RoughTiger69 I think you need the latest version (+1.3.0 with UI support)
If you are using an older version, you need to specify that you are continuing an execution (Change the "Configuration/Args/continue_pipeline" to True)
EDIT: clearml 1.3.x will work with clearml-server 1.2

👍

DeliciousBluewhale87 out of curiosity , what do you mean by "deployment functionality" ? is it model serving ?

Yep that makes sense to me too. Done

First I would check the CLI command it will basically prefill it for you:
https://clear.ml/docs/latest/docs/apps/clearml_task
Specifically to your question, working directory "." is the root of the git repo
But I would avoid adding it manually, use the CLI, it will either use ask you to provide info or take the git repo details from the local copy

now it has log, but only the initial one

So the subprocesses are not logged ?

Is there a way to document these non-standard entry points

@<1541954607595393024:profile|BattyCrocodile47> you should see the "run" in the Args section under Configuration
in case of HF you should see "-m huggingface" and then the rest in the Args section
(if this does not work, then I assume this is a bug 🙂 )
The idea is of course that you can always enqueue and reproduce, so if that part is broken we should fix it 😊

Hi RipeAnt6

What would be the best way to add another model from another project say C to the same triton server serving the previous model?

You can add multiple call to cleaml-serving , each one with a new endpoint and a new project/model to watch, then when you launch it it will setup all endpoints on a single Triton server (the model optimization loading is taken care by Triton anyhow)

I think it should be treated as failed,

I'm not sure where I stand on default behavior, it it could easily be an argument for the pipeline controller

Hmm so you are saying you have to be logged out to make the link work? (I mean pressing the link will log you in and then you get access)

Hi AttractiveShrimp45
Well, I would use the Task.connect to add a section with any configuration your are using. for example
Task.current_task().connect(my_dict_with_conf_for_data, name="dataset51")wdyt?

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 🙂

Hi SubstantialElk6
Generally speaking here, the idea is that actual code creates a Dataset (i.e. Dataset class created from code), plus you can add some metric reporting (like table reporting) to create a preview of the data stored for better visibility, or maybe create some statistics as part of the data ingest script. Then this ingest code can be relaunched / automated. The created Dataset itself can be tagged renamed added key/value for better cataloging. wdyt?

Hi DilapidatedDucks58 ,
I'm not aware of anything of this nature, but I'd like to get a bit more information so we could check it.
Could you send the web-server logs ? either from the docker or the browser itself.

WickedGoat98 let me check...

I see..
Generally speaking If that is the case, I would think it might be better to use the docker mode, it offers way more stable environment, regardless on the host machine runinng the agent. Notice there is no need to use custom containers, as the agent will basically run the venv process, only inside a container, allowing you to reuse offf the shelf containers.

If you were to add this, where would you put it? I can use a modified version of 

clearml-agent

Yep, that would b...

WickedGoat98 what's the clearml version you are using?

Yey!

I'm running agent inside docker.

So this means venv mode...

Unfortunately, right now I can not attach the logs, I will attach them a little later.

No worries, feel free to DM them if you feel this is to much to post them here

WickedGoat98 this is awesome! Let me know how I could help 🙂
BTW: I checked regrading the plot comparison, this is a BE issue due to the size of the plot, I was told a fix will be deployed in a day or two.

I see... In the triton pod, when you run it, it should print the combined pbtxt. Can you print both before/after ones? so that we could compare ?

GiganticTurtle0 , let me add some background. The idea is that at some point you had your code running on your machine (when developing it for example),
when you actually executed the code itself in development, you call 'task.init' (to track the development process for example). This Task.init call, did the analysis of the code and python package dependencies and stored in on the Task. Then when you clone the Task, it already lists all the python packages your code directly imports (see "In...

Basically it is the same as "report_scatter2d"

RipeGoose2 good news 🙂

WickedGoat98 Nice!!!
BTW: The fix should solve both (i.e. no need to manually cast), I'll make sure the fix is on GitHub so you'll be able to verify 🙂

Since you are running in venv mode, adding the OS environment before the clearml-agent, will basically make sure it will propagate to the process itself.
ReassuredTiger98 make sense ?

Hi @<1533620191232004096:profile|NuttyLobster9>
First nice workaround!
Second could you send the full log? When the venv is skipped then pytorch resolving should be skipped as well, and no error should be raised...
And Lastly could you also send the log of the task that executed correctly (the one you cloned), because you are correct it should have been the same

"Fix TF 2.4 keras load/save model"

Okay I think I know what's going on (there is a race that for some reason on CoLab acts differently).
As a quick hack you can do the following:
Task._report_subprocess_enabled = False task = Task.init(...) task.set_initial_iteration(0)

Also I would suggest using Task.execute_remotely
https://clear.ml/docs/latest/docs/references/sdk/task#execute_remotely