Not the file the UI

I also have task_override that adds a version which changes each run

It's just a tag, so no real difference

The agents are docker containers, how do I modify the startup script so it creates a queue?

Hmm actually not sure about that, might not be part of the helm chart.
So maybe the easiest is:
from clearml.backend_api.session.client import APIClient c = APIClient() c.queues.create(name="new_queue")

LazyFish41 just making sure, you built a container from the docker file, and used it as base docker image for the Task, is that correct ?
Also notice the cleaml-agent will not change the entry point of the docker meaning if the entry point does not end with plain bash, it will not actually run anything

BTW:
This is very odd "~/.clearml/venvs-builds.3/3.6/bin/python" it thinks it is using "python 3.6" but it is linked with python 2.7 ...
No idea how that could happen

Just making sure, the original code was executed on python 3?

Yes, that sounds like the issue, is the file actually there ?

force_analyze_entire_repo to to True 🙂
(false is the default)

I started a last one to confirm!

You mean a second run, just to make sure ?

I think the main issue is that for some reason the container running changed one of the files inside the temp folder. then the host machine is "stuck" with a file that the root user owned/changed, and now it cannot reuse / delete the temp folder.
I think the fix is to make sure the container deleted the temp folder when it is done

Like this one:
https://demoapp.trains.allegro.ai/projects/380af79766304cf78b40e15656013d89/experiments/c41d64546aa54414b7c848a5261e85c7/output/debugImages

Are you saying this component should pull a specific git repo?
PipelineDecorator.component( ..., )seems like there is no reference to a specific repo (arguments repo and repo_branch etc are missing) is that correct?

Hi RipeAnt6

What would be the best way to add another model from another project say C to the same triton server serving the previous model?

You can add multiple call to cleaml-serving , each one with a new endpoint and a new project/model to watch, then when you launch it it will setup all endpoints on a single Triton server (the model optimization loading is taken care by Triton anyhow)

It's just the print (_ repr _) not showing the data
for w in client.workers.get_all(): print(w.data)

Question - why is this the expected behavior?

It is 🙂 I mean the original python version is stored, but pip does not support replacing python version. It is doable with conda, but than you have to use conda for everything...

So are you saying the large file size download is the issue ? (i.e. network issues)

😂

I'm trying to create a task that is not in repository root folder.

JuicyFox94 If the Task is not in a repo folder, you mean in a remote repository right ?
This means the repo should be in the form of " https://github.com/ " or "ssh://"
It failed in deducing this is a remote repository (maybe we can improve the auto detection?!)

Also, the IDs as an entry in the Configuration will not be clickable in the web interface, right?

No, but on the other hand, it will be editable if you clone the Task.
Which brings me to a different scenario,
In the original one, the Main Task created the Dataset, i.e. Output Dataset (and stored it both ways).
I could think of a situation the Task is using the Dataset as input (say preprocessing or traing), then we might want to enable users to clone and change the Input dataset. wdyt?

Hi FancyWhale93 , in your clear.conf configure default output uri, you can specify the file server as default, or any object storage:
https://github.com/allegroai/clearml-agent/blob/9054ea37c2ef9152f8eca18ee4173893784c5f95/docs/clearml.conf#L409

CleanWhale17 per your request :)

An automated ML Pipeline 👍 Automated Data Source Integration 👍 Data Pooling and Web Interface for Manual Annotation of Images(Seg. / Classif) [Allegro Enterprise] or users integrate with open-source Storage of Annotation output files(versioned JSON) 👍 Online-Training  Support(for Dataset Shifts) [Not Sure what you mean] Data Pre-processessing (filter/augment) [Allegro Enterprise] or users integrate with open-source Data-set visualization(stats...

quick update, still trying to reproduce ...

Can you post here the docker-compose.yml you are spinning? Maybe it is the wring one?
Step 4 here:
https://github.com/thepycoder/asteroid_example#deployment-phase

GrittyHawk31
what are you getting when you are running:
docker psand what are you getting with:
netstat -natp | grep LISTEN

Containers are not running

? but you are running the docker-compose, how come no containers are running ?

https://github.com/allegroai/clearml/blob/fcad50b6266f445424a1f1fb361f5a4bc5c7f6a3/examples/optimization/hyper-parameter-optimization/hyper_parameter_optimizer.py#L86
you can just pass the instance of the OptunaOptimizer, you created, and continue the study

yes, that makes sense to me.
What is your specific use case, meaning when/how do you stop / launch the hpo?
Would it make sense to continue from a previous execution and just provide the Task ID? Wdyt?

Hi YummyFish22
Looks like the task does not have "Task.init" call on the main script (or an import of clearml)? could that be the case?

Thanks you !
I'll look into it

Hmm, in the credentials popup there should be a "secure connect" checkbox, it tells it to use https instead of http. Can you verify?

Different question. How can I pass PYTHONPATH env variable to a task, run by agent (so python can find classes inside m subdirectories)?

Hi HelpfulHare30
By default the working directory will be added to the python path, this means if I have under execution:
Working Dir: "." Script: "src/script.py"The root git repo will be added to the python path.
BTW: next RC you could add a flag to the agent to always add the git repo