I think it is free 🙂 (registration that is)

well you can always do:
os.system('sed ...')🙂

AttractiveCockroach17 can I assume you are working with the hydra local launcher ?

Hi CleanWhale17 , at least for the moment, the code although open ( https://github.com/allegroai/trains-web ) has no external theme/customization interface.
That said we do have some thoughts on it.., What did you have in mind ?

Thanks @<1634001106403069952:profile|DefeatedMole42>
A follow up, (1) how are you spinning the agent ? (2) could it be the docker image "ultralytics/yolov5" does not have Bash as entry point ?
you can force that with

@PipelineDecorator.component(return_values=['int'], cache=False,
                             task_type='training',
                             docker="ultralytics/yolov5",
                             docker_args="--entrypoint /bin/bash",
                             pa...

Hi UnsightlyShark53 , just a quick FYI, you can also log the entire config file config.json this will be stored as model configuration, and you can see it in the input/output models under the artifacts tab.
See example here you can path either the path to the configuration file, or the dictionary itself after you loaded the json, whatever is more convenient :)

Hmmm, that actually connects with something we were thinking about: introducing sections to the hyper parameters. This way we could easily differentiate between the command line arguments and other types of parameters. DilapidatedDucks58 what do you think?

EnviousStarfish54

and the 8 charts are actually identical

Are you plotting the same plot 8 times?

Hi LivelyLion31 I missed your S3 question, apologies. What did you guys end up doing?
BTW you could always upload the entire TB log folder as artifact, it's simple task.upload_artifact('tensorboard', './tblogsfolder')

is there a way to increase the size of the text input for fields or a better way to handle lists?

No 😞

Maybe an easier way to use connect_configuration instead ? it will take an entire dict and store it as text (format is hocon, which is YAML/Json compatible, which means it is hard to break when editing)

Is there any references (vlog/blog) on deploying real-time model and do the continuous training pipeline in clear-ml?

Something along the lines of this one ?
https://clear.ml/blog/creating-a-fully-automatic-retraining-loop-using-clearml-data/
Or this one?
https://www.youtube.com/watch?v=uNB6FKIi8Wg

NICE! MoodyCentipede68 this is awesome 🙂

What about Calling Taskl.init Without the agent?

When I look at the details, model artifact in the ClearML UI, it's been saved the usual way, and no tags that I added in the OutputModel constructor are there.

Did you disable the autologging ? Are you saying the tags not appearing is a bug (it might be) ?

Also, I don't mind auto logging either if I have control over publishing the model or not directly from that script, and adding tags etc, like OutputModel.

Sure you can publish models / add tags etc, wither from the UI or pr...

No worries 🙂

I get what you're saying. Only problem is in the case of AutoLogging, I don't have the model id, for the model being saved.

Task.models['output'] should return all the model objects the autologging created

DeliciousBluewhale87 could you send the new log?

Hi MagnificentSeaurchin79
This means the tensorflow was not directly imported in the repository (which is odd, it might point to the auto package analysis failing to find a the package, if this is the case please let me know)
Regardless, if you need to make sure a package is listed in the requirements either import it or use.
Task.add_requirements('tensorflow') or Task.add_requirements('tensorflow', '2.3.1')

DilapidatedDucks58 You might be able to, check the links, they might be embedded into the docker, so you can map diff png file from the host 😛
BTW: what would you change the icons to?

Yey! BTW: what the setup you are running it with ? does it include "manual" tasks? Do you also report on completed experiments (not just failed ones)? Do you filter by iteration numbers?

https://github.com/allegroai/clearml/commit/737ca91d2a6f42b5f1ef19815fac93e183ba5398

Could you also provide the full log?

Hi @<1654294828365647872:profile|GorgeousShrimp11>

can you run a pipeline on a

schedule

or are schedules only for Tasks?

I think one tiny details got lost here, Pipelines (the logic driving them) are a type of Task, this means you can clone and enqueue them like other tasta
(Task.enqueue / Task.clone)
Other than that looks good to me, did I miss anything ?

(only works for pyroch because they have diff wheeks for diff cuda versions)

No (this is deprecated and was removed because it was confusing)
https://github.com/allegroai/clearml-agent/blob/cec6420c8f40d92ab1cd6cbe5ca8f24cf351abd8/docs/clearml.conf#L101

BTW: the agent will resolve pytorch based on the install CUDA version.

You’ll just need the user to 

name them

 as part of loading them in the code (in case they are loading multiple datasets/models).

Exactly! (and yes UI visualization is coming 🙂 )

Hi @<1523701868901961728:profile|ReassuredTiger98>

is there something like a clearml context manager to disable automatic logging?

Sure just do a wildcard with the files you actually want to autolog the rest will be ignored:
None

task = Task.init(..., auto_connect_frameworks={'pytorch' : '*.pt'}

Hi @<1523715429694967808:profile|ThickCrow29>

Is there a way to specify a callback upon an abort action from the user

You mean abort of the entire pipeline?
None

Hmm I cannot think of something that will provide something a per user basis.
Wouldn't a global set of credentials that the agent is using be enough ?
(on the local machine, user can keep using the "definitions.py")