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dpnp-quickstart

NumPy-compatible array operations optimized for Intel hardware. Use when the user wants to migrate or port NumPy code to dpnp, asks whether a NumPy hot path can

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Preis unbestätigt★ 21 GitHub-StarsVerzeichnis aktualisiert · 9. Okt. 2026agent-skill

Übersicht

NumPy-compatible array operations optimized for Intel hardware. Use when the user wants to migrate or port NumPy code to dpnp, asks whether a NumPy hot path can run on an Intel CPU or GPU, needs to check dpnp installation or SYCL device selection with dpctl, hits a NumPy API dpnp does not implement, or wants to compare dpnp against NumPy. Covers install, device control, fallback patterns, and profiling.

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dpnp quickstart

Purpose

Runs NumPy-style array operations on Intel CPUs and GPUs through dpnp, which mirrors the NumPy API over SYCL device memory. Covers installation, SYCL device selection with dpctl, migrating existing NumPy code, falling back to NumPy for unimplemented APIs, and measuring whether the move actually paid off.

Prefer this over plain NumPy when the arrays are large and the work is math-heavy on Intel hardware. Prefer plain NumPy when the arrays are small — dpnp has dispatch overhead that a small array cannot amortize.

When to Use This Skill

Use this skill when:

The user is migrating or porting NumPy code to Intel CPU or GPU execution.
The user asks whether a NumPy hot path can run on an Intel GPU.
The user needs to check a dpnp install, or which SYCL device will be used.
The user hit a NumPy API that dpnp does not implement.
The user wants to compare dpnp against NumPy.

Use dpnp for:

Large arrays (>10,000 elements)
Math-heavy operations (linear algebra, FFT, reductions)
Intel CPU/GPU acceleration

Do not use this skill — stick with NumPy — for:

Small arrays (<1,000 elements)
I/O operations
APIs not yet implemented in dpnp

Quick Start

Run this first to confirm the environment before changing any user code.

# Conda (officially recommended)
conda install -c https://software.repos.intel.com/python/conda -c conda-forge --override-channels dpnp

# Alternative: pip (may have native dependency issues)
pip install dpnp

# verify installation
python -c "import dpnp; print(dpnp.__version__)"

Then confirm which device the arrays will actually land on:

import dpctl
import dpnp as np

print(dpctl.select_default_device())

x = np.arange(100_000)
print(x.sycl_device)

Report what this prints. Do not claim GPU execution if the output shows a CPU device — the default SYCL device is the GPU only when one is visible.

Implementation Guide

Confirm the environment — install and print the device, as in Quick Start.

Swap the import. dpnp is a drop-in NumPy replacement for the covered API:

# Drop-in NumPy replacement
import dpnp as np

# Create arrays
x = np.array([1, 2, 3, 4])
y = np.arange(1000000)

# Operations work like NumPy
result = np.sum(y)
dot_product = np.dot(x, x)

Port the hot path. Array creation, reductions, and linear algebra keep their NumPy spelling:

import dpnp as np

# Array creation
a = np.zeros((100, 100))
b = np.ones(1000)
c = np.linspace(0, 10, 100)

# Math operations
sum_val = np.sum(a)
mean_val = np.mean(b)
std_val = np.std(c)

# Linear algebra
mat = np.random.randn(100, 100)
result = np.dot(mat, mat.T)

Add fallbacks for uncovered APIs. dpnp implements a subset of NumPy, and coverage is per-parameter, not just per-function. Check with dir(dpnp) or the documentation, and guard the call:

import dpnp
import numpy as np

def safe_unique(x):
    try:
        return dpnp.unique(x)
    except (NotImplementedError, TypeError):
        host_x = dpnp.asnumpy(x) if isinstance(x, dpnp.ndarray) else x
        return np.unique(host_x)

