Hi, i was reading this thread and wondered which version of clearml-server and clearml-agent has this taken effect with?

Hi, how may i task.init() within these sub processes without write access to the 3rd party scripts and python executables?

Hi, building a container with vscode is not possible. If i have an alternative location for the vscode, where should i indicate in the configuration?

where should i indicate in the configuration?

Any idea?

Hi, i'm gonna hijack this thread a bit. My community uses ClearML and is looking at various model deployment strategies. We are looking at a seamless integration with Triton but noted they Triton does not support deployment strategies. ClearML-Serving seems to but the strategies are rather limited. Is there a roadmap to expand Clearml-serving?

Hi HelpfulDeer76 , I'm facing similar issues. Would you mind describing in detail how you deploy clearml-agent? Is it running as a pod on k8s?

The problem is resolved by doing a git push. Somehow the git diff didn't capture the difference in requirements.txt in the project. I can't reproduce the same issue after this as well.

I think a related question is, ClearML replies heavily on Triton (Good thing) but Triton only support a few frameworks out of the box. So this 'engine' need to make sure its can work with Triton and use all its wonderful features such as request batching, GPU reuse...etc.

Yeah me 3.

Hi i missed this. Is there a recording?

Hi, I was expecting to see the container rather then the actual physical machine. For example, in the file panel on the left of the jupyter panel, I see the file contents of the physical machine. I was expecting this to be the container.

Hi, it looks like the entire http://clear.ml domain is offline for more than 12 hours. Main pages and documentation are inaccessible as well.

Having same issues. Looks like Google DNS can't resolve the DNS at all.
` %nslookup app.clear.ml - 8.8.8.8
Server: 8.8.8.8
Address: 8.8.8.8#53

** server can't find app.clear.ml: SERVFAIL `

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.

ok that's the latest? Will do.

Hi thanks for the examples! I will look into them. Quite a fair bit of my teams uses tf datasets to pull data directly from object stores, so tfrecords and stuff are heavily involved. I'm trying to figure if they should version the raw data or the tfrecords with ClearML, and if downloading entire set of data to local can be avoided as tf datasets is able to handle batch downloading quite well.

Yeah that sounds good. But from user perspective, especially the untrained, they wouldn't know what to point to. Example, some may think it's an exe, some think it's a zip bundle, and others think it's any github repo with the word vscode.

Do you mean by this that you want to be able to seamlessly deploy models that were tracked using ClearML experiment manager with ClearML serving?

Ideally that's best. Imagine that i used Spacy (Among other frameworks) and i just need to add the one or two lines of clearml codes in my python scripts and i get to track the experiments. Then when it comes to deployment, i don't have to worry about Spacy having a model format that Triton doesn't recognise.

Do you want clearml serving ...

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.

Hi, i can't seem to find the source. What are the kind of situations where it will try to install torch outside of user requirements?

clearml-serving does not support Spacy models out of the box among many others and that Clearml-Serving only supports following;
Support Machine Learning Models (Scikit Learn, XGBoost, LightGBM)
Support Deep Learning Models (Tensorflow, PyTorch, ONNX).
An easy way to extend support to different models would be a boon.

I believe in such scenarios, a custom engine would be required. I would like to know, how difficult is it to create a custom engine with clearml-serving? For example, in this...

Is there anyway to see an error log from that?

Try set docker_force_pull: true under agent section of your agent's clearml.conf.

like create multiple datasets?
create parent (all) - upload to S3
create child1 (first 100k)
create child2 (second 100k)...blah blah

Then only pull indices from children. Technically workable but not sure if its best approach since different ppl have different batch sizes in mind.

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?

Previously we had similar issues when we switched images used in agent. Might want to check on that.

Thanks SuccessfulKoala55 . Just pm'ed him.

Thanks SuccessfulKoala55 . I can try my hand on a patch. But the pod spinning is handled by the k8s glue, which has no link to the client side. How should the client pass the key over to k8s glue during runtime via clearml server?

what feature on this paid roadmap are you referring to? I am indeed communicating with Noem on paid features.

And any roadmap on this? The organisation's on ssh auth is firm. This can end up not possible to use ClearML for remote execution.