Sorry i don't quite understand this. The task itself was submitted as I run the code on the client. I suppose the dependancies requirements would be copied over as the experiment is cloned?

Its hard to tell, but the agent change was a significant one. Unless python versions has something to do with it.

Yes, as listed in the snippet. The torch library is torchvision.

Hi,
It did, nvidia/cuda:10.1-runtime-ubuntu18.04.

So if i need to set this every time, what is the following config for? And how do i pass in new env parameters?
` default_docker: {
# default docker image to use when running in docker mode
image: "dockerrepo/mydocker:custom"

    # optional arguments to pass to docker image
    # arguments: ["--ipc=host", ]
    arguments: ["--env GIT_SSL_NO_VERIFY=true",]
} `

This is a env var?
CLEARML_CONFIG_FILE

Can i somehow perform an export or backup?

Hi, the problem is the same.

I noticed that its not checking out the latest version in gitlab. This latest version would contain the requirements.txt.
Using cached repository in "/root/.clearml/vcs-cache/pytorchmnist.f220373e7227ec760b28c7f4cd99b534/pytorchmnist" warning: redirecting to Note: checking out 'cfb833bcc70f3e10d3b6a96cfad3225ed682382b'.But i'm guessing this block below applied the diff..does it include the requirements.txt though?
` HEAD is now at cfb833b Upload New Fil...

Ok. I noted this is due to the venv_update setting. It needs to be disabled as it has a dependancy on the internet url. We can close this.

running git diff on my terminal in this repo gave nothing. nothing at all.

Ok that works. thanks.

Yes for both clearml and clearml-agent

Thanks that did solve the problem, the tasks are running again.

Is there anyway to see an error log from that?

Hi AgitatedDove14 . I'm trying out passing env via the code instead.
task.set_base_docker("nvcr.io/nvidia/tensorflow:19.11-tf2-py3 --env TRAINS_AGENT_GIT_USER=git_username_here --env TRAINS_AGENT_GIT_PASS=git_password_here")So the strange thing is when my k8sglue pulls a task, this happens.
` Pulling task xxxxxxxxxx launching on kubernetes cluster
Pushing task xxxxxxxxxx into temporary pending queue
Kubernetes scheduling task id=xxxxxxxxxxxx
skipping docker argument TRAINS_AGENT_GIT_USE...

I think in general, the 'published' action can be considered an 'approval'. The question is, how do we control who has the authority to 'publish'? The Web UI today does not support any uploads outside of the coding environment, would be nice it would be supported. But for now, the only workaround is to include parameters that stores document urls in the user properties.

Do you have more info on vault?
Actually it only make sense if the entire department or organisation are saving their models in a common repo. In our case this is not possible due to client security (e.g. training data from clients can potentially be 'reverse engineered' from trained models in future). So each department and even projects will need their own repo.

Thanks SuccessfulKoala55 , how might I do this clean up? Does this increase with more use of ClearML? And to add, we save all artifacts onto a remote S3 server.

Hi, it make sense if i only had to change hyperparameters, but it's not so when i am still changing the model architecture (training code) and train and repeat.

Hi, i dont't think clearml agent actually ran at that point in time. All i can see in the pod is
apt install of libpthread-stubs, libx11, libxau and libxcb1 packages. pip install of clearml-agentAfter the above are successful, the pod just hang there.

ok thanks! will try it out.

Thanks SuccessfulKoala55 . I can try my hand on a patch. But the pod spinning is handled by the k8s glue, which has no link to the client side. How should the client pass the key over to k8s glue during runtime via clearml server?

No i didn't indicate this particular issue on the git issue. Only the apply template.yml is on the issue.

[root@2c7498711bef elasticsearch]# curl -XGET `
yellow open events-training_stats_scalar-d1bd92a3b039400cbafc60a7a5b1e52b 4hAFNtGkRr-CHNGnUYfbTA 1 1 4724 271 660.9kb 660.9kb
yellow open events-log-d1bd92a3b039400cbafc60a7a5b1e52b M3qgFy1HRU2PibDOr1YOdw 1 1 1221 20 1013.6kb 1013.6kb
red open worker_stats_d1bd92a3b039400cbafc60a7a5b1e52b_2021-05 EQK8mnlhRxCrrKK3clcUFA 1 1
red open queue_metrics_d1bd92a3b039400cbafc60a7a5b1e52b_...

What's the diff between template-yaml and --overrides-yaml? I used the latter to ensure the gpu is passed in.

This is probably the whole script.

kubectl get nodes
pip install clearml-agent
python k8s_glue_example.py

Yes it is! But ClearML didn't support multi node training out of the box in a way that it streamline the process. So we are trying to figure out a way to do it.

I think the default action of clearml-agent k8s glue when running a task is to create a virtual env and installing the dependancies. So i'm just checking how to change that behaviour to look at global instead.

Space is way above nominal. What created this folder that it's trying to process? What processing is this?
Processing /tmp/build/80754af9/attrs_1604765588209/workIs there any paths in the agent machine that i can clear out to remove any possible issues from previous versions?

Hi CostlyOstrich36 , What you described is task. I was referring to the pipeline controller.

Is there enterprise support for k8s glue on OpenShift?