Convert at API boundaries, not inside loops. Use dpnp.asnumpy() when a downstream library needs a NumPy array. Pandas, scikit-learn, and many NumPy-based libraries usually expect NumPy arrays: use dpnp for the numeric hot path, then convert once with asnumpy() before calling host-oriented libraries. Repeated device-to-host copies inside tight loops can erase acceleration gains.
Constrain the device when the code must be portable. There is no ambient current-device setting and no context manager to enter — placement is an argument at construction. Inspect what is visible with dpctl, then pin allocation with device= or by reusing an existing array's queue, when the same code runs across workstations, containers, and cloud VMs with different SYCL devices:
```
import dpctl
import dpnp

print([d.filter_string for d in dpctl.get_devices()])   # what is actually there

x = dpnp.arange(100_000, device="cpu")                  # pin this array
print(x.sycl_device)

y = dpnp.zeros(x.size, sycl_queue=x.sycl_queue)         # keep the next one beside it
```
Validate, then measure. Compare against NumPy with numpy.testing.assert_allclose() for critical math before making any performance claim.

Performance

No verified benchmark numbers ship with this skill yet. Do not state a speedup that measurement in the user's own environment does not support.

To measure:

Warm up once before timing, to avoid measuring first-run compilation.
Compare against NumPy with the same inputs and dtype.
Profile end-to-end pipelines, including conversions and host-library calls.

Gotchas & Limitations

First run is slow: JIT compilation happens on first execution. Time the second run.
Not all NumPy APIs available: Check compatibility with dir(dpnp) or documentation.
Data transfer cost: Converting between dpnp and NumPy arrays has overhead. Avoid in tight loops.
Small arrays slower: dpnp has dispatch overhead. Use NumPy for small arrays (<1,000 elements).
Parameter-level gaps: a function existing in dpnp does not mean every NumPy keyword argument is accepted — TypeError on an unexpected keyword is the common symptom.
Device assumptions: the default SYCL device is whatever is visible. Code that works on a GPU workstation can land on CPU in CI without erroring.

References

File	Load it when
`references/official-sources.md`	you need to check a claim against upstream documentation — API coverage for a given release, install prerequisites, or which `dpnp` version implements a function

Two questions in this skill should not be answered from memory, and this is where they get answered: is this NumPy API covered (coverage is per keyword argument and changes between releases) and is this package available for the user's platform.

Quelle ansehen ↗

Dateimetadaten

name: dpnp-quickstart
description: >-
  NumPy-compatible array operations optimized for Intel hardware. Use when the user
  wants to migrate or port NumPy code to dpnp, asks whether a NumPy hot path can run
  on an Intel CPU or GPU, needs to check dpnp installation or SYCL device selection
  with dpctl, hits a NumPy API dpnp does not implement, or wants to compare dpnp
  against NumPy. Covers install, device control, fallback patterns, and profiling.
license: Apache-2.0
compatibility: "Requires dpnp and dpctl. GPU execution requires the Intel GPU driver stack and a SYCL-visible device."
metadata:
  intel-skill-type: "tool-skill"
  version: "1.0"

Originaltext anzeigen

---
name: dpnp-quickstart
description: >-
  NumPy-compatible array operations optimized for Intel hardware. Use when the user
  wants to migrate or port NumPy code to dpnp, asks whether a NumPy hot path can run
  on an Intel CPU or GPU, needs to check dpnp installation or SYCL device selection
  with dpctl, hits a NumPy API dpnp does not implement, or wants to compare dpnp
  against NumPy. Covers install, device control, fallback patterns, and profiling.
license: Apache-2.0
compatibility: "Requires dpnp and dpctl. GPU execution requires the Intel GPU driver stack and a SYCL-visible device."
metadata:
  intel-skill-type: "tool-skill"
  version: "1.0"
---

# dpnp quickstart

## Purpose

Runs NumPy-style array operations on Intel CPUs and GPUs through `dpnp`, which
mirrors the NumPy API over SYCL device memory. Covers installation, SYCL device
selection with `dpctl`, migrating existing NumPy code, falling back to NumPy for
unimplemented APIs, and measuring whether the move actually paid off.

