Thanks RobustRat47 !
Should we put somewhere this requirement ? (i.e. nvidia drivers) ?
Is this really a must ?

RobustSnake79 this one seems like scalar type graph + summary table, correct?
BTW: I'm not sure how to include the "Recommendation" part 🙂

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?

Working on it as we speak 🙂 Hopefully in the next release (probably next week)

I am running from noebook and cell has returned

Well the Task will close when you shut down the notebook 🙂

JitteryCoyote63 okay... but let me explain a bit so you get a better intuition for next time 🙂
The Task.init call, when running remotely, assumes the Task object already exists in the backend, so it ignores whatever was in the code and uses the data stored on the trains-server, similar to what's happening with Task.connect and the argparser.
This gives you the option of adding/changing the "output_uri" for any Task regardless of the code. In the Execution tab, change the "Output Destina...

Regarding this, does this work if the task is not running locally and is being executed by the trains agent?

This line: "if task.running_locally():" makes sure that when the code is executed by the agent it will not reset it's own requirements (the agent updates the requirements/installed_packages after it installs them from the requiremenst.txt, so that later you know exactly which packages/versions were used)

task = Task.init(...) if task.running_locally(): # wait for the repo detection and requirements update task._wait_for_repo_detection() # reset requirements task._update_requirements(None)🙂

GiddyTurkey39 my bad 😞 try this one
task._update_requirements({})

could you send the entire log here?
i.e. from the "docker-compose" command line and onward

ReassuredTiger98 maybe we should add an option to send a text next to the abort?
(Actually it is just a matter of passing the argument)
wdyt?

Hi GiddyTurkey39
Are you referring to an already executed Task or the current running one?
(Also, what is the use case here? is it because the "installed packages are in accurate?)

force_analyze_entire_repo to to True 🙂
(false is the default)

Can you please tell me if it is possible to set up slack monitoring in clearml?

It is 🙂
This one?
https://clear.ml/docs/latest/docs/guides/services/slack_alerts

Yes that's the part that is supposed to only pull the GPU usage for your process (and sub processes) instead of globally on the entire system

GiddyTurkey39

A flag would be really cool, just in case if theres any problem with the package analysis.

Trying to think if this is a system wide flag (i.e. trains.conf) or a flag in task.init.
What do you think?

I think task.init flag would be great!

👍

I think we added it somewhere in 0.14, anyhow I just checked the Logger doc, it is there now 🙂

@<1523710674990010368:profile|GreasyPenguin14> make sure it to uses https not ssh:
edit ~/clearml.conf

force_git_ssh_protocol: false

and that you have both git_user & git_pass set in your clearml.conf

... Would not work for huge llm style models.

yes I agree... but then if the model is small enough then you can just keep it in memory ...

When I start the serving containers it can't retrieve the model:

Hi BrightRabbit75
I think you need to pass the credentials for your S3 account to the clearml-serving containers
Basically just add AWS_ACCESS_KEY_ID , AWS_SECRET_ACCESS_KEY to your docker compose:

https://github.com/allegroai/clearml-serving/blob/4b52103636bc7430d4a6666ee85fd126fcb49e2e/docker/docker-compose-triton-gpu.yml#L110
https://github.com/allegroai/clearml-serving/blob/4b52103636bc7430d4a6666e...

I want to be able to compare scalars of more than 10 experiments, otherwise there is no strong need yet

Make sense, in the next version, not the one that will be released next week, the one after with reports (shhh do tell anyone 🙂 ) , they tell me this is solved 🎊

Nice!

Ohh sorry I missed that and answered on the original message, nvm 🙂 all is well now

Nicely done DeterminedToad86 🙂
Wasn't this issue resolved by torch?

The --template-yaml allows you to use foll k8s YAML template (the overrides is just overrides, which do not include most of the configuration options. we should probably deprecate it

ThickDove42 you need the latest cleaml-agent RC for the docker setup script (next version due next week)
pip install clearml-agent==0.17.3rc0

named as 

venv_update

 (I believe it's still in beta). Do you think enabling this parameter significantly helps to build environments faster?

This is deprecated... it was a test to use the a package that can update pip venvs, but it was never stable, we will remove it in the next version

Yes, I guess. Since pipelines are designed to be executed remotely it may be pointless to enable an 

output_uri

 parameter in the 

PipelineDecorator.componen...

If you passed the correct path it should work (if it fails it would have failed right at the beginning).
BTW: I think it is clearml-agent --config-file <file here> daemon ...

LOL 😊