It gets rerouted to http://app.clearml.home.ai/dashboard . with the same network error.

OK I will send here shortly.

Do u have an example of how I can define the packages to be installed for every steps of the pipeline?

And just a suggestion which maybe I can post in GitHub issue too.
It is not very clear what are the purpose of the project name and name, even after I read the --help. Perhaps this is something that can be made clearer when updating the docu?

SuccessfulKoala55 Nope. I didn't even get to enter my name. I suspect there is some mistake in mapping the data folder.
Was using the template in https://github.com/allegroai/clearml-helm-charts to deploy.

U want to share your clearml.conf here?

JuicyFox94 and SuccessfulKoala55 Thanks alot. Indeed it is caused by dirty cookies.

Example i build my docker image using a image in docker hub. In this image, i installed torch and cupy packages. But when i run my experiment in this image, the packages are not found.

Yes, I ran the experiment inside.

Hi TimelyPenguin76 , nope. I don't see any errors. That's why not sure what went wrong

Ok. Can I check that only the main script was stored in the task but not the dependent packages?

I guess the more correct way is to upload to some repo where the remote task can still pull from it?

I not very sure tbh. Just want to see if this is useful....

I got SSL error few days back and I solved it by adding cert to /etc/ssl/certs and perform update-ca-certificates .

export REQUESTS_CA_BUNDLE=/etc/ssl/certs/ca-certificates.crt

Add this. Note that verify might not work with sdk.aws.s3.verify but sdk.aws.s3.credentials . Pls see the attached image.

Example:
aws {
s3 {
credentials: [
{
` ...

Hi Bart, yes. Running with inference container.

Hello CostlyOstrich36 I am facing an issue now. basically i installed all necessary python packages in my docker image. But somehow, the clearml-agent does not seems to be able to detect these global packages. I don't see them in the "installed packages". Any advice?

@<1526734383564722176:profile|BoredBat47> Just to check if u need to do update-ca-certificates or equivalent?

SuccessfulKoala55 i tried comment off fileserver, clearml dockers started but it doesn't seems to be able to start well. When I access clearml via webbrowser, site cannot be reached.

Just to confirm, I commented off these in docker-compose.yaml.

apiserver:
command:
- apiserver
container_name: clearml-apiserver
image: allegroai/clearml:latest
restart: unless-stopped
volumes:
- /opt/clearml/logs:/var/log/clearml
`...

I figured out that it maybe possible to do these
experiment_task = Task.current_task()
OutputModel(experiment_task ).update_weights(' http://model.pt ') to attach it to the ClearML experiment task.

Ah I think I was not very clear on my requirement. I was looking at porting project level, not entire clearml data over. Is it possible instead?

I was browsing clearml agent gihub and saw this. Isn't this for spinning up clearml-agent in a docker and perform like a daemon?

Nice. It is actually dataset.id .

I see. Was wondering any advantage to do it any of the ways.

Thanks AgitatedDove14 . Specifically, I wanted to use my own clearml server and Triton. Thus, I attempted to use --engine-container-args during launch but error saying no such flag. Looked into --help but I guessed it is not updated yet.

Not exactly sure yet but I would think user tag for deployed make sense as it should be a deliberated user action. And additional system state is required too since a deployed state should have some pre-requitise system state.

I would also like to ask if clearml has different states for a task, model, or even different task types? Right now I dun see differences, is this a deliberated design?

@<1523701070390366208:profile|CostlyOstrich36> This is output_uri or where do I put this url?

Yes. The training working well with cuda

Is there a way to troubleshoot further?

Yup, was thinking of bash script.

The intent is to generate some outputs from the clearml task and thinking probably to package it into a docker image for ease of sharing to others that are not plug into our network and able to run the image directly.

A more advanced case will be to decide how long this job should run amd terminate after that. This is to improve the usage of gpu

CostlyOstrich36 I mean the dataset object in clearml as well as the data that is tied to this object.

The intent is to bring over to another clearlml setup and keep some form of traceability.

To clarify, there might be cases where we get helm chart /k8s manifests to deploy a inference services. A black box to us.

Users may need to deploy this service where needed to test out against other software components. This needs gpu resources which a queue system will allow them to queue up and eventually get this deployed instead of hard resource allocation to this purpose

@<1523701205467926528:profile|AgitatedDove14> I still trying to figure out how to do so. Coz when I add a task in queue, clearml agent basically creates a pod with the container. How can I make a task that does a helm install or kubectl create deployment.yaml?