Ohh, the controller task itself holds the artifacts ?

How can i get loaded model in Preporcess class in ClearML Serving?

ComfortableShark77
You mean your preprocess class needs a python package or is it your own module ?

AbruptHedgehog21 what exactly do you store as a Mode file ? is this a python object pickled ?

when I duplicate the experiment and clone it remote, the call is ignored and the recorded values are used?

Yes ScantChimpanzee51 exactly.
Think of it as the inital value you want to put on the Task when you are running the code on your machine, later when you clone the Task, you can edit the base docker image in the UI (or with the API), of course the new value is used when the agent spins this Task, and to avoid the actual docker (the one you changed in the UI) to be overwritten by ...

My task starts up and checks the mounted EFS volume for x data, if x data does not exist there, it then pulls x data from S3.

BoredHedgehog47 you can just use StorageManager and configure clearml cache for the EFS, it will essentially do the same 🙂
Regrading helm chart with EFS,
you need to configure the clearml-glue pod template with the EFS mount
example :
https://github.com/kubernetes-sigs/aws-efs-csi-driver/blob/e7f647f4e6fc76f983d61522e635353005f1472f/examples/kubernetes/volu...

Yes, in tandem with the experiments (because they constantly log to the server).
That said, with 0.16 we added offline mode, so you can run in offline mode, then import the experiment into the system.

This is odd, what is the parameter?
I assume it needs sorting and one time this is Integer, and the next it is a String, so the server cannot sort based on it. Could that be ?

creating a dataset with parents worked very well and produced great visuals on the UI!

woot woot!

I tried the squash solution, however this somehow caused a download of all the datasets into my

so this actually works, kind or like git squash, bottom line it will repackage the data from all the different versions into one new version. This means downloading the data from all squashed versions, then repackaging it into a single new version. Make sense ?

Will this still be considered as 

global site-packages

This is a pip settings, I "think" it inherits from the local user's installation, but I would actually install with "sudo pip" that will definitely be "inherited"

Hi ConvolutedSealion94
You can archive / delete the SERVING-CONTROL-PLANE Task from the DevOps project in the UI.
Do notice you will need to make sure the clearml-serving is updated with a new sesison ID or remove it (i.e. take down the pods / docker-compose)
Make sense ?
Were you able to interact with the service that was spinned? (how was it spinned?)

Is it being used to ssh to the instance?

It is used for the SSH client so it "knows" the SSH server (does that make sense) ?

I put two models in the same endpoint, then only one was running,

without providing version number, you are overriding the models (because this is the same endpoint)

I started another docker container having a different port number and then the curls with the new model endpoint (with the new port) started working

Seems like misconfiguration on the first one?

, which apparently I can't specify when I establish the model endpoint but I need to re compose the docker container by...

but I still clearml-agent will raise the same error

which one?

JitteryCoyote63 are you running the agent in docker mode ?

This is the reason you are getting an error 🙂
Basically the session asks the agent to setup a new SSH server with credentials on the remote machine, this is not an issue inside a container, as this is an isolated environment, but when running in venv mode the User running the agent is not root, hence it cannot spin/configure an SSH server.
Make sense ?

actually the issue is that the packages are not being detected 😞
what happens if you do the following?
Task.add_requirements("tensorflow") task = Task.init(...)

okay, now it should work 🙂

Could it be it checks the root target folder and you do not have permissions there only on subfolders?

Hmmm, let me check

Hi JitteryCoyote63 ,
When you shutdown the task (manually with close() or when the process finish) it wait for the uploads...

Why do you need to specifically wait for all the artifacts upload? (currently you can stop the artifacts upload thread and wait for all the artifacts, but that seems like a bad hack)

I see. If you are creating the task externally (i.e. from the controller), you should probably call. task.close() it will return when everything is in order (including artifacts uploaded, and other async stuff).
Will that work?

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 🙂

ComfortableShark77 are you saying you need "transformers" in the serving container?
CLEARML_EXTRA_PYTHON_PACKAGES: "transformers==x.y"https://github.com/allegroai/clearml-serving/blob/6005e238cac6f7fa7406d7276a5662791ccc6c55/docker/docker-compose.yml#L97

Very lacking wrt to how things interact with one another

If I'm reading it correctly, what you are saying is that some of the "big picture" / holistic approach on how different parts interact with one another is missing, is that correct?

I think ClearML would benefit itself a lot if it adopted a documentation structure similar to numpy ecosystem

Interesting thought, what exactly would you suggest we "borrow" in terms of approach?

ScantWorm7
Tensorboard is automatically captured and sent to the trains server. This is in addition to the local copy of your TB files. Actually in most cases the local copy is redundant

Hi BoredHedgehog47
You mean like EFS for caching ?

Could it be there is a Task.init being called Before this code snippet ?

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...

CleanWhale17 what is " Online-Training  Support(for Dataset Shifts" ?

EnviousPanda91 please feel free to PR if it works 🙂
https://github.com/allegroai/clearml/blob/86586fbf35d6bdfbf96b6ee3e0068eac3e6c0979/clearml/binding/frameworks/catboost_bind.py#L114