Python pickle memory error

Python Memory Error | How to Solve Memory Error in Python

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.

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.

Out of Memory Error in Python

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:

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.

if you wanna limit the python vm memory usage, you can try this:
1、Linux， ulimit command to limit the memory usage on python
2、you can use resource module to limit the program memory usage;

if u wanna speed up ur program though giving more memory to ur application, you could try this:
1threading, multiprocessing
2pypy
3pysco on only python 2.5

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

Code #2: In order to restrict memory use, the code puts a limit on the total address space

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.

If your problem is still not solved and you need help regarding Python Memory Error. Comment Down below, We will try to solve your issue asap.

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Python pickle memory error

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:

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Python Memory Error

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Introduction to Python Memory Error

Memory Error is a kind of error in python that occurs when where the memory of the RAM we are using could not support the execution of our code since the memory of the RAM is smaller and the code we are executing requires more than the memory of our existing RAM, this often occurs when a large volume of data is fed to the memory and when the program has run out of memory in processing the data.

Syntax of Python Memory Error

When performing an operation that generates or using a big volume of data, it will lead to a Memory Error.

Web development, programming languages, Software testing & others

Code:

Output:

For the same function, let us see the Name Error.

Code:

The numpy operation of generating random numbers with a range from a low value of 1 and highest of 10 and a size of 10000 to 100000 will throw us a Memory Error since the RAM could not support the generation of that large volume of data.

How does Memory Error Works?

Most often, Memory Error occurs when the program creates any number of objects, and the memory of the RAM runs out. When working on Machine Learning algorithms most of the large datasets seems to create Memory Error. Different types of Memory Error occur during python programming. Sometimes even if your RAM size is large enough to handle the datasets, you will get a Memory Error. This is due to the Python version you might be using some times; 32-bit will not work if your system is adopted to a 64-bit version. In such cases, you can go uninstall 32-bit python from your system and install the 64-bit from the Anaconda website. When you are installing different python packages using the pip command or other commands may lead to improper installation and throws a Memory Error.

In such cases, we can use the conda install command in python prompt and install those packages to fix the Memory Error.

Example:

Another type of Memory Error occurs when the memory manager has used the Hard disk space of our system to store the data that exceeds the RAM capacity. Upon working, the computer stores all the data and uses up the memory throws a Memory Error.

Avoiding Memory Errors in Python

The most important case for Memory Error in python is one that occurs during the use of large datasets. Upon working on Machine Learning problems, we often come across large datasets which, upon executing an ML algorithm for classification or clustering, the computer memory will instantly run out of memory. We can overcome such problems by executing Generator functions. It can be used as a user-defined function that can be used when working with big datasets.

Generators allow us to efficiently use the large datasets into many segments without loading the complete dataset. Generators are very useful in working on big projects where we have to work with a large volume of data. Generators are functions that are used to return an iterator. Iterators can be used to loop the data over. Writing a normal iterator function in python loops the entire dataset and iters over it. This is where the generator comes in handy it does not allow the complete dataset to loop over since it causes a Memory Error and terminates the program.

The generator function has a special characteristic from other functions where a statement called yield is used in place of the traditional return statement that returns the output of the function.

A sample Generator function is given as an example:

Code:

Output:

In this sample generator function, we have generated integers using the function sample generator, which is assigned to the variable gen_integ, and then the variable is iterated. This allows us to iter over one single value at a time instead of passing the entire set of integers.

In the sample code given below, we have tried to read a large dataset into small bits using the generator function. This kind of reading would allow us to process large data in a limited size without using up the system memory completely.

Code:

Output:

There is another useful technique that can be used to free memory while we are working on a large number of objects. A simple way to erase the objects that are not referenced is by using a garbage collector or gc statement.

Code:

The import garbage collector and gc.collect() statement allows us to free the memory by removing the objects which the user does not reference.

There are additional ways in which we can manage the memory of our system CPU where we can write code to limit the CPU usage of memory.

Code:

This allows us to manage CPU usage to prevent Memory Error.

Some of the other techniques that can be used to overcome the Memory Error are to limit our sample size of we are working on, especially while performing complex machine learning algorithms. Or we could update our system with more memory, or we can use the cloud services like Azure, AWS, etc. that provides the user with strong computing capabilities.

Another way is to use the Relational Database Management technique where open-source databases like MySQL are available free of cost. It can be used to store large volumes of data; also, we can adapt to big data storage services to effectively work with large volumes.

Conclusion

In detail, we have seen the Memory Error that occurs in the Python programming language and the techniques to overcome the Name Error. The main take away to remember in python Memory Error is the memory usage of our RAM where the operations are taking place, and efficiently using the above-mentioned techniques will allow us to overcome the Memory Error.

