Do you accidentally know if there are any plans for an implementation with the logger variable, so that in case of something it would be possible to write to different tables?

CheerfulGorilla72 what do you mean "an implementation with the logger variable" ? pytorch-lighting defaults to the TB logger, which clearml will automatically catch and log into the clearml-server, you can always add additional logs with clearml interface Logger.current_logger().report_???
What am I mis...

Where is the cleamlr-server running? GCP as well?

It should be under script.diff:
'script': {'binary': '', 'repository': '', 'tag': '', 'branch': '', 'version_num': '', 'entry_point': '', 'working_dir': '', 'requirements': {'pip': ''}, 'diff': ''}For some reason this is empty in your case, are you seeing it in the UI?
If you are querying the current task (i.e. running) it might not be there yet.
You can call this internal function that returns only after the repo detection is done.
task._wait_for_repo_detection()

No, clearml uses boto, this is internal boto error, which points bucket size limit, see the error itself

SpotlessFish46 So the expected behavior is to have the single script inside the diff, but you get empty string ?

It should have worked....
Can you run the examples from the repo and see if they work?

Ooops 😞
task.get_tags()
task.set_tags()

SpotlessFish46 unless all the code is under "uncommitted changes" section, what you have is a link to the git repo + commit id

SpotlessFish46
yes you can access the entire code in the incomitted changes, you can test it with:task = Task.get_task(task_id='aabb') task_dict = task.export_task()2. correct, but then if you need the entire code base you need to clone the arepo and apply the uncommitted changes. Basically trains-agent does that when execute with build
trains-agent build --id aabb --target ~/my_task_env3. See (2)

WhimsicalLion91

What would you say the use case for running an experiment with iterations

That could be loss value per iteration, or accuracy per epoch (iteration is just a name for the x-axis in a sense , this is equivalent to time series)
Make sense?

Try to set this line in your clearml.conf to true:
https://github.com/allegroai/clearml/blob/6e6271fb91f2aeb2aa7a13c6d07d4e635baaa670/docs/clearml.conf#L177

Should I use 

update_weights_package

Yes
BTW, config.pbtxt you should pass when "registering" the endpoint with the CLI

I want to keep the above setup, the remote branch that will track my local will be on 

fork

 so it needs to pull from there. Currently it recognizes 

origin

 so it doesn’t work because the agent then can’t find the commit.

So you do not want to push the change set ?
You can basically add the entire change set (uncomitted changes) from the last pushed commit).
In your clearml.conf, set store_code_diff_from_remote: true
https://github.com/allegroai...

DAG which get scheduled at given interval and

Yes exactly what will be part of the next iteration of the controller/service

an example achieving what i propose would be greatly helpful

Would this help?
from trains.automation import TrainsJob job = TrainsJob(base_task_id='step1_task_id_here') job.launch(queue_name='default') job.wait() job2 = TrainsJob(base_task_id='step2_task_id_here') job2.launch(queue_name='default') job2.wait()

Hmm let me check, I think we changed the offline mode to use the latest API version (because by definition it cannot know what's the server).
Let me check if you can override it

GrievingTurkey78 I'm not sure I follow, are you asking how to add additional scalars ?

Hello guys, i have 4 workers (2 in default and 2 in service queue on same machine)

Hi @<1526734437587357696:profile|ShaggySquirrel23>
I think what happens is one agent is deleting it's cfg file when it is done, but at least in theory each one should have it's own cfg
One last request can you try with the agent's latest RC version 1.5.3rc2 ?

Oh you achieve exactly the same with plotly and te restapi/python interface.
Basically pull data from tasks , create visualization and log it on one if the Task or on a new one

Correct

😞 I'll pass to the guys

Hi @<1691620877822595072:profile|FlutteringMouse14>

Do I have to use Hydra

You can, and then the entire configuration is fully captured by ClearML (automatically) while you can still override values with the manual "key.sub=value" both in the UI and in the CLI

Otherwise you can connect nested dict with task.connect (these will be flattened with / for sub keys).
Or you can connect configuration files ( task.connect_configuration ) and edit them as is in the UI (with override of...

Hi @<1539055479878062080:profile|FranticLobster21>

hey, how do I use local files as dependencies?

You mean like a repository ?

Can I specify in task what local files do I use that should be packaged?

In a git repo?

Basically the agent can do two things, either replicate a single script or clone a git repo + uncommitted changes

I think that what happened was you are running it on the host machine (not inside the docker)
I probably missed a " somewhere

This is a Sagemaker notebook instances

Yes I think this is the issue

What is the difference to 

file_history_size

Number of unique files per titles/series combination (aka how many images to store in the history, when the iteration is constantly increasing)

This is odd, Can you send the full Task log? (remove any pass/user/repo that you think is sensitive)

If I install using

pip install -r ./requirements.txt

then pip installs the packages in the order of the requirements file.

Actually this is not how it works, pip will install in any way it sees fit, and it is not consistent between versions (it has to do with dependency resolving)

However, during the installation process from ClearML, it installs the packages in order UNLESS there's a custom path provided, then it's saved for last

Correct because the custom (I...

Notice the pipeline step/Task at execution is not aware of the pipeline context

WickedGoat98 Forever 🙂
The limitation is on the storage size

Hi WickedGoat98

Will I need to wrap their execution in python by system calls?

That would probably be the easiest solution 🙂

Then you can plug it into your pipeline as a preprocessing Task:

You can check this example:
https://github.com/allegroai/trains/tree/master/examples/pipeline