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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

Revisar el código fuente Ver en GitHub

Precio sin confirmar★ 21 Estrellas de GitHubRegistro actualizado · 9 oct 2026agent-skill

Resumen

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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Documentación de origen, no instrucciones para este sitio. Revisa los permisos antes de ejecutar comandos.

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.

Ver fuente ↗

Metadatos del archivo

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"

Ver texto original

---
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.

Revisar el código fuente

Precio y costes de ejecución

Obtener el skill: Precio sin confirmar
Ejecutarlo: Requisitos sin confirmar. Consulta los costes del agente, API y servicios en la fuente.
Licencia: Apache-2.0
Precio sin confirmar: No hemos confirmado el precio. Los enlaces existentes al código y a la instalación siguen disponibles.

Obtener gratis no significa ejecutar gratis. El precio no es una evaluación de seguridad. Enviar información de precio →

Fuente del skill registrada

La ruta de instrucciones está registrada. No implica pruebas de ejecución, seguridad ni compatibilidad.

Revisar antes de instalar: Evitar instalación automática

Licencia: Apache-2.0

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
Falta aprobación de revisión por IA
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

Abrir auditoría completa

Las herramientas son indicios de metadatos, no compatibilidad probada. Los prompts son sugerencias.

Empieza con una tarea pequeña

1Lee la fuente y confirma entradas, resultados, dependencias y permisos.
2Pide un plan al agente. Aprueba la configuración y los costes antes de probar en un entorno aislado.
3Comprueba resultados y archivos modificados. Informa solo de lo ejecutado y conserva la revisión de la fuente.

Consulta dependencias, claves API y costes externos en la fuente. Un repositorio público no implica servicios gratuitos.

Fuente y notas de uso

IndexadoRevisión estática

Los metadatos y revisiones son orientativos. Popularidad, descubrimiento y ejecución correcta son hechos distintos.

Repositorio fuente: intel/skills
Licencia: Apache-2.0
Versión: 1.0
Último push de GitHub: 29 sept 2026
Registro actualizado: 9 oct 2026
Ruta de instrucciones: skills/dpnp-troubleshooting/SKILL.md @ 902833d826e7

Versión declarada en el registro; consulta las versiones de la fuente.

Calidad

55/100

Prometedor

Confianza

56/100

Do not auto-install

Auditoría

70/100

Requiere revisión

Abrir auditoría completa Ver informe de evaluación

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
Falta aprobación de revisión por IA
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: —
Resultados: —

Copiar no es instalar. Los recuentos requieren un informe de instalación correcta, no garantizan calidad general.

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La API Registry expone señales de decisión, confianza, auditoría, casos de uso e instalación sin raspar la interfaz.

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      "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"
  }
}

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Creador: intel
Fuente: intel/skills
Indexado por: Índice comunitario de OpenAgentSkill

La atribución enlaza al repositorio público o al perfil del creador. Los creadores pueden reclamar la ficha para actualizar las señales de propiedad.

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