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Hi,
I am trying to deploy DNN inference/training workloads in pytorch using GPUs provided by DevCloud. I tried the tutorial "Intel_Extension_For_PyTorch_GettingStarted" [Github Link] following the procedure:
qsub -I -l nodes=1:gpu:ppn=2 -d .
export LD_LIBRARY_PATH=/glob/development-tools/versions/oneapi/2023.0.1/oneapi/intelpython/latest/envs/pytorch/lib/python3.9/site-packages/torch/lib/:$LD_LIBRARY_PATH
patch < ./codes_for_ipynb/gpu.patch
./q ./run.sh
The compute node obtained is the [11th Gen Intel(R) Core(TM) i9-11900KB @ 3.30GHz] host cpu integrated with Intel® UHD Graphics (Tiget Lake Xe) GPU.
However, the output file (returned run.sh.e) shows the following error:
RuntimeError: Number of dpcpp devices should be greater than zero!
Is there any step I am missing for accessing GPU resources on the DevCloud?
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Hi, Thanks for posting in Intel communities
The way you queried to get GPU node on DevCloud is correct, and you don't have to explicitly set the environment variable. Just
source /opt/intel/oneapi/setvars.sh
does the job.
FYI, prerequisites to run the sample are ( https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_PyTorch_GettingStarted#prerequisites )
It requires Discrete GPUs
Unfortunately, Intel DevCloud currently has only Integrated GPUs.
Thanks,
Rajashekar
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Hi, Good day to you.
We have not heard back from you. Is your issue resolved with the above solution? If this resolves your issue, make sure to accept this as a solution. This would help others with similar issue.
Regards,
Rajashekar
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Hi,
We assume that your issue is resolved. If you need any additional information, please post a new question as this thread will no longer be monitored by Intel.
Regards,
Rajashekar
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