Memory error pycharm

Впервые я столкнулся с Memory Error, когда работал с огромным массивом ключевых слов. Там было около 40 млн. строк, воодушевленный своим гениальным скриптом я нажал Shift + F10 и спустя 20 секунд получил Memory Error.

Memory Error — исключение вызываемое в случае переполнения выделенной ОС памяти, при условии, что ситуация может быть исправлена путем удаления объектов. Оставим ссылку на доку, кому интересно подробнее разобраться с этим исключением и с формулировкой. Ссылка на документацию по Memory Error.

Если вам интересно как вызывать это исключение, то попробуйте исполнить приведенный ниже код.

print('a' * 1000000000000)

Почему возникает MemoryError?

В целом существует всего лишь несколько основных причин, среди которых:

• 32-битная версия Python, так как для 32-битных приложений Windows выделяет лишь 4 гб, то серьезные операции приводят к MemoryError
• Неоптимизированный код
• Чрезмерно большие датасеты и иные инпут файлы
• Ошибки в установке пакетов

Как исправить MemoryError?

Ошибка связана с 32-битной версией

Тут все просто, следуйте данному гайдлайну и уже через 10 минут вы запустите свой код.

Как посмотреть версию Python?

Идем в cmd (Кнопка Windows + R -> cmd) и пишем python. В итоге получим что-то похожее на

Python 3.8.8 (tags/v3.8.8:024d805, Feb 19 2021, 13:18:16) [MSC v.1928 64 bit (AMD64)]

Нас интересует эта часть [MSC v.1928 64 bit (AMD64)], так как вы ловите MemoryError, то скорее всего у вас будет 32 bit.

Как установить 64-битную версию Python?

Идем на официальный сайт Python и качаем установщик 64-битной версии. Ссылка на сайт с официальными релизами. В скобках нужной нам версии видим 64-bit. Удалять или не удалять 32-битную версию — это ваш выбор, я обычно удаляю, чтобы не путаться в IDE. Все что останется сделать, просто поменять интерпретатор.

Идем в PyCharm в File -> Settings -> Project -> Python Interpreter -> Шестеренка -> Add -> New environment -> Base Interpreter и выбираем python.exe из только что установленной директории. У меня это

C:/Users/Core/AppData/LocalPrograms/Python/Python38

Все, запускаем скрипт и видим, что все выполняется как следует.

Оптимизация кода

Пару раз я встречался с ситуацией когда мои костыли приводили к MemoryError. К этому приводили избыточные условия, циклы и буферные переменные, которые не удаляются после потери необходимости в них. Если вы понимаете, что проблема может быть в этом, вероятно стоит закостылить пару del, мануально удаляя ссылки на объекты. Но помните о том, что проблема в архитектуре вашего проекта, и по настоящему решить эту проблему можно лишь правильно проработав структуру проекта.

Явно освобождаем память с помощью сборщика мусора

В целом в 90% случаев проблема решается переустановкой питона, однако, я  просто обязан рассказать вам про библиотеку gc. В целом почитать про Garbage Collector стоит отдельно на авторитетных ресурсах в статьях профессиональных программистов. Вы просто обязаны знать, что происходит под капотом управления памятью. GC — это не только про Python, управление памятью в Java и других языках базируется на технологии сборки мусора. Ну а вот так мы можем мануально освободить память в Python:

Memory error python sklearn

Впервые я столкнулся с Memory Error, когда работал с огромным массивом ключевых слов. Там было около 40 млн. строк, воодушевленный своим гениальным скриптом я нажал Shift + F10 и спустя 20 секунд получил Memory Error.

Что такое MemoryError в Python?

Memory Error — исключение вызываемое в случае переполнения выделенной ОС памяти, при условии, что ситуация может быть исправлена путем удаления объектов. Оставим ссылку на доку, кому интересно подробнее разобраться с этим исключением и с формулировкой. Ссылка на документацию по Memory Error.

Если вам интересно как вызывать это исключение, то попробуйте исполнить приведенный ниже код.

Почему возникает MemoryError?

