How can I find out Python interpreter memory consumption?
I tried memory-profiler but it shows running code memory consumption.
PS. It seems tracemalloc does what I want
I have a Python program that reads lines of files and analyzes them. The program intentionally reads many lines into the RAM.
The program started getting MemoryError while appending a line (as str) to list. When I check in the task manager (the program runs on Windows 10), I see that the memory of the program is on 1635MB (stable) and the total memory use of the machine is below 50%.
I read that Python does not limit the memory, so what could be the reason?
Technical details:
I use Python 3.6.5 on Windows 10, 64-bit 16GB RAM machine. I run the program from the PowerShell terminal and not through the IDE.
I see that the memory of the program is on 1635MB
Windows EXEs compiled as 32-bit have, by default, a 2GB memory limit even when on 64-bit OS SKUs where plenty more memory is available. You're at 1.6 GB, so you're probably bumping up against this limit.
Make sure you are running the 64-bit version of Python.exe. Python.org's download page defaults to 32-bit for unknown reasons. But if you browse to the bottom of their download page for a given release, you can find the x86-64 version for 64-bit architecture.
I am currently working on RnD in TensorFlow (CPU Version), but unable to decide on the basic requirement for my system for training on large datasets or may be I stumbled upon a possible bug in TensorFlow library.
The Official TensorFlow documentation, nowhere suggests any specific requirement for the system to be building and running TensorFlow programs on. From what I can understand, if that can be run over Windows, Linux, Mac along with Android, iOS and also over embedded systems like RaspberryPi, I suppose there should not be any such hardware requirement for the same.
However, while in the process of initial research, I tried running the TensorFlow Seq2Seq model (translating English to French https://www.tensorflow.org/tutorials/seq2seq), where the training and test datasets end up taking around 7-8 GB of diskspace initially and 20-22Gb on a whole. Once the translate.py python script is executed, it ends up choking the memory and pushing disk utilization to 98% and 100% respectively.
My current system runs Windows 8.1 64 bit OS, Core i5 5200U clocking at 2.2 GHz, 8GB RAM and around 70GB free space on HDD (specifically allotted for TensorFlow usage). But even after allowing my system to run over 7-8 hours (with no other application running) it got stuck multiple times and usually after the memory utilization peeks to around 100% after tokenizing the datasets.
Though I am not sure, but I suppose the TensorFlow learning graph is being created inside the RAM and once it expands to around all the memory space, the program ends up in un-ending loop waiting for memory to get cleared and then increase the learning graph.
So the whole drills down to 3 questions:
Does TensorFlow uses RAM for building and saving Learning Graph? If so, is it possible to get choked in a similar fashion?
From a business perspective, is there a minimum hardware requirement for training such a system?
If it is not the system requirement, can this be a possible bug in TensorFlow library which pushes it into an unending loop waiting for memory to get cleared?
Update
After running the python script for over 30 hours continuously, the process seems to have stuck at the same place for past 14 hours while "Reading development and training data". Refer image below for further investigation:
As soon as I was about to shut down the program, the same started responding again and I waited for another 15-20 minutes and finally I got the answer from the OS itself. It was indeed low RAM that was causing the problem. Attaching the screen grab of the Windows Alert of system running low on memory for reference, incase anyone gets caught in same situation.
UPDATE
I tried taking a VM instance on Google Cloud Platform. This machine had 2 x Intel Xeon (R) each running at 2.23 GHZ, with 13GB RAM and 50GB storage. But the result was same in this situation also even though the application was utilising more than 10.5 GB RAM. Seems like this tutorial script needs a very intense system probably a Super Computer with atleast 32 GB RAM to run and execute completely. I might look to write/arrange my own dataset now. However, this must be taken as future enhancement to use Persistent Storage (HDD/SSD) to create the Graph instead of RAM so as to avoid chocking of Memory.
I was trying to probe the Python 32bit memory limit. So I wrote the little program
a=[]
while 1:
a.append(chr(65))
and watched the Windows task manager for the memory consumption of python.exe.
Firstly, I was surprised that it is occationally reduced (almost halved sometimes). Second, the amount only goes up to about 500MB (I believe on another 64bit machine it rose endlessly).
