Awesome GitHub RepositoriesPython Profilers

py-spy is a sampling profiler and process debugger for Python. It allows for the analysis of running processes to identify performance bottlenecks and diagnose hanging programs without requiring code changes or restarts. The tool operates by reading the memory of a running process from the outside, which enables non-invasive sampling and state collection without pausing execution. It can resolve binary symbols to capture performance data from native extensions written in compiled languages and generate visual flame graphs for both native extensions and subprocesses. The project provides capa

Scalene is a high-performance diagnostic utility designed to measure resource consumption during the execution of Python applications. It functions as a line-level monitor, providing granular insights that pinpoint the specific source code responsible for performance overhead. The tool distinguishes itself through statistical profiling that captures stack traces and resource usage without requiring manual instrumentation of the source code. It tracks CPU, GPU, and memory consumption by intercepting library-level calls and hardware driver commands, allowing for the analysis of both managed and

VizTracer is a Python runtime instrumentation system and execution profiler used to trace and visualize code execution. It functions as a multi-process performance analyzer and trace visualizer, providing an interactive timeline and flamegraph interface to identify performance bottlenecks and analyze call sequences. The project distinguishes itself by its ability to aggregate execution data from multiple threads, subprocesses, and asynchronous tasks into a single unified report. It also features live process instrumentation, allowing users to attach to and detach from running Python applicati

pyinstrument is a statistical sampling profiler for Python that records the call stack at regular intervals to identify performance bottlenecks with low overhead. It tracks wall-clock time, including I/O and external service calls, and provides specialized profiling for asynchronous programs by attributing time spent awaiting tasks to the calling function. The project converts captured execution data into interactive HTML reports, JSON, and flamecharts. It includes a call stack visualizer to simplify the analysis of execution paths and supports the profiling of individual cells within interac

This project is a collection of diagnostic tools designed for auditing IP quality, analyzing network stability, profiling server environments, and benchmarking hardware performance. It provides a suite of utilities to evaluate virtual private servers through hardware performance benchmarking and system environment diagnostics. The toolset includes a streaming service unlock checker to determine regional content access, an IP reputation audit tool for blacklist and geolocation verification, and a network quality analyzer for measuring latency and throughput. It covers broader capability areas

Criterion 是一个基于统计学的 Rust 微基准测试库和性能回归工具。它提供了一个用于隔离和测量小段代码的框架，利用统计分析消除噪声，确保执行速度测量结果的可靠性和可重复性。 该工具通过性能可视化套件脱颖而出，可生成 HTML 报告和图表以跟踪性能趋势和吞吐量。它包含一个将当前执行时间与存储基准进行对比的系统，以识别并防止性能下降。 该库涵盖了异步函数测量、用于输入缩放的参数化基准测试以及代码吞吐量计算。它还支持集成自定义硬件指标和处理器计数器，以在运行期间捕获底层数据。 通过用于基准测试过滤的命令行界面和用于验证持续集成流水线中执行情况的验证模式，该工具提供了完善的自动化支持。

This project is a diagnostic utility for monitoring and analyzing memory consumption in Python applications. It provides tools for tracking resource usage at the process level and performing detailed, line-by-line analysis to identify memory leaks and performance bottlenecks. The tool distinguishes itself through its ability to aggregate memory metrics across entire process trees, capturing the total resource impact of both parent and child processes. It supports time-series visualization of memory usage over the duration of a script, allowing for the identification of long-term consumption p

vprof 是一个专为 Python 设计的可视化性能分析工具，用于识别执行瓶颈并监控内存消耗。它作为一个 CPU 和内存分析器，将性能数据转换为交互式可视化图表，以分析处理器时间和调用栈。 该项目通过一套视觉诊断工具脱颖而出，包括用于可视化调用栈的火焰图，以及将执行频率和持续时间直接映射到源代码上的热力图。它还包含一个远程性能监视器，能够从运行中的服务器捕获特定函数的指标，并将数据流式传输到独立的分析工具中。 该工具涵盖广泛的功能领域，包括基于采样的 CPU 分析、通过垃圾回收器跟踪实现的行级内存监控，以及用于离线分析的性能数据持久化。这些工具可用于审计源代码效率并识别内存泄漏。

Segment Anything Fast is a high-performance computer vision inference engine and image segmentation framework built for PyTorch. It provides a specialized environment for automated object isolation and mask generation, designed to process large-scale visual datasets with increased throughput. The project distinguishes itself through a suite of system-level optimization strategies that accelerate deep learning model performance. By utilizing graph-based model compilation, just-in-time kernel fusion, and hardware-aware quantization, it reduces computational latency and memory footprint. These t