- What is Memory Error?
- Types of Python Memory Error
- How to put limits on Memory and CPU Usage
- Ways to Handle Python Memory Error and Large Data Files
What is Memory Error?
Python Memory Error or, in layman’s terms, you’ve run 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 big datasets. Rather than loading the complete dataset into memory, save it to your hard disc and access it in batches.
Your program has run out of memory, resulting in a memory error. This indicates that your software generates an excessive number of items. In your case, you’ll need to look for areas of your algorithm that are taking a significant amount of RAM.
A memory error occurs when an operation runs out of memory.
Types of Python Memory Error
Following are the types of Python Memory Error
- Unexpected Memory Error in Python
- Python Memory Error Due to Dataset
- Python Memory Error Due to Improper Installation of Python
- Out of Memory Error in Python
- Memory error in Python when 50+GB is free and using 64bit python?
Unexpected Memory Error in Python
If you receive an unexpected Python Memory Error while having plenty of RAM, it’s possible that you’re running a 32-bit Python installation.
A Simple Solution to Unexpected Python Memory Error: Your software has used up all of the virtual address space available to it. It’s most likely because you’re using a 32-bit Python version. Because 32-bit applications are limited to 2 GB of user-mode address space in Windows (and most other operating systems).
We Python Poolers advocate installing a 64-bit version of Python (if possible, upgrade to Python 3 for other reasons); it will consume more memory, but it will also have access to a lot more memory space (and more physical RAM as well).
The problem is that 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.
Python Memory Error Due to Dataset
Another choice, if you’re working with a huge dataset, is dataset size, which has previously been mention in relation to 32-bit and 64-bit versions. Loading a huge dataset into memory and running calculations on it, as well as preserving intermediate results of such computations, can quickly consume memory. If this is the case, generator functions can be quite useful. Many prominent Python libraries, such as Keras and TensorFlow, include dedicated generator methods and classes.
Python Memory Error Due to Improper Installation of Python
Memory Error can also be cause by improper Python package installation. In reality, before resolving the issue, we had manually installed python 2.7 and the packages that I need on Windows. After wasting nearly two days trying to figure out what was wrong, we reloads everything using Conda and the issue was resolved.
Conda is probably installing improved memory management packages, which is the major cause. So you might try installing Python Packages using Conda to see if it fixes the Memory Error.
Out of Memory Error in Python
If an attempt to allocate a block of memory fails on most systems, an “Out of Memory error” is return, however, the core cause of the problem almost never has anything to do with being “out of memory.” That’s because the memory manager on almost every modern operating system will happily use your available hard disc space to store pages of memory that don’t fit in RAM; your computer can usually allocate memory until the disc fills up, which may result in a Python Out of Memory Error (or a swap limit is reached; in Windows, see System Properties > Performance Options > Advanced > Virtual memory).
To make matters worse, every current allocation in the program’s address space might result in “fragmentation,” which prevents further allocations by dividing available memory into chunks that are individually too tiny to satisfy a fresh allocation with a single contiguous block.
- When operating on a 64bit version of Windows, a 32bit application with the LARGEADDRESSAWARE flag enabled has to access to the whole 4GB of address space.
- Four readers have contacted in to say that the gcAllowVeryLargeObjects setting eliminates the .NET restriction. No, it doesn’t. This setting permits objects to take up more than 2GB of memory, but it limits the number of elements in a single-dimensional array to 231 entries.
How can I explicitly free memory in Python?
If you’ve written a Python program that uses a huge input file to generate a few million objects, and it’s using up a lot of memory, what’s the best approach to inform Python that part of the data is no longer needed and maybe freed?
This problem has a simple solution:
With gc.collect, you may force the garbage collector to release an unreferenced memory ().
As illustrated in the example below:
import gc gc.collect()
Memory error in Python when 50+GB is free and using 64bit python?
On some operating systems, the amount of RAM that a single CPU can manage is limited. So, even if there is sufficient RAM available, your single thread (=running on one core) will not be able to handle it anymore. But I’m not sure if this applies to your Windows version.
How to put limits on Memory and CPU Usage
To limit a program’s memory or CPU use while it is executing. So that we don’t have any memory problems. To accomplish so, the Resource module may be utilized, and both tasks can be completed successfully, as demonstrated in the code below:
Code1: Restrict CPU time
# importing libraries import signal import resource import os # checking time limit exceed def time_exceeded(signo, frame): print("Time's up !") raise SystemExit(1) def set_max_runtime(seconds): # setting up the resource limit soft, hard = resource.getrlimit(resource.RLIMIT_CPU) resource.setrlimit(resource.RLIMIT_CPU, (seconds, hard)) signal.signal(signal.SIGXCPU, time_exceeded) # max run time of 15 millisecond if __name__ == '__main__': set_max_runtime(15) while True: pass
Code 02: In order to restrict memory use, the code puts a limit on the total address space
# using resource import resource def limit_memory(maxsize): soft, hard = resource.getrlimit(resource.RLIMIT_AS) resource.setrlimit(resource.RLIMIT_AS, (maxsize, hard))
Ways to Handle Python Memory Error and Large Data Files
Allocate More Memory
A default memory setup may restrict some Python tools or modules. Check to see if your tool or library may be re-configure to allocate extra RAM.
That is a platform built to handle very huge datasets and allow data transformations and machine learning algorithms to be applied on top of it.
Weka is a fantastic example of this, since you may increase RAM as a parameter when running the app.
Work with a Smaller Sample
Use a Smaller Sample Size Makes you so sure you require all of the data?
Take a sample of your data at random, such as the first 1,000 or 100,000 rows. Before fitting a final model to all of your data, use this smaller sample to work through your problem (using progressive data loading techniques). I believe that this is an excellent practice for machine learning in general since it allows for rapid spot-checks of algorithms and results turnaround.
You may also compare the amount of data utilized to fit one method to the model skill in a sensitivity analysis. Perhaps you can use a natural point of declining returns as a guideline for the size of your smaller sample.
Use a Computer with More Memory
Is it necessary for you to use your computer?
You might be able to acquire access to a much bigger computer with a lot more RAM.
Renting compute time on a cloud provider like Amazon Web Services, which provides computers with tens of gigabytes of RAM for less than a dollar per hour, is an excellent example.
Use a Relational Database
Relational databases are a common method of storing and retrieving huge datasets.
Internally, data is save on a disc, which may be loads in batches and searched using a standard query language (SQL).
Most (all?) programming languages and many machine learning tools can connect directly to relational databases, as can free open-source database solutions like MySQL or Postgres. You may also use SQLite, which is a lightweight database.
Use a Big Data Platform
In some cases, you may need to resort to a big data platform.