В целом существует всего лишь несколько основных причин, среди которых:

• 32-битная версия Python, так как для 32-битных приложений Windows выделяет лишь 4 гб, то серьезные операции приводят к MemoryError
• Неоптимизированный код
• Чрезмерно большие датасеты и иные инпут файлы
• Ошибки в установке пакетов

Как исправить MemoryError?

Ошибка связана с 32-битной версией

Тут все просто, следуйте данному гайдлайну и уже через 10 минут вы запустите свой код.

Как посмотреть версию Python?

Идем в cmd (Кнопка Windows + R -> cmd) и пишем python. В итоге получим что-то похожее на

Нас интересует эта часть [MSC v.1928 64 bit (AMD64)], так как вы ловите MemoryError, то скорее всего у вас будет 32 bit.

Как установить 64-битную версию Python?

Идем на официальный сайт Python и качаем установщик 64-битной версии. Ссылка на сайт с официальными релизами. В скобках нужной нам версии видим 64-bit. Удалять или не удалять 32-битную версию — это ваш выбор, я обычно удаляю, чтобы не путаться в IDE. Все что останется сделать, просто поменять интерпретатор.

Идем в PyCharm в File -> Settings -> Project -> Python Interpreter -> Шестеренка -> Add -> New environment -> Base Interpreter и выбираем python.exe из только что установленной директории. У меня это

Все, запускаем скрипт и видим, что все выполняется как следует.

Оптимизация кода

Пару раз я встречался с ситуацией когда мои костыли приводили к MemoryError. К этому приводили избыточные условия, циклы и буферные переменные, которые не удаляются после потери необходимости в них. Если вы понимаете, что проблема может быть в этом, вероятно стоит закостылить пару del, мануально удаляя ссылки на объекты. Но помните о том, что проблема в архитектуре вашего проекта, и по настоящему решить эту проблему можно лишь правильно проработав структуру проекта.

Явно освобождаем память с помощью сборщика мусора

В целом в 90% случаев проблема решается переустановкой питона, однако, я просто обязан рассказать вам про библиотеку gc. В целом почитать про Garbage Collector стоит отдельно на авторитетных ресурсах в статьях профессиональных программистов. Вы просто обязаны знать, что происходит под капотом управления памятью. GC — это не только про Python, управление памятью в Java и других языках базируется на технологии сборки мусора. Ну а вот так мы можем мануально освободить память в Python:

Источник

How to avoid memory overloads using SciKit Learn

A small technique we found while text-mining 40,000 documents

This article is a part of our making of series on “Islam, media subject”, a study on the perception of Islam by french national daily newspapers. You can see our first published article here and the previous one here.

In “Islam, media subject”, we wanted to quantify how media were treating “Islam”. To do that we used text-mining tools and techniques to analyse the articles published in daily newspapers from France.

One of those techniques was the analysis of (co)occurrences of terms in the articles thanks to Document-Term matrices like bellow. Those matrices hold occurrences of terms (all terms compose the vocabulary) through a set of documents (the corpus). They constitute one of the basic components of many techniques like Topic Modeling, Clustering, study of similarities, etc.

As you can see on this schema, the vocabulary is constituted of part-of-speech tagged words. This is a part of our analysis because we wanted to be able to study what were the terms used in our corpus associated with “Islam”.

But this is not a part of DTM, so you can ignore it for the rest of this article. However if you’re interested in it you can read the first article of this series that details it a bit.

In order to create those kind of matrices, we used a feature took from the Sci-Kit Learn libray (or sklearn, a Python library providing machine learning algorithms and techniques) called CountVectorizer. This feature was exactly what we were looking for because it took in charge the creation of DTM while letting us be able to study more deeply what terms were used in the different parts of our corpus thanks to a vocabulary (as shown above).

Why is it taking so much memory?

This vocabulary is a Python dictionary associating terms to a matrix columns. As we can see in the previous scheme, those terms can be single words or association of words (or n-grams). In our case we used n-grams between 1 and 3 terms and that’s what made this vocabulary get really huge quickly.

In this chart, we can see that the main problem lies in this vocabulary. For instance, when the number of documents in our corpus gets above 14,000 the vocabulary is taking 750MB while the corpus itself (a pandas DataFrame) takes

250MB and the associated DTM is taking only

180MB. This also illustrate the way Python deal with memory allocation for dictionaries (like vocabulary). As we can see, it first allocate an amount of memory, then, when it got filled by new elements, it doubles its space, and so on. That’s why we see a huge gap between 13,000 and 14,000 documents.

