Python memory error ошибка

What is Memory Error?
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
How can I explicitly free memory in Python?
Memory error in Python when 50+GB is free and using 64bit python?
How do you set the memory usage for python programs?
How to put limits on Memory and CPU Usage
Ways to Handle Python Memory Error and Large Data Files
1. Allocate More Memory
2. Work with a Smaller Sample
3. Use a Computer with More Memory
4. Use a Relational Database
5. Use a Big Data Platform
Summary

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.

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

# 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.

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.

Источник

Python Memory Error

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.

Code:

## Numpy operation which return random unique values
import numpy as np
np.random.uniform(low=1,high=10,size=(10000,100000))

Output:

Python Memory Error 1

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

Code:

## Functions which return values
def calc_sum(x,y):
    op = x + y
    return(op)

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:

Python Memory Error 2

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:

def sample_generator():
    for i in range(10000000):
        yield i
gen_integ= sample_generator()
for i in gen_integ:
    print(i)

Output:

A sample Generator

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:

def readbits(filename, mode="r", chunk_size=20):
    with open(filename, mode) as f:
        while True:
            data = f.read(chunk_size)
            if not data:
                break
            yield data
def main():
    filename = "C://Users//Balaji//Desktop//Test"
    for bits in readbits(filename):
        print(bits)

Output:

large dataset into small bits

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:

import gc
gc.collect()

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:

import resource 
def limit_memory(Datasize): 
        min_, max_ = resource.getrlimit(resource.RLIMIT_AS) 
        resource.setrlimit(resource.RLIMIT_AS, (Datasize, max_))

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.

An Example of MemoryError

To have a look at this error in action, let’s start with a particularly greedy piece of code. In the code below, we start with an empty array and use nested arrays to add strings to it. In this case, we use three levels of nested arrays, each with a thousand iterations. This means at the end of the program, the array s has 1,000,000,000 copies of the string «More.«

s = []
for i in range(1000):
   for j in range(1000):
       for k in range(1000):
           s.append("More")

Output

As you might expect, these million strings are a bit much for, let’s say, a laptop to handle. The following error is printed out:

C:codePythonMemErrvenv3KScriptspython.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

In this case, the traceback is relatively simple as there are no libraries involved in this short program. After the traceback showing the exact function call which caused the issue, we see the simple but direct MemoryError.

Two Ways to Handle A MemoryError in Python

Appropriate Python Set-up

This simplest but possibly least intuitive solution to a MemoryError actually has to do with a potential issue with your Python setup. In the event that you have installed the 32-bit version of Python on a 64-bit system, you will have extremely limited access to the system’s memory. This restricted access may cause MemoryErrors on programs that your computer would normally be able to handle.

Attention to Large Nested Loops

If your installation of Python is correct and these issues still persist, it may be time to revisit your code. Unfortunately, there is no cut and dry way to entirely remove this error outside of evaluating and optimizing your code. Like in the example above, pay special attention to any large or nested loops, along with any time you are loading large datasets into your program in one fell swoop.

In these cases, the best practice is often to break the work into batches, allowing the memory to be freed in between calls. As an example, in the code below, we have broken out earlier nested loops into 3 separate loops, each running for 333,333,333 iterations. This program still goes through one million iterations but, as the memory can be cleared through the process using a garbage collection library, it no longer causes a MemoryError.

An Example of Batching Nested Loops

import gc

s = []
t = []
u = []

for i in range(333333333):
   s.append("More")
gc.collect()

for j in range(333333333):
   t.append("More")
gc.collect()

for k in range(333333334):
   u.append("More")
gc.collect()

How to Avoid a MemoryError in Python

Python’s garbage collection makes it so that you should never encounter issues in which your RAM is full. As such, MemoryErrors are often indicators of a deeper issue with your code base. If this is happening, it may be an indication that more code optimization or batch processing techniques are required. Thankfully, these steps will often show immediate results and, in addition to avoiding this error, will also vastly shorten the programs’ runtime and resource requirements.

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Источник

Содержание

Python Memory Error | How to Solve Memory Error in Python
What is Memory Error?
Types of Python Memory Error
Unexpected Memory Error in Python
The easy solution for Unexpected Python Memory Error
Python Memory Error Due to Dataset
Python Memory Error Due to Improper Installation of Python
Out of Memory Error in Python
How can I explicitly free memory in Python?
Memory error in Python when 50+GB is free and using 64bit python?
How do you set the memory usage for python programs?
How to put limits on Memory and CPU Usage
Ways to Handle Python Memory Error and Large Data Files
1. Allocate More Memory
2. Work with a Smaller Sample
3. Use a Computer with More Memory
4. Use a Relational Database
5. Use a Big Data Platform
Summary
What is memory error in python
Что такое MemoryError в Python?
Почему возникает MemoryError?
Как исправить MemoryError?
Ошибка связана с 32-битной версией
Как посмотреть версию Python?
Как установить 64-битную версию Python?
Оптимизация кода
Явно освобождаем память с помощью сборщика мусора
Debugging and preventing memory errors in Python
Our basic setup
Why do we need to address OOMs?
OOMs in synchronous requests on a server
Worker processes running out of memory
How can we debug these errors?
Attempts at memory profiling
Memory limits to the rescue (sort of)
Thread stack size
Behavior when limiting address space
Bringing it all Together
What about memory leaks?

