Hi WickedElephant66
in the pipeline component, import the required package it should auto detect it, or in the decorator function add the argument "packages"
https://github.com/allegroai/clearml/blob/0397f2b41e41325db2a191070e01b218251bc8b2/clearml/automation/controller.py#L2941

DistressedGoat23
you can now access the weights model object
pip install 1.8.1rc0
then:
` def callback(_, model_info):
model_info.weights_object # this is your xgboost object
model_info.name = "my new name"
return model_info

WeightsFileHandler.add_pre_callback(callback) `

JitteryCoyote63 I think that with 0.17.2 we stopped mounting the venv build to the host machine. Which means it is all stored inside the docker.

Fix in the next version 🙂

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...

It uses only one CPU core, could I use multiprocessing somehow?

Hi EcstaticMouse10
Hmm, yes it should be multi core:
https://github.com/allegroai/clearml/blob/a9774c3842ea526d222044092172980ae505e24f/clearml/datasets/dataset.py#L1175
wdyt?

before exposing our IP to the world, I suggest going over security advisory in the docs: None
as a general note, do not expose your server, the open source version is not designed for it, just put it inside your VPN and it will be fine

Specifically for this one, this is the auto generated docstring from the actual code, so PR to the
https://github.com/allegroai/clearml/blob/e53a76b713910adaf87578c69e86f8154d4ab4c1/clearml/logger.py#L152

I'll make sure we fix the example, because as you pointed, it is broken :(

Hi DepressedChimpanzee34
Why do you need to have the configuration added manually ? isn't the cleaml.conf easier ? If not I think OS environments are easier no? I run run above code, everything worked with no exception/warning... What is the try/except solves exactly ?

Hmm let me test the call itself.

Are they expanded in the "api_server" ? (I verified on a linux machine, same error, the env in the api_server is not being resolved)

DefiantHippopotamus88 you can create a custom endpoint and do that, but it will be running I the same instance , is this what you are after? Notice that Triton actually supports it already, you can check the pytorch example

Hi IntriguedRat44
Sorry, I missed this message...
I'm assuming you are running in manual mode (i.e. not through the agent), in that case we do not change the CUDA_VISIBLE_DEVICES.
What do you see in the resource monitoring? Is it a single GPU or multiple GPUs?
(Check the :monitor:gpu in the Scalar tab under results,)
Also what's the Trains/ClearML version you are suing and the OS ?

Hi @<1658281099807166464:profile|SmallCamel52>

Lack of authentication in all versions of the fileserver component

Are you leaving the fileserver open to the world ?

Thanks!

GreasyLeopard35
I can update that the fix to UniformIntegerParameterRange should be pushed with tomorrows release 🙂
(which would fix in turn LogUniformParameterRange)

Hmm StrangePelican34
Can you verify you call Task.init before TB is created ? (basically at the start of everything)

GreasyLeopard35 from the implementation:
https://github.com/allegroai/clearml/blob/fcad50b6266f445424a1f1fb361f5a4bc5c7f6a3/clearml/automation/parameters.py#L215
Which basically returns the "self.base" (default) 10 to the power of the selected value:
10**-3 = 0.001
So how would I get a negative value ?

Hi @<1529271085315395584:profile|AmusedCat74>

ClearML Scheduler where it doesn't reuse the task

What do you mean by doesn't reuse the Task, do you mean you want each time the scheduler is launched to basically overwrite the previous run ?

That is awesome!
If you feel like writing a bit about the use-case and how you solved it, I think AnxiousSeal95 will be more than happy to publish something like that 🙂

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

With pleasure 🙂

My pleasure 🙂

Does a pipeline step behave differently?

Are you disabling it in the pipeline step ?
(disabling it for the pipeline Task has no effect on the pipeline steps themselves)

Yes, experiments are standalone as they do not have to have any connecting thread.
When would you say a new "run" vs a new "experiment" ? when you change a parameter ? change data ? change code ?
If you want to "bucket them" use projects 🙂 it is probably the easiest now that we have support for nested projects.

The address is valid. If i just go to the files server address on my browser,

@<1729309131241689088:profile|MistyFly99> what is the exact address of those files? (including the http prefix) and what is the address of the web application ?

Hi ScaryBluewhale66

TaskScheduler I created. The status is still

running

. Any idea?

The TaskScheduler needs to actually run in order to trigger the jobs (think cron daemon)
Usually it will be executed on the clearml-agent services queue/mahine.
Make sense ?

BTW: StickyMonkey98 if you feel like writing a few examples I think it will be easy to push into the docs, so that at least we improve iteratively...

hmm, yes, but then this kind of a hacky solution... The original #340 was about packaging source code that was not in git... Now we want to add "data" (even if ephemeral) on to it, no?
My thinking is somehow make sure a Task can reference a "Dataset" to be downloaded before it starts by the agent ?!