So we can guess that the next allocated memory space, for this dictionary alone, will be around 1.5GB when we will reach a bigger number of documents. So as the number of documents get bigger, you will reach a point where the allocated memory space is so big that CountVectorizer can’t compute the DTM anymore without throwing a Memory Error.

How to resolve this memory overload?

First, if you don’t need to, don’t use CountVectorizer. Sklearn have other less memory-consuming features like HashingVectorizer . However it didn’t allow us to have an in-memory vocabulary (since text got hashed). This made us stick with CountVectorizer in order to be able to query this vocabulary to study terms usage in our corpus.

But if you need to use CountVectorizer, what you can do is to separate this costful operation into several smaller operations and construct the Document-Term matrix with multiple operations, not one. That’s what we’re going to see next.

Divide the Document-Term matrix creation process in smaller parts

This sounds like an easy task to do. It seems you “just” have to separate your documents in small batches (chunks); then you apply CountVectorizer on them to create small DTMs, then combine them to get a big DTM, right? Well, no. If you do that, you will end up with incompatible matrices. Because every chunks’ DTM will have columns corresponding to different vocabulary terms. Indeed, the vocabulary depends on the texts content.

Different set of texts will produce different vocabularies and this is what prevents you to concatenate those matrices; simply because it doesn’t make any sense to concatenate two matrices having columns representing different terms. So, in order to separate this DTM creation in smaller parts, you should instead:

1. Create a single vocabulary of the whole corpus,
2. separate your corpus in different chunks,
3. for each chunk create a corresponding DTM with the vocabulary created earlier (this way you can be sure your matrices will have the same shape),
4. and finally concatenate your matrices.

We’ve summed this in the schema bellow. Remember that the Part-of-Speech Tagging process is not mandatory at all, but if you want to use such technique then you should do it with the depicted order. You can also look at a simplified version of what we implemented in this gist.

Benefits

This approach have several benefits. First, if your corpus computation fail (for various reasons) you could take back computation to where it failed (if you took care of saving computed parts during the process). This saves a lot of time and helps to keep a relatively good mental sanity.

Second, this solution could be parallelized/distributed since we have small independent corpus chunks and a single shared vocabulary, it won’t be an issue to process those matrices independently.

And finally, it makes computation of DTM smaller in terms of memory consumption. That’s why it’s less prone to break with a Memory Error.

Improvements to do

We didn’t spend additional time on this issue but we can imagine different improvements or different approaches to improve our solution.

As I’ve said earlier, the biggest problem lies in the memory space took by our vocabulary and our solution don’t reduce this space. And things will get worse when the corpus gets bigger (like in CountVectorizer); to a point where this vocabulary will get too big to do anything on the side. That’s why we should consider different approaches to reduce this space.

• Reduce the size of the vocabulary
  You could, for instance, remove terms appearing only once in the whole corpus or terms appearing too many times (like tool words, stop words). However, this approach won’t be enough to reduce its size significantly.
• Get the vocabulary out of the RAM, store it in a database
  This would be a huge improvement. By storing this vocabulary in a database would save a lot of memory space for other parts of the analysis. We didn’t look for what would be the “perfect” database for this job but if we can manage to have a working solution with PostgreSQL it will be good enough for us.
• Use a different data structure
  Python dictionaries are taking a lot of memory space. Heading for a similar data structure but with a memory consumption focus could be the solution. The only clue I have right now is to look at Trie (and the marisa-trie implementation).

If you know simpler, more efficient or just different approaches that could tackle this issue, feel free to comment!

Источник

How to reduce memory used by Random Forest from Scikit-Learn in Python?