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.

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

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.

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

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

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?

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?

How do you set the memory usage for python programs?

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

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?

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

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).

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.

Источник

What is memory error in python

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

Debugging and preventing memory errors in Python

At Brex, we use Python extensively for Data Science and Machine Learning applications. On the Data Platform team, we often need to write applications that can execute Python code written by our Data Science (DS) team, like on-demand feature computation or inference for predictive models.

Because these Python services interact with real-life data, there can be significant variance in the amount of data processed per request. For example, a request to “run inference to predict customer X’s future spending” might have very different memory usage requirements, depending on how much data is available on customer X’s past spending patterns.

This set the stage for one of our most recent reliability challenges: dealing with out of memory errors (OOMs) in our model-serving services, equipped with the knowledge that some of the requests will not necessarily be “well-behaved.” This one was quite an interesting challenge to debug, so we figured it was worthwhile to share some of our learnings with the broader community.

Our basic setup

Let’s start off with some basic info about our setup. Everything that follows (unless specifically called out) has been tested on this specific setup:

Python version: 3.7.3 (I know, upgrading is on our list)
All of our Python services get built into docker images using Bazel (after being packaged into a binary executable)
Our services run in a Kubernetes cluster on AWS
All of our Python services are running on a standard gRPC server process
You will see some references to async workloads — the basic setup for these are Python Celery workers consuming from a queue backed by Redis

Why do we need to address OOMs?

Perhaps the answer to this is quite obvious (i.e., OOMs cause services to fail), but it’s worth digging into what exactly happens when a python process “runs out of memory.” The Python process itself is blissfully unaware of the memory limit imposed on it by the OS.¹ Instead of self-limiting the amount of memory it will allocate, the Python process will simply attempt to allocate more memory to do whatever it needs to do (for example, to load in some more data from a database) — only to immediately receive a SIGKILL from the OS when it hits its memory limit.

This not only stops the process from completing the task(s) it was previously running, but it will also not allow for any kind of “graceful” shutdown. This is probably the worst possible thing that could happen to your process while running in production — in addition to preventing any ongoing requests from completing, it is also extremely difficult to debug. After all, the inability to shut down gracefully prevents any server-side errors, metrics, or traces from being generated to aid in service observability.

To be more concrete, consider the following two different cases where we saw real-life OOMs happen at Brex.

OOMs in synchronous requests on a server

This case is essentially the same case outlined in the intro: a server runs out of memory while processing a request. The dangerous thing here is that our servers are almost always handling more than one request at a time — when a single one of these requests causes a process to run out of memory, all requests being handled by that process will immediately fail. Even worse: since a graceful shutdown is not possible, we cannot control how they fail, so that we can’t even ensure the client receives a retriable error message.

Worker processes running out of memory

In addition to processing requests synchronously, we also have Python processes that act as workers consuming tasks from a queue. In this case, each task is performed sequentially by each worker, so the problem above does not exist. However, because the SIGKILL makes a graceful shutdown impossible, these tasks simply remain unacknowledged by the worker. For our specific Celery setup, this means we end up hitting the visibility timeout, so that our workers repeatedly attempt to consume these OOM-inducing tasks ad-infinitum (more elaborate async setups will, of course, have additional retry redundancies, which ensure this does not happen).

How can we debug these errors?

All this doom and gloom brings us to the question: how do we even begin to address this problem? Our initial intuition here was fairly straightforward — some combination of the following hypotheses must be causing the service to run out of memory:

There was a memory leak in our server implementation, causing it to use more and more memory in each request and eventually run out of memory.
While none of the individual request were using too much memory, the combination of all requests at any given time would use too much memory in aggregate.
There are requests which, on their own, legitimately use more memory than a single process has available.²

In a server that handles a relatively constant number of requests over time, you might think that a memory leak would be quite apparent as simply monotonically increasing memory usage. You would be correct. In this case, however, we were dealing with a server that has very bursty workloads, so there was no opportunity to spot the traditional slowly increasing memory usage over time.

Solving this issue came down to testing these hypotheses against what we saw in reality. In a perfect world, we could distinguish between these hypotheses by measuring:

A. The amount of memory allocated directly attributable to handling each request

B. The percentage of (A) deallocated after the process finishes responding to each request

The world we live in, however, is d̶a̶r̶k̶ ̶a̶n̶d̶ ̶f̶u̶l̶l̶ ̶o̶f̶ ̶t̶e̶r̶r̶o̶r̶s̶ not perfect, and these two numbers are not readily available. This is mainly because these OOMs tended to happen at times when our service was handling a large number of concurrent requests at once. This is not surprising. After all, any of the three hypotheses laid out would cause the probability of an OOM to increase when the server is handling a large number of concurrent requests. Each request in our server is handled by a separate long-lived thread (spawned from a ThreadPoolExecutor) — a fairly standard setup for Python gRPC servers. Because we cannot measure the memory used by each individual thread², we cannot easily measure the amount of memory used in each individual request.