Prefer this over plain NumPy when the arrays are large and the work is math-heavy
on Intel hardware. Prefer plain NumPy when the arrays are small — `dpnp` has
dispatch overhead that a small array cannot amortize.

## When to Use This Skill

Use this skill when:

- The user is migrating or porting NumPy code to Intel CPU or GPU execution.
- The user asks whether a NumPy hot path can run on an Intel GPU.
- The user needs to check a `dpnp` install, or which SYCL device will be used.
- The user hit a NumPy API that `dpnp` does not implement.
- The user wants to compare `dpnp` against NumPy.

Use `dpnp` for:

- Large arrays (>10,000 elements)
- Math-heavy operations (linear algebra, FFT, reductions)
- Intel CPU/GPU acceleration

Do **not** use this skill — stick with NumPy — for:

- Small arrays (<1,000 elements)
- I/O operations
- APIs not yet implemented in dpnp

## Quick Start

Run this first to confirm the environment before changing any user code.

```bash
# Conda (officially recommended)
conda install -c https://software.repos.intel.com/python/conda -c conda-forge --override-channels dpnp

# Alternative: pip (may have native dependency issues)
pip install dpnp

# verify installation
python -c "import dpnp; print(dpnp.__version__)"
```

Then confirm which device the arrays will actually land on:

```python
import dpctl
import dpnp as np

print(dpctl.select_default_device())

x = np.arange(100_000)
print(x.sycl_device)
```

Report what this prints. Do not claim GPU execution if the output shows a CPU
device — the default SYCL device is the GPU only when one is visible.

## Implementation Guide

1. **Confirm the environment** — install and print the device, as in Quick Start.
2. **Swap the import.** `dpnp` is a drop-in NumPy replacement for the covered API:

   ```python
   # Drop-in NumPy replacement
   import dpnp as np

   # Create arrays
   x = np.array([1, 2, 3, 4])
   y = np.arange(1000000)

   # Operations work like NumPy
   result = np.sum(y)
   dot_product = np.dot(x, x)
   ```

3. **Port the hot path.** Array creation, reductions, and linear algebra keep
   their NumPy spelling:

   ```python
   import dpnp as np

   # Array creation
   a = np.zeros((100, 100))
   b = np.ones(1000)
   c = np.linspace(0, 10, 100)

   # Math operations
   sum_val = np.sum(a)
   mean_val = np.mean(b)
   std_val = np.std(c)

   # Linear algebra
   mat = np.random.randn(100, 100)
   result = np.dot(mat, mat.T)
   ```

4. **Add fallbacks for uncovered APIs.** `dpnp` implements a subset of NumPy, and
   coverage is per-parameter, not just per-function. Check with `dir(dpnp)` or the
   documentation, and guard the call:

   ```python
   import dpnp
   import numpy as np

   def safe_unique(x):
       try:
           return dpnp.unique(x)
       except (NotImplementedError, TypeError):
           host_x = dpnp.asnumpy(x) if isinstance(x, dpnp.ndarray) else x
           return np.unique(host_x)
   ```

5. **Convert at API boundaries, not inside loops.** Use `dpnp.asnumpy()` when a
   downstream library needs a NumPy array. Pandas, scikit-learn, and many
   NumPy-based libraries usually expect NumPy arrays: use `dpnp` for the numeric
   hot path, then convert once with `asnumpy()` before calling host-oriented
   libraries. Repeated device-to-host copies inside tight loops can erase
   acceleration gains.
6. **Constrain the device when the code must be portable.** There is no ambient
   current-device setting and no context manager to enter — placement is an argument
   at construction. Inspect what is visible with `dpctl`, then pin allocation with
   `device=` or by reusing an existing array's queue, when the same code runs across
   workstations, containers, and cloud VMs with different SYCL devices:

   ```python
   import dpctl
   import dpnp

   print([d.filter_string for d in dpctl.get_devices()])   # what is actually there

   x = dpnp.arange(100_000, device="cpu")                  # pin this array
   print(x.sycl_device)

   y = dpnp.zeros(x.size, sycl_queue=x.sycl_queue)         # keep the next one beside it
   ```

7. **Validate, then measure.** Compare against NumPy with
   `numpy.testing.assert_allclose()` for critical math before making any
   performance claim.

