Laboratory
Achieving confident refactoring through experimentation with Python 2.7 & 3.3+ (by joealcorn)
pyflame
By uber-archive
Our great sponsors
| Laboratory | pyflame | |
|---|---|---|
| 2 | 1 | |
| 1,273 | 2,869 | |
| - | - | |
| 0.0 | 0.1 | |
| 11 months ago | over 4 years ago | |
| Python | C++ | |
| MIT 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.
Laboratory
Posts with mentions or reviews of Laboratory.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-11-14.
-
Scientist: A Ruby library for carefully refactoring critical paths
Yes, at the bottom it lists all similar libraries in other languages. Python has https://github.com/joealcorn/laboratory
- Suture: A Ruby gem that helps you refactor your legacy code
pyflame
Posts with mentions or reviews of pyflame.
We have used some of these posts to build our list of alternatives
and similar projects.
-
Does anyone use performance profiling / flamegraphs for optimizing ML algorithms?
I know profiling and continuous profiling have become popular for understanding system-wide performance characteristics. I.e. companies like Neflix, Uber (pyflame) , Doordash, Paypal, and many more use profiling and flamegraphs to optimize their application code often by decreasing latency -- I never hear about them using it for their ML related code (this is what my question is ultimately about).
What are some alternatives?
When comparing Laboratory and pyflame you can also consider the following projects:
pdb++
py-spy - Sampling profiler for Python programs
memory_profiler - Monitor Memory usage of Python code
icecream - 🍦 Never use print() to debug again.
line_profiler
profiling
python-uncompyle6 - A cross-version Python bytecode decompiler
python-statsd - Python Client for the Etsy NodeJS Statsd Server