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

概览

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.

查看来源 ↗

文件元数据

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"

查看原始文本

---
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**.

查看并核实来源

获取价格与运行成本

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
Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
Low GitHub adoption signal
缺少 AI 审查批准
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

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-quickstart/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

60/100

仅限沙盒

审计

72/100

高风险

打开完整审计查看评估报告

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
缺少 AI 审查批准
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: —
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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      "documentation": "Strong README/SKILL.md context",
      "agentOutcomes": "No agent outcome data yet"
    },
    "outcome_evidence": {
      "total": 0,
      "successes": 0,
      "failures": 0,
      "not_relevant": 0,
      "success_rate": null,
      "recent_success_rate": null,
      "recent_failure_rate": null,
      "install_attempts": 0,
      "install_success_rate": null,
      "risk_blocked": 0,
      "setup_required": 0,
      "avg_output_quality": null,
      "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": "11d 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"
  }
}

创作者工具

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Registry 收录

可认领

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-quickstart?metric=listed&label=Listed)](https://www.openagentskill.com/skills/intel-dpnp-quickstart?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/intel-dpnp-quickstart?metric=trust&label=Trust)](https://www.openagentskill.com/skills/intel-dpnp-quickstart?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/intel-dpnp-quickstart?metric=audit&label=Audit)](https://www.openagentskill.com/skills/intel-dpnp-quickstart/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/intel-dpnp-quickstart?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/intel-dpnp-quickstart?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

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