DefiantHippopotamus88 you can create a custom endpoint and do that, but it will be running I the same instance , is this what you are after? Notice that Triton actually supports it already, you can check the pytorch example

We do upload the final model manually.

wait you said upload manually, and now you are saying "saved automatically", I'm confused.

With pleasure 🙂

CooperativeFox72 could you expand on "not working"?
If you have a yaml file, I would do:
` # local_path = './my_config.yaml'
path = task.connect_configuration(local_path, name=name)
if task.running_locally():
with open(local_path, "r") as config_file:
my_params_dict = yaml.load(config_file, Loader=yaml.FullLoader)
my_params_dict['change_me'] = 'new value'
my_params_text = yaml.dump(my_params_dict)

store back the change, my_params assumed to be the content of the param file (tex...

I want to be able to access the data just avoid reporting the experiment results

Yes, you are correct 😞
If you just want to skip the logging you can always add an if to the Tasl.init call ?!

Hi @<1657918706052763648:profile|SillyRobin38>
You mean remove the entire serving session? is it still running somewhere ?
(for example if you take the docker-compose down it will be marked aborted automatically after 2 hours)

Ok no it only helps if as far as I don't log the figure.

you mean if you create the natplotlib figure and no automagic connect you still see the mem leak ?

Woot woot!
awesome, this RC is stable you can feel free to use it, the official release is probably due to be out next week :)

Oh that makes sense! I'll pass it to the documentation guys 🙏

Hi OutrageousSheep60

Is there a way to instantiate a

clearml-task

while providing it a

Dockerfile

that it needs to build prior to executing the task?

Currently not really, as at the aned the agent does need to pull a container,
But you can cheive basically the same by adding the "dockerfile" script as --docker_bash_setup_script Notice of course that this is an actual bash script not Docker script, so no need for "RUN" prefix.
wdyt?

ElegantCoyote26 what you are after is:
docker run -v ~/clearml.conf:/root/clearml.conf -p 9501:8085
Notice the internal port (i.e. inside the docker is 8080, but the external one is changed to 9501)

As you said you just need to clone, righr click clone?

Still not supported 😞

Hi IrritableGiraffe81
You can access the model object with, task.models['output']
To set the model metadata I would recommend making sure you have the latest clearml package, I think this is relatively new addition

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

Hi @<1792364603552829440:profile|TestyBeetle31>
Yeah so sorry we finally changed the repository name:
None

Where is the broken this link coming from, we will fix it (we are working on it, and some of the services do not auto forward

Hi AttractiveWoodpecker16
I think is the correct channel for that question.
(any chance you can move your thread there?)
Specifically just email billing@clear.ml they will cancel (no need to worry about the beginning of the month, just explain and they will not charge over Nov)

EDIT: I know they are working on making it a one click in the UI, main limit is what happens with the data that was stored and was above the free tier threshold, anyhow I think next version will sort that as well.

I can't think of any hack that will satisfy your IT other than than an actual vault...
wdyt?

This line 🙂
None
Notice Triton (and so is clearml-serving) needs the pytorch model to be converted into torchscript, so that the triton backend can load it

I'd prefer to use config_dict, I think it's cleaner

I'm definitely with you

Good news:

new 

best_model

 is saved, add a tag 

best

,

Already supported, (you just can't see the tag, but it is there :))

My question is, what do you think would be the easiest interface to tell (post/pre) store, tag/mark this model as best so far (btw, obviously if we know it's not good, why do we bother to store it in the first place...)

Correct

Sure thing 🙂

Thanks ShakyJellyfish91 ! please let me know what you come up with, I would love for us to fix this issue.

So inside the pipeline logic you can do Task.current_task().id
Or inside a component Task.current_task().parent

While if I just download the right packages from the requirements.txt than I don't need to think about that

I see you point, the only question how come these packages are not automatically detected ?

SmugOx94 could you please open a GitHub issue with this request, otherwise we might forget 🙂
We might also get some feedback from other users

Does it work if I launch the clearml-agent on a docker and pip doesn't know the packages to install

Not sure I follow... the "detect_with_pip_freeze" flag (when set) will tell clearml (at runtime) to create the "installed packages" directly from pip freeze (instead of analyzing the code)

LovelyHamster1
Also you can use pip freeze instead of the static code analysis , on your development machines set:
detect_with_pip_freeze: false
https://github.com/allegroai/clearml/blob/e9f8fc949db7f82b6a6f1c1ca64f94347196f4c0/docs/clearml.conf#L169

I would like to force the usage of those requirements when running any script

How would you force it? Will you just ignore the "Installed Packages" section ?

Back to the feature request, if this is taken care of (both adding a missed package, and the S3 upload), do you still believe there is a room for this kind of feature?