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dpnp-troubleshooting

Diagnosing dpnp failures on Intel CPUs and GPUs. Use when dpnp raises NotImplementedError or an unexpected TypeError, when the import fails or a SYCL runtime li

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价格未确认★ 21 GitHub Stars目录更新于 · 2026年10月9日agent-skill

概览

Diagnosing dpnp failures on Intel CPUs and GPUs. Use when dpnp raises NotImplementedError or an unexpected TypeError, when the import fails or a SYCL runtime library is missing, when no SYCL device is visible, when dpctl reports a device the user did not expect, or when dpnp code runs slower than the NumPy it replaced. Covers the fallback pattern for unimplemented APIs, install repair, forcing CPU execution, and the handoff to libraries that only accept NumPy arrays.

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dpnp troubleshooting

Purpose

Turns a dpnp failure into the next command to run. Covers the five things that actually go wrong: an API that is not implemented, an import that cannot find the SYCL runtime, no visible device, a device that is not the one expected, and code that got slower instead of faster.

Prefer this skill over reading the traceback and guessing. Each symptom below has a check that produces an answer, and most of them are one line.

When to Use This Skill

Use this skill when:

dpnp raises NotImplementedError, AttributeError, or a TypeError about a keyword argument.
import dpnp fails, or fails on a missing libsycl shared object.
No SYCL device is found, or dpctl lists a different device than expected.
dpnp code is slower than the NumPy it replaced.
Another library rejects a dpnp array.

Do not use this skill to plan a migration (dpnp-quickstart), to size a workload against device memory (dpnp-memory), or as a source of speedup figures.

Quick Start

Three commands answer most questions before any code changes:

python -c "import dpnp; print(dpnp.__version__)"
python -c "import dpctl; print([d.filter_string for d in dpctl.get_devices()])"
python -c "import dpnp; print(dpnp.arange(4).sycl_device)"

Version, what is visible, and where an array actually lands. Report what they print rather than what they were expected to print.

Implementation Guide

NotImplementedError or a rejected keyword. dpnp implements a subset of NumPy, and coverage is per keyword argument as well as per function — a function that exists can still reject a signature. Do not gate on hasattr(dpnp, "name"); the attribute can be there and the call still fail. Guard the call instead:

import dpnp
import numpy

def safe_call(device_func, host_func, x):
    try:
        return device_func(x)
    except (NotImplementedError, AttributeError, TypeError):
        host = dpnp.asnumpy(x) if isinstance(x, dpnp.ndarray) else x
        return dpnp.array(host_func(host))

unique = safe_call(dpnp.unique, numpy.unique, dpnp.array([1, 2, 2, 3]))

Import failures. ImportError on the module name means it is not installed; OSError on libsycl.so means the package is there and the SYCL runtime is not. Install from the Intel conda channel, or the runtime alone from pip:
```
conda install -c https://software.repos.intel.com/python/conda \
  -c conda-forge --override-channels dpnp dpctl

pip install intel-cmplr-lib-rt      # SYCL runtime only
```
No device, or the wrong device. dpctl.get_devices() returning an empty list means the driver stack is not visible to SYCL; dpnp then has only the host to fall back to. To pin execution while debugging, select the device explicitly at allocation, which is clearer than relying on process-wide state:
```
import dpctl
import dpnp

cpu = dpctl.SyclDevice("opencl:cpu:0")
arr = dpnp.arange(1000, device=cpu)
```
The same restriction from outside the process is ONEAPI_DEVICE_SELECTOR=opencl:cpu (it replaced the older SYCL_DEVICE_FILTER, which no longer has an effect on current runtimes).
Slower than NumPy. Three causes, in the order they occur:
- The array is too small, and dispatch dominates. Below roughly a thousand elements NumPy is the right answer; dpnp earns its keep on large arrays.
- The first call was timed. It includes compilation, so time the second:
```
import time
import dpnp

x = dpnp.random.randn(100000)
dpnp.sin(x)                                  # warm up, discard
start = time.perf_counter()
dpnp.sin(x)
print(f"{time.perf_counter() - start:.4f}s")
```
- A conversion sits inside the loop. dpnp.asnumpy() copies device to host every call; hoist it above the loop, or keep the whole loop on the device.
Another library rejects the array. pandas, scikit-learn, PyTorch, and TensorFlow check for a NumPy array and refuse anything else. Convert once at the boundary with dpnp.asnumpy() — dpnp-interop has the per-library patterns.

