Yes. The training working well with cuda

SdK meaning I run the agent using clearml-agent daemon ....
Alternatively I understand I can also run the agent using docker run allegroai/clearml-agent:latest. But I cannot figure out how to add --restart, --queue, -- gpus flag to the container

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

Yea. Added an issue. We can follow up from there. Really hope that clearml serving can work, is a nice project.

Alright. Thanks CostlyOstrich36

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: [
{
` ...

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.

U want to share your clearml.conf here?

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?

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?

seems like it was broken for numpy version 1.24.1.
Tried with numpy 1.23.5 and it works.

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.

Hi @<1523701070390366208:profile|CostlyOstrich36> , basically

• I uploaded dataset using clearml Datasets. The output_uri is pointed to my s3, thus the dataset is stored in s3. My s3 is setup with http only.
• When I retrieve the dataset for training, using Dataset.get() , I encountered ssl cert error as the url to retrieve data was https://<s3url>/... instead of s3://<s3url>/... which is http. This is weird as the dataset url is without https.
• I am not too sure why and I susp...

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
`...

Nice. It is actually dataset.id .

https://clear.ml/docs/latest/docs/integrations/storage/
Try add the <path to your cert> for s3.credentials.verify.

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.

Clearml 1.1.1. Yes, i have boto3 installed too.

Just to add, when I run the pipeline locally it works as well.

When I run as regular remote task it works. But when I run as a step in pipeline, it cannot access the same folder in my local machine.

It return false. Just to share abit more, I have the requirements.txt in gitlab with my codes and are in folders. Do I need to provide a gitlab path?

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

OK I will send here shortly.

Hi CostlyOstrich36 I have run this task locally at first. This attempt was successful.

When I use this task to run in a pipeline (task was run remotely), it cannot find the external package. This seems logical but I not sure how to resolve this.

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?

Nope. In my local drive

Thanks AgitatedDove14 and TimelyMouse69 . The intention was to have some traceability between the two setups. I think the best way is to enforce some naming convention (for project and name) so we can know how they are related? Any better suggestions?

Hi Bart, yes. Running with inference container.