Hi DilapidatedDucks58 ,
Are you running in docker or venv mode?
Do the works share a folder on the host machine?
It might be syncing issue (not directly related to the trains-agent but to the facts you have 4 processes trying to simultaneously access the same resource)

BTW: the next trains-agent RC will have a flag (default off) for torch-nightly repository support 🙂

👍

and of course:
task.set_parameters_as_dict(params)

Hi StrangePelican34 , you mean poetry as package manager of the agent? The venvs cache will only work for pip and conda, poetry handles everything internally:(

Okay, let me check soemthing

Oh :)
task.get_parameters_as_dict()

But a warning instead of an error would be good.

Yes, that makes sense, I'll make sure we do that

Does this sound like a reasonable workflow, or is there a better way maybe?

makes total sense to me, will be part of next RC 🙂

Hi GiddyTurkey39
us the config file connect to the Task via Task.connect_configuration ?

Oh i get it now, can you test:
git ls-remote --get-url githuband then
git ls-remote --get-url

Assuming this is a followup on:
https://clearml.slack.com/archives/CTK20V944/p1626184974199700?thread_ts=1625407069.458400&cid=CTK20V944

This depends on how you set it with the clearml-serving --endpoint my_model_entry
curl <serving-engine-ip>:8000/v2/models/my_model_entry/versions/1

When is clearml-deploy coming to the open source release?

Currently available under clearml-serving (more features are being worked on, i.e. additional stats and backends)
https://github.com/allegroai/clearml-serving

ReassuredTiger98 regrading the agent error, can you see the package some_packge in the "Installed Packages" in the UI? Was it installed ? are you using pip or conda as package manager in the agent (check the clearml.conf) is the agent running in docker mode ?

GrievingTurkey78 short answer no 😞
Long answer, the files are stored as differentiable sets (think changes set from the previous version(s)) The collection of files is then compressed and stored as a single zip. The zip itself can be stored on Google but on their object storage (not the GDrive). Notice that the default storage for the clearml-data is the clearml-server, that said you can always mix and match (even between versions).

Is it possibe to launch a task from Machine C to the queue that Machine B's agent is listening to?

Yes, that's the idea

Do I have to have anything installed (aside from the 

trains

 PIP package) on Machine C to do so?

Nothing, pure magic 🙂

(without having to execute it first on Machine C)

Someone some where has to create the definition of the environment...
The easiest to go about it is to execute it one.
You can add to your code the following line
task.execute_remotely(queue_name='default')This will cause you code to stop running and enqueue itself on a specific queue.
Quite useful if you want to make sure everything works, (like run a single step) then continue on another machine.
Notice that switching between cpu...

That is correct.
Obviously once it is in the system, you can just clone/edit/enqueue it.
Running it once is a mean to populate the trains-server.
Make sense ?

WackyRabbit7 I guess we are discussing this one on a diff thread 🙂 but yes, should totally work, that's the idea

ElegantKangaroo44 my bad 😞 I missed the nuance in the description

There seems to be an issue in the web ui -> viewing plots in "view in experiment table" doesn't respect the "scalars to display" one sets when viewing in "view in fullscreen".

Yes the info-panel does not respect the full view selection, It's on the to do list to add this ability, but it is still no implemented...

Hmm so VSCode running locally connected to the remote machine over the SSH?
(I'm trying to figure out how to replicate the setup for testing)

We actually added a specific call to stop the local execution and continue remotely , see it here: https://github.com/allegroai/trains/blob/master/trains/task.py#L2409

My bad I meant the output of mount

They all "inherit" the same user / environment from one another

GiganticTurtle0 quick update, a fix will be pushed, so that casting is based on the Actual value passed not even type hints 🙂
(this is only in case there is no default value, otherwise the default value type is used for casting)

Hi ElegantCoyote26 , yes I did 🙂
It seems cometml puts their default callback logger for you, that's it.

Sure thing 🙂

If the manual execution (i.e. pycharm) was working it should have stored it on the Pipeline Task.

"This is Not a an S3 endpoint... what is the files server you configured for it?

basically use the template 🙂 we will deprecate the override option soon

CooperativeFox72 of course, anything trains related, this is the place 🙂
Fire away

Thanks ElegantCoyote26 I'll look into it. Seems like someone liked our automagical approach 🙂