memory_profiler VS pyflame

memory_profiler

pythonprofilers/memory_profiler: Monitor Memory usage of Python code

# 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

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.

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).