Ok the doc needs fix (edited)

suggestion?

@<1523710674990010368:profile|GreasyPenguin14> make sure it to uses https not ssh:
edit ~/clearml.conf

force_git_ssh_protocol: false

and that you have both git_user & git_pass set in your clearml.conf

at means I need to pass a single zip file to 

path

 argument in 

add_files

 , right?

actually the opposite, you pass a folder (of files) to add_files. Then add_files remembers the files location (and pre calculates the hash of the files content). When you call upload it will actually compress the files that changed into a zip file (or files depending on the chunk size), and upload the files to the destination (as specified in the upload call...

In the UI you can see all the agents and their IDs
Then you can so

clearml-agent daemon --stop <agent id>

Alright I have a followup question then: I used the param --user-folder “~/projects/my-project”, but any change I do is not reflected in this folder. I guess I am in the docker space, but this folder is not linked to my the folder on the machine. Is it possible to do so?

Yes you must make sure the docker can mount a persistent folder for you to work on.
Let me check what's the easiest way to do that

@<1541954607595393024:profile|BattyCrocodile47>

Is that instance only able to handle one task at a time?

You could have multiple agents on the same machine, each one with its own dedicated GPU, but you will not be able to change the allocation (i.e. now I want 2 GPUs on one agent) without restarting the agents on the instance. In either case, this is for a "bare-metal" machine, and in the AWS autoscaler case, this goes under "dynamic" GPUs (see above)

Well, in that case, just change the order it should solve it (I'll make sure we have that as the default:

conda_channels: ["pytorch", "conda-forge", "defaults", ]

It should solve the issue 🙂

YEY

that embed seems to be slightly off with regards to where the link is actually pointing to

I think this is the Slack preview... 😞

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

It talks about referencing an issue.

Yes please, just better visibility 🙏

pipeline, can I control the tags that the tasks a pipeline creates? 

add_pipeline_tags

  adds tags from pipeline to the tasks I suppose? But I also need to clear existing tags in those created tasks

add_pipeline_tags will add the unique ID of the pipeline execution, if you want to add specific tags you can use the task_overrides and provide:
pipe.add_step(..., task_overrides={'tags': ['my', 'tags']})

single task in the DAG is an entire ClearML

pipeline

.

just making sure detials are not lost, "entire ClearML pipeline ." : the pipeline logic is process A running on machine AA.
Every step of that pipeline can be (1) subprocess, but that means the exact same environement is used for everything, (2) The DEFAULT behavior, each step B is running on a different machine BB.

The non-ClearML steps would orchestrate putting messages into a queue, doing retry logic, and tr...

Hi ThickDove42 ,
Yes, but by the time you will be able to access it, it will be in a display form (plotly), not very convient.
If this is something you need to re-use, I would argue that it is an artifact and should be stored as artifact (then accessing it is transparent) , obviously you can both report as table and upload as artifact, no harm in that.
what do you think?

and the clearml server version ?

it does appear on the task in the UI, just somehow not repopulated in the remote run if it’s not a part of the default empty dict…

Hmm that is the odd thing... what's the missing field ? Could it be that it is failing to Cast to a specific type because the default value is missing?
(also, is issue present in the latest clearml RC? It seems like a task.connect issue)

correct (i.e. not frontend)

WhimsicalLion91 I guess import/export is going to be more challenging, doable though. You will need to get all the Tasks, then collect all the artifacts, then collect all the reported logs (console/plots/etc). Then import everything back to your own server...
Exporting a single Task
task.export_task and Task.import_task
If you need all the scalars :
task.get_reported_scalars(...)And the console logs:
Task.get_reported_console_output

Hi, I changed it to 1.13.0, but it still threw the same error.

This is odd, just so we can make the agent better, any chance you can send the Task log ?

OH I see. I think you should use the environment variable to override it:
None
so add to the docker args something like

-e CLEARML_AGENT__AGENT__PACKAGE_MANAGER__POETRY_INSTALL_EXTRA_ARGS=

ohh, the copy paste thing when you generate credentials ?

Releasing an RC

By default the pl Trainer will output everything to TB, which we automatically store. But verify that TB is installed

LOL 🙂

Hmmm maybe 

 I thought that was expected behavior from poetry side actually

I think this is the expected behavior, hence bug?!

None
The thing is the server will not return a 502 error, only a 500 error,
None
Could it be the k8s ingress maybe ?

correct. notice you need two gents one for the pipeline (logic) and one for the pipeline components.
that said you can run two agents on the same machine 🙂

StraightDog31 can you elaborate? where are the parameters stored? who is trying to access them, and maybe for what purpose ?

Nice!

script, and the kwcoco not imported directly (but from within another package).

fyi: usually the assumption is that clearml will only list the directly imported packages, as these will pull the respective required packages when the agent will be installing them ... (meaning that if in the repository you are never actually directly importing kwcoco, it will not be listed (the package that you do import directly, the you mentioned is importing kwcoco, will be listed). I hope this ...

Hi TenseOstrich47
Thanks for following up!
Should be solved in the upcoming release (I think ETA is next week) 🙂