Are you developers? So you must know Python. Coming with Python, whether you’re a first-time programmer or have experience with other languages, Python can be simple to learn. However, there are some errors in this programming language. Thus, in this article, ArrowHiTech will provide you with one of the errors called memory error in python. In detail, we will go through the definition, types and how to solve Python memory error. So let’s get started right now.
What is Python memory error?
Python memory error means that you’ve ran out of memory in your RAM to run your code. When you get this error, it means you’ve loaded all of the data into memory. Batch processing is recommended for large datasets. Rather than putting your complete dataset into memory, you should save it to your hard drive and access it in batches. Your software has run out of memory, leading in a memory error. This indicates that your program generates an overwhelming number of items. In your case, you’ll need to look for areas of your algorithm that are taking a significant amount of RAM.
Types of Python Memory Error
#1 Unexpected Memory Error in Python
If you receive an unexpected Python Memory Error despite having plenty of RAM, it’s possible that you’re using a 32-bit Python installation.
Your software has used almost all of the virtual address space available to it. Because you’re using a 32-bit Python version. The reason is that 32-bit applications are limited to 2 GB of user-mode address space in Windows (and most other operating systems), Additionally, 32-bit Python has only 4GB of RAM. Because of the operating system overhead, this can decrease even more if your operating system is 32-bit.
So ArrowHiTech recommends you to install a 64-bit version of Python (if possible, upgrade to Python 3). Because it will use greater memory. however, it will also have access to a lot more storage space (and RAM as well).
#2 Memory error in python Due to Dataset
Loading a huge dataset into memory and running computations on it, as well as storing intermediate results of such computations, can quickly fill out memory. Many famous Python libraries, such as Keras and TensorFlow, include dedicated generator methods and classes.
#3 Python Memory Error Due to Improper Installation of Python
Memory Error can also be caused by improper Python package installation. Before resolving the issue, we had manually installed python 2.7 and the programs that I need on Windows. Finally, after wasting nearly two days trying to figure out what was wrong, we reinstalled everything with Conda. Therefore, the issue was resolved.
#4 Out of Memory Error in Python
On modern operating systems, the memory manager will use the available hard disk space to store pages of memory. Because it doesn’t fit in RAM. Therefore, your computer can usually distribute memory until the disk gets full. As a result, it leads to a Python Out of Memory Error (or a swap limit is reached- If you want to see, please access System Properties > Performance Options > Advanced > Virtual memory in Windows).
How to solve Python memory error?
#1 Free memory in Python
With gc.collect(), you can force the garbage collector to release an unreferenced memory.
#2 Set the memory usage for python programs
If you want to keep the memory usage of the Python to a minimum, try this:
- Use the ulimit command to set a memory limit for python.
- You can utilize the resource module to limit the amount of memory used by the program;
If you want to speed up your program by giving it extra memory, consider the following:
- Threading, multiprocessing
- Pysco on only python 2.5
#3 Put limits on Memory and CPU Usage
To limit a program’s memory or CPU usage while it is executing.Because we don’t have any memory problems. Thus, the Resource module can be used, and both tasks can be completed successfully, as shown in the code below:
Restrict CPU time:
# importing libraries
# checking time limit exceed
def time_exceeded(signo, frame):
print(“Time’s up !”)
# setting up the resource limit
soft, hard = resource.getrlimit(resource.RLIMIT_CPU)
resource.setrlimit(resource.RLIMIT_CPU, (seconds, hard))
# max run time of 15 millisecond
if __name__ == ‘__main__’:
In order to restrict memory use, the code puts a limit on the total address space
# using resource
soft, hard = resource.getrlimit(resource.RLIMIT_AS)
resource.setrlimit(resource.RLIMIT_AS, (maxsize, hard))
#4 Handle memory error python and large data files
Allocate More Memory
A default memory setup in Python may limit some tools or modules. Check whether your tool or library can be re-configured to allocate more RAM. That is, a platform intended to handle very huge datasets and allowing data transforms and machine learning algorithms to be applied on top of it.
Choose a Smaller Sample Size
Take a sample of your data at random, like the first 1,000 or 100,000 rows. Before fitting a final model on all of your data, use this smaller sample to work through your problem (using progressive data loading techniques).
Use a Computer with More Memory
You might be able to acquire access to a much bigger computer with a lot more memory.
Renting compute time on a cloud provider like Amazon Web Services, which offers machines with tens of gigabytes of RAM for less than a dollar per hour, is an excellent example.
Use a Relational Database
Most (all?) programming languages, as well as many machine learning tools, can connect directly to relational databases using free open-source database technologies such as MySQL or Postgres. A lightweight approach, such as SQLite, is also an option.
Use a Big Data Platform
You may need to use a big data platform in some cases.
You have learned a variety of strategies and methods for working with Python memory error in this article. Have you tried any of these methods before? Let us know. Additionally, if your problem has not been resolved and you require assistance with Python Memory Error. So please CONTACT US. We are experienced professionals in the field of ECommerce, Web/mobile apps, CMS website development as well as Salesforce and Software Consultant & Development.