Performance

No measured numbers ship with this skill, and a fix here is not evidence of a speedup. When a change is meant to make something faster, measure it:

Warm up first, then time the steady state.
Compare against the NumPy original on the same inputs and dtype.
Time the whole pipeline, including conversions — a loop body that got faster while the surrounding transfers got more frequent is a net loss.

Gotchas & Limitations

hasattr is not a coverage check. The attribute can exist and the call still raise. try/except is the only reliable gate.
A fallback that converts inside a loop is its own bug. Correct, and slower than never having moved to dpnp.
The default device is whatever is visible. Code that runs on a GPU workstation lands on a CPU in CI without raising, so "it worked locally" says nothing about where it ran.
intel-cmplr-lib-rt fixes the runtime, not the driver. A GPU that the kernel driver does not expose stays invisible whatever is installed in the environment.
Not covered: driver installation, container device passthrough, and multi-GPU scheduling.

References

File	Load it when
`references/official-sources.md`	you need the current install channels, the API coverage of the installed release, or the device selection environment variables — all three change between releases and must not be answered from memory

Two things here should never be answered from memory: which install channel and package names are current, and whether a given NumPy API is covered in the user's release. Both are documented upstream and both have already changed.

查看来源 ↗

文件元数据

name: dpnp-troubleshooting
description: >-
  Diagnosing dpnp failures on Intel CPUs and GPUs. Use when dpnp raises
  NotImplementedError or an unexpected TypeError, when the import fails or a SYCL
  runtime library is missing, when no SYCL device is visible, when dpctl reports a
  device the user did not expect, or when dpnp code runs slower than the NumPy it
  replaced. Covers the fallback pattern for unimplemented APIs, install repair,
  forcing CPU execution, and the handoff to libraries that only accept NumPy
  arrays.
license: Apache-2.0
compatibility: "Requires dpnp and dpctl. Install commands assume conda or pip with the Intel channel or index."
metadata:
  intel-skill-type: "tool-skill"
  version: "1.0"

查看原始文本

---
name: dpnp-troubleshooting
description: >-
  Diagnosing dpnp failures on Intel CPUs and GPUs. Use when dpnp raises
  NotImplementedError or an unexpected TypeError, when the import fails or a SYCL
  runtime library is missing, when no SYCL device is visible, when dpctl reports a
  device the user did not expect, or when dpnp code runs slower than the NumPy it
  replaced. Covers the fallback pattern for unimplemented APIs, install repair,
  forcing CPU execution, and the handoff to libraries that only accept NumPy
  arrays.
license: Apache-2.0
compatibility: "Requires dpnp and dpctl. Install commands assume conda or pip with the Intel channel or index."
metadata:
  intel-skill-type: "tool-skill"
  version: "1.0"
---

# dpnp troubleshooting

## Purpose

Turns a `dpnp` failure into the next command to run. Covers the five things that
actually go wrong: an API that is not implemented, an import that cannot find the
SYCL runtime, no visible device, a device that is not the one expected, and code
that got slower instead of faster.

Prefer this skill over reading the traceback and guessing. Each symptom below has
a check that produces an answer, and most of them are one line.

## When to Use This Skill

Use this skill when:

- `dpnp` raises `NotImplementedError`, `AttributeError`, or a `TypeError` about a
  keyword argument.
- `import dpnp` fails, or fails on a missing `libsycl` shared object.
- No SYCL device is found, or `dpctl` lists a different device than expected.
- `dpnp` code is slower than the NumPy it replaced.
- Another library rejects a `dpnp` array.

Do **not** use this skill to plan a migration (`dpnp-quickstart`), to size a
workload against device memory (`dpnp-memory`), or as a source of speedup
figures.

## Quick Start

Three commands answer most questions before any code changes:

```bash
python -c "import dpnp; print(dpnp.__version__)"
python -c "import dpctl; print([d.filter_string for d in dpctl.get_devices()])"
python -c "import dpnp; print(dpnp.arange(4).sycl_device)"
```

Version, what is visible, and where an array actually lands. Report what they
print rather than what they were expected to print.

