So now you don’t have any failures but gpu usage issue?
I didnt run the hyper_parameter_optimzer.py, as I was thinking if there is already a problem with the base, no use with running the series of experiments
How about running the ClearML agent in docker mode?
Prev, we had our clearml-agent run in the bare-metal machine instead in docker formation. There wasnt any issue.. Though I havent tried with 0.17.2 version
We have k8s on ec2 instances in the cloud. I'll try it there 2morrow and report back..
let me run the clearml-agent outside the k8 system.. and get back to u
Hi TimelyPenguin76 ,
Instead of running the hyper_parameter_optimizer.py, I tried running the base_template_keras_simple.py instead.. It seems that I didnt use the GPU, however when i ssh into clearml-glueq-id-ffaf55c984ea4dbfb059387b983746ba:gpuall pod, and ran nvidia-smi, it gave an output.
Hi DeliciousBluewhale87
So now you don’t have any failures but gpu usage issue? How about running the ClearML agent in docker mode? You can choose an Nvidia docker image and all the Cuda installations and configuration will be part of the image.
What do you think?
Hi DeliciousBluewhale87
Can you share the version you are using? Did you get any other logs? maybe from the pod?
Could not load dynamic library 'libcupti.so.11.0'; dlerror: libcupti.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64 2021-03-11 09:11:17.368793: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcupti.so'; dlerror: libcupti.so: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64 2021-03-11 09:11:17.368810: E tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1415] function cupti_interface_->Subscribe( &subscriber_, (CUpti_CallbackFunc)ApiCallback, this)failed with error CUPTI could not be loaded or symbol could not be found.Seems that it unable to load the GPU as well. hmmm .. Below is how, I ran the agentclearml-agent daemon --queue 238_q --docker nvidia/cuda:10.1-cudnn7-runtime --force-current-version --foreground