memory_profiler
Monitor Memory usage of Python code (by pythonprofilers)
profiling
By what-studio
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| memory_profiler | profiling | |
|---|---|---|
| 6 | - | |
| 4,214 | 3,001 | |
| 1.4% | - | |
| 3.7 | 1.5 | |
| 14 days ago | - | |
| Python | Python | |
| GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
memory_profiler
Posts with mentions or reviews of memory_profiler.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-04-10.
- Ask HN: C/C++ developer wanting to learn efficient Python
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8 Most Popular Python HTML Web Scraping Packages with Benchmarks
memory_profiler
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Check Python Memory Usage
pythonprofilers/memory_profiler: Monitor Memory usage of Python code
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Profiling and Analyzing Performance of Python Programs
# https://github.com/pythonprofilers/memory_profiler pip install memory_profiler psutil # psutil is needed for better memory_profiler performance python -m memory_profiler some-code.py Filename: some-code.py Line # Mem usage Increment Occurrences Line Contents ============================================================ 15 39.113 MiB 39.113 MiB 1 @profile 16 def memory_intensive(): 17 46.539 MiB 7.426 MiB 1 small_list = [None] * 1000000 18 122.852 MiB 76.312 MiB 1 big_list = [None] * 10000000 19 46.766 MiB -76.086 MiB 1 del big_list 20 46.766 MiB 0.000 MiB 1 return small_list
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Profiling Python code with memory_profiler
What do you do when your Python program is using too much memory? How do you find the spots in your code with memory allocation, especially in large chunks? It turns out that there is not usually an easy answer to these question, but a number of tools exist that can help you figure out where your code is allocating memory. In this article, I’m going to focus on one of them, memory_profiler.
- What Is Your Favorite Profilerperformance Tool
profiling
Posts with mentions or reviews of profiling.
We have used some of these posts to build our list of alternatives
and similar projects.
We haven't tracked posts mentioning profiling yet.
Tracking mentions began in Dec 2020.
What are some alternatives?
When comparing memory_profiler and profiling you can also consider the following projects:
py-spy - Sampling profiler for Python programs
line_profiler
pyflame
Laboratory - Achieving confident refactoring through experimentation with Python 2.7 & 3.3+
filprofiler - A Python memory profiler for data processing and scientific computing applications
python-uncompyle6 - A cross-version Python bytecode decompiler
icecream - 🍦 Never use print() to debug again.
memory_profiler vs py-spy
profiling vs py-spy
memory_profiler vs line_profiler
profiling vs line_profiler
memory_profiler vs pyflame
profiling vs pyflame
memory_profiler vs Laboratory
profiling vs Laboratory
memory_profiler vs filprofiler
profiling vs filprofiler
memory_profiler vs python-uncompyle6
profiling vs icecream