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How can I use GPU again on Google Colab after exceeding usage limit?

I have used Google Colab in Free version to run my Tensorflow codes. After 12 hours, it gives error message which is "You cannot currently connect to a GPU due to usage limits in Colab." I applied "factory reset runtime" to use GPU again but it does not work. Furthermore, I terminated all sessions and started again but this process does not give any solution too. Is there any method to use GPU again on Google Colab Free version. Thanks.

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Mert Ege Avatar asked Apr 09 '20 17:04

Mert Ege


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How do I get around the limits on Colab?

To avoid hitting your GPU usage limits, we recommend switching to a standard runtime if you are not utilizing the GPU. Choose Runtime > Change Runtime Type and set Hardware Accelerator to None. For examples of how to utilize GPU and TPU runtimes in Colab, see the Tensorflow With GPU and TPUs In Colab example notebooks.

How long does colab usage limit last?

Colab Pro and Pro+ limit sessions to 24 hours.

How many hours of GPU is free in Colab?

Click on “Notebook settings” and select “GPU”. That's it. You have a free 12GB NVIDIA Tesla K80 GPU to run up to 12 hours continuously for free. It is worth mentioning both Google Colab and Kaggle offer awesome GPU power.

How long can you use a GPU on a Colab?

Edit after thread got archived: The usage limit is pretty dynamic and depends on how much/long you use colab. I was able to use the GPUs after 5 days; however, my account again reached usage limit right after 30mins of using the GPUs (google must have decreased it further for my account).

How to enable GPU in Google Colab notebooks?

Google provides the use of free GPU for your Colab notebooks. Enabling GPU. To enable GPU in your notebook, select the following menu options −. Runtime / Change runtime type You will see the following screen as the output −. Select GPU and your notebook would use the

Should I have more than one Google account for Colab?

My suggestion is to have multiple google accounts for colab, so you could use the other accounts when facing usage limits. Sorry for not replying to comments. I was away from reddit for quite a while.

What is the difference between GPU and TPU in Google Colab?

As per the information provided by Google’s Colab documentation, A GPU provides 1.8TFlops and has a 12GB RAM while TPU delivers 180TFlops and provides a 64GB RAM. Google Colab is a great alternative for Jupyter Notebook for running high computational deep learning and machine learning models.


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2 Answers

If you use GPU regularly, runtime durations will become shorter and shorter and disconnections more frequent. The cooldown period before you can connect to another GPU will extend from hours to days to weeks. Google tracks everything. They not only know your accounts's usage but also the usage of accounts that appear related to that account and will adjust usage limits accordingly if they even suspect someone of trying to abuse the system. They will never give you an explicit reason why the runtime disconnected or why you can't connect to GPU other than the generic message about "usage limits". Neither will they ever give users a straightforward way to track their usage because all that would do is make it easier for people to skirt the restrictions. If your account is basically blacklisted they will never actually tell you your account is blacklisted because that creates more headaches for them. You'll just get the same message about usage limits when trying to connect forever. They prefer to have users confused and guessing because that keeps all the power with Google. And for a free service, who's to say there's anything wrong with that.

edit: For Colab Pro they likely won't fatally restrict an account for over-usage but they can significantly restrict it by extending the cooldown period to 3-5 days, reducing runtime durations from 24 hrs to 6-8 hrs, etc. Keep in mind this is for people running multiple accounts multiple times a week for the maximum duration. If you're just using a single account once or twice a week you shouldn't have a problem.

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stephanie schaye Avatar answered Sep 18 '22 15:09

stephanie schaye


Colab's free version works on a dynamic usage limit, which is not fixed and size is not documented anywhere, that is the reason free version is not a guaranteed and unlimited resources.
Basically, the overall usage limits and timeout periods, maximum VM lifetime, GPU types available, and other factors vary over time. Colab does not publish these limits, in part because they can (and sometimes do) vary quickly.

GPUs and TPUs are sometimes prioritized for users who use Colab interactively rather than for long-running computations, or for users who have recently used less resources in Colab. As a result, users who use Colab for long-running computations, or users who have recently used more resources in Colab, are more likely to run into usage limits and have their access to GPUs and TPUs temporarily restricted. Users interested in having higher and more stable usage limits can use Colab Pro.

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Tfer3 Avatar answered Sep 18 '22 15:09

Tfer3