RobustSnake79 I have not tested, but I suspect that currently all the reports will stay in TB and not passed automagically into ClearML
It seems like something you would actually want to do with TB (i.e. drill into the graphs etc.) no?

RobustSnake79 this one seems like scalar type graph + summary table, correct?
BTW: I'm not sure how to include the "Recommendation" part 🙂

SmugOx94 could you please open a GitHub issue with this request, otherwise we might forget 🙂
We might also get some feedback from other users

Hi LovelyHamster1 ,
you mean totally ignore the "installed packages" section, and only use the requirements.txt ?

Hmm, we could add an optional test for the python version, and the fail the Task if the python version is not found. wdyt?

Won't it be too harsh to have system wide restriction like that ?

Yes, as long as the client is served from http://app.something.com it will look for the api server at http://api.something.com

model upload and registration i should pass something like

'xgboost': False

or

'xgboost': False, 'scikit': False

?

Exactly! which framework are you using ?

about 2, I refer to the names of the models.

Hmm that is a good point to test, usually this is based on the Task name (I think), so if the Task name contains the HPO params in the name it should be the same on the model name. Do you see the HPO params on the Task name ? Should we open a Gi...

The problem is that I currently don't have a way to get them "from outside".

Maybe as a hack (until we add the model object)
` class MyModelCB:
current_args = dict()
@classmethod
def callback(load_save, model_info):
if load_save != "save":
return model_info
model_info.name = "my new name" + str(current_args) # make a name from args
return model_info

WeightsFileHandler.add_pre_callback(MyModelCB.callback)
MyModelCB.current_args = {"args": "value"} `wdyt?

LudicrousParrot69 you mean post execution or while you are executing the hyperparameter optimizer ?

Essentially the example provide just prints out ids to the log file,

What do mean?

The easiest would be as an artifact (I think).
Let's assume you put it into a csv file (with pandas or mnaually)
To upload (from the pipeline Task itself):
task.upload_artifacts(name='summary', artifact_object='~/my/summary.csv')Then if you want to grab it from anywhere else:
task = Task.get_task(task_id='HPO controller Task id here') my_csv = Task.artifacts['summary'].get_local_copy()
If you want to store as dict it might be even easier:
` task.upload_artifacts(name='summary', artifa...

The default cleanup service should work with S3 with a correctly configured clearml service agent if I understand the workings correctly.

Yes I think you are correct

I am referring to the UI.

In that case, no 😞 . This is actually a backend server change (from the UI it should be relatively simple). Is this somehow a showstopper ?

Bad news, there isn't a nice interface to get the table from the Optimizer object (I will make sure we add it, no reason not to).
But you can very easily get all the information you need and more:
all_the_tasks = an_optimizer.get_top_experiments(top_k=100)Then for every task in the list you can get All the information:
for task in all_the_tasks: task_params_as_dict = task.get_parameters() task_scalars = task.get_last_scalar_metrics()Basically the Task object enables you to que...

You can run this code from anywhere. The 'base_task_id' is actually the pipeline controller Task ID.
BTW: Next version will have a nicer interface to query it, but this code will work on the current version

LudicrousParrot69 ,
Are you trying to post execution parse the attached Table, then put it into a CSV on the HPO Task ?

, is the team open to PRs from external people?

Yes please do! PRs are welcomed! I thought we fixed the GitHub readme to reflect it, anyhow I'll make sure we do 🙂

basically

would allow blocking the machine from being scaled-in when

Oh this is what I was missing 🙂 That makes sense to me!
So what you are saying is that the AWS autoscaler agent, when it is launching a Task, inside the container you will set "protection flag" when the Task ends, you will unset "protection flag"
Is that correct?

Okay that kind of makes sense, now my followup question is how are you using the ASG? I mean the clearml autoscaler does not use it, so I just wonder on what the big picture, before we solve this little annoyance 🙂

Working on it as we speak 🙂 Hopefully in the next release (probably next week)

I see now, give me a minute I'll check

Long story short, the Task requirements are async, so if one puts it after creating the object (at least in theory), it might be too late.
Make sense ?

Xeon E3-1240: 4 - 5 hours!wow... yes definitely worth upgrading 🙂

it works if I run the same command manually.

What do you mean?
Can you do:
docker run -it <my container here> bashThen immediately get an interactive bash ?

FreshParrot56 we could add this capability, but the main caveat is that f your version depends on multiple parent versions you still need to download and extract all the parent versions, which means that when you clear them you might hurt later performance. Does that make sense? What is the use-case / scenario for you?

Hmm, this means the step should have included the git repo itself, which means the code should have been able to import the .py
Can you see the link to the git repository on the Pipeline step Task ?

OK, I got it by modifying the .conf file and putting the credentials on node

Nice! 🙂

Hi DepressedFox45
Basically move the import into the function, it will automatically detect the package.
@PipelineDecorator.component(...) def step_one(...): import sklearn import pandas as pd # stuffMake sense ?

Oh, is your pipeline code a part of a git repository ?

Back to the feature request, if this is taken care of (both adding a missed package, and the S3 upload), do you still believe there is a room for this kind of feature?