Our solution to this problem was to essentially force our server to execute each request one at a time, so that we could more easily measure the amount of memory used per request. The easiest way to do this would be to simply turn down the concurrency of the gRPC server to 1.³ If this were at all a reasonable option for us, we would have taken it without hesitation. However, this would have meant that any requests coming in beyond our concurrency capacity would queue until previous requests were processed. This might have helped us debug our OOM problem, but it would have definitely made us worse off in our overall reliability.

In this case, we had to get more creative: in addition to executing each request on a thread off of the main server process, we would also execute each request entirely asynchronously in an isolated worker process, as in the diagram below.⁴

As you can see, the actual results from the asynchronous computation are not used at all — we simply use the async execution to run each request in an isolated manner.⁵ In our asynchronous worker, we can simply measure the amount of memory used before and after each request is executed (we used psutil.Process.full_memory_info to obtain the resident memory used). This allows us to gain a very detailed understanding of the memory allocated for (A) and deallocated⁶ after (B) each request so we can effectively distinguish between hypotheses (1) and (2). In addition, because requests were running without any contention for resources, we could directly attribute any OOMs that occurred to the specific requests being processed on that worker at that point in time. This, in addition to measuring the memory used before a request was processed, allowed us to differentiate between (1) and (3).

So, what was our conclusion? Are you holding your breath? At the end of the day, hypothesis (3) was the main cause of our memory issues in this specific instance. This became quite obvious when we saw the asynchronous behavior: a single request was repeatedly causing a worker process to OOM and being continuously retried, as Celery was never able to acknowledge the task after consuming it from the Redis queue.

Attempts at memory profiling

We should briefly mention that we attempted to profile the memory used by this server in order to identify which bits of code were allocating memory that was causing our OOM. We attempted to use tracemalloc , largely following the steps outlined in this blog post, creating an asynchronous thread which would report the top memory allocation tracebacks in our code.

Unfortunately, after deploying this piece of instrumentation to production we almost immediately saw a significant increase in the number of OOMs reported for our service. From the few logs we were able to recover after this attempt, it was clear that the vast majority of the memory allocation was in fact coming from the usage of tracemalloc itself. At the end of the day, using this profiling tool ended up creating far too much overhead to be useful.

Memory limits to the rescue (sort of)

The process above for debugging allowed us to understand when and how requests cause OOMs, but it comes with a couple of drawbacks:

It is inherently reactive, as it allows the OOM to continue to happen on the gRPC server process, and
As outlined, this would require quite a significant amount of upkeep for what ends up being just a debugging tool at the end of the day.

In an ideal scenario, we would be able to prevent the OOM error in the first place, which essentially requires stopping the Python process from allocating more memory than it is allowed. From an implementation perspective, the desired behavior would be that the Python interpreter would, instead of attempting to allocate more than its fair share of OS memory, simply raise a MemoryError in the codepath attempting such an allocation. Then, at the very top level of the execution stack in the gRPC server, we can handle such errors and return a retriable error code to the client (in this case the RESOURCE_EXHAUSTED gRPC code seems appropriate).

In a world where we can trust clients to be well-behaved (thankfully, we live in such a world on the Data Platform team at Brex), clients can back off and retry, hopefully reaching either (a) the same exact process at a later point in time, when there is less contention for memory or (b) another instance entirely of the same service which is using less memory (provided a load balancer is appropriately employed).

Of course, we wouldn’t be discussing this scenario unless there were a reasonable method to implement it. Enter: the Python resources module.

I was quite surprised to find that there were very few instances documenting this use case for the Python resources module (aside from one excellent and succinct blog post by Carlos Becker, which much of this discussion will build on). After spending some time working with the resources module, I now have some suspicions as to why there is scarce information on this use case: imposing resource limits without a thorough understanding of Python’s memory management system can lead to unexpected behavior (more on this in a bit). Nevertheless, this module does allow us to set a limit on the memory a process is allowed to access, effectively turning OOMs (which affect the entire process) into MemoryErrors (which only affect a single execution path/thread, and can be handled appropriately by the caller). In fact, setting up memory limits is as simple as running the following function at the very top of your process:

The snippet above will ensure the process’s heap (where Python allocates the vast majority of its data) does not grow beyond the limits imposed by its cgroup.⁷ If you are running your python process on a Kubernetes cluster, this will correspond to the memory resource limit for the container running said process.

