I want to know the state of the gpu used by mine when using Colab.
I tried
!nivdia-smi
while it can not work along with my program so It shows no process.
I never tried but there are ways to ssh into colab so you can run nvidia-smi on ssh and run your code on regular colab cell.
Click on the menu "Runtime" >> "Manage Session".
I think there's the information you need.
change
!nivdia-smi
to
!nvidia-smi
Related
my colab restart after showing memory error.
I am trying to train cnn model on google colab notebook but after fist epoch my notebook restart itself by showing the error of memory allocation issue.
I was running the same one day before and it was running fine.
Logs are attached here.
It sounds like you might be running out of memory in google Colab.
One way to get around this might be to use your own computer. The method for this with anaconda is:
open anaconda
type in to anaconda: pip install jupyter
Once it is downloaded, type: jupyter notebook
this then opens a webpage, in the webpage there is a dropdown (I think called new) which you should click on, then click on terminal
in the terminal, you should type in (all on one line): jupyter notebook --NotebookApp.allow_origin='https://colab.research.google.com' --port=8889 --NotebookApp.port_retries=0
There will then be 3 output links, copy the second one.
back in colab, click the dropdown in the top right next to connect, then click connect to local runtime
Then input the copied URL into the prompt, then connect.
I use Colab pro, open a session in a browser and type commands in the terminal. I especially install new software. But when I close the browser, my colab setting are restarted and I have to reinstall all those software again. Is there any way I can keep the software that are installed through terminal?
As you noted, Colab automatically destroys VMs after detecting user inactivity.
Colab Pro+ has a feature called background execution, which is exactly what you asked for: VMs persist after you close your browser. Note that Colab Pro+ costs 5x more than Colab Pro (as of 2022-01-09).
Alternatively, if the process of setting up the environment does not take a long time, I would put all the installation commands in the first cell, using shell access (!apt install my-things) or bash magic (%%bash). Thus, installing the software is done with one cell execution.
I'm Attempting to run "First steps with Tensorflow" locally, outside of colab. Not really familiar with colab so I don't know how to access the "dataframes" such as "california_housing_dataframe", etc. Evidently colab "knows" how to access the dataframes in the example but I am attempting to run the exercise natively on my local system.
Thank You
I think you should have Pandas library locally installed. Then, I think it would run natively.
I understand the GUI (such as those powered by tkinter) does not work on Google Colab, any alternatives at this point?
Error message
TclError: no display name and no $DISPLAY environment variable in google's colab
To use these notebooks you need to install binary MoebInv libraries and their dependencies.
In short, you simply need to execute it in CoLab or your Ubuntu-18.04 desktop the next cell
I can't be able to run the tensorflow code with GPU when I ran it from a jupyter notebook. Same code runs no problem, if I ran in a python script.
I followed the main installation link:
https://www.tensorflow.org/install/install_windows
Also tried:
http://bailiwick.io/2017/11/05/tensorflow-gpu-windows-and-jupyter/
No problems outside notebook when I ran in a python script file.
Most likely the problem is similar to this:
Tensorflow not running on GPU in jupyter notebook
More specifically my test:
I can see both devices CPU and GPU via python a script
I can see only CPU via notebook
Thanks a lot for any help in advance!
Very late, but short answer:
Here is a Tutorial on how to set up a GPU-based Jupyterlab instance with Docker (which makes the installation faster).
I hope this helps!
I removed all existing environments and created a new one, which resolved the issue.
(Also, I had to apply the following to get around an issue caused by removed environments:
https://github.com/jupyter/notebook/issues/2301
)