To solve the MemoryError in Python, upgrade your RAM in your system. As a result, it improves the system’s performance and reduces the chance of getting the MemoryError.
MemoryError in Python
The MemoryError occurred when the RAM ran out of memory. For example, this may happen when we have uploaded a large set of data into our memory. While working with machine learning and artificial intelligence tasks, we must properly maintain the memory.
Machine Learning uses a large data set for processing. This cannot be performed when we have low RAM space.
MemoryError also occurs due to improper installation of python in your system. So again, if python is not installed correctly, then there is a chance for this to happen.
To free the already used spaces, we can use a command to garbage the unused memory.
import gc gc.collect()
This code collects all the unreferenced memory. By doing this, we can get the free memory to operate.
To solve the MemoryError, we need to allocate more memory. With small memories, we cannot handle this error if the data set is large and use the proper system which matches all the requirements.
If the data sets are large, try to test with the sample data. Instead of testing with a large data set, we can extract the sample data from the data set and can be used for the testing.
We can use the Big data platform for working with large data. There are many platforms on which we can work with large data sets. The Keras and Tensorflow are the ML frameworks in which we use large data sets.
MemoryError due to infinite loops
Infinite loops can create memory limit exceptions. For example, using the while loop without incrementing or decrementing creates an infinite loop. In that case, it causes a memory limit error.
arr =  while(True): arr.append(input())
The arr list is appended with values up to some length in this program. After that, the memory limit is raised. You can use the relational database for working with large data files. It provides feasibility for storing and retrieving large data fields.
There are many online virtual machines in which you can work with large data sets. If this memory limit happens, you can follow these activities to handle the memory limit error.
We can use a cloud-based working environment to work in the cloud system so that memory-limit error is handled.
The MemoryError will not happen in a small program where you work with small data sets. Instead, it occurs when we work with large data sets or when the program’s complexity increases.
To handle memory limits, there is a technique called dynamic programming. We can use dynamic programming to optimize the code so that time and space complexity can be reduced, or you can increase your RAM size to read and process the data faster without fear of Memory overflow.
That’s it for this tutorial.
Krunal Lathiya is an Information Technology Engineer. By profession, he is a web developer with knowledge of multiple back-end platforms including Python. Krunal has written many programming blogs which showcases his vast knowledge in this field.