That was easy enough, wasn’t it? Oh, what is that? Your process is now raising MemoryErrors if you look at it funny? Ok, maybe I missed something here…

Thread stack size

When we initially tested out this fix, it almost immediately crashed and burned in our development environment. The problem ended up being simple: we were not accounting for the fairly massive default Python stack size on Linux systems (roughly 10Mb by default). This is the size of the C stack, which gets allocated on the heap of the process as soon as any thread is created. If there is insufficient memory to spawn new threads, then your multi-threaded program will have an absolutely miserable time. Considering the Very Large amount of memory used by a single Python thread, and the relatively low memory limits we were dealing with (originally, each process was set up to have a 2Gb limit), this meant that a process would very quickly reach its memory limit due to the threads it was spawning alone.

The solution to this? Well, quite simply: set the stack size to some lower and more reasonable value (in our case, we ended up with 2Mb). This, of course, does not come without its dangers — if the Python process is making calls which result in the C stack size growing significantly, then this can be problematic. For our purposes, the 2Mb limit is quite comfortable (we could probably get away with going far lower than that):

Behavior when limiting address space

It is worth noting for posterity here (and for others that happen to stumble upon this problem) that the choice to use RLIMIT_DATA was not a simple one. In fact, we initially attempted to set RLIMIT_AS (as in the original post that described this solution), but we found that this created some fundamentally weird problems. To get a flavor of what I’m talking about, you can try running this Python snippet yourself:⁸

The snippet above will error out once it gets to thread 18 — this is far lower than the memory limit of 1Gb should allow for. Through some experimentation and learning about how Python actually manages its memory (it essentially allocates any interesting bits of memory used on the heap), we ended up concluding that using RLIMIT_DATA was more appropriate. In practice, this proved to be fairly well-behaved: the code above runs swimmingly when we swap out RLIMIT_AS with RLIMIT_DATA — in fact, in that case you only begin to see failures when we attempt to spawn hundreds of threads, which is consistent with the default stack size of 10Mb discussed above.

Bringing it all Together

After the long process of learning and debugging that led to the info above, we can now summarize exactly how we changed our gRPC servers to behave more nicely under memory contention. For starters, at the very top of our entry point, we added:

Finally, in our actual server code, we can simply wrap all of our endpoints with the following decorator:⁹

And that’s that! Well, almost… this solution will correctly handle most allocation failures — for example, if one attempts to load in too much data from a database inside of an endpoint handler. However, there are some potential edge cases that should also be addressed, if one’s intent is to be thorough (which we, of course, all are). Specifically, there are some errors that are caused by memory allocation failures but do not come up as MemoryErrors:

Exceptions that are raised from MemoryErrors, but which do not themselves preserve the exception type. It is relatively straightforward to account for these, by simply recursing down into an exception’s __cause__ and inspecting if it is a MemoryError instance
Exceptions raised when the interpreter cannot start new threads due to memory contention (ie: if there is insufficient memory available to allocate a new stack). It is easy to reproduce this error yourself using a python interpreter:

Accounting for both of these types of exceptions is relatively straightforward, if a bit awkward for the latter one:

Finally, we can slightly change our decorator above to use this new error handler:

This solution, of course, will not get rid of all your memory problems, of course. If you operate in a memory constrained environment (which all atom-bound computation does, at the end of the day), you will always have some point at which you run out of memory. However, this solution will allow you to (a) better isolate failures due to badly behaving requests and (b) to fail gracefully whenever your application does run into memory contention.

What about memory leaks?

Going back to our initial hypotheses when debugging these OOMs: the solution above will help whenever cases 2 and 3 are hit, but in the case of memory leaks (our first hypothesis), this would not be very useful. In fact, the changes above might actually reduce a service’s reliability if said service has a significant memory leak. Consider the example of a server that leaks some memory on every request made to it. After some time, that process will eventually consume enough memory that it cannot process any new requests. If the solution above is applied, this process will stay alive in a “bad state” indefinitely. On the other hand, if we remove the memory limits using the resources module, the resulting OOMs will kill the process (at which point whatever orchestration method you are using — in our case, Kubernetes — should restart the process with low memory usage). How, then, can we ensure that memory leaks don’t end up negatively affecting our reliability?

It is important to start right off the bat here with a lecture: the way to avoid operational issues due to memory leaks is to fix them. I have seen many cases where production services rely on an orchestration mechanism to restart them periodically in order to avoid leaking memory. While this might prove a workable short-term solution, it is hardly an engineering standard we should build towards. Instead, one should aim to (a) be alerted whenever a potential memory leak occurs and (b) have some mechanism to limit the negative effects of a memory leak in a production environment, when it does occur.

With that out of the way, how does the solution proposed here perform for our criteria (a) and (b)? Well, (a) is already handled pretty well: if a server is leaking memory, then this solution will lead to the server eventually being unable to respond to most requests (which ought to trigger some of your automated alerting, right?). For (b), however, the existing solution is woefully insufficient. The solution: to modify the server’s liveness and readiness checks to take into account used and available memory. While we will not go into the details of the code to do so (it should be relatively straightforward… and plus, you’re probably getting tired of reading about OOMs by now), we can briefly describe a couple of potential solutions here.

