out-of-memory

问题 If i have following code to send data repeatedly to client every 10ms: setInterval(function() { res.write(somedata); }, 10ms); What would happen if the client is very slow to receive the data? Will server get out-of-memory error? Edit: actually the connection is kept alive, sever send jpeg data endlessly (HTTP multipart/x-mixed-replace header + body + header + body.....) Because node.js response.write is asynchronous, so some users guess it may store data in internal buffer and wait until low

问题 If i have following code to send data repeatedly to client every 10ms: setInterval(function() { res.write(somedata); }, 10ms); What would happen if the client is very slow to receive the data? Will server get out-of-memory error? Edit: actually the connection is kept alive, sever send jpeg data endlessly (HTTP multipart/x-mixed-replace header + body + header + body.....) Because node.js response.write is asynchronous, so some users guess it may store data in internal buffer and wait until low

问题 I have saved an image to a file and when I want to read the file and set the image to the imageView , I face with OOM exception. This is my code : File tempImageFile = new File(Environment.getExternalStorageDirectory().getPath() + "/AHOORATempImage"); FileOutputStream fileOutputStream = new FileOutputStream(tempImageFile); fileOutputStream.write(data); fileOutputStream.flush(); fileOutputStream.close(); imageView.setImageURI(Uri.fromFile(tempImageFile)); and This is part of error in logCat :

问题 I'm new to python and ubuntu. i got killed after running python code. The file I'm using for the code is around 2.7 GB and I have 16 GB RAM with one tera hard ... what should I do to avoid this problem because I'm searching and found it seems to be out of memory problem I used this command free -mh I got total used free shared buff/cache available Mem: 15G 2.5G 9.7G 148M 3.3G 12G Swap: 4.0G 2.0G 2.0G the code link I tried Link import numpy as np import matplotlib.pyplot as plt class

问题 I have this same error as others when running php ~/composer.phar update : The following exception is caused by a lack of memory and not having swap configured Check https://getcomposer.org/doc/articles/troubleshooting.md#proc-open-fork-failed-errors for details Fatal error: Uncaught exception 'ErrorException' with message 'proc_open(): fork failed - Cannot allocate memory' in phar:///home/tea/composer.phar/vendor/symfony/console/Symfony/Component/Console/Application.php:974 Stack trace: 0

问题 We are on 16GB AWS instance and I am finding it to be really slow. When I ran ps -aux | grep apache I can see about 60+ apache processes. When I ran watch -n 1 "echo -n 'Apache Processes: ' && ps -C apache2 --no-headers | wc -l && free -m" It is showing almost all memory being used by apache. When I ran curl -L https://raw.githubusercontent.com/richardforth/apache2buddy/master/apache2buddy.pl | perl to see how to optimize Apache, it suggested me to increase number of MaxRequestWorkers so I

问题 I`m using a script to remove duplicates on mongo, it worked in a collection with 10 items that I used as a test but when I used for the real collection with 6 million documents, I get an error. This is the script which I ran in Robomongo (now known as Robo 3T): var bulk = db.getCollection('RAW_COLLECTION').initializeOrderedBulkOp(); var count = 0; db.getCollection('RAW_COLLECTION').aggregate([ // Group on unique value storing _id values to array and count { "$group": { "_id": { RegisterNumber

问题 I have some code in TensorFlow which takes a base model, fine-tunes (trains) it with some data, and then uses the model to predict() using some other data. All this is encapsulated in a main() method of a module and works fine. When I run this code in a loop over different base models, however, I end up with an OOM after, e.g., 7 base models. Is this expected? I would expect that Python cleans up after each main() call. Does TensorFlow not do that? How can I force it to? Edit: here's an MWE

问题 I have some code in TensorFlow which takes a base model, fine-tunes (trains) it with some data, and then uses the model to predict() using some other data. All this is encapsulated in a main() method of a module and works fine. When I run this code in a loop over different base models, however, I end up with an OOM after, e.g., 7 base models. Is this expected? I would expect that Python cleans up after each main() call. Does TensorFlow not do that? How can I force it to? Edit: here's an MWE

问题 My original list_ function has over 2 million lines of code and I get a memory error when I run the code that calculates . Is there a way I could could go around it. The list_ down below isa portion fo the actual numpy array. Pandas data: import pandas as pd import math import numpy as np bigdata = 'input.csv' data =pd.read_csv(Daily_url, low_memory=False) #reverses all the table data values data1 = data.iloc[::-1].reset_index(drop=True) list_= np.array(data1['Close'] Code: number = 5 list_=