by Emery Berger
% pip install -U scalene
Scalene is a high-performance CPU and memory profiler for Python that does a number of things that other Python profilers do not and cannot do. It runs orders of magnitude faster than other profilers while delivering far more detailed information.
- Scalene is fast. It uses sampling instead of instrumentation or relying on Python's tracing facilities. Its overhead is typically no more than 10-20% (and often less).
- Scalene is precise. Unlike most other Python profilers, Scalene performs CPU profiling at the line level, pointing to the specific lines of code that are responsible for the execution time in your program. This level of detail can be much more useful than the function-level profiles returned by most profilers.
- Scalene separates out time spent running in Python from time spent in native code (including libraries). 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 profiles memory usage. In addition to tracking CPU usage, Scalene also points to the specific lines of code responsible for memory growth. It accomplishes this via an included specialized memory allocator.
- Scalene produces per-line memory profiles, making it easier to track down leaks.
- Scalene profiles copying volume, making it easy to spot inadvertent copying, especially due to crossing Python/library boundaries (e.g., accidentally converting
numpyarrays into Python arrays, and vice versa). - NEW! Scalene now reports the percentage of memory consumed by Python code vs. native code.
- NEW! Scalene now highlights hotspots (code accounting for significant percentages of CPU time or memory allocation) in red, making them even easier to spot.
Below is a table comparing the performance and features of various profilers to Scalene.
Function-granularity profilers report information only for an entire function, while line-granularity profilers (like Scalene) report information for every line
- Time is either real (wall-clock time), CPU-only, or both.
- Efficiency: 🟢 = fast, 🟡 = slower, 🔴 = slowest
- Mem Cons.: tracks memory consumption
- Unmodified Code: works on unmodified code
- Threads: works correctly with threads
- Python/C: separately attributes Python/C time and memory consumption
- Mem Trend: shows memory usage trends over time
- Copy Vol.: reports copy volume, the amount of megabytes being copied per second
Scalene prints annotated source code for the program being profiled
(either as text or as HTML via the --html option) and any modules it
uses in the same directory or subdirectories (you can optionally have
it --profile-all and only include files with at least a
--cpu-percent-threshold of time). Here is a snippet from
pystone.py. The "sparklines" summarize memory consumption over time (at the top, for the whole program).
Positive net memory numbers indicate total memory allocation in megabytes; negative net memory numbers indicate memory reclamation.
The following command runs Scalene on a provided example program.
% scalene test/testme.py
To see all the options, run with --help.
% scalene --help
usage: scalene [-h] [--outfile OUTFILE] [--html]
[--profile-interval PROFILE_INTERVAL] [--wallclock]
[--cpu-only] [--profile-all]
[--cpu-percent-threshold CPU_PERCENT_THRESHOLD]
Scalene: a high-precision CPU and memory profiler.
https://github.com/emeryberger/scalene
% scalene yourprogram.py
optional arguments:
-h, --help show this help message and exit
--outfile OUTFILE file to hold profiler output (default: stdout)
--html output as HTML (default: text)
--profile-interval PROFILE_INTERVAL
output profiles every so many seconds.
--wallclock use wall clock time (default: virtual time)
--cpu-only only profile CPU time (default: profile CPU, memory, and copying)
--profile-all profile all executed code, not just the target program (default: only the target program)
--cpu-percent-threshold CPU_PERCENT_THRESHOLD
only report profiles with at least this percent of CPU time (default: 1%)
Scalene is distributed as a pip package and works on Mac OS X and Linux platforms (including Ubuntu in Windows WSL2).
You can install it as follows:
% pip install -U scalene
or
% python3 -m pip install -U scalene
As an alternative to pip, you can use Homebrew to install the current version of Scalene from this repository:
% brew tap emeryberger/scalene
% brew install --head libscalene
NEW: You can also install Scalene on Arch Linux via the AUR
package. Use your favorite AUR helper, or
manually download the PKGBUILD and run makepkg -cirs to build. Note that this will place
libscalene.so in /usr/lib; modify the below usage instructions accordingly.
For technical details on Scalene, please see the following paper: Scalene: Scripting-Language Aware Profiling for Python (arXiv link).
If you use Scalene to successfully debug a performance problem, please add a comment to this issue!
Logo created by Sophia Berger.