Monitor the memory available to the process in the readiness check — if the memory is consistently below a certain threshold (i.e., the threshold you estimate is necessary to process each marginal request), then have the readiness check fail. If this state persists for an extended amount of time, then fail the liveness check as well (which will cause Kubernetes to restart your pod/process).
Keep track of the number of times your server encounters an “allocation error” over some recent time interval. If that value exceeded some percentage threshold of all requests, then have the liveness check return a failed response.

Of course, both of these methods suffer from the issue that they will kill your application, potentially interrupting requests that would otherwise have succeeded! This is why, at the end of the day, all you can hope for is to detect and fix any potential memory leaks in order to ensure your server is reliable!

[1] By default. We will explore ways to change this in a later part of this post.

[2] In fact, “thread memory usage” is not even a well-defined quantity, let alone an easily measurable one.

[3] This could be accomplished by simply setting the max number of workers for the ThreadPoolExecutor mentioned above to 1.

[4] Ok, this is a half-truth. The asynchronous workflow was built independently to handle similar requests to the synchronous workflow, and that ended up having the nice consequence of making OOM errors much easier to debug. Nevertheless, the strategy we present here is a perfectly sound strategy for cases in which one wishes to debug such errors while still using a completely synchronous workflow.

[5] Note that, in order to execute the asynchronous request in the first place, we had to deploy additional resources in our Kubernetes cluster. This might seem counter-intuitive, since we were attempting to address an issue that arises from a resource-constrained environment. The reality here is that we were not resource constrained in the absolute sense: we are far below our instance limits, so it is quite easy to deploy new instances to handle the async requests. However, the resources were constrained on each single node, which is what generated the OOMs in the first place.

[6] This is also a half-truth. Python’s memory management system is more complex than this, since it manages memory allocation differently for objects under 512 bytes. Specifically, the memory allocator will not necessarily return memory back to the OS when small objects are deleted. In our particular case (and I suspect this is fairly generalizable) the bulk of the memory allocation was coming from large objects, so the measures of resident memory before vs after a request is processed were quite representative of the “true” memory allocated/deallocated by the python process during the request lifecycle.

[7] If you are running an application in a docker container, the cgroup memory limit is the same as the limit imposed by Docker on the container.

[8] We originally tested this out using the python:3.7.3 docker image, but have since confirmed the same behavior on 3.9.7.

[9] In practice, we use a gRPC server interceptor here — the decorator should serve the same purpose, though, and is significantly easier to follow if you are not very familiar with interceptors.

Источник

This Python tutorial will assist you in resolving a memory error. We’ll look at all of the possible solutions to memory errors. There are some common issues that arise when memory is depleted.

What is Memory Error?

The python memory error occurs when your python script consumes a large amount of memory that the system does not have.

The python error occurs it is likely because you have loaded the entire data into memory.

The python operation runs out of memory it is known as memory error, due to the python script creates too many objects, or loaded a lot of data into the memory.

You can also checkout other python File tutorials:

How To Read Write Yaml File in Python3
Read and Write CSV Data Using Python
How To Convert Python String To Array
How to Trim Python string

Unexpected Memory Error Due to 32-bit Python

If you encounter an unexpected Python Memory Error while running a Python script. You may have plenty of memory but still receive a Memory Error. You should check if you are using 32-bit Python libraries, and if so, you should update to 64-bit.

Because 32-bit applications consume more memory than 64-bit applications. Windows allocates 2 GB of user-mode address space to 32-bit applications.

Python Memory Error Due to Dataset

This is yet another possibility for a python memory error to occur when working with a large dataset. Your Python scripts are loading a large dataset into memory and performing operations on it, which can rapidly fill up your memory. You must scan your script and correct any errors in the code, or use third-party Python libraries if they are available.

Python Memory Error Due to inappropriate package

Improper python package installation is also causing memory error, We can use Conda for package installation and management. The Conda is installing better memory management packages. The Conda is an open-source package management system and environment management system that runs on Windows, macOS and Linux. The Conda quickly installs, runs and updates packages and their dependencies.

How To Free memory in Python Using Script

Python uses garbage collection and built-in memory management to ensure the program only uses as much RAM as required.

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

Источник

What exactly is a Memory Error?
Python Memory Errors There are Several Types of Python Memory Errors
In Python, an unexpected memory error occurs
Unexpected Python Memory Error: A Simple Solution
Memory Error in Python Because of the Dataset
Memory Error in Python Python was installed incorrectly
Python Out of Memory Error
In Python, how do I manually free memory?
How can you make python scripts use less memory?
How can I set memory and CPU usage limits?
How to Deal with Python Memory Errors and Big Data Files
Increase the amount of memory available
Use a Smaller Sample Size
Make use of a computer that has more memory
Make use of a database that is relational
Use a big data platform to your advantage
Error Monitoring Software
Summary

What exactly is a Memory Error?

