Maybe you heard that Google Colab has P100 GPU, It is way more faster than other all GPUs except V100 (V100 is avaliable in only Colab Pro.). As Its powerful, its pretty rare in Colab Free (P100). I didnt get "Tesla P100" in Colab before. So I tried to code a program that Factory Resets Runtime until getting "Tesla P100-PCIE..." text in nvidia-smi (If you create a cell which contains !nvidia-smi in code, You'll get your GPU's model) . I tried to do with Selenium but It failed cause of "This browser may not be secure" error. Then I tried to do with Javascript (Google DevConsole) but It failed cause of an error that I dont know what does it mean. So Im here.
[Q] How to get "Tesla P100" in Google Colab in programmaticly way?
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I am using colab pro. About 4 months ago, I experienced slow learning of the tensorflow model. The learning speed is so slow, and as a result of checking it myself today, I was able to confirm that the gpu was detected normally, but the GPU POWER was off. The volatile GPU Util is also allocated as 0 , but it looks like the GPU is not being utilized for training. When I looked for the cause, there was a saying that the data I/O bottleneck was, so I also modified the DATALOADER, and when I ran the same code and dataset in a different COLAB account, I was able to see that the GPU allocation worked well and the time was also shortened. If there is a problem with the os settings or if there is something I need to fix, please let me know. have a good day
I figured out that the problem was simply a path problem. As we've gotten feedback before, it seems like there's been a bottleneck in loading images through folders.
It was solved by specifying the path of the dataset as content/ .
I am trying to train a model for image recognition using Yolo version 3 with this notebook:
https://drive.google.com/file/d/1YnZLp6aIl-iSrL4tzVQgxJaE1N2_GfFH/view?usp=sharing
But for some reason, everything works fine but the final training. The training starts, and after 5-10 minutes (randomly) it stops working. The browser becomes unresponsive (I am unable to do anything inside that tab), and after several minutes Colab completely disconnects.
I have tried this 10 and more times and I always get the same result. I tried it on both Chrome Canary and regular Chrome (last versions), as well inside anonymous windows, but I always get the same result.
Any ideas? Why is that happening?
Eager to know your thoughts about this.
All the best,
Fab.
Problem solved. Tried the same process on Firefox and discovered that the auto-saving feature of Google drive was conflicting with the process! So... I had to simply use the "playground" of colab instead as explained here:
https://stackoverflow.com/questions/58207750/how-to-disable-autosave-in-google-colab#:~:text=1%20Answer&text=Open%20the%20notebook%20in%20playground,Save%20a%20copy%20in%20Drive.
No idea why Chrome didn't give me any feedback about that, but Firefox saved my day!
Following #fabrizio-ferrari answer, I disabled output saving and the problem persisted.
Runtime -> Change runtime type -> Omit code cell output when saving this notebook
I moved to firefox and the problem disappeared.
When I run this code in google colab
n = 100000000
i = []
while True:
i.append(n * 10**66)
it happens to me all the time. My data is huge. After hitting 12.72 GB RAM, but I don't immediately get to the crash prompt and the option to increase my RAM.
I have just this Your session crashed after using all available RAM. View runtime logs
What is the solution ? Is there another way ?
You either need to upgrade to Colab Pro or if your computer itself has more RAM than the VM for Colab, you can connect to your local runtime instead.
Colab Pro will give you about twice as much memory as you have now. If that’s enough, and you’re willing to pay $10 per month, that’s probably the easiest way.
If instead you want to use a local runtime, you can hit the down arrow next to “Connect” in the top right, and choose “Connect to local runtime
The policy was changed. However, currently, this workaround works for me:
Open and copy this notebook to your drive. Check if you already have 25gb RAM by hovering over the RAM indicator on the top right (this was the case for me). If not, follow the instructions in the colab notebook.
Source: Github
To double the RAM size of Google Colab use this Notebook, it gives a 25GB RAM! Note: set Runtime type to "None" to double RAM, then change it again to GPU or TPU.
https://colab.research.google.com/drive/155S_bb3viIoL0wAwkIyr1r8XQu4ARwA9?usp=sharing
as you said 12GB
this needs a large RAM,
if you need a small increase you can use colab pro
If you need a large increase and using a deep learning framework my advice you should use :
1- the university computer (ACADEMIC & RESEARCH COMPUTING)
2- using a platform like AWS, GCP, etc 3- you may use your very professional computer using GPU (I didn't recommend this)
Noob question here. Trying to set up Colab for work due to the situation right now.
If I download python packages or datasets in Google Colab using wget or pip, does that consume my data? To be clear, I only want to run code on Colab, and not download the models or files on my local system from colab.
Asking because my data limits are pretty low (1GB per day) and one large pre-trained model can finish it all up.
No, it won't consume (much) of your data.
Google Colab runs on Google Cloud. If it downloand some data, it travel to Google Cloud, not to your computer.
Only the text you type, the output text, and some images travel to your computer. Only the notebook contents. So, it consumes only a little for you.
Recently google colab consumes too much of internet data . Approx 4GB in 6 hours of training for single notebook . What can be the issue ?
Yes I have the same issue. It normally works fine but, there is sudden spike in the internet data. Check this. In the process it wasted 700 Mb in just 20 minutes, and I have mobile internet, so this creates a problem sometimes. Didn't find the answer but it seems like there is some kind of synchronization going on between the browser and the colab platform.
One thing you could do is to open the notebook in Playground mode as shown in this link How to remove the autosave option in Colab. This only happens because of the fact that Colab is saving everytime and there is a constant spike in the network. It becomes difficult when you use only mobile data. So, it is a safe option to open the notebook in Playground mode, so that the synchronization doesn't continue as usual.