'cprofile' 검색 결과

Measure Python execution time and memory use to locate slow or resource-heavy code. Use profiling results to guide improvements in data structures, caching, input and output operations, algorithms, and parallel processing.

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• Finding slow functions in Python applications
• Reducing memory use and investigating leaks
• Improving data processing and database queries

cProfile、メモリプロファイラー、パフォーマンスベストプラクティスを使用してPythonコードをプロファイルおよび最適化。低速なPythonコードのデバッグ、ボトルネックの最適化、アプリケーションパフォーマンスの改善時に使用。

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Investigate bugs and slow applications through reproducible examples, logs, environment comparisons, and hypothesis testing. The skill structures root cause analysis and performance measurement, helping developers narrow down failures and verify that a fix works.

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• Investigate bugs that occur only occasionally
• Measure and locate performance bottlenecks
• Collect evidence and reproduce production failures

Helps write and improve Python code with type hints, tests, and organized package settings. Covers dependency management, asynchronous tasks that wait on external operations, and profiling to identify performance bottlenecks before making changes.

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• Refactoring Python code with type hints and tests
• Organizing package settings and dependencies
• Implementing concurrent input and output tasks

Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.

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Profiles Python code for performance bottlenecks and memory issues. Use when Python code is slow or when profiling for optimization before a release.

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Systematically find and fix performance problems in applications, frontend and backend, by measuring first. Use this skill whenever the user reports slow endpoints, high latency, poor throughput, memory leaks, large bundles, or slow page loads; wants to profile with cProfile, py-spy, Chrome DevTools, Node --cpu-prof, or perf; run load tests with locust or k6; set performance budgets or SLOs; optimize database queries (EXPLAIN, indexes, N+1); add caching (Redis); improve frontend performance (code splitting, lazy loading, image optimization, memoization, virtualization); or tune the network (CDNs, gzip/br, HTTP/2/3). Also trigger for p50/p95/p99 latency, throughput, error rate, connection pooling, async vs threads, GIL, and memory leak investigations.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.