That sounds about right to me 🙂

GiganticTurtle0 this one worked for me 🙂
` from clearml import Task
from clearml.automation.controller import PipelineDecorator

@PipelineDecorator.component(return_values=["msg"], execution_queue="myqueue1")
def step_1(msg: str):
msg += "\nI've survived step 1!"
return msg

@PipelineDecorator.component(return_values=["msg"], execution_queue="myqueue2")
def step_2(msg: str):
msg += "\nI've also survived step 2!"
return msg

@PipelineDecorator.component(return_values=["m...

PompousBeetle71 just making sure, and changing the name solved it?

SmoothArcticwolf58 could you copy paste the entire query and what is the expected results vs reality ?

I think you are correct, it seems like it is missing requirements to boto/azure/google (I will make sure this is added). In the meantime, you can stop the "triton serving engine" Task, reset it, add boto3 to the installed packages and relaunch.
That said your main issue might be packaging the python model. Basically you need to create a model from the entire folder (with whatever there is inside the folder), then Triton should be able to run it (if the config.pbtxt is correct).
` m = OutputMo...

The versions don't need to match, any combination will work.

For example, the

Task

object is heavily overloaded and its documentation would benefit from being separated into logical units of work. It would also make it easier for the ClearML team to spot any formatting issues.

This is a very good point (the current documentation is basically docstring, but we should create a structured one)

... but some visualization/inline code with explanation is also very much welcome.

I'm assuming this connected with the previous po...

I assume every fit starts reporting from step 0 , so they override one another. Could it be?

By default SSH server is not running in a lot of scenarios (k8s for example, Windows, MacOS)...

So in a simple "all-or-nothing"

Actually this is the only solution unless preemption is supported, i.e. abort running Task to free-up an agent...
There is no "magic" solution for complex multi-node scheduling, even SLURM will essentially do the same ...

Hi GrotesqueOctopus42

creates a graph of the neural network and would be nice to have it on the experiment logs aswell

I think the main issue is displaying later in the UI, thoughts?
BTW: is this useful for you outside f very local TF debugging ?

.I am using pipeline from tasks method and not pipeline from decorator.

Wait I'm confused nowm if this is a pipeline from Tasks then the Tasks themselves should have clearml in the "installed packages", no? and if they do not, how were they created?

GrievingTurkey78

maybe since the package is not directly imported in my code it is possible to get a different version to what I have locally (?).

If these are derivative packages (i.e. imported by other packages) they are not automatically logged when executing the Task manually (in order to keep the "installed packages as lean as possible on the one hand but specify also specify the important packages for you)
That said, when the "trains-agent" executed the task it will store nack...

Google to the rescue:
https://github.com/pytorch/pytorch/issues/47917

there is a semaphore warning, not sure if it’s related

Can you resend it?
Is the Task marked as closed when the process ends ?

tell me please, does the agent always create a virtual environment?

Yes, but it inherits from the container preinstalled system environment

is it possible to make the agent run the script in an already prepared docker container without creating a virtual environment in the container?

You can set the CLEARML_AGENT_SKIP_PIP_VENV_INSTALL=1 environment variable

logger.report_scalar(title="loss", series="train", iteration=0, value=100)
logger.report_scalar(title="loss", series="test", iteration=0, value=200)

Hi @<1661542579272945664:profile|SaltySpider22> I'm not sure I understand the answer to my parallel quesion

@<1535793988726951936:profile|YummyElephant76> oh you mean like jupyter server was running, then inside the notebook you would start a new venv, in that venv "notebook" package was missing, hence it failed detecting the notebook ?

Yey! nice!

is how you would create different queues,

SarcasticSquirrel56 you can create them from the UI, when the server is already running
(if you are saying, how do I create them in the first installaiton, then yes you are correct, this is possible in the helm chart, I think 😞 )

Oh my bad, post 0.17.5 😞
RC will be out soon, in the meantime you can install directly from github:
pip install git+

👍

Hi all! Does anyone know a solution to my issue with deploying models saved on azure on the clearml-serving docker container?

Hi NuttyCamel41
The easiest is to map the clearml.conf to both the serving and triton containers in your docker-compose.yaml (or k8s secrets) and make sure the conf file has the credentials to access the azure blob. wdyt ?

Oh it makes sense now 🙏

Hi ItchyJellyfish73
The behavior should not have changed.

"force_repo_requirements_txt" was always a "catch all option" to set a behavior for an agent, but should generally be avoided

That said, I think there was an issue with v1.0 (cleaml-server) where when you cleared the "Installed Packages" it did not actually cleared it, but set it to empty.
It sounds like the issue you are describing.
Could you upgrade the clearml-server and test?

So two folders with artifacts per experiment. I was wondering if there was a more efficient solution and if it could be combined.

Not sure I follow, is two subfolders for two different things are not they it is supposed to be ?

And your ~/clearml,conf ?

YEY!

Hi CloudySwallow27

how can I just "define" it on my local PC, but not actually run it.

You can use the clearml-task CLI
https://clear.ml/docs/latest/docs/apps/clearml_task#how-does-clearml-task-work
Or you can add the following line in your code, that will cause the execution to stop, and to continue on a remote machine (basically creating the Task and pushing it into an execution queue, or just aborting it)
task = Task.init(...) task.execute_remotely()https://clear.ml/do...