Introduction to Memory Profiling in Python

By neub9
3 Min Read

Profiling Python code is an essential practice for understanding how the code functions and identifying opportunities for optimization. While you may have profiled Python scripts for time-related metrics, such as execution times, profiling for memory is equally important. Memory profiling can help identify memory leaks, resource utilization, and potential scaling issues.

In this tutorial, we will explore profiling Python code for memory usage using the Python package memory-profiler. To get started, install the memory-profiler Python package using pip in a dedicated virtual environment for your project:

pip3 install memory-profiler

Additionally, ensure that you have matplotlib installed in the project’s virtual environment to utilize the plotting capabilities available in memory-profiler.

Next, let’s create a Python script (e.g., with a function `process_strs` that involves memory-intensive operations. Here’s the code for the `process_strs` function:

from memory_profiler import profile

def process_strs(reps=10**6):
str1 = ‘python’ * reps
str2 = ‘programmer’ * reps
str3 = str1 + str2
del str2
return str3


Running the script should provide useful insights into memory usage, illustrating the memory allocation and deallocation process. Additionally, you can run the `mprof` command to generate a .dat file with memory usage data, and use the `mprof plot` command to create a visual representation of the memory usage data.

You can also log the memory usage statistics to a log file using the `@profile` decorator and the `stream` argument.

Furthermore, the `memory_usage` function can be utilized to understand the resources required for a specific function to execute, sampled at regular intervals. This function provides a granular view of memory utilization and allows for adjustments to the sampling interval.

In summary, this tutorial has provided an introduction to profiling Python scripts for memory usage using the memory-profiler package. We explored various features of memory-profiler, such as plotting memory usage and logging memory usage statistics. This information can be invaluable for optimizing memory performance in Python applications.

Lastly, the author of this tutorial, Bala Priya C, is a developer and technical writer with expertise in areas such as DevOps, data science, and natural language processing. She is passionate about sharing her knowledge with the developer community through tutorials, how-to guides, and opinion pieces.

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