torch.cuda.is_available() is False

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pbena64
Level 2
torch.cuda.is_available() is False

I am trying to load a pickle file of a pre-trained model to my code recipe but get the following error message:

Error_tensorflow.png

I have already selected the "ai-exec-t4-gpu" in the "Containerized Execution" tab of the environment. I do not understand exactly what could be going wrong here.

Appreciate your help!

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1 Solution
SarinaS
Dataiker

Hi @pbena64,

Since there are several different configuration points that are relevant, it's indeed impossible to say without investigating a diagnostic. It does seem like the intended setup is that if you use one of these configurations you would have access to a GPU. 

Thanks,
Sarina

 

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6 Replies
SarinaS
Dataiker

Hi @pbena64,

There are a number of different configurations that are involved here. If you would like us to take a look, please open a support ticket with the following attached:
- an instance diagnostic of the DSS instance
- a job diagnostic of the job that fails with the referenced error 

Then we can take a look.

Thank you,
Sarina 

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pbena64
Level 2
Author

Hi @SarinaS,

Thanks for the help. In my case, I don't see either of the "Actions > Download job diagnosis" and "Administration > Maintenance > Diagnostic tool." to download the logs (please see below screenshots). Could it be something with the configuration? Thanks!

Regards,

diagnostics.pngjob_diagnostics.png

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SarinaS
Dataiker

Hi @pbena64,

In that case, it appears that you are not an administrator of the DSS instance. Please reach out to your internal DSS administrator about the current issue. If they need any assistance troubleshooting, then they can open a support ticket with us the following attached:
- an instance diagnostic of the DSS instance
- a job diagnostic of the job that fails with the referenced error  

Thanks!
Sarina

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pbena64
Level 2
Author

Hi @SarinaS,

That is correct. I will reach out to the admins. Thanks a lot!

But, in general, if I select the "ai-exec-t4-gpu", or "ai-exec-t4-gpu-4g" from the "Containerized execution>>Build for" and save and update the environment, and in my recipe import torch and run torch.cuda.is_available(), I should get access to gpu, right? I mean no other setting has to be changed from my side?

Appreciate it!

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SarinaS
Dataiker

Hi @pbena64,

Since there are several different configuration points that are relevant, it's indeed impossible to say without investigating a diagnostic. It does seem like the intended setup is that if you use one of these configurations you would have access to a GPU. 

Thanks,
Sarina

 

pbena64
Level 2
Author

Hi @SarinaS,

Thank you very much for your help. I will follow up with our internal administrator.

Regards,

 

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