June 24, 2020 by Piotr PЕ‚oЕ„ski Random forest June 24, 2020 by Piotr PЕ‚oЕ„ski —>

The Random Forest algorithm from scikit-learn package can sometimes consume too much memory:

• max_depth=None ,
• min_samples_split=2 ,
• min_samples_leaf=1 ,

which means that full trees are built. Bulding full trees is by design (see Leo Breiman, Random Forests article from 2001). The Random Forest creates full trees to fit the data well. If there will be one tree in the Random Forest, then the model will overfit the data. However, in the Random Forest there are created set of trees (for example 100 trees). To overcome the overfitting (and increase stability) the bagging and random subspace sampling are used. (Bagging — selecting subset of rows for training, random subspace sampling — selecting subset of columns in each node split search).

In the case of large data sets or complex datasets, the full tree can be really deep and have thousands of nodes. Such single decision tree will use a lot of memory and thus the memory consumption of the Random Forest will grow very fast. In this post I will show how to reduce memory consumption of the Random Forest. In the example I will use Adult Income dataset.

Let’s load packages and the data

The dataset has 32,561 rows and 15 columns (including the target column). We see that data use about 3.8 MB in the memory (similar memory is also needed to store the data on the hard drive disk).

Let’s use 25% of the data for testing and the rest for training.

I create the Random Forest Classifier with default parameters. This means that full trees will be built. There will be created 100 trees (the default of n_estimators ).

Let’s train the model:

Check the depth of the first tree in the Random Forest

Let’s check the depth of all the trees in the Forest:

Check the size of single tree in the disk after saving with joblib:

Our dataset size was 3.8 MB so the resulting Random Forest is about 13 times larger than the dataset! The dataset was pretty small, you can easily imagine how the Random Forest size will explode for larger files (the complexity of the dataset matters a lot because it determines the depth of the full tree).

Before changing anything in the Random Forest let’s check its performance.

Reduce memory usage of the Scikit-Learn Random Forest

The memory usage of the Random Forest depends on the size of a single tree and number of trees. The most straight forward way to reduce memory consumption will be to reduce the number of trees. For example 10 trees will use 10 times less memory than 100 trees. However, the more trees in the Random Forest the better for performance and I will search for other hyper-parameters to control the Random Forest size.

The simplest way to reduce the memory consumption is to limit the depth of the tree. Shallow trees will use less memory. Let’s train shallow Random Forest with max_depth=6 (keep number of trees as default 100 ):

Let’s save the shallow Decision Tree to the disk:

You see, the full single tree size was: 0.52 MB while the shallow tree size is 0.01 MB. Let’s save the whole forest:

The Random Forest with full trees has size 49.67 MB and the shallow Random Forest size is 0.75 MB so 66 times less!

Let’s check the performance of such shallow tree:

The perfomance is better! The shallow Random Forest has about 4% better logloss (the lower value the better). So we reduced the size of Random Forest by 66 times and increase the perfomance! 🙂

The shallow trees can be also obtained by tuning min_samples_split or min_samples_leaf (or even other hyper-parameters, like: min_weight_fraction_leaf , max_features , max_leaf_nodes ). However, I prefer to tune max_depth because it is more intuitive.

Extra tip for saving the Scikit-Learn Random Forest in Python

While saving the scikit-learn Random Forest with joblib you can use compress parameter to save the disk space. In the joblib docs there is information that compress=3 is a good compromise between size and speed. Example below:

Compressed Random Forest is 6 times smaller!

The same obervation about memory consumption should be valid for Extra Trees Classifier and Extra Trees Regressor .

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Источник

Я недавно запускал программу, а затем Pycharm выскочил из сообщения об ошибке из-за нехватки памяти, что может быть вызвано слишком большим объемом прочитанных данных. Pycharm имеет максимальный объем памяти по умолчанию в сети. Появившееся сообщение составляет 1024 МБ, которое выделено Pycharm. Внутренней памяти не хватает!

1. Описание:

Содержимое файла конфигурации pycharm64.exe.vmoptions

-Xms128m
-Xmx1024m
-XX:ReservedCodeCacheSize=240m
-XX:+UseConcMarkSweepGC
-XX:SoftRefLRUPolicyMSPerMB=50
-ea
-Dsun.io.useCanonCaches=false
-Djava.net.preferIPv4Stack=true
-XX:+HeapDumpOnOutOfMemoryError
-XX:-OmitStackTraceInFastThrow

Конфигурация по умолчанию следующая, которую можно настроить в соответствии с вашим собственным ПК:

Xmx 512 МБ JVM максимально выделяемой памяти
 XX: MaxPermSize 250 МБ Установить максимальный размер памяти без кучи
 XX: ReservedCodeCacheSize 150 МБ Установить максимальное значение кеша кода

заметка:

-Xms и -Xmx — это опция команды java для установки доступного объема памяти при запуске приложения и доступного размера памяти во время выполнения.