## Performance

No verified benchmark numbers ship with this skill yet. Do not state a speedup
that measurement in the user's own environment does not support.

To measure:

- Warm up once before timing, to avoid measuring first-run compilation.
- Compare against NumPy with the same inputs and dtype.
- Profile end-to-end pipelines, including conversions and host-library calls.

## Gotchas & Limitations

- **First run is slow**: JIT compilation happens on first execution. Time the second run.
- **Not all NumPy APIs available**: Check compatibility with `dir(dpnp)` or documentation.
- **Data transfer cost**: Converting between dpnp and NumPy arrays has overhead. Avoid in tight loops.
- **Small arrays slower**: dpnp has dispatch overhead. Use NumPy for small arrays (<1,000 elements).
- **Parameter-level gaps**: a function existing in `dpnp` does not mean every
  NumPy keyword argument is accepted — `TypeError` on an unexpected keyword is the
  common symptom.
- **Device assumptions**: the default SYCL device is whatever is visible. Code that
  works on a GPU workstation can land on CPU in CI without erroring.

## References

| File | Load it when |
|---|---|
| [`references/official-sources.md`](references/official-sources.md) | you need to check a claim against upstream documentation — API coverage for a given release, install prerequisites, or which `dpnp` version implements a function |

Two questions in this skill should not be answered from memory, and this is where
they get answered: **is this NumPy API covered** (coverage is per keyword argument
and changes between releases) and **is this package available for the user's
platform**.

Quelle prüfen

Preis und Betriebskosten

Skill beziehen: Preis unbestätigt
Ausführen: Anforderungen unbestätigt. Agenten-, API- und Dienstkosten an der Quelle prüfen.
Lizenz: Apache-2.0
Preis unbestätigt: Der Preis ist noch nicht bestätigt. Vorhandene Quell- und Installationslinks bleiben verfügbar.

Kostenloser Bezug bedeutet nicht kostenlosen Betrieb. Preise sind keine Sicherheitsbewertung. Preisinformation einreichen →

Skill-Quelle erfasst

Ein Anleitungspfad ist erfasst. Das ist kein Ausführungstest und keine Sicherheits- oder Kompatibilitätsgarantie.

Vor Installation prüfen: Automatische Installation vermeiden

Lizenz: Apache-2.0

Dependency or permission surface needs review
Permission surface may require sandboxing
Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
Low GitHub adoption signal
KI-Prüffreigabe fehlt
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
Quality score needs review
Permission surface needs review: shell or command execution, filesystem or document access
GitHub adoption: 21 GitHub stars
Stars/forks activity: 21 stars, 9 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, external package install surface
Permission surface: shell or command execution, filesystem or document access

Vollständiges Audit öffnen

Tools sind Metadatenhinweise, keine getestete Kompatibilität. Prompts sind Vorschläge.

Mit einer kleinen Aufgabe beginnen

1Quelle lesen und Eingaben, Ergebnisse, Abhängigkeiten sowie Berechtigungen prüfen.
2Agent um einen Plan bitten. Einrichtung und Kosten vor einem isolierten Test genehmigen.
3Ergebnisse und geänderte Dateien prüfen. Nur tatsächliche Ausführungen melden und die Quellrevision aufbewahren.

Prüfe Abhängigkeiten, API-Schlüssel und externe Kosten in der Quelle. Öffentliche Repositories bedeuten nicht, dass alle Dienste kostenlos sind.

Quelle und Nutzungshinweise

ErfasstStatisch geprüft

Metadaten und Prüfungen dienen der Orientierung. Beliebtheit, Quellenerfassung und erfolgreiche Ausführung sind verschiedene Fakten.

Quell-Repository: intel/skills
Lizenz: Apache-2.0
Version: 1.0
Letzter GitHub-Push: 29. Sept. 2026
Verzeichnis aktualisiert: 9. Okt. 2026
Anleitungspfad: skills/dpnp-quickstart/SKILL.md @ 902833d826e7

Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.

