauthorized access.

  • skill_execution (Skill Execution): whether the expected skill was selected, decoys were avoided, and the workflow executed.

  • skill_efficiency (Tool Productivity): tool-call productivity (legacy wire id; routing is scored under Discoverability).

  • accuracy (Accuracy): final-answer correctness against the reference answer.

  • goal_accuracy (Goal Accuracy): whether the user's goal was achieved.

  • behavior_check (Behavior Check): whether the expected workflow behavior was followed.

  • token_efficiency (Token Efficiency): actual uncached prompt plus completion usage (50% of Efficiency).

</details>

Freshness

Regenerate this benchmark when the skill, evaluation dataset, target agent/model, evaluator version, environment, or scoring policy changes.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Node.js

The author says: No DOCA install required to read this skill (it is an overlay loaded against any DOCA artifact skill); the validation steps within this skill require a live DOCA install at /opt/mellanox/doca on a host or BlueField with a built DOCA-linked binary.

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
NVIDIA
Publisher
NVIDIA (Official)
Source
NVIDIA/skills · skills/doca-bare-metal-deployment · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download doca-bare-metal-deployment.zip