2. Решение 1. Измените файл конфигурации прямо в Pycharm.

Этот способ относительно простой и удобный.

1. Нажмите «Справка» на панели инструментов PyCharm, «Справка-> Найти действие».

Введите «Параметры виртуальной машины», ниже отобразится «Изменить параметры настраиваемой виртуальной машины», а затем нажмите Enter, чтобы перейти к открытию PyCharm.файл конфигурации vmoption。

2. Измените -Xmx1024m на -Xmx2048m или другие значения, затем сохраните и выйдите из PyCharm.

3. Перезапустите Pycharm, и все готово! ! !

3. Решение 2. Найдите файл конфигурации в общем каталоге установки и измените его.

Фактически, этот метод такой же, как и метод первый, но через путь установки Pycharm найдитеpycharm64.exe.vmoptionsНастройте файл, а затем измените его.

Найдите файл конфигурации в пути установки в соответствии с вашей собственной установкой и измените его!

Редактировать файл конфигурации pycharm
D:Program FilesPyCharm 4.5.4binpycharm.exe.vmoptions
D:Program FilesPyCharm 4.5.4binpycharm64.exe.vmoptions
 Адрес ищется в соответствии с фактическим путем установки.
 Кроме того, в 64-битной системе рекомендуется установить 64-битную jre, чтобы вы могли использовать bin  pycharm64.exe.
 32-битная jre может использовать только bin  pycharm.exe

What is Memory Error?

Python Memory Error or in layman language is exactly what it means, you have run out of memory in your RAM for your code to execute.

When this error occurs it is likely because you have loaded the entire data into memory. For large datasets, you will want to use batch processing. Instead of loading your entire dataset into memory you should keep your data in your hard drive and access it in batches.

A memory error means that your program has run out of memory. This means that your program somehow creates too many objects. In your example, you have to look for parts of your algorithm that could be consuming a lot of memory.

If an operation runs out of memory it is known as memory error.

Types of Python Memory Error

Unexpected Memory Error in Python

If you get an unexpected Python Memory Error and you think you should have plenty of rams available, it might be because you are using a 32-bit python installation.

The easy solution for Unexpected Python Memory Error

Your program is running out of virtual address space. Most probably because you’re using a 32-bit version of Python. As Windows (and most other OSes as well) limits 32-bit applications to 2 GB of user-mode address space.

We Python Pooler’s recommend you to install a 64-bit version of Python (if you can, I’d recommend upgrading to Python 3 for other reasons); it will use more memory, but then, it will have access to a lot more memory space (and more physical RAM as well).

The issue is that 32-bit python only has access to ~4GB of RAM. This can shrink even further if your operating system is 32-bit, because of the operating system overhead.

For example, in Python 2 zip function takes in multiple iterables and returns a single iterator of tuples. Anyhow, we need each item from the iterator once for looping. So we don’t need to store all items in memory throughout looping. So it’d be better to use izip which retrieves each item only on next iterations. Python 3’s zip functions as izip by default.

Must Read: Python Print Without Newline

Python Memory Error Due to Dataset

Like the point, about 32 bit and 64-bit versions have already been covered, another possibility could be dataset size, if you’re working with a large dataset. Loading a large dataset directly into memory and performing computations on it and saving intermediate results of those computations can quickly fill up your memory. Generator functions come in very handy if this is your problem. Many popular python libraries like Keras and TensorFlow have specific functions and classes for generators.

Python Memory Error Due to Improper Installation of Python

Improper installation of Python packages may also lead to Memory Error. As a matter of fact, before solving the problem, We had installed on windows manually python 2.7 and the packages that I needed, after messing almost two days trying to figure out what was the problem, We reinstalled everything with Conda and the problem was solved.