The computer has 4GB memory, windows boot>3GB is supposingly active and I patched the python.exe with imagecfg.exe -l. No other relevant processes are running and total memory does not exceed 40%. I believe the very same procedure worked on another computer though.
Any suggestions how I can check if my python can go up to 3GB now?
Is Python generally slower on Windows vs. a *nix machine? Python seems to blaze on my Mac OS X machine whereas it seems to run slower on my Window's Vista machine. The machines are similar in processing power and the vista machine has 1GBs more memory.
I particularly notice this in Mercurial but I figure this may simply be how Mercurial is packaged on windows.
I wanted to follow up on this and I found something that I believe is 'my answer'. It appears that Windows (vista, which is what I notice this on) is not as fast in handling files. This was mentioned by tony-p-lee.
I found this comparisons of Ubuntu vs Vista vs Win7. Their results are interesting and like they say, you need to take the results with a grain of salt. But I think the results lead me to the cause. Python, which I feel was indirectly tested, is about equivalent if not a tad-bit faster on Windows.. See the section "Richards benchmark".
Here is their graph for file transfers:
(source: tuxradar.com)
I think this specifically help address the question because Hg is really just a series of file reads, copies and overall handling. Its likely this is causing the delay.
http://www.tuxradar.com/content/benchmarked-ubuntu-vs-vista-vs-windows-7
No real numbers here but it certainly feels like the start up time is slower on Windows platforms. I regularly switch between Ubuntu at home and Windows 7 at work and it's an order of magnitude faster starting up on Ubuntu, despite my work machine being at least 4x the speed.
As for runtime performance, it feels about the same for "quiet" applications. If there are any GUI operations using Tk on Windows, they are definitely slower. Any console applications on windows are slower, but this is most likely due to the Windows cmd rendering being slow more than python running slowly.
Maybe the python has more depend on a lot of files open (import different modules).
Windows doesn't handle file open as efficiently as Linux.
Or maybe Linux probably have more utilities depend on python and python scripts/modules are more likely to be buffered in the system cache.
I run Python locally on Windows XP and 7 as well as OSX on my Macbook. I've seen no noticable performance differences in the command line interpreter, wx widget apps run the same, and Django apps also perform virtually identically.
One thing I noticed at work was that the Kaspersky virus scanner tended to slow the python interpreter WAY down. It would take 3-5 seconds for the python prompt to properly appear and 7-10 seconds for Django's test server to fully load. Properly disabling its active scanning brought the start up times back to 0 seconds.
With the OS and network libraries, I can confirm slower performance on Windows, at least for versions =< 2.6.
I wrote a CLI podcast-fetcher script which ran great on Ubuntu, but then wouldn't download anything faster than about 80 kB/s (where ~1.6 MB/s is my usual max) on either XP or 7.
I could partially correct this by tweaking the buffer size for download streams, but there was definitely a major bottleneck on Windows, either over the network or IO, that simply wasn't a problem on Linux.
Based on this, it seems that system and OS-interfacing tasks are better optimized for *nixes than they are for Windows.
Interestingly I ran a direct comparison of a popular Python app on a Windows 10 x64 Machine (low powered admittedly) and a Ubuntu 14.04 VM running on the same machine.
I have not tested load speeds etc, but am just looking at processor usage between the two. To make the test fair, both were fresh installs and I duplicated a part of my media library and applied the same config in both scenarios. Each test was run independently.
On Windows Python was using 20% of my processor power and it triggered System Compressed Memory to run up at 40% (this is an old machine with 6GB or RAM).
With the VM on Ubuntu (linked to my windows file system) the processor usage is about 5% with compressed memory down to about 20%.
This is a huge difference. My trigger for running this test was that the app using python was running my CPU up to 100% and failing to operate. I have now been running it in the VM for 2 weeks and my processor usage is down to 65-70% on average. So both on a long and short term test, and taking into account the overhead of running a VM and second operating system, this Python app is significantly faster on Linux. I can also confirm that the Python app responds better, as does everything else on my machine.
Now this could be very application specific, but it is at minimum interesting.
The PC is an old AMD II X2 X265 Processor, 6GB of RAM, SSD HD (which Python ran from but the VM used a regular 5200rpm HD which gets used for a ton of other stuff including recording of 2 CCTV cameras).