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: [
{
` ...

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

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

Clearml 1.1.1. Yes, i have boto3 installed too.

Is there a way to troubleshoot further?

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

Nope. In my local drive

Nice. That should work. Thanks

This is what I got. and when I see http400 error in the console.

Yes. But I not sure what's the agent running. I only know how to stop it if I have the agent id

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.

By the way, how can I start up the clearml agent using the clearml-agent image instead of SDK? Do u have an example of the docker run command that includes the queue, gpus etc?

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?

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

Thanks @<1523701205467926528:profile|AgitatedDove14> . what I could think of is to write a task that may run python subproecss to do "helm install". In those python script, we could point to /download the helm chart from somewhere (e.g. nfs, s3).

Does this sound right to u?
Anything that I was wondering is if we could pass the helm charts /files when we uses clearml sdk, so we could minimise the step to push them to the nfs/s3.

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.

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

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
`...

Can clearml-serving does helm install or upgrade? We have cases where the ml models do not come from the ml experiments in clearml. But would like to tap on clearml q to enable resource queuing.

Hi Bart, yes. Running with inference container.

Alright. Thanks CostlyOstrich36

Yup. But I happened to reinstall my server and the data is lost. And the agent continue running.

@<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?

@<1523701205467926528:profile|AgitatedDove14> I looking at a queue system which clearml q offers that allow user to queue job to deploy an app / inference service. This cam be as simple as a pod or a more complete helm chart.

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...

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?

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

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?

U want to share your clearml.conf here?

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