Python Memory Error or, in layman’s terms, you’ve run out of Random access memory (RAM) to sustain the running of your code. This error indicates that you have loaded all of the data into memory. For large datasets, batch processing is advised. Instead of packing your complete dataset into memory, please save it to your hard disk and access it in batches.

Your software has run out of memory, resulting in a memory error. It indicates that your program generates an excessive number of items. You’ll need to check for parts of your algorithm that consume a lot of memory in your case.

A memory error occurs when an operation runs out of memory.

Python has a fallback exception, as do all programming languages, for when the interpreter runs out of memory and must abandon the current execution. Python issues a MemoryError in these (hopefully infrequent) cases, giving the script a chance to catch up and break free from the present memory dearth. However, because Python’s memory management architecture is based on the C language’s malloc() function, It is unlikely that all processes will recover – in some situations, a MemoryError will result in an unrecoverable crash.

A MemoryError usually signals a severe fault in the present application. A program that takes files or user data input, for example, may encounter MemoryErrors if it lacks proper sanity checks. Memory restrictions might cause problems in various situations, but we’ll stick with a simple allocation in local memory utilizing strings and arrays for our code example.

The computer architecture on which the executing system runs is the most crucial element in whether your applications are likely to incur MemoryErrors. Or, to be more particular, the architecture of the Python version you’re using. The maximum memory allocation granted to the Python process is meager if you’re running a 32-bit Python. The maximum memory allocation limit fluctuates and is dependent on your system. However, it is generally around 2 GB and never exceeds 4 GB.

64-bit Python versions, on the other hand, are essentially restricted only by the amount of memory available on your system. Thus, in practice, a 64-bit Python interpreter is unlikely to have memory problems, and if it does, the pain is much more severe because it would most likely affect the rest of the system.

To verify this, we’ll use psutil to get information about the running process, specifically the psutil.virtual memory() method, which returns current memory consumption statistics when called. The print() memory usage method prints the latter information:

Python Memory Errors There are Several Types of Python Memory Errors

In Python, an unexpected memory error occurs

Even if you have enough RAM, you could get an unexpected Python Memory Error, and you may be using a 32-bit Python installation.

Unexpected Python Memory Error: A Simple Solution

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 recommend installing a 64-bit version of Python (if possible, update to Python 3 for various reasons); it will use more memory, but it will also have much more memory space available (and more physical RAM as well).

The problem is Python 32-bit only has 4GB of RAM. So it can be reduced due to operating system overhead even more if your operating system is 32-bit.

For example, the zip function in Python 2 accepts many iterables and produces a single tuple iterator. In any case, for looping, we only require each item from the iterator once. As a result, we don’t need to keep all of the things in memory while looping. As a result, it’s preferable to utilize izip, which retrieves each item only on subsequent cycles. Thus, by default, Python 3’s zip routines are called izip.

Memory Error in Python Because of the Dataset

Another choice, if you’re working with a huge dataset, is dataset size. The latter has already been mentioned concerning 32-bit and 64-bit versions. Loading a vast dataset into memory and running computations on it, and preserving intermediate results of such calculations can quickly consume memory. If this is the case, generator functions can be pretty helpful. Many major Python libraries, such as Keras and TensorFlow, include dedicated generator methods and classes.

Memory Error in Python Python was installed incorrectly

Improper Python package installation can also result in a Memory Error. In fact, before resolving the issue, we had manually installed python 2.7 and the programs that I need on Windows. We replaced everything using Conda after spending nearly two days attempting to figure out what was wrong, and the issue was resolved.

Conda is probably installing improved memory management packages, which is the main reason. So you might try installing Python Packages with Conda to see if that fixes the Memory Error.

Conda is a free and open-source package management and environment management system for Windows, Mac OS X, and Linux. Conda is a package manager that installs, runs, and updates packages and their dependencies in a matter of seconds.

Python Out of Memory Error

When an attempt to allocate a block of memory fails, most systems return an “Out of Memory” error, but the core cause of the problem rarely has anything to do with being “out of memory.” That’s because the memory manager on almost every modern operating system will gladly use your available hard disk space for storing memory pages that don’t fit in RAM. In addition, your computer can usually allocate memory until the disk 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 can result in “fragmentation,” which prevents further allocations by dividing available memory into chunks that are individually too small to satisfy a new allocation with a single contiguous block.

When operating on a 64bit version of Windows, a 32bit application with the LARGEADDRESSAWARE flag set has access to the entire 4GB of address space.
Four readers have contacted in to say that the gcAllowVeryLargeObjects setting removes the.NET restriction. No, it doesn’t. This setting permits objects to take up more than 2GB of memory, limiting the number of elements in a single-dimensional array to 231 entries.

In Python, how do I manually free memory?

If you’ve written a Python program that uses a large input file to generate a few million objects, and it’s eating up a lot of memory, what’s the best approach to tell Python that some of the data is no longer needed and may be freed?

