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✅ Translation Quality ReviewVerdict: PASS | Model: claude-sonnet-4-6 | Date: 2026-04-08 📝 Translation Quality
Summary: The translation is of high quality overall. All modified and newly added sections are accurately translated with natural Chinese phrasing and consistent technical terminology. The new 'Caching Compiled Code' section is well handled. Minor improvements could be made to a few word choices for naturalness, but these are non-critical. Formatting and mathematical content are fully preserved with no syntax errors detected. Technical terminology is consistently and correctly applied throughout, including 'JIT编译', '即时编译器', '类型推断', '多线程', '并行化' etc. The newly added '### 缓存编译代码' section is accurately and fluently translated, clearly conveying both the concept and the practical usage Mathematical expressions and LaTeX formatting are fully preserved with no errors Code comments within code blocks are appropriately translated into Chinese (e.g., '# 抽取冲击', '# 更新财富', '# 对每条样本路径'), improving accessibility for Chinese readers The glossary term 'stochastic volatility' → '随机波动率' is correctly applied in the numba_ex4 exercise section Suggestions:
🔍 Diff Quality
Summary: The translation target document is correctly updated to mirror all structural and content changes from the English source, with the heading map accurately reflecting added, removed, and renamed headings. This review was generated automatically by action-translation review mode. |
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Pull request overview
Automated zh-cn translation sync to align the Numba lecture with upstream “Modernize Numba lecture” changes from QuantEcon/lecture-python-programming.
Changes:
- Updated
lectures/numba.mdcontent/structure (reworded intro, revised sections, removed “编译类”, added “缓存编译代码”). - Refreshed code examples and surrounding explanations to match the modernized source (e.g., updated random-number usage, decorator usage, and headings).
- Updated translation sync state metadata in
.translate/state/numba.md.yml.
Reviewed changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated 4 comments.
| File | Description |
|---|---|
lectures/numba.md |
Updates the zh-cn Numba lecture translation to match upstream modernized content and examples. |
.translate/state/numba.md.yml |
Updates translation-sync bookkeeping (source SHA, sync date, mode, section count). |
Comments suppressed due to low confidence (1)
lectures/numba.md:263
- 这里用
@jit装饰并在注释中说明“这段代码会抛出错误”，但如果@jit未强制nopython=True，某些 Numba 版本/设置可能会回退到对象模式而不抛错，从而与讲解目的不一致。建议将该示例改为@njit（或@jit(nopython=True)），以确保无法推断类型时确实触发编译错误。
```{code-cell} ipython3
@jit
def bootstrap(data, statistics, n_resamples):
bootstrap_stat = np.empty(n_resamples)
n = len(data)
for i in range(n_resamples):
resample = np.random.choice(data, size=n, replace=True)
bootstrap_stat[i] = statistics(resample)
return bootstrap_stat
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| ```{note} | ||
| 在旧版本的 Numba 中，当 `@jit` 装饰器无法推断所有类型时，会静默回退到"对象模式"，这几乎不能带来速度提升。当前版本的 Numba 默认使用 `nopython` 模式，这意味着编译器坚持进行完整的类型推断，如果失败则引发错误。您在其他代码中经常会看到 `@njit`，它只是 `@jit(nopython=True)` 的别名。由于 nopython 模式现在是默认模式，`@jit` 和 `@njit` 是等价的。 | ||
| ``` |
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{note} 块里写到“当前版本的 Numba 默认使用 nopython 模式，因此 @jit 和 @njit 等价”。这会让文中后续多个示例（例如期待编译失败抛错、以及并行化加速）变得依赖 Numba 版本/配置，且在不少环境下 @jit 仍可能回退到对象模式而不报错。建议改为显式使用 @njit（或 @jit(nopython=True)）并相应更新这段说明，避免读者得到不一致的结果。
| from numba import prange | ||
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| @njit(parallel=True) | ||
| @jit(parallel=True) | ||
| def compute_long_run_median_parallel(w0=1, T=1000, num_reps=50_000): | ||
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此处使用 @jit(parallel=True) + prange 来演示并行化，但 Numba 的并行化优化通常要求处于 nopython 编译路径；如果 @jit 发生对象模式回退，parallel=True 可能不会生效（或行为与讲解不一致）。建议改为 @njit(parallel=True)（或 @jit(nopython=True, parallel=True)）以确保示例确实并行执行。
| return x | ||
| ``` | ||
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| 这会将编译后的代码存储在磁盘上，以便后续会话可以跳过编译步骤。 |
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cache=True 的说明写成“后续会话可以跳过编译步骤”。在 Jupyter / Jupyter Book 这类交互式环境中，Numba 的磁盘缓存通常要求函数定义在可定位的模块文件中；在笔记本代码单元里启用缓存可能不会生效，甚至可能直接报错。建议在文字里注明该限制（或将示例调整为更适用于本讲义构建环境的写法）。
| 这会将编译后的代码存储在磁盘上，以便后续会话可以跳过编译步骤。 | |
| 这通常会将编译后的代码存储在磁盘上，从而在后续会话中避免重复编译。 | |
| 但需要注意的是，Numba 的磁盘缓存通常要求函数定义在可定位的模块文件中；在 Jupyter / Jupyter Book 这类交互式环境的代码单元里，`cache=True` 可能不会生效，甚至可能直接报错。 |
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| ```{code-cell} ipython3 | ||
| time1 / time3 # 计算速度提升倍数 | ||
| time1 / time3 # Calculate speed gain |
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这一行代码注释仍是英文（“Calculate speed gain”），与其余中文讲义不一致。建议将注释翻译为中文以保持语言一致性。
| time1 / time3 # Calculate speed gain | |
| time1 / time3 # 计算加速比 |
Automated Translation Sync
This PR contains automated translations from QuantEcon/lecture-python-programming.
Source PR
#512 - Modernize Numba lecture
Files Updated
lectures/numba.md.translate/state/numba.md.ymlDetails
This PR was created automatically by the translation action.