This, however, requires that I slightly modify the clearml helm chart with the aws-autoscaler deployment, right?

Correct 🙂

The "Optimizer task" will continue to run as long as there are sub-Tasks it launched.
Is anything else running/pending ?

Ssh is used to access the actual container, all other communication is tunneled on top of it. What exactly is the reason to bind to 0.0.0.0 ? Maybe it could be a flag that you, but I'm not sure in what's the scenario and what are we solving, thoughts?

Hmm what do you mean? Isn't it under installed packages?

Also, on the ClearML dashboard, I can see the 

clearml-agent

 log:

Is the clearml-agent running in docker mode ?

See https://github.com/allegroai/clearml-session/issues/3

Which means there will be atleast multiple published models entries of same model over time?

Only the specific one will be published (not all the Models the Task created)

Hmm, Notice that it does store sym links to parent data versions (to save on multiple copies of the same file). If you call get_mutable_local_copy() you will get a standalone copy

Could that be it ?

'config.pbtxt' could not be inferred. please provide specific config.pbtxt definition.

This basically means there is no configuration on how to serve the mode, i.e. size/type of lower (input) layer and output layer.
You can wither store the configuration on the creating Task, like is done here:
https://github.com/allegroai/clearml-serving/blob/b5f5d72046f878bd09505606ca1147d93a5df069/examples/keras/keras_mnist.py#L51
Or you can provide it as standalone file when registering the mo...

Which would also mean that the system knows which datasets are used in which pipelines etc

Like input artifacts per Task ?

RC3 is out with a few fixes, please feel free to test 🙂

What do you have under the "installed packages" ?

LudicrousParrot69 we are working on adding nested project which should help with the humongous mass the HPO can create. This is a more generic solution for the nesting issue. (since nesting inside a table is probably not the best UX solution 🙂 )

I think this should work 🤞

Hmm that makes sense, I "think" the enterprise offering has a solution for that as well (i.e. full separation over static cluster), but probably the best way to constituent this avenue is talk to Sales (I'm assuming they'll setup a call to discuss the details)

Going back to the open source, I think that adding the credentials as part of the source code might allow to have "credentials" auto populate as part of the remote execution, wdyt?

yes, looks like. Is it possible?

Sounds odd...
Whats the exact project/task name?
And what is the output_uri?

I double checked the code it's always being passed 😞

ohhh yes and that is the issue 😞

YummyFish22 can you point to the huggingface example you are using?

cuda 10.1, I guess this is because no wheel exists for torch==1.3.1 and cuda 11.0

Correct

how can I enforce a specific wheel to be installed?

You mean like specific CUDA wheel ?
you can simple put the http link to the wheel in the "installed packages", it should work

Hi StoutElephant16
You mean like cron Job?
(Unfortunately if this is the case, then currently no CLI for that, but it is a great idea, maybe open a github issue to make sure we do not forget to add it 😄 )

Sure,
https://github.com/allegroai/clearml/blob/a621b4fa20020d97e939f7c4b9f84a0a621d7d34/examples/optimization/hyper-parameter-optimization/base_template_keras_simple.py#L48

programmatically or from the UI?

If this is the case, then we do not change the maptplotlib backend
Also

I've attempted converting the 

mpl

 image to 

PIL

 and use 

report_image

  to push the image, to no avail.

What are you getting? error / exception ?

You cannot change the user once you have mount the shared folder with wither CIFS or NFS

Is there no await/synchronize method to wait for task update?

Yes, but then we will have to relaunch it (not unthinkable), but I'm still looking for the intimidate value of doing all that work, wdyt?

Hi EagerOtter28
The agent knows how to do the http->ssh conversion on the fly, in your cleaml.conf (on the agent's machine) set force_git_ssh_protocol: true
https://github.com/allegroai/clearml-agent/blob/42606d9247afbbd510dc93eeee966ddf34bb0312/docs/clearml.conf#L25

I can't see any reason it should not work 😀

In our case this is not possible due to client security (e.g. training data from clients can potentially be 'reverse engineered' from trained models in future).

Hmm I see, wouldn't it make more sense to separate clients like a multi-tenant SAAS solution ?

When you are running the base-task, are you proving any arguments to it?
Can you share the "execution" Tab? and the Args tab of the base-task ?