Qualität

55/100

Vielversprechend

Vertrauen

60/100

Nur Sandbox

Audit

72/100

Riskant

Vollständiges Audit öffnen Eval-Bericht ansehen

Dependency or permission surface needs review
Permission surface may require sandboxing
Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
Low GitHub adoption signal
KI-Prüffreigabe fehlt
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
Quality score needs review
Permission surface needs review: shell or command execution, filesystem or document access
GitHub adoption: 21 GitHub stars
Stars/forks activity: 21 stars, 9 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, external package install surface
Permission surface: shell or command execution, filesystem or document access

Verified installs: —
Ergebnisse: —

Kopieren ist keine Installation. Zahlen benötigen eine Erfolgsmeldung und garantieren keine allgemeine Qualität.

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Die Registry API stellt Entscheidungs-, Vertrauens-, Audit-, Use-Case- und Installationssignale ohne UI-Scraping bereit.

Installations-API öffnen LLM-Textformat Manifest öffnen Auto-Auflösungsplan

Weitere Details

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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": [
      "design-creative",
      "agent-skill"
    ],
    "known_risks": [
      "AI review approval is missing",
      "This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
      "Low GitHub adoption signal",
      "Quality score needs review",
      "Permission surface needs review: shell or command execution, filesystem or document access",
      "GitHub adoption: 21 GitHub stars",
      "Stars/forks activity: 21 stars, 9 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, external package install surface"
    ]
  },
  "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": 72,
    "risk_level": "risky",
    "risk_label": "Risky",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required",
      "Low GitHub adoption signal",
      "AI review approval is missing",
      "This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
      "Quality score needs review",
      "Permission surface needs review: shell or command execution, filesystem or document access"
    ]
  },
  "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": "Design and creative production",
    "scenario": "Design and creative",
    "maintenance": "12d since push",
    "risk": "Risky"
  },
  "alternative_skills": [
    {
      "slug": "robium-ai-architect",
      "name": "architect",
      "url": "https://www.openagentskill.com/skills/robium-ai-architect",
      "stars": 21,
      "install_command": "npx skills add robium-ai/robium --skill architect",
      "trust_score": 69,
      "audit_score": 73
    }
  ],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "Low GitHub adoption signal",
    "Audit risk risky exceeds max_risk=medium",
    "High-risk permission hints: Shell or command execution",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required"
  ],
  "agent_contract": {
    "task_input": "Use dpnp-quickstart 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: 68/100 Manual review",
      "Audit: 72/100 Risky",
      "Safety: 44/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "intel-dpnp-quickstart (dpnp-quickstart)",
      "install_command": "npx skills add intel/skills --skill dpnp-quickstart",
      "risk_summary": "Risky; 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-quickstart",
      "task": "Use dpnp-quickstart 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-quickstart",
    "api": "https://www.openagentskill.com/api/agent/skills/intel-dpnp-quickstart",
    "audit": "https://www.openagentskill.com/skills/intel-dpnp-quickstart/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=intel-dpnp-quickstart&task=Use%20dpnp-quickstart%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20dpnp-quickstart%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20dpnp-quickstart%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/intel-dpnp-quickstart/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/intel-dpnp-quickstart"
  }
}

Für Ersteller

Quelle des Eintrags

Registry-indexiert

Beanspruchbar

Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.

Ersteller: intel
Quelle: intel/skills
Indexiert von: OpenAgentSkill Community-Index

Die Zuordnung verlinkt auf das öffentliche Repository oder Creator-Profil. Creator können den Eintrag beanspruchen, um Eigentümersignale zu aktualisieren.

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Dieser Registry-indexiert-Eintrag wird intel zugeschrieben, ist aber noch nicht offiziell markiert. Beanspruche ihn, um ein verifiziertes Eigentümersignal hinzuzufügen und künftige Launch-, Installations- und Audit-Updates vertrauenswürdiger zu machen.

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