Ok. That brings me back to the spawned pod. At this point, clearml-agent and its config would be a controbuting factor. Is the absence of /tmp/.clearml_agent.xxxxxx.cfg an issue?

Ok thks.

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...

I'm using this feature, in this case i would create 2 agents, one with cpu only queue and the other with gpu queue. And then at the code level decide with queue to send to.

Then you pass the tolerations definition through a different pod template?

Yup.

Hi SuccessfulKoala55 , just to add, my clearml.conf (client) and clearml.agent.conf (agent) can have differing values. I'm not sure which one takes precedence and if this could be the cause.

ok. Any idea what can go on between the setting up of clearml-agent and initialising the clearml-agent itself? Does the clearml-agent try to communicate with any internet address. From another perspective, it looks like a long time out issue. I happen to be deploying on a disconnected on-premise setup.

This would be solved if --env GIT_SSL_NO_VERIFY=true is passed to the k8s pod that's spawned to run the job. Currently its not.

clearml=1.0.3
python=3.8.10
clearml-data upload --id 12314jhg42342j4j --storagehttp://ecs.ai is an on-prem DELL EMC ECS that serves as our S3 storage configured with s self signed cert.

Got that thanks. Just to better understand. When clearml-data upload my recursive folder of image data, it convert it into a compressed form with a different folder structure than the original datasets.

When my software pull the data, i'm returned a str. How would we manipulate the data from there?

ah ok, so if i see Jax's workspace on https://app.community.clear.ml/dashboard , then i'm on the right track? How regular does this reset then?

the hackathon is 3 days.

Hi, the latest k8sglue-example.py was last commited about 4 months ago. Are you refering to that version?

We are deploying ClearML Server via the docker-compose.

For ClearML-Agent. We have the choice of Docker or K8S preferred (Using the Glue).
For K8S, we can't get the glue to work ( https://clearml.slack.com/archives/CTK20V944/p1614525898114200?thread_ts=1613923591.002100&cid=CTK20V944 ) so we can't make an assessment of whether it actually works for us.

Just to put a ping for those on this side of the timezone to look at. Thanks.

Okay this part I missed, why would you need to add additional "catalog" when you have the UI?

Yeah this is the part i am trying to reconcile. I don't see any UI for datasets, Or is this a feature of hyperdatasets and i just mixed them up.

I'm using the k8s Glue.

Hi, this is the setup.

client
from clearml import Task, Logger task = Task.init(project_name='DETECTRON2',task_name='Train',task_type='training') task.set_base_docker("quay.io/fb/detectron2:v3 --env GIT_SSL_NO_VERIFY=true --env TRAINS_AGENT_GIT_USER=testuser --env TRAINS_AGENT_GIT_PASS=testuser" ) task.execute_remotely(queue_name="single_gpu", exit_process=True)
k8s_glue_example.py spawned a pod and starts running.

ClearML UI -> Experiment -> Results -> Console.
` At the top it will pri...

I see, so its a path. Another question, as far as i can tell, clearml-data will download entire datasets before starting training. This isn't very ideal when we are dealing with billions of datasets (E.g. WE might want to download a subset at a time, send to GPU for training and then use the CPU to concurrently pull another subset.). Any comments on this?

Hi, for both of them,  args.lastiter  is the exact same value. But when plotted out, they are 2 actually iterations apart.

Hi, the idea is to load the gituser and password into the --env by loading it via a env var so the client could access the resources without divulging the credentials in source code and it would be removed after completion since the container would be removed. Its actually doing well with ClearML except the part that the agent seems to print the content of docker_cmd on running the task.

I would like to note that this behaviour doesn't exist with the clearml-agent daemon though. It only exis...

To note, the latest codes have been pushed to the Gitlab repo.

unfortunately, our security posture is so strict that we cannot have an agent git user that have unfettered read access to all repos.

Hi, clearml-agent==0.17.2rc3 did work. I'm on a 1.19 k8s cluster, and has this error when a task is pulled. Is the glue not compatible with 1.19?
` Pulling task 3a90802d1dfa4ec09fbccba0beffbaa8 launching on kubernetes cluster
Pushing task 3a90802d1dfa4ec09fbccba0beffbaa8 into temporary pending queue
Kubernetes scheduling task id=3a90802d1dfa4ec09fbccba0beffbaa8
kubectl output:
Flag --replicas has been deprecated, has no effect and will be removed in the future.
Flag --generator has been depre...

Yeah me 3.

Ok that worked. So every time i have changes in codes, i will have to rerun the experiment on my own machine that doesn't have any GPUs?

Kinda defeat the purpose of using ClearML Agent.

Hi SuccessfulKoala55 I was refering to the Task.init() or any other SDK API that we use in our training codes.

Hi we did a check. Only 7.16.1 and 6.8.21 and above mitigates the attack. What's the current version that ClearML is using?

No, i can't see the files. But i can see if i don't use ':port' in the URL when uploading. I can't access the machine today, i'll try to check the S3 logs when i'm back.

ok that's the latest? Will do.