![]() ![]() ![]() Scalene's overhead is just a 35% slowdown. Slowdown: the slowdown when running a benchmark from the Pyperformance suite.When Scalene is profiling a program launched in the background (via &), you can suspend and resume profiling.Ĭomparison to Other Profilers Performance and Featuresīelow is a table comparing the performance and features of various profilers to Scalene.Scalene supports decorators to profile only specific functions.Scalene can produce reduced profiles (via -reduced-profile) that only report lines that consume more than 1% of CPU or perform at least 100 allocations. ![]() Scalene profiles copying volume, making it easy to spot inadvertent copying, especially due to crossing Python/library boundaries (e.g., accidentally converting numpy arrays into Python arrays, and vice versa).Scalene identifies lines with likely memory leaks.Scalene produces per-line memory profiles.Scalene separates out the percentage of memory consumed by Python code vs.It accomplishes this via an included specialized memory allocator. In addition to tracking CPU usage, Scalene also points to the specific lines of code responsible for memory growth. Scalene reports GPU time (currently limited to NVIDIA-based systems).Scalene also separates out system time, making it easy to find I/O bottlenecks.Scalene highlights hotspots (code accounting for significant percentages of CPU time or memory allocation) in red, making them even easier to spot.Most Python programmers aren't going to optimize the performance of native code (which is usually either in the Python implementation or external libraries), so this helps developers focus their optimization efforts on the code they can actually improve. Scalene separates out time spent in Python from time in native code (including libraries).Scalene performs profiling at the line level and per function, pointing to the functions and the specific lines of code responsible for the execution time in your program.We tested CPU profiler accuracy and found that Scalene is among the most accurate profilers, correctly measuring time taken. Its overhead is typically no more than 10-20% (and often less). It uses sampling instead of instrumentation or relying on Python's tracing facilities. This talk presented at P圜on 2021 walks through Scalene's advantages and how to use it to debug the performance of an application (and provides some technical details on its internals). Scalene Overview Scalene talk (P圜on US 2021) The generated file profile.html is self-contained and can be saved for later use. Hover over bars to see breakdowns of CPU and memoryĬonsumption, and click on underlined column headers to sort theĬolumns. Tab in a web browser with an interactive user interface (all processing is done Scalene has both a CLI and a web-based GUI (demo here).īy default, once Scalene has profiled your program, it will open a # do not import profile! profile def slow_function(): ![]()
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