I need to get the CPU speed in Cpu tab in Task manager i.e at What Frequency CPU is running? . How Can I get that speed using command prompt?
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Whenever I use Jupyter notebook, it is using 99/100% of my CPU (according to task Manager). I am using windows 10.
Is there any way I can limit this CPU usage? I want to use my pc for some other task while keeping the jupyter kernel running.
Thanks in advance.
I'm on Windows 11, using WSL2 (Windows Subsystem for Linux). I recently upgraded my RAM from 32 GB to 64 GB.
While I can make my computer use more than 32 GB of RAM, WSL2 seems to be refusing to use more than 32 GB. For example, if I do
>>> import torch
>>> a = torch.randn(100000, 100000) # 40 GB tensor
Then I see the memory usage go up until it hit's 30-ish GB, at which point, I see "Killed", and the python process gets killed. Checking dmesg, it says that it killed the process because "Out of memory".
Any idea what the problem might be, or what the solution is?
According to this blog post, WSL2 is automatically configured to use 50% of the physical RAM of the machine. You'll need to add a memory=48GB (or your preferred setting) to a .wslconfig file that is placed in your Windows home directory (\Users\{username}\).
[wsl2]
memory=48GB
After adding this file, shut down your distribution and wait at least 8 seconds before restarting.
Assume that Windows 11 will need quite a bit of overhead to operate, so setting it to use the full 64 GB would cause the Windows OS to run out of memory.
Vivado consumes all of the free memory space in my machine during synthesis and for this reason, the machine either hangs or crashes after a while.
I encountered this issue when using Vivado 2018.1 on Windows 10 (w/ 8GB RAM) and Vivado 2020.1 on CentOS 7 (w/ 16GB RAM).
Is there any option in Vivado to limit its memory usage?
If this problem happens when you are synthesizing multiple out of context modules, try reducing the Number of Jobs when you start the run.
I use my school's server for deep learning experiment. I stopped the python script but somehow the GPU memory was not released. I don't have the root to kill the processes, how can I clear the GPU memory?
I tried 'sudo kill -9' and 'nvidia-smi', but it said 'insufficient permissions' (I am using the university's server).
Currently I have a simpliest VPS: 1 core, 256 MB of RAM, Ubuntu 12.04 LTS. My application seems to be running fine enough (I'm using unicorn and nginx) but when I run my rake jobs:work command for my delayed_jobs, unicorn process is getting killed.
I was wondering if it is related to the RAM amount ?
When the unicorn process is up and running, free -m command shows me that around 230 MB of RAM are occupied.
I was wondering, how much RAM would I need in overall ? 512 ? 1024 ?
Which one should I go with ?
Would be very glad to receive any answers!
Thank you
Your DJ worker would run another instance of your Rails application, so you need to make sure that you have at least enough RAM for that other instance plus allowance for other processes you are running.
Check ps aux for the memory usage of your Rails app.
Run top and see how much physical memory is free (while your Rails app is running).
My guess is you'll have to bump up your RAM to 512 MB. You of course don't want your memory use to spill over to swap.
Of course, besides that, you also need to make sure that your application and database are optimized enough that there are no incredible spikes in memory usage.
You can start with
ulimit -S -a
to find out the limits of your environment