We guess Conda is installing better memory management packages and that was the main reason. So you can try installing Python Packages using Conda, it may solve the Memory Error issue.

Most platforms return an “Out of Memory error” if an attempt to allocate a block of memory fails, but the root cause of that problem very rarely has anything to do with truly being “out of memory.” That’s because, on almost every modern operating system, the memory manager will happily use your available hard disk space as place to store pages of memory that don’t fit in RAM; your computer can usually allocate memory until the disk fills up and it may lead to Python Out of Memory Error(or a swap limit is hit; in Windows, see System Properties > Performance Options > Advanced > Virtual memory).

Making matters much worse, every active allocation in the program’s address space can cause “fragmentation” that can prevent future allocations by splitting available memory into chunks that are individually too small to satisfy a new allocation with one contiguous block.

¹ If a 32bit application has the LARGEADDRESSAWARE flag set, it has access to s full 4gb of address space when running on a 64bit version of Windows.

² So far, four readers have written to explain that the gcAllowVeryLargeObjects flag removes this .NET limitation. It does not. This flag allows objects which occupy more than 2gb of memory, but it does not permit a single-dimensional array to contain more than 2^31 entries.

How can I explicitly free memory in Python?

If you wrote a Python program that acts on a large input file to create a few million objects representing and it’s taking tons of memory and you need the best way to tell Python that you no longer need some of the data, and it can be freed?

The Simple answer to this problem is:

Force the garbage collector for releasing an unreferenced memory with gc.collect(). 

Like shown below:

import gc

gc.collect()

Memory error in Python when 50+GB is free and using 64bit python?

On some operating systems, there are limits to how much RAM a single CPU can handle. So even if there is enough RAM free, your single thread (=running on one core) cannot take more. But I don’t know if this is valid for your Windows version, though.

How do you set the memory usage for python programs?

Python uses garbage collection and built-in memory management to ensure the program only uses as much RAM as required. So unless you expressly write your program in such a way to bloat the memory usage, e.g. making a database in RAM, Python only uses what it needs.

Which begs the question, why would you want to use more RAM? The idea for most programmers is to minimize resource usage.

How to put limits on Memory and CPU Usage

To put limits on the memory or CPU use of a program running. So that we will not face any memory error. Well to do so, Resource module can be used and thus both the task can be performed very well as shown in the code given below:

Code #1: 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 #2: 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

1. Allocate More Memory

Some Python tools or libraries may be limited by a default memory configuration.

Check if you can re-configure your tool or library to allocate more memory.

That is, a platform designed for handling very large datasets, that allows you to use data transforms and machine learning algorithms on top of it.

A good example is Weka, where you can increase the memory as a parameter when starting the application.

2. Work with a Smaller Sample

Are you sure you need to work with all of the data?

Take a random sample of your data, such as the first 1,000 or 100,000 rows. Use this smaller sample to work through your problem before fitting a final model on all of your data (using progressive data loading techniques).

I think this is a good practice in general for machine learning to give you quick spot-checks of algorithms and turnaround of results.

You may also consider performing a sensitivity analysis of the amount of data used to fit one algorithm compared to the model skill. Perhaps there is a natural point of diminishing returns that you can use as a heuristic size of your smaller sample.

3. Use a Computer with More Memory

Do you have to work on your computer?

Perhaps you can get access to a much larger computer with an order of magnitude more memory.

For example, a good option is to rent compute time on a cloud service like Amazon Web Services that offers machines with tens of gigabytes of RAM for less than a US dollar per hour.

4. Use a Relational Database

Relational databases provide a standard way of storing and accessing very large datasets.

Internally, the data is stored on disk can be progressively loaded in batches and can be queried using a standard query language (SQL).

Free open-source database tools like MySQL or Postgres can be used and most (all?) programming languages and many machine learning tools can connect directly to relational databases. You can also use a lightweight approach, such as SQLite.

5. Use a Big Data Platform

In some cases, you may need to resort to a big data platform.

Summary

In this post, you discovered a number of tactics and ways that you can use when dealing with Python Memory Error.

Are there other methods that you know about or have tried?
Share them in the comments below.

Have you tried any of these methods?
Let me know in the comments.

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