## Implementation Guide

1. **`NotImplementedError` or a rejected keyword.** `dpnp` implements a subset of
   NumPy, and coverage is per keyword argument as well as per function — a
   function that exists can still reject a signature. Do not gate on
   `hasattr(dpnp, "name")`; the attribute can be there and the call still fail.
   Guard the call instead:

   ```python
   import dpnp
   import numpy

   def safe_call(device_func, host_func, x):
       try:
           return device_func(x)
       except (NotImplementedError, AttributeError, TypeError):
           host = dpnp.asnumpy(x) if isinstance(x, dpnp.ndarray) else x
           return dpnp.array(host_func(host))

   unique = safe_call(dpnp.unique, numpy.unique, dpnp.array([1, 2, 2, 3]))
   ```

2. **Import failures.** `ImportError` on the module name means it is not
   installed; `OSError` on `libsycl.so` means the package is there and the SYCL
   runtime is not. Install from the Intel conda channel, or the runtime alone
   from pip:

   ```bash
   conda install -c https://software.repos.intel.com/python/conda \
     -c conda-forge --override-channels dpnp dpctl

   pip install intel-cmplr-lib-rt      # SYCL runtime only
   ```

3. **No device, or the wrong device.** `dpctl.get_devices()` returning an empty
   list means the driver stack is not visible to SYCL; `dpnp` then has only the
   host to fall back to. To pin execution while debugging, select the device
   explicitly at allocation, which is clearer than relying on process-wide state:

   ```python
   import dpctl
   import dpnp

   cpu = dpctl.SyclDevice("opencl:cpu:0")
   arr = dpnp.arange(1000, device=cpu)
   ```

   The same restriction from outside the process is
   `ONEAPI_DEVICE_SELECTOR=opencl:cpu` (it replaced the older
   `SYCL_DEVICE_FILTER`, which no longer has an effect on current runtimes).

4. **Slower than NumPy.** Three causes, in the order they occur:

   - The array is too small, and dispatch dominates. Below roughly a thousand
     elements NumPy is the right answer; `dpnp` earns its keep on large arrays.
   - The first call was timed. It includes compilation, so time the second:

     ```python
     import time
     import dpnp

     x = dpnp.random.randn(100000)
     dpnp.sin(x)                                  # warm up, discard
     start = time.perf_counter()
     dpnp.sin(x)
     print(f"{time.perf_counter() - start:.4f}s")
     ```

   - A conversion sits inside the loop. `dpnp.asnumpy()` copies device to host
     every call; hoist it above the loop, or keep the whole loop on the device.

5. **Another library rejects the array.** pandas, scikit-learn, PyTorch, and
   TensorFlow check for a NumPy array and refuse anything else. Convert once at
   the boundary with `dpnp.asnumpy()` — `dpnp-interop` has the per-library
   patterns.

## Performance

No measured numbers ship with this skill, and a fix here is not evidence of a
speedup. When a change is meant to make something faster, measure it:

- Warm up first, then time the steady state.
- Compare against the NumPy original on the same inputs and dtype.
- Time the whole pipeline, including conversions — a loop body that got faster
  while the surrounding transfers got more frequent is a net loss.

## Gotchas & Limitations

- **`hasattr` is not a coverage check.** The attribute can exist and the call
  still raise. `try`/`except` is the only reliable gate.
- **A fallback that converts inside a loop is its own bug.** Correct, and slower
  than never having moved to `dpnp`.
- **The default device is whatever is visible.** Code that runs on a GPU
  workstation lands on a CPU in CI without raising, so "it worked locally" says
  nothing about where it ran.
- **`intel-cmplr-lib-rt` fixes the runtime, not the driver.** A GPU that the
  kernel driver does not expose stays invisible whatever is installed in the
  environment.
- Not covered: driver installation, container device passthrough, and multi-GPU
  scheduling.

## References

| File | Load it when |
|---|---|
| [`references/official-sources.md`](references/official-sources.md) | you need the current install channels, the API coverage of the installed release, or the device selection environment variables — all three change between releases and must not be answered from memory |

Two things here should never be answered from memory: **which install channel and
package names are current**, and **whether a given NumPy API is covered in the
user's release**. Both are documented upstream and both have already changed.