Recommended Articles

This is a guide to Python Memory Error. Here we discuss the introduction, working and avoiding memory errors in python, respectively. You may also have a look at the following articles to learn more –

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In this tutorial, we are going to find out some of the possible causes that can lead to the Python Pickle Dump memory error, and after that, we will provide some possible recovery methods that you can use to try to fix the problem.

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I am now the author of a package called klepto (and the author of the including dill ).Designed for very simple storage of recovered and real physical objects, klepto provides a simple dictionary interface for databases, a cache for storage devices, and disk storage. Below I’ll show you storing LOBs in a huge archive, a directory, which is a directory on the filesystem, where one file is important for each entry. I choose object serialization (it is more measured, but uses dill so you can sell almost any object) and choose any cache. Using memory.cache allows me to quickly access an archive of directories without having to keep the entire archive in memory. Interacting with the database or file may take a while, but interacting with memory is fast … because you can fill the archive memory cache as you wish.

  >>> Klepto>>> Import d = klepto.archives.dir_archive ('foo', cached = True, serialized = True)>>> ddir_archive ('stuff' ,, cached = True)>>> import numpy>>> # add three sale offers to the cache p  memory>>> d ['big1'] Numpy = .arange (1000)>>> d ['big2'] = numpy.arange (1000)>>> d ['big3'] = numpy.arange (1000)>>> Extract numbers from the cache memory in the entire archive on the hard disk>>> d.dump ()>>> Clear # cache memory>>> d.clair ()>>> ddir_archive ('stuff' ,, cached = True)>>> # only legion caching entries often from archive>>> d.load ('big1')>>> d ['grand1'] [- 3:]Table ([997, 998, 999])>>> 

klepto offers fast and flexible access to large amounts of memory, and if the archive allows us parallel access (like some databases), you can read the results in parallel. It is also easy to share the results of different parallel processes or on different machines. Here, for example, I am creating a second archive pointing to the directory of the same archive. Transferring keys between two objects is easy, and the process is no different from other processes.

  >>> f = klepto.archives.dir_archive ('foo', cached = True, serialized = True)>>> fdir_archive ('stuff' ,, cached = True)>>> # add very small objects to the first cache>>> d ['small1'] = Lambda x: x ** 2>>> d ['small2'] equals (1,2,3)>>> #Clean objects in your archive>>> d.dump ()>>> # Load one of the closest objects into the cache>>> second f.load ('small2')>>> thendir_archive ('foo', 'small2': (1, 2, 3),cacheable = True) 

You can also choose from different levels of compression of information about the file and aboutYou want the files to appear in memory. Much has to do with differentParameters for file backends and indexes. Interfacehowever, it is the same.

Regarding your other questions about deleting junk files and editing parts of the dictionary, klepto can do both of these things, because a person can load and delete objects out of cache separately, empty, load and synchronization with the server side, archive or others created using other dictionary methods.

I created a class with a list (content,
binaries) is so large that it takes up a lot of memory.

When I select the list dump for the first time, it assigns a 1.9 GB file to
Disk. I can get the content back, but when I try to clear it
Again (with or without additions) get this:

Follow-up call (last call):
File ““, line 1, is in
File “c: Python26 Lib pickle.py”, 1362, line in dump
Pickler (file, log) .dump (obj)
The file “c: Python26 Lib pickle.py “, ray 224, in the dump
self.save (obj)
Save file “c: Python26 Lib pickle.py”, zone 286, in obj)
f (self, # call an unrelated approach with explicit self
File “c: Python26 Lib pickle.py”, line 600, in save_list
self._batch_appends (iter (obj))
File “c: Python26 Lib pickle.py”, line 615, to “c: Python26 Lib pickle _batch_appends”
save (x)
File.py “, web 286, save to obj)
f (self, # call an unbound method with explicit self
File “c: Python26 Lib pickle.py”, line 488, in save_string
self.write (STRING + repr (obj) + ‘ n’)
Memory error

I am trying to get this error either by trying to load the complete list or by keeping
it was found in “segments”, i.e. in a list of 2229 elements, i.e. from
Command line. I’ve tried individual pieces of
using Pickle.The list is in files, that is, 500 climatic zones have been recorded in their own
The file is still the same error.

I created the following sequence while trying to dump most of the dump list into
Segments – X and Y were separated by indices of 500 elements, pattern doesn’t work
by [1000: 1500]:

I am assuming the available hard drive is exhausted, so I tried
“Waiting” for landfills in the hope that a series of garbage
Free up some memory – But it won’t help at all.

1. The Gets List was indeed compiled from various sources
2. The marketing mailing list can be successfully deleted
3. the program restarts gracefully and loads the
list4. The list cannot be (re) unloaded without a MemoryError

Any ideas (other than specific ones – don’t save all of these files
Greedy content for the list! Although this is a simple “answer” I see under
dot ).

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Prendi Un Problema Con L’errore Di Memoria Ram Python Pickle Dump
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