This problem has a simple solution:

You can cause the garbage collector to release an unreferenced memory() by using gc.collect.
As illustrated in the example below:

Do you get a memory error when there are more than 50GB of free space in Python, and you’re using 64-bit Python?
On some operating systems, the amount of RAM that a single CPU can handle is limited. So, even if there is adequate RAM available, your single thread (=one core) will not be able to take it anymore. However, we are not certain that this applies to your Windows version.

How can you make python scripts use less memory?

Python uses garbage collection and built-in memory management to ensure that the application consumes as much memory as needed. So, unless you explicitly construct your program to balloon memory utilization, such as creating a RAM database, Python only utilizes what it requires.

Which begs the question of why you’d want to do it in the first place – consume more RAM in the first place. For most programmers, the goal is to use as few resources as possible.

If you wish to keep Python’s memory use low, virtual machine to a minimum, try this:

On Linux, use the ulimit command to set a memory limit for Python.
You can use the resource module to limit how much memory the program uses

Consider the following if you wish to speed up your software by giving it more memory: Multiprocessing, threading.
On only python 2.5, use pysco

How can I set memory and CPU usage limits?

To limit the amount of memory or CPU used by an application while it is running. So that we don’t have any memory problems. To accomplish so, the Resource module can be used, and both tasks can be completed successfully, as demonstrated in the code below:

Code 1: Limit CPU usage

# libraries being imported
import signal
import resource
import os

# confirm_exceed_in_time.py 
# confirm if there is an exceed in time limit
def exceeded_time(sig_number, frame):
    print("Time is finally up !")
    raise SystemExit(1)
 
def maximum_runtime(count_seconds):
    # resource limit setup 
    if_soft, if_hard = resource.getrlimit(resource.RLIMIT_CPU)
    resource.setrlimit(resource.RLIMIT_CPU, (count_seconds, if_hard))
    signal.signal(signal.SIGXCPU, exceeded_time)
 
# set a maximum running time of about 25 millisecond
if __name__ == '__main__':
    maximum_runtime(25)
    while True:
        pass

Code #2: To minimize memory usage, the code restricts the total address space available.

# using resource
import resource
 
def limit_memory(maxsize):
    if_soft, if_hard = resource.getrlimit(resource.RLIMIT_AS)
    resource.setrlimit(resource.RLIMIT_AS, (maxsize, if_hard))

How to Deal with Python Memory Errors and Big Data Files

Increase the amount of memory available

A default memory setup may limit some Python tools or modules.

Check to see if your tool or library may be re-configured to allocate more RAM.

That is a platform built to handle massive datasets and allow data transformations. On top of that and machine learning algorithms will be applied.
Weka is a fantastic example of this, as you may increase memory as a parameter when running the app.

Use a Smaller Sample Size

Are you sure you require all of the data?

Take a random sample of your data, such as the first 5,000 or 100,000 rows, before fitting a final model to Use this smaller sample to work through your problem instead of all of your data (using progressive data loading techniques).

It is an excellent practice for machine learning in general, as it allows for quick spot-checks of algorithms and results turnaround.

You may also compare the amount of data utilized to fit one algorithm 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.

Make use of a computer that has more memory

Is it necessary for you to use your computer? Of course, – it is possible to lay your hand’s on a considerably larger PC with significantly more memory. A good example is renting computing time from a cloud provider like Amazon Web Services, which offers workstations with tens of gigabytes of RAM for less than a dollar per hour.

Make use of a database that is relational

Relational databases are a standard method of storing and retrieving massive datasets.

Internally, data is saved on a disk, loaded 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 can also use SQLite, which is a lightweight database.

Use a big data platform to your advantage

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

Error Monitoring Software

Airbrake’s powerful error monitoring software delivers real-time error monitoring and automatic exception reporting for all of your development projects. Airbrake’s cutting-edge web dashboard keeps you up to current on your application’s health and error rates at all times. Airbrake effortlessly interacts with all of the most common languages and frameworks, no matter what you’re working on. Furthermore, Airbrake makes it simple to adjust exception parameters while providing you complete control over the active error filter system, ensuring that only the most critical errors are collected.

Check out Airbrake’s error monitoring software for yourself and see why so many of the world’s greatest engineering teams rely on it to transform their exception handling techniques!

Summary

In this article, you learned about various strategies and methods for coping with Python Memory Error.

Would you mind letting us know in the comments section if you have used any of these methods?

Источник

A programming language will raise a memory error when a computer system runs out of RAM Random Access Memory or memory to execute code.

If it fails to execute a Python script, the Python interpreter will present a MemoryError exception for the Python programming. This article will talk about the MemoryError in Python.

the `MemoryError` in Python

A memory error is raised when a Python script fills all the available memory in a computer system. One of the most obvious ways to fix this issue is to increase the machine's RAM.

But buying a new RAM stick is not the only solution for such a situation. Let us look at some other possible solutions to this problem.

