👍 no worries

hmm I assume the reason is the cookie / storage changed?

Hi CourageousDove78
Not the cleanest, but you can basically pass everything here:
https://allegro.ai/clearml/docs/rst/references/clearml_api_ref/index.html#post--tasks.get_all
Reasoning is that it is passed almost as is to the server for the actual query.

Hi GiddyTurkey39
Are you referring to an already executed Task or the current running one?
(Also, what is the use case here? is it because the "installed packages are in accurate?)

CheerfulGorilla72 could it be the server address has changed when migrating ?

Fully automatic, just have them defined and Task.init and everything else will work out of the box.
Notice the Env will override clearml.conf, so you can have clearml.conf with other default values inside the container, and have the Env override the definition
(not to worry, it is Not a must to have clearml.conf , it's just a nice way to add default values)

Any chance you can share the Log?
(feel free to DM it so it will not end up public)

sounds good 🙂

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

ReassuredTiger98 both are running with pip as package manager, I thought you mentioned conda as package manager, no?
agent.package_manager.type = pipAlso the failed execution is looking for "ruamel_yaml_conda" but it is nowhere to be found on the original one?! how is that possible ?

Hi CleanPigeon16
I think now the issue is missing git credentials, did you pass git_user / git_pass to the AWS autoscaler ?

Could it be it defaulted to the demo server instead of your own server?

SmugOx94 Yes, we just introduced it 🙂 with 0.16.3
Discussion was here (I'll make sure to update the issue that the version is out)
https://github.com/allegroai/trains/issues/222
In your trains.conf add the following line:
sdk.development.store_code_diff_from_remote = trueIt will store the diff from the remote HEAD instead of the local one.

The azure section:
https://github.com/allegroai/trains/blob/master/docs/trains.conf#L117

OddAlligator72 FYI you can also import / export an entire Task (basically allowing you to create it from scratch/json, even without calling Task.create)
Task.import_task(...) Task.export_task(...)

Hmm it should probably be pinned

ContemplativeGoat37 I think there was an issues just lije you described and it was solved in later versions, upgrade to the latest clearml package version, you should be fine 🙂

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

None
notice there is a scroll_id there, you might need to call the API multiple times until you scroll over All the events
could that be it?

In terms of creating dynamic pipelines and cyclic graphs, the decorator approach seems the most powerful to me.

Yes that is correct, the decorator approach is the most powerful one, I agree.

Hi RoundMosquito25
What do you mean by "local commits" ?

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...

RobustRat47 I think you have to use the latest clearml package for that (1.6.0)

LethalDolphin75 Yes you are correct, we should add here:
https://github.com/allegroai/clearml/blob/400c6ec103d9f2193694c54d7491bb1a74bbe8e8/clearml/automation/optuna/optuna.py#L210
elif isinstance(p, UniformLogarithmicParameterRange): hp_type = 'suggest_float' hp_params = dict(low=p.min_value, high=p.max_value if p.include_max else p.max_value - p.step_size, log=True, step=p.step_size)btw: I'm not sure if the ...

clearml - WARNING - Could not retrieve remote configuration named 'hyperparams'

What's the clearml-server version you are working with ?

In both logs I see (even in the single GPU log, it seems you "see" two GPUs, is that correct?)
GPU 0,1 Tesla V100-SXM2-32GB (arch=7.0)

Last question, this is using relatively old clearml version (0.17.5), can you test with the latest version (1.1.1)?

While I'll look into it, you can do:
from clearml import OutputModel output_model = OutputModel() output_model.update_weights("best_model.onnx")

Hi YummyMoth34 they will keep on trying to send reports.
I think they try for at least several hours.

Hi SarcasticSparrow10 ,
So the bad news is the UI is actually escaping the query, so you cannot search regexp from the UI. The good news, you can do achieve that from python:
from trains import Task tasks = Task._query_tasks(task_name='exp.*i1')

yes

EnviousStarfish54

oh, this is a bit different from my expectation. I thought I can use artifact for dataset or model version control.

You totally can use artifacts as a way to version data (actually we will have it built in in the next versions)

Getting an artifact programmatically:
Task.get_task(task_id='aabb'). artifacts['artifactname'].get()

Models are logged automatically. No need to log manually