Memory error pickle dump

Происхождение новой ошибки памяти numpy и pickle?

Я столкнулся с проблемой, когда хочу создать обучающий набор изображений для глубокого обучения. Изображения представляют собой файлы .tif с оттенками серого 299×299. В каждой категории почти 3000 изображений. Я собирал эти данные в файл pickle размером около 6.5 Go. Тогда мне удалось его без проблем загрузить.

Однако недавно я снова хочу поработать с этим обучающим набором, и когда я запустил тот же код, что и раньше, у меня возникла эта ошибка памяти:

MemoryError Traceback (последний вызов последним) в —-> 1 X = X.reshape (X.shape [0], 299, 299, 1) .astype (‘float32’)

MemoryError: невозможно выделить 6,06 ГиБ для массива с формой (18204, 299, 299, 1) и типом данных float32

Кроме того, я сохранил старый файл рассола (размером 6.5 Go), и когда я попытался загрузить его в тот же самый CNN, что и раньше, у меня также возникла ошибка памяти:

MemoryError Traceback (последний вызов последним) в —-> 1 X_train = pickle.load (open (‘X_train.pickle’, ‘rb’))

Это очень странно, потому что я ничего не менял, тот же компьютер, та же версия python (3.7), те же изображения, тот же код, те же версии для pickle и numpy (был обновлен только тензорный поток, но я не вижу никакой ссылки).

У меня такое ощущение, что что-то не так с памятью моего компьютера, но я всегда использовал тот же компьютер для предыдущего кода.

Пожалуйста, дайте мне знать, если у вас есть какие-нибудь подсказки, которые могут объяснить или решить эту проблему!

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Re: Memory error while saving dictionary of size 65000X50 using pickle

I didn’t have the problem with dumping as a string. When I tried to
save this object to a file, memory error pops up.

I am sorry for the mention of size for a dictionary. What I meant by
65000X50 is that it has 65000 keys and each key has a list of 50
tuples.

I was able to save a dictionary object with 65000 keys and a list of
15-tuple values to a file. But I could not do the same when I have a
list of 25-tuple values for 65000 keys.

You exmple works just fine on my side.

save this object to a file, memory error pops up.

I am sorry for the mention of size for a dictionary. What I meant by
65000X50 is that it has 65000 keys and each key has a list of 50
tuples.

>
You exmple works just fine on my side.

I can get the program

for i in xrange(65000):
d[i]=[(x,) for x in range(50)]
print «Starting dump»
s = pickle.dumps(d)

to complete successfully, also, however, it consumes a lot
of memory. I can reduce memory usage slightly by
a) dumping directly to a file, and
b) using cPickle instead of pickle
i.e.

import cPickle as pickle

for i in xrange(65000):
d[i]=[(x,) for x in range(50)]
print «Starting dump»
pickle.dump(d,open(«/tmp/t.pickle»,»wb»))

The memory consumed originates primarily from the need to determine
shared references. If you are certain that no object sharing occurs
in your graph, you can do
import cPickle as pickle

for i in xrange(65000):
d[i]=[(x,) for x in range(50)]
print «Starting dump»
p = pickle.Pickler(open(«/tmp/t.pickle»,»wb»))
p.fast = True
p.dump(d)

With that, I see no additional memory usage, and pickling completes
really fast.

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memory error with pickle in _load_models() #474

I run auto-sklearn with 4 hours time budgets, when finished, it reports a memory error in automl.py -> _fit() -> self._load_models()，because it use pickle to load model file. How can I tackle this error?

The text was updated successfully, but these errors were encountered:

We fixed this in the development branch (this was reported before as #444). Therefore, you can either wait until we create a new release or use the development branch in the meantime.

I’m getting memory error with pickle in load_models while running auto-sklearn in parallel. The stack trace is as follows:

Traceback (most recent call last):
File «/home/ushmal/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py», line 258, in _bootstrap
self.run()
File «/home/ushmal/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py», line 93, in run
self._target(*self._args, **self._kwargs)
File «/home/ushmal/PycharmProjects/automl/automl_parallel_sparse.py», line 72, in spawn_classifier
automl.fit(X_train, y_train, dataset_name=dataset_name)
File «/home/ushmal/anaconda3/envs/py36/lib/python3.6/site-packages/autosklearn/estimators.py», line 664, in fit
dataset_name=dataset_name,
File «/home/ushmal/anaconda3/envs/py36/lib/python3.6/site-packages/autosklearn/estimators.py», line 337, in fit
self._automl[0].fit(**kwargs)
File «/home/ushmal/anaconda3/envs/py36/lib/python3.6/site-packages/autosklearn/automl.py», line 996, in fit
load_models=load_models,
File «/home/ushmal/anaconda3/envs/py36/lib/python3.6/site-packages/autosklearn/automl.py», line 208, in fit
only_return_configuration_space=only_return_configuration_space,
File «/home/ushmal/anaconda3/envs/py36/lib/python3.6/site-packages/autosklearn/automl.py», line 370, in _fit
data_manager_path = self._backend.save_datamanager(datamanager)
File «/home/ushmal/anaconda3/envs/py36/lib/python3.6/site-packages/autosklearn/util/backend.py», line 318, in save_datamanager
pickle.dump(datamanager, fh, -1)
MemoryError

I’m using a sparse array input to train the models.

According to your traceback, the memory error happens in the beginning of the fit method. Could you please open a new issue? Please also post the size of the dataset (shape, NNZ) and your amount of RAM?

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Encounter «Memory Error» when converting imagenet dataset #18

Hi,
When I was trying to using the Alexnet model, I first of all tried to follow your instruction to download val224_compressed.pkl and executed the command «python convert.py»
But when I was converting, it always come to the error message «Memory Error».
I am curious about how to deal with this issue, since I think the memory of the machine I used is big enough, which is 64 GB.
Thanks !

The text was updated successfully, but these errors were encountered:

I ran into the same problem as well, the 244244 file was dumped okay with 7.5G and the 299299 pkl file was empty with 0B

I saw the same issue. I separated the 224 and 299 dump processing loops and cleared variables that were no longer used. Still it dies in dump_pickle, which must be making another copy.
So, I looked around and found that scikit learn has a joblib.dump that can replace pkl.dump in dump_pickle, and it doesn’t use as much memory while writing out the files.
I think you’ll still need to separate the 224 and 299 processing, as mine was running out of 32G memory while doing a transpose. too many copies of the same data going on. With joblib, memory use goes up to 27G, and no error. This could probably use a db, instead of all this image info in a dict.

same problem on 24G RAM windows PC with python 3.6.6 and torch 1.1.0

I finished my job by following convert.py, thx to @jnorwood

this new convert.py will takes about 16Gb memory.

import os
import numpy as np
import tqdm
from utee import misc
import argparse
import cv2
import joblib

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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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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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