Switch to `64-bit` Installation of Python

Commonly, a MemoryError exception occurs when using a 32-bit installation. A 32-bit Python installation can only access RAM approximately equal to 4 GB.

If the computer system is also 32-bit, the available memory is even less. In most cases, even 4 GB of memory is enough. Still, Python programming is a multi-purpose language.

It gets used in significant domains such as machine learning, data science, web development, app development, GUI Graphical User Interface, and artificial intelligence.

One should not get limited due to this threshold. To fix this, all you have to do is install the 64-bit version of the Python programming language.

A 64-bit computer system can access 2⁶⁴ different memory addresses or 18-Quintillion bytes of RAM. If you have a 64-bit computer system, you must use the 64-bit version of Python to play with its full potential.

Generator Functions in Python

When working on machine learning and data science projects, one must deal with massive datasets. Loading such gigantic datasets directly into the memory, performing operations over them, and saving the modifications can quickly fill up a system’s RAM.

This anomaly can cause substantial performance issues in an application. One way to fix this is to use generators. Generators generate data on the fly or whenever needed.

Python libraries such as Tensorflow and Keras provide utilities to create generators efficiently. One can also build generators using any libraries using pure Python.

To thoroughly learn about Python generators, refer to this article.

Optimizing Your Code in Python

One can resolve a MemoryError exception by optimizing their Python code. The optimization includes tasks such as:

Getting rid of the garbage and unused data by deallocating or freeing the new or allocated memory.
Saving fewer data to the memory and using generators instead.
Using the batching technique breaking a massive dataset into smaller chunks of data to compute smaller pieces of data to obtain the final result.

This technique is generally used while training gigantic machine learning models such as image classifiers, chatbots, unsupervised learning, and deep learning.
To solve problems, use state-of-the-art algorithms and robust and advanced data structures such as graphs, trees, dictionaries, or maps.
Using dynamic programming to retain pre-calculated results.
Using powerful and efficient libraries such as Numpy, Keras, PyTorch, and Tensorflow to work with data.

Note that these techniques apply to all programming languages, such as Java, JavaScript, C, and C++.

Additionally, optimization improves the time complexity of a Python script, drastically improving the performance.

Источник

Python memory error ошибка

What is Memory Error?

Types of Python Memory Error

Unexpected Memory Error in Python

The easy solution for Unexpected Python Memory Error

Python Memory Error Due to Dataset

Python Memory Error Due to Improper Installation of Python

How can I explicitly free memory in Python?

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

How do you set the memory usage for python programs?

How to put limits on Memory and CPU Usage

Ways to Handle Python Memory Error and Large Data Files

1. Allocate More Memory

2. Work with a Smaller Sample

3. Use a Computer with More Memory

4. Use a Relational Database

5. Use a Big Data Platform

Summary

Introduction to Python Memory Error

Syntax of Python Memory Error

How does Memory Error Works?

Avoiding Memory Errors in Python

Conclusion

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An Example of MemoryError

Output

Two Ways to Handle A MemoryError in Python

Appropriate Python Set-up

Attention to Large Nested Loops

An Example of Batching Nested Loops

How to Avoid a MemoryError in Python

Track, Analyze and Manage Errors With Rollbar

Python Memory Error | How to Solve Memory Error in Python

What is Memory Error?

Types of Python Memory Error

Unexpected Memory Error in Python

The easy solution for Unexpected Python Memory Error

Python Memory Error Due to Dataset

Python Memory Error Due to Improper Installation of Python

Out of Memory Error in Python

How can I explicitly free memory in Python?

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

How do you set the memory usage for python programs?

How to put limits on Memory and CPU Usage

Ways to Handle Python Memory Error and Large Data Files

1. Allocate More Memory

2. Work with a Smaller Sample

3. Use a Computer with More Memory

4. Use a Relational Database

5. Use a Big Data Platform

Summary

What is memory error in python

Что такое MemoryError в Python?

Почему возникает MemoryError?

Как исправить MemoryError?

Ошибка связана с 32-битной версией

Как посмотреть версию Python?

Как установить 64-битную версию Python?

Оптимизация кода

Явно освобождаем память с помощью сборщика мусора

Debugging and preventing memory errors in Python

Our basic setup

Why do we need to address OOMs?

OOMs in synchronous requests on a server

Worker processes running out of memory

How can we debug these errors?

Attempts at memory profiling

Memory limits to the rescue (sort of)

Thread stack size

Behavior when limiting address space

Bringing it all Together

What about memory leaks?

What is Memory Error?

Unexpected Memory Error Due to 32-bit Python

Python Memory Error Due to Dataset

Python Memory Error Due to inappropriate package

How To Free memory in Python Using Script

What exactly is a Memory Error?

Python Memory Errors There are Several Types of Python Memory Errors

In Python, an unexpected memory error occurs

the `MemoryError` in Python

Switch to `64-bit` Installation of Python