Hi AgitatedDove14 , that's what i am trying to figure out as well. The task has nothing to do with torch, and the requirements.txt doesn't have any torch packages as well.

AlertBlackbird30 , Actually the log says 10.2.
docker_cmd = nvidia/cuda:10.2-devel-ubuntu18.04 -e GIT_SSL_NO_VERIFY=true

I meant the dataset id.

Ok thanks.

Congrats on v1.0. 🎉

so the clearml-agent daemon needs higher privilege?

I managed to find out why. The docker image I'm using is not set as root user thus the error. But I'm wondering why this is the case as docker best practices does indicate we should use a non root on production images.

Clearing the cache entirely works. Thanks.

Executing task id [228caa5d25d94ac5aa10fa7e1d02f03c]:
repository = https://192.168.50.88:18443/tkahsion/pytorchmnist
branch = master
version_num = cfb833bcc70f3e10d3b6a96cfad3225ed682382b
tag = 
docker_cmd = nvidia/cuda:10.1-runtime-ubuntu18.04
entry_point = pytorch_mnist.py
working_dir = .
Warning: could not locate requested Python version 3.9, reverting to version 3.6
Using base prefix '/usr'
New python executable in /root/.clearml/venvs-builds/3.6/bin/python3.6
Also creating executable i...

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

Nice, what are the names of the talks?

Hi thanks. How about Agent, does its docker mode or k8s mode require docker.sock to be exposed?

This is the log i extracted.

It would make sense on a very large resource cluster. Unfortunately we only have less than 50 GPUs to share across. A multi-tenant SAAS would cut the resources into even more smaller clusters and not help with efficiency. Or would you have a suggestion?

Going back to the open source, I think that adding the credentials as part of the source code might allow to have "credentials" auto populate as part of the remote execution, wdyt?

Not sure how this will work when i can't supply the credentials to ClearML programatically.

Ah ok. So it will be fixed on the ClearML server web UI as well? (See screenshots).

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.

Ok thanks. that explains alot. We have been doing this wrongly the whole time, thinking that the clearml.conf on the client side would be acknowledged by the remote agent execution. In reality, only the API section is utilised.

Ok thks.

first line to make sure kubectl is connected to k8s.

Ok, that seems clearer, thanks.

does the bash script need clearml-agent to be able to communicate to the https clearml-server first? If yes, there's a chicken/egg problem here.

Hi,
basically i run this block first and ended the script.
task = Task.init(project_name="afro-nmt", task_name=args.taskname, continue_last_task=args.taskid) Logger.current_logger().report_scalar(title="BLEU",series="JW300",value=args.jwbleu, iteration=args.lastiter)Then i run another script, with series different.
` task = Task.init(project_name="afro-nmt", task_name=args.taskname, continue_last_task=args.taskid)
Logger.current_logger().report_scalar(title="BLEU",series="SS900",value=arg...

The first is probably done using pipeline controllers, the second using Datasets or HyperDatasets. Its not very clear how the last one is achieved, especially on the searchable data catalogs.

The agent is running on a disconnected server on docker mode. I have a client that runs clearml-session and i saw from the agent's logs that the installation of vscode fails.

Thanks. Have a better understanding now.

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, when i tried ip:port, it references the right host and bucket....BUT... the file is not found on the ECS S3 even though i can see from the logs that it states Completed model upload to s3://ecs.ai:80/clearml-models/artifacts/ ...

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...

Its actually in your documentation. Its removed since 0.17 apparently.
https://allegro.ai/clearml/docs/docs/release_notes/ver_0_17.html#clearml-agent-0-17-2

And this is my logs, it tried to install something and encountered permission denied. It wouldn't if it obeyed the force_repo_requirements_txt.

1620664917916 Kahs-MacBook-Pro.local info ClearML Task: created new task id=024a421c0e174650a1c7ff64af756c26 ClearML results page: `
1620664920359 Kahs-MacBook-Pro.local info ClearML Mon...