forfreeuf.blogg.se - Python library reference the python profiler

The total memory usage at any time will be displayed in the Mem usage column and increment in memory usage due to the execution of a particular statement in the Increment column.

It shows two columns named Mem usage and Increment next to each line of function which is decorated with We can see that it starts with some memory and then increases memory as arrays are created and decrease memory as an array are deallocated. The output generated using the memory profiler is very self-explanatory. memory_profiler ¶ġ098330 Filename : example1. It can be easily installed using pip/conda. It's built on top of psutil module of python. It's work almost like line_profiler which profiles time.

memory_profiler: It's written totally in python and monitors process which is running python code as well as line by line memory usage by code.

We'll be concentrating on how to use memory_profiler to analyze memory usage of python code as a part of this tutorial. The Python has many libraries which provide memory usage report like memory_profiler, memprof, guppy/hpy, etc. It also has libraries like cProfile, profile, line_profiler, etc which can be used to analyze the running time of various parts of code but it does not provide any information on memory usage. The python has been the go-to language nowadays for data analysis of big datasets. But due to the rise in data over time, it sometimes happens that we run out of memory and need to monitor which part of the code is using how much of memory to further understand how we can avoid it from happening again in the future.

This is the reason we generally do no monitor primary memory usage. With the rise in the primary memory of computer systems, we generally do not run out of memory. How to Profile Memory Usage in Python using memory_profiler? ¶