![]() The purpose of Python memory profilers is to find memory leaks and optimize memory usage in your Python applications. Hence, we need the help of Python memory profilers. Also, it may jeopardize the stability of the application due to unpredictable memory spikes. However, it is not practical as this may result in a waste of resources. The quick-fix solution is to increase the memory allocation. Once it reaches its peak, memory problems occur. Maybe an object is hanging to a reference when it’s not supposed to be and builds up over time. There are instances where developers don’t know what’s going on. ![]() ![]() This is when development experiences memory errors. If the code execution exceeds the memory limit, then the container will terminate. Also, Python relies on its Memory Management system by default, instead of leaving it to the user.Īs Python code works within containers via a distributed processing framework, each container contains a fixed amount of memory. This is primarily because Python is applied to Data Science and ML applications and works with vast amounts of data. However, Python applications are prone to memory management issues. Profiling applications always involve issues such as CPU, memory, etc.
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