查看并核实来源

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获取 Skill: 价格未确认
运行 Skill: 尚未确认运行要求，请查看来源中的 Agent、API 和服务费用。
许可证: Apache-2.0
价格未确认: 我们尚未确认此 Skill 的价格，现有来源与安装入口仍可使用。

免费获取不代表免费运行，价格标签不代表安全评级。提交价格信息 →

已记录技能来源

已记录技能指令路径，不代表本站运行测试、安全保证或兼容性认证。

安装前审查: 避免自动安装

许可证: Apache-2.0

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
缺少 AI 审查批准
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 21 GitHub stars
Stars/forks activity: 21 stars, 9 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

打开完整审计

工具列表来自元数据，并非已测试的兼容性；Agent 提示词是建议的交接方式。

从一个小任务开始

1阅读来源，确认输入、预期输出、依赖和权限。
2先让 Agent 提出计划，批准环境配置和费用，再进行隔离的小规模测试。
3检查输出和变更文件，只报告实际执行结果，并保留来源版本以便复现。

请在来源中核实依赖、API 密钥及第三方费用。公开仓库不代表所有服务免费。

来源与使用须知

已收录静态检查通过

仓库元数据和审核信号仅供参考。受欢迎、已发现来源、成功运行是不同的事实。

来源仓库: intel/skills
许可证: Apache-2.0
版本: 1.0
最近 GitHub 推送: 2026年9月29日
目录更新于: 2026年10月9日
技能指令路径: skills/dpnp-troubleshooting/SKILL.md @ 902833d826e7

版本来自目录元数据，使用前请核实来源发布记录。

质量

55/100

有潜力

信任

56/100

Do not auto-install

审计

70/100

需审查

打开完整审计查看评估报告

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
缺少 AI 审查批准
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 21 GitHub stars
Stars/forks activity: 21 stars, 9 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

Verified installs: —
结果: —

复制不等于安装。安装数需有成功安装回报，不代表全面的质量保证。

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      "production_outcomes": 0,
      "last_outcome_at": null,
      "label": "No agent outcome data yet"
    },
    "auto_install": {
      "allowed": false,
      "sandbox_required": true,
      "reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
    },
    "best_for": [
      "automation",
      "agent-skill"
    ],
    "known_risks": [
      "AI review approval is missing",
      "Low GitHub adoption signal",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "GitHub adoption: 21 GitHub stars",
      "Stars/forks activity: 21 stars, 9 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 70,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Low GitHub adoption signal",
      "AI review approval is missing",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "GitHub adoption: 21 GitHub stars",
      "Stars/forks activity: 21 stars, 9 forks; issue activity unavailable in current metadata"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 55,
    "label": "Promising"
  },
  "supply": {
    "track": "Coding and developer agents",
    "scenario": "Coding agents",
    "maintenance": "13d since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "Low GitHub adoption signal",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "AI review approval is missing",
    "Quality score needs review"
  ],
  "agent_contract": {
    "task_input": "Use dpnp-troubleshooting in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 64/100 Manual review",
      "Audit: 70/100 Needs review",
      "Safety: 26/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "intel-dpnp-troubleshooting (dpnp-troubleshooting)",
      "install_command": "npx skills add intel/skills --skill dpnp-troubleshooting",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "intel-dpnp-troubleshooting",
      "task": "Use dpnp-troubleshooting in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/intel-dpnp-troubleshooting",
    "api": "https://www.openagentskill.com/api/agent/skills/intel-dpnp-troubleshooting",
    "audit": "https://www.openagentskill.com/skills/intel-dpnp-troubleshooting/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=intel-dpnp-troubleshooting&task=Use%20dpnp-troubleshooting%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20dpnp-troubleshooting%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20dpnp-troubleshooting%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/intel-dpnp-troubleshooting/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/intel-dpnp-troubleshooting"
  }
}

创作者工具

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此列表来自公开来源，维护者认领获批前不会标记为官方。

创作者: intel
来源: intel/skills
收录方: OpenAgentSkill 社区索引

归属链接指向公开仓库或创作者主页。创作者可认领列表以更新所有权信号。

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这条 Registry 收录列表归属于 intel，但尚未标记为官方。认领后可增加已验证所有者信号，使后续发布、安装和审计更新更值得信赖。

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[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/intel-dpnp-troubleshooting?metric=listed&label=Listed)](https://www.openagentskill.com/skills/intel-dpnp-troubleshooting?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
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