This may not useful. It’s just a challenge I have set up for myself. Let’s say you have a big array. What can you do so that the program does not benefit from caching, cache line prefetching or the fact that the next memory access can only be determined after the first access finishes. So we have our array: array
Tag: memory
Can i have too many features in a logistic regression?
I’m building a model to predict pedestrian casualties on the streets of New York, from a data set of 1.7 million records. I decided to build dummy features out of the ON STREET NAME column, to see what predictive power that might provide. With that, I have approximately 7500 features. I tried running that, and I immediately get an alert
How to stop loop running out of memory?
I’ve come back to programming after a long haitus so please forgive any stupid errors/inefficient code. I am creating an encryption program that uses the RSA method of encryption which involves finding the coprimes of numbers to generate a key. I am using the Euclidean algorithm to generate highest common factors and then add the coprime to the list if
What does .contiguous() do in PyTorch?
What does x.contiguous() do for a tensor x? Answer There are a few operations on Tensors in PyTorch that do not change the contents of a tensor, but change the way the data is organized. These operations include: narrow(), view(), expand() and transpose() For example: when you call transpose(), PyTorch doesn’t generate a new tensor with a new layout, it
Download pdf in memory python
I want to open a pdf in my Python program. So far that works. Right now I open the pdf from my local disk, but I want it to fetch the pdf from the internet, instead of opening it from my local drive. Note that I don’t wish to save the existing_pdf, once I fetched it from the internet I
What does .view() do in PyTorch?
What does .view() do to a tensor x? What do negative values mean? Answer view() reshapes the tensor without copying memory, similar to numpy’s reshape(). Given a tensor a with 16 elements: To reshape this tensor to make it a 4 x 4 tensor, use: Now a will be a 4 x 4 tensor. Note that after the reshape the
Artificially creating memory usage in Python
I’m trying to create a pure memory intensive script in Python for testing purposes but every script that I try also increases my cpu. I’ve read this post and I also tried, among others: in order to copy an array to another array but once again I had cpu variations as well. UPDATED So, how can I cause a standard
Why is lxml.etree.iterparse() eating up all my memory?
This eventually consumes all my available memory and then the process is killed. I’ve tried changing the tag from schedule to ‘smaller’ tags but that didn’t make a difference. What am I doing wrong / how can I process this large file with iterparse()? I can easily cut it up and process it in smaller chunks but that’s uglier than
Python: setting memory limit for a particular function call
In a Python script, I want to set a memory limit for a certain function call. I looked at how to limit heap size; however, I don’t want to limit the memory of the entire running Python process — i.e. setting the memory limit before and after the function call. Is there any way to make a function call with
How do I make Python remember settings?
I wrote the beautiful python example code below. Now how do I make it so when I exit then restart the program it remembers the last position of the scale? Edit: I tried the following code But get the following error Answer Write the scale value to a file and read it in on startup. Here’s one way to do