Hi DeliciousBluewhale87
Hmm, good question.
Basically the idea is that if you have ingestion service on the pods (i.e. as part of the yaml template used by the k8s glue) you can specify to the glue what are the exposed ports, so it knows (1) what's the maximum of instances it can spin, e.g. one per port (2) it will set the external port number on the Task, so that the running agent/code will be aware of the exposed port.
A use case for it would be combing the clearml-session with the k8s gl...

Thank you AttractiveWoodpecker16 !
Removing the uncommitted changes so that you can launch it from an agent? Or is it visual only?

Hi AgitatedTurtle16 could you verify you can access the API server with curl?

Makes sense to add it to docker run by default if GPUs are mentioned in agent.

I think this is an arch thing, --privileged is not needed on ubuntu flavor, that said you can always have it if you add it here:
https://github.com/allegroai/clearml-agent/blob/178af0dee84e22becb9eec8f81f343b9f2022630/docs/clearml.conf#L149

clearml-agent daemon --gpus 0 --queue default --docker
But docker still sees all GPUs.

Yes --gpus should be enough, are you sure regrading the --privileged flag ?

And other question is clearml-serving ready for serious use?

Define serious use? KFserving support is in the pipeline, if that helps.
Notice that clearml-serving is basically a control plane for the serving engine, not to neglect the importance of it, the heavy lifting is done by Triton 🙂 (or any other backend we will integrate with, maybe Seldon)

Where do you store those ?

Any reason not to ?

Yes, I was referring to logging the "clearlm-data" Dataset ID on the Task itself, not an external database.
Make sense?

StorageHelper is used internally.
I'll make sure we remove it from the examples/docs

If this is the case and assuming you were able to use clearml to upload them, this means that adding the

AWS_ACCESS_KEY_ID
AWS_SECRET_ACCESS_KEY

To your env file should just work
https://github.com/allegroai/clearml-serving/blob/main/docker/example.env

Make sense?

RipeGoose2 you are not limited to the automagic
From anywhere in your code you can always do:
from trains import Logger Logger.current_logger().report_plotly(...)So you can add any manual reporting on top of the one generated by lightning .
Sounds good?

https://allegro.ai/clearml/docs/rst/references/clearml_python_ref/logger_module/logger_logger.html#clearml.logger.Logger.report_image

Q. Would someone mind outlining what the steps are to configuring the default storage locations, such that any artefacts or data which are pushed to the server are stored by default on the Azure Blob Store?

Hi VivaciousPenguin66
See my reply here on configuring the default output uri on the agent: https://clearml.slack.com/archives/CTK20V944/p1621603564139700?thread_ts=1621600028.135500&cid=CTK20V944
Regrading permission setup:
You need to make sure you have the Azure blob credenti...

a CI for the vscode extension ? So spin server + agent & connect to it as part of the CI?

No 😞

GreasyPenguin14 makes total sense.
In that case I would say variants to the accuracy make sense to me, I would suggest:
title='trains', series='accuracy/day' and title='trains', series='accuracy/night'
Regrading hierarchy, from the implementation perspective a unique identifier is always the combination of title/series (or in other words metric/variant), introducing another level is a system wide change.
This means it might be more challenging than expected ...

WickedGoat98

I will try to collect the installation steps in a document and share it to the community once ready

Thank you! this will be awesome !

We're here if you need anything 🙂

My typos are killing us, apologies :
change -t to -it it will make it interactive (i.e. you can use bash 🙂 )

BTW: why use CLI? the idea of clearml it becomes part of the code, even in the development process, this means add "Task.init(...)" at the beginning of the code, this creates the Tasks and logs them as part of the development. Which means that xecuting them is essentially cloning and enqueuing in the UI. Of course you can automate it directly as part of the code.

OmegaConf is the configuration, the overrides are in the Hyperparameters "Hydra" section
None

You are correct, the agent will clone the git and install the requirements, as written in the task installed packages section. Regrading the git branch, notice it will pull the specific commit id as stated in the execution section, and it will apply any uncommitted changes. You can edit the execution section and change the commit to the latest in a specific version (you should probably also clear the uncommitted changes of you do that)

Okay verified, it's the 'Agg' backend

Hi JealousParrot68
no need for decorators, you can just pass the function to schedule_function=<function goes here> 🙂
See scheduler here
https://github.com/allegroai/clearml/blob/8708967a5ef4d8529a1a5ea417672e3ebbb258d7/clearml/automation/scheduler.py#L485
And triggers here:
https://github.com/allegroai/clearml/blob/8708967a5ef4d8529a1a5ea417672e3ebbb258d7/clearml/automation/trigger.py#L193
https://github.com/allegroai/clearml/blob/8708967a5ef4d8529a1a5ea417672e3ebbb258d7/clea...

BTW: if you need to set env variables you can also add -e PYTHONPATH=/new/path to the docker args

to add an init script or to expand its capacity,

@<1546665634195050496:profile|SolidGoose91> I seem to see it in the wizard here, what am I missing?

Hi SarcasticSquirrel56

But if I then clone the task, and execute it by sending it to a queue, the experiment succeeds,

I'm assuming that on the remote machine the "files_server" is not configured the same way as the local execution. for example it points to an S3 bucket the credentials for the bucket are missing.
(in your specific example I'm assuming that the plot is non-interactive which means this is actually a PNG stored somewhere, usually the file-server configuration). Does tha...

Sen the full Task log, you can DM it if it is easier

So does that mean "origin" solves the issue ?

Hi ExasperatedCrocodile76
This is quite the hack, but doable 🙂
`
file_path = task.connect_configuration(name = 'augmentations', configuration = 'augmentations.py')

import importlib

module_name = 'augmentations'

spec = importlib.util.spec_from_file_location(module_name, file_path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module) `
https://stackoverflow.com/a/54956419

@<1523720500038078464:profile|MotionlessSeagull22> you cannot have two graphs with the same title, the left side panel presents graph titles. That means that you cannot have a title=loss series=train & title=loss series=test on two diff graphs, they will always be displayed on the same graph.
That said, when comparing experiments, all graph pairs (i.e. title+series) will be displayed as a single graph, where the diff series are the experiments.