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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 main features of pythonprofilers/memory_profiler are: Python Profilers, Memory Usage Analyzers, Line-Level Resource Monitors, Time Series Analysis, Child Process Memory Tracers, Bytecode Instrumentation Agents, Python Tooling, Function Decorators.
Open-source alternatives to pythonprofilers/memory_profiler include: rfjakob/earlyoom — earlyoom is a Linux OOM killer daemon that monitors system memory and terminates processes to prevent system freezes.… plasma-umass/scalene — Scalene is a high-performance diagnostic utility designed to measure resource consumption during the execution of… nvdv/vprof — vprof is a visual profiling tool for Python designed to identify execution bottlenecks and monitor memory consumption.… crazyguitar/pysheeet — pysheeet is a technical reference library providing a curated collection of code snippets and implementation patterns… mahmoud/boltons — Boltons is a comprehensive utility toolkit and extension of the Python standard library. It provides a collection of… pylons/pyramid — Pyramid is a Python web framework and WSGI toolkit designed for building web applications. It functions as a URL…
earlyoom is a Linux OOM killer daemon that monitors system memory and terminates processes to prevent system freezes. It acts as a memory resource monitor and process termination manager, tracking available RAM and swap space to ensure the operating system remains responsive. The project distinguishes itself by selecting termination targets based on the largest resident set size rather than relying solely on kernel OOM scores. It provides granular control through regular expression-based process filtering to protect specific applications and can terminate entire process groups to ensure compl
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
vprof is a visual profiling tool for Python designed to identify execution bottlenecks and monitor memory consumption. It functions as a CPU and memory profiler that transforms performance data into interactive visualizations to analyze processor time and call stacks. The project distinguishes itself through a suite of visual diagnostics, including flame graphs for stack visualization and heatmaps that map execution frequency and duration directly onto source code. It also includes a remote performance monitor capable of capturing function-specific metrics from a running server and streaming
pysheeet is a technical reference library providing a curated collection of code snippets and implementation patterns for advanced Python development, system integration, and high-performance computing. It serves as a comprehensive guide for implementing low-level network programming, native C extensions, and asynchronous and concurrent programming. The project provides specialized frameworks for the development and deployment of large language models, including tools for distributed GPU inference and high-performance serving. It also includes detailed patterns for high-performance computing