I'm trying to profile a python application in pycharm, however when the application terminates and the profiler results are displayed Pycharm requires all 16gb of RAM that I have, which makes pycharm unusable.
Said python application is doing reinforcement learning, so it does take a bit of time to run (~10 min or so), however while running it does not require large amounts of RAM.
I'm using the newest version of PyCharm on Ubuntu 16.04 and CProfile is used by Pycharm for profiling.
I'd be very glad if one of you knows a solution.
EDIT: It seems this was an issue within PyCharm, which has since been fixed (as of 2017/11/21)
It's a defect within PyCharm: https://youtrack.jetbrains.com/issue/PY-25768
Related
I use a jupyter notebook to execute a pyton script. The script calls the association_rules function from the mlxtend framework. Calling this function the ram literally explodes from 500 MB used to over 32 GB. But that would not be the problem. The Problem is if i execute the script locally on my windows 10 PC the ram maxes out but everything is still running. When i do the same on a unix server (Xfce) the server crashes. Is there something i can do prevent the server from crashing and to guarantee that the script continues?
Upadate:
I basically missed the fact that windows is swapping ram all the time, the only difference is that windows does not crash. I'm quite sure this would be solved on linux by fixing the swapping configuration. So basically the question is obselete.
Update:
I have made some wrong assumptions. The windows PC was already swapping, and the swapping partition went out of memory as well. So on all machine the same Problem appeared and all them crashed. In the end it was a mistake on the data preprocessing. Sorry for the unconvenience and please see this question as not relevant any more.
Run the script using the nice command to assign priority.
I am a beginner learning to program python using VS code so my knowledge about both the VS code and the python extension is limited. I am facing two very annoying problems.
Firstly, when the python extension starts the memory usage of vs code jumps from ~300 mb to 1-1.5 Gbs. If I have any thing else open then everything gets extremely sluggish. This seems to me a bit abnormal. I have tried disabling all other extensions but the memory consumption remains the same. Is there a way (or some settings that I can change to reduce the memory consumption?
Secondly, the intellisense autocomplete takes quite a bit of time (sometimes 5-10 mins) before it starts to kick in. Also it stops working sometimes completely. Any pointers what could be causing that?
PS: I am using VS code version 1.50 (September update) and python anaconda 4.8.3.
VSCode as a code editor, in addition to the memory space occupied by VSCode itself, it needs to download the corresponding language services and language extensions to support, so it occupies some memory space.
For memory, it is recommended that you uninstall unnecessary third-party extensions and duplicate language services. In addition, this is a good habit if we use virtual environments in VSCode. The folder of the virtual environment exists in the project, and the installation package is stored in the project without occupying system resources.
For automatic completion, this function is provided by the corresponding language service and extension. please try to reload VSCode and wait for the language service to load before editing the code.
Therefore, you can try to use the extension "Pylance", which not only provides outstanding language service functions but also provides automatic completion.
At least for the intellisense, you could try changing
"python.jediEnabled": false
in your settings.json file. This will allow you to use a newer version of the intellisense, but it might need to download first.
But beyond that, I’d suggest using Pycharm instead. It’s quite snappy, and it has a free version.
I am running a Python script using Spyder 2.3.9. I have a fairly large script and when running it through with (300x600) iterations (a loop inside another loop), everything appears to be working fine and takes approximately 40 minutes. But when I increase the number to (500x600) iterations, after 2 hours, the output yields:
It seems the kernel died unexpectedly. Use 'Restart kernel' to continue using this console.
I've been trying to go through the code but don't see anything that might be causing this in particular. I am using Python 2.7.12 64bits, Qt 4.8.7, PyQt4 (API v2) 4.11.4. (Anaconda2-4.0.0-MacOSX-x86_64)
I'm not entirely sure what additional information is pertinent, but if you have any suggestions or questions, I'd be happy to read them.
https://github.com/spyder-ide/spyder/issues/3114
It seems this issue has been opened on their GitHub profile, should be addressed soon given the repo record.
Some possible solutions:
It may be helpful, if possible, to modify your script for faster convergence. Very often, for most practical purposes, the incremental value of iterations after a certain point is negligible.
An upgrade or downgrade of the Spyder environment may help.
Check your local firewall for blocked connections to 127.0.0.1 from pythonw.exe.
If nothing works, try using Spyder on Ubuntu.
I am developing a program for simulation (kind of like numerical solver). I am developing it in an ipython notebook. I am wondering if the speed of the code running in the notebook is the same as the speed of the code running from terminal ?
Would browser memory or overhead from notebook and stuff like that makes code run slower in notebook compared to native run from the terminal?
One of the things that might slow things a lot would be if you had a lot of print statements in your simulation.
If you run the kernels server and browser on the same machine, assuming your simulation would have used all the cores of your computer, yes using notebook will slow things down. But no more than browsing facebook or Youtube while the simulation is running. Most of the overhead of using IPython is actually when you press shift-enter. In pure python prompt the REPL might react in 100ms, and in IPython 150 or alike. But if you are concern about performance, the overhead of IPython is not the first thing you should be concern about.
I have found that Jupyter is significantly slower than Ipython, whether or not many print statements are used. Nearly all functions suffer decreased performance, but especially if you are analyzing large dataframes or performing complex calculations, I would stick with Ipython.
I tested learning the same small neural net (1) under Jupyter and (2) running Python under Anaconda prompt (either with exec(open(foo.py).read()) under python or with python foo.py directly under Anaconda prompt).
It takes 107.4 sec or 108.2 sec under Anaconda prompt, and 105.7 sec under Jupyter.
So no, there is no significant difference, and the minor difference is in favor of Jupyter.
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).