Thanks!

quick update, still trying to reproduce ...

Ohh I see.
In your web app, look for the "?" icon (bottom left corner), click on it, it should open the full platform documentation

JitteryCoyote63 fix should be pushed later today 🙂
Meanwhile you can manually add the Task.init() call to the original script at the top, it is basically the same 🙂

UnsightlyShark53 Awesome, the RC is still not available on pip, but we should have it in a few days.
I'll keep you posted here :)

Hi OutrageousGrasshopper93
I think that what you are looking for is Task.import_task and Task.export
https://allegro.ai/docs/task.html#trains.task.Task.import_task
https://allegro.ai/docs/task.html#trains.task.Task.export_task

BTW: which clearml version are you using ?
(I remember there was a change in the last one, or the one before, making the config loading differed until accesses)

ReassuredTiger98 when you look for task "dca2e3ded7fc4c28b342f912395ab9bc" there are no artifacts ?
Could you add some prints? this should have worked...

My pleasure 🙂

Ohh, like a query based only of the stored url ?
Do you also has the creating Task ?

BTW: what happens if you pass the same s3://bucket to Task.init output_uri ? I assume you are getting the same access issue ?

FierceHamster54 are you sure you have write permissions ?

I have to specify the full uri path ?

No it should be something like " s3://bucket "

the model files management is not fully managed like for the datasets ?

They are 🙂

This would be my only improvement, otherwise awesome!!!
output_model.update_weights(weights_filename=os.path.join(training_data_path, 'runs', 'train', 'yolov5s6_results', 'weights', 'best.onnx'))

👍

Hi FierceHamster54

Do I need to instantiate a task inside my component ? Seems a bit redundant....

Yes, so the idea is that the Task (along the code) will be automatically linked with the output model, for better traceability.
That said you can "import" a model into the system (i.e. it was created somewhere else and you want to register it with InputModel.import_model
https://clear.ml/docs/latest/docs/clearml_sdk/model_sdk#importing-models
I guess "Input" from that perspecti...

do I still need to specify a OutputModel

No need, only if you want to upload a local model file (but I assume in this case, no new model is created)

Hmmm, let me check

I'm assuming your are looking for the AWS autoscaler, spinning EC2 instances up/down and running daemons on them.
https://github.com/allegroai/clearml/blob/master/examples/services/aws-autoscaler/aws_autoscaler.py

https://clear.ml/docs/latest/docs/guides/services/aws_autoscaler

The point is, " leap" is proeperly installed, this is the main issue. And although installed it is missing the ".so" ? what am I missing? what are you doing manually that does Not show in the log?
In other words how did you install it "menually" inside the docker when you mentioned it worked for you when running without the agent ?

hmm DeliciousKoala34
what are you getting if you put this at the top of your code (the one you are running in the remote docker)
import os print([(k, os.environ[k]) for k in os.environ if k.startswith("CLEARML_")])

Hmm, could it be that the working dir is outside of the git repo?

And this is with the latest pycharm plugin 1.1.0 ?

Hi DeliciousKoala34
This means the pycharm plugin was not able to run git on your local machine.
Whats your OS ?
could it be that if you open cmd / shell "git" is not in the path ?

In your code, can you print the following:
import os print(os.environ.keys())There should be a few keys the Pycharm plugin is sending from the local machine, pointing to the git repo

It is the folder the clearml creates and the folder we create ourself to store the predictions

I see... If that is the case, the only solution I can think of is manually uploading the files with StorageManager(...) then get the url, and register it as debug_media or artifact:
logger.report_media("image", "type a", iteration=iteration, url=" ") task.upload_artifact('a link', artifact_object=' ')

Hi WittyOwl57
That's actually how it works (original idea/design was borrowed from libclound), basically you need to create a Drive, then the storage manger will use it.
Abstract class here:
https://github.com/allegroai/clearml/blob/6c96e6017403d4b3f991f7401e68c9aa71d55aa5/clearml/storage/helper.py#L51
Is this what you had in mind ?

SmarmySeaurchin8 what do you think?
https://github.com/allegroai/trains/issues/265#issuecomment-748543102

task.connect_configuration

I'll make sure we have conda ignore git:// packages, and pass them to the second pip stage.

This is awesome, thank you!