How to Handle Memory Error in Python?

A MemoryError indicates that the Python interpreter has exhausted its memory allocation for our Python program. This might be due to a problem with the Python environment's setup or a problem with the program itself fetching too much stuff at once.

What is a Memory Error?

Python Memory Error or, in simpler terms, we have run out of memory space in our RAM for the program to execute.

This problem is most likely caused by loading all the data into the system's memory. Batch processing is recommended for huge datasets. Rather than putting the complete data into memory, retain it on the hard drive and retrieve it in chunks.

A memory error indicates that our software has reached its memory limit. This signifies that our software generates an excessive number of objects. In this case, we must look for areas of our program that may take a significant amount of RAM.

A memory error occurs when a task runs out of storage.

An Example of MemoryError

Let's start with a highly greedy chunk of code to see this issue in action. In the script below, we begin with an empty list and add Python strings to it using nested lists. In this scenario, three layers of nested lists with one thousand iterations each are used. This indicates that the list has 1,000,000,000 repetitions of the string "More" after the program.

Code

Output

C:\code\Python\MemErr\venv\3K\Scripts\Python.exe C:/code/Python/MemErr/main.py
Traceback (most recent call last):
  File "C:/code/Python/MemErr/main.py", line 6, in <module>
   
s.append("More")
MemoryError

Because no modules are included in this tiny application, the traceback error is quite easy. Following the traceback that shows the specific function call that triggered the problem, we see the basic but direct MemoryError.

Ways to Handle a

Appropriate Python Set-up

This simplest, but maybe least apparent, remedy to a MemoryError has to deal with a possible problem with the Python setup. If we install the 32-bit edition of the Python application on a 64-bit machine, we will have very restricted access to the system's RAM. This limited access may result in memory errors on programs that our computer should be able to handle properly.

Attention to Large Nested Loops

If the Python installation is adequate, but these problems remain, it might be necessary to rethink the code. Sadly, there is no easy solution to eliminate this issue other than analyzing and optimizing our code. Pay close attention to any huge or nested loops, and each time we load enormous datasets into our program in one fell swoop, like in the instance above.

In these instances, dividing the work into groups is typically desirable, enabling the ram to be released between calls. For instance, in the program below, we have separated prior nested loops into three independent loops, each of which runs for 333,333,333 iterations. This program still runs one million times, but because we can empty the memory through a garbage collection tool, it no longer throws a MemoryError.

See the following Example of Batching the Nested Loops

Code

How to Put limits on Memory and CPU Usage

We will now see how to restrict a running program's use of memory or CPU. to prevent memory errors. We may complete both tasks by using the Resource module, as demonstrated in the following code:

Code

Output

The time limit is exceeded by the program
An exception has occurred; use %tb to see the full traceback.

SystemExit: 1

The code limits the overall address space to control memory consumption.

Code

Output

Memory limit is exceeded by the code

Avoiding Memory Errors in Python

The most common cause of Memory Errors by programs is while working with huge datasets. When dealing with Machine Learning projects, we frequently encounter big datasets that, when performing a Machine Learning algorithm for regression or clustering, cause the computer ram to run out of space immediately. We can solve such issues by running Generator functions. It may be implemented as a user-defined method when dealing with large datasets.

Without importing the entire dataset, we can easily divide big datasets into several pieces thanks to Python generator functions. Generators are incredibly helpful when working on huge projects dealing with many rows of data. The functions of the generator return iterator objects. The data may be looped using these iterator objects. A typical iterator method is written in Python loops and iterates over the full dataset. This is when the generator function comes in helpful; it prevents the entire dataset from looping over because doing so results in the Memory Error and causes the application to crash.

The generator functions in Python differ from other regular functions in that a keyword called yield is included in the place of the customary return keyword, which typically returns the function's result. However, it not terminates the function once the yield command is executed.

An instance Generator function is provided:

Code

Use a Relational Database

Large datasets may be stored and accessed consistently using relational databases.

Fundamentally, the data is kept on a disc and may be accessed by normal query languages and gradually loaded in batches (SQL).

The majority (all?) of computer languages and several machine learning applications can easily connect with relational databases as an open-source database solution like MySQL or Postgres. Additionally, a lightweight method like SQLite is an option.

Conclusion

We have covered the Python programming language's Memory Error in great length and methods for dealing with Name Errors. The most important thing to remember about the Memory Error is how much RAM is used for the tasks. We may get beyond the Memory Error by effectively utilizing the abovementioned strategies.