because step can be constructed with multiple

sub-components

but not all of them might be added to the UI graph

Just to make sure I fully understand when we decorate with @sub_node we want that to also appear in the UI graph (and have it's own Task / metrics etc)
correct?

Hi ConvolutedSealion94
Just making sure, you spinned the docker-compose of the clearml serving as well ?

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

Sure, something like : https://github.com/feast-dev/feast

Just curious about the timeout, was it configured by clearML or the GCS? Can we customize the timeout?

I'm assuming this is GCS, at the end the actual upload is done GCS python package.
Maybe there is an env variable ... Let me google it

WickedGoat98 Notice this is not the "clearml-agent-services" docker but "clearml-agent" docker image
Also the default docker image is "nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04"
Other than that quite similar :)

Will they get ordered ascending or descending?

Good point, I'll check the docs... but I think they do not specify
https://clear.ml/docs/latst/docs/references/sdk/task#taskget_tasks
From the code it seems the ordered is not guaranteed.
You can however pass '-last_update' : order_by which will give you the latest updated first
` task_filter = {
'page_size': 2,
'page': 0,
'order_by': ['last_metrics.{}.{}'.format(title, series), '-last_update']
}
Task.get_tasks(...

JitteryCoyote63 Should be quite safe, there is no major change that I'm aware of on the ClearML side that can effect it.
That said, wait for after the weekend, we are releasing a new ClearML package, I remember there was something with the model logging, it might not directly have something to do with ignite, but worth testing on the latest version.

Great, please feel free to share your thoughts here 🙂

PompousBeetle71 I think that was you saw as tags in previous version was actually systems tags, now we also have users tags (i.e. .tags). If you still want to access the system tags can you try:
InputModel('aabbcc')._get_base_model().data.system_tags

BTW: how are you using them? should we have a direct interface to those ?

PompousBeetle71 notice that starting with this version when you set model tags they will be stored as user tags , which you can change and edit in UI. So if you still need the system tags you have to access them directly.

PompousBeetle71 you can also use ModelOutput.update_weights_package to store multiple files at once (they will all be packaged into a single zip, and unpacked when you get them back via ModelInput). Would that help?

Ohh I see now, okay there are two entries on an artifact, the actual artifact (link to file somewhere) and the text preview of the artifact . I think the "preview" is the issue

So this is a UI visual issue?

This is only in the preview right ?

Any plans to add unpublished state for clearml-serving?

Hmm OddShrimp85 do you mean like flag, not being served ?
Should we use archive ?
The publish state, basically locks the Task/Model so they are not to be changed, should we enable unlocking (i.e. un-publish), wdyt?

MysteriousBee56 I would do Task.create()
you can get the full Task internal representation with task.data
Then call task._edit(script={'repo': ...}) to edit/update all the Task entries.
You can check the dull details of the task object here: https://github.com/allegroai/trains/blob/master/trains/backend_api/services/v2_8/tasks.py#L954
BTW: when you have a sample script working, consider PR-ing it, I'm sure it will be useful for others 🙂 (also a great way to get us involved with debuggin...

Hi @<1524560082761682944:profile|MammothParrot39>
The traditional solution is git submodules, basically main repo links to other repos. This way the agent can fully reproduce the full env.
Another option is to install the second repo as Python package with link to the repo and commit
And a third option is having the second repo as part of the docker.
Regrading env variables, you can add '-e env=val' as part if the docker arts section
Wdyt?

Great! btw: final v1.2.0 should be out after the weekend

Hi @<1610083503607648256:profile|DiminutiveToad80>
Yes, it does. They are also cached by default (on the machine with the agent)
None

Hi ShallowArcticwolf27

However, the AMI for version 0.16.1 has the following docker-compose file

I think we moved the docker-compose yaml when we upgraded from trains to clearml. Any reason your are installing the old docker-compose ?

I don't have the compose file, or at least can't seem to find it in 

/opt

you can manually take down all dockers with:
docker psthen docker stop <container id> for each container id

Interesting...
We could followup the .env configuration, and allow the clearml-task to add configuration files from cmd line. This will be relatively easy to add. We could expand the Environment support (that somewhat exists), and add the ability to read variables from .emv and Add them to an "hyperparemeter" section, named Environment. wdyt?

Hi ShallowArcticwolf27

from the command line to a remote machine while loading a local 

.env

 file as a configuration object?

Where would the ".env" go to ? Are we trying to pass it to the remote machine somehow ?

Hi ShallowArcticwolf27

Does the 

clearml-task

 cli command currently support remote repositories with that are intended to be used with ssh

It does 🙂

but the 

git@

 prefix used for gitlab's ssh it seems to default to looking for the repository locally

git@ is always the prefix for SSH repositories (it does not actually mean it uses it, it's what git will return when asked on the origin of the repository. The agent knows (if SSH credentials ...

Does it mean I can use clearml-serving helm chart alone

Unrelated, the clearml-serving can be deployed on k8s or with docker-compose regardless of where/how clearml-server is deployed

If a Task is in the 'Completed' I think the only option is to 'Reset' it (see image).

In the UI yes, in code you can do task.mark_aborted(force=True)

You do clear the previous run execution but I think for a repetitive task this is fine.

I would avoid that, no?

CloudyHamster42
RC probably in a few days, but notice that it will just remove the warnings, I still can't reproduce the double axis issue.

It will be helpful if you could send a small script to reproduce the problem.

Maybe this example code can help ? https://github.com/allegroai/trains/blob/master/examples/manual_reporting.py

No worries, I would love for us to come up with a nice solution 🙂