What is the new problem?

Hi @<1544853721739956224:profile|QuizzicalFox36> ,
You can use StorageManager.download_file() to easily fetch files.
None

Hi @<1561885921379356672:profile|GorgeousPuppy74> , you can get the key/secret pair from the ClearML UI.
I think this is the documentation you were looking for: None
You can access the settings page via the profile icon at the top right.

Hi JitteryCoyote63 , you can get around it using the auto_connect_frameworks parameter in Task.init()

You'll need ES/Mongo to run the ClearML server

I think the set_default_upload_uri is for all output models, while set_upload_destination is for a specific model/file

However, now when I go in the Results -> Debug Samples tab, the s3 credential window pops up. Every time that I refresh the page

RattyLouse61 , What version of ClearML are you running, I think this issue was solved in 1.3.0 release

I meant writing a new pipeline controller that will incorporate the previous pipelines as steps. What is the error that you're getting? Can you provide a snippet?

Let me see if I can find it

VexedCat68 , you can iterate through all 'running' tasks in a project and abort them through the api. The endpoint is tasks.stop

@<1590514584836378624:profile|AmiableSeaturtle81> , I would suggest opening a github feature request then 🙂

You can add torch to the installed packages section manually to get it running but I'm curious why it wasn't logged. How did you create the original experiment?

Hi @<1523707653782507520:profile|MelancholyElk85> , in Task.init() you have the auto_connect_frameworks Parameter.

None

Hi @<1523702220866981888:profile|ShallowGoldfish8> , you can use the StorageManager module to upload/download files

Hi ClumsyElephant70 ,
What about
# pip cache folder mapped into docker, used for python package caching docker_pip_cache = ~/.clearml/pip-cache # apt cache folder mapped into docker, used for ubuntu package caching docker_apt_cache = ~/.clearml/apt-cache

Hi @<1544853695869489152:profile|NonchalantOx99> , how are you running the pipeline? What are the clearml & server versions?

Do you have a snippet that reproduces this?

https://stackoverflow.com/questions/12973777/how-to-run-a-shell-script-at-startup

@Alex Finkelshtein, if the parameters you're using are like this:

parameters = { 'float': 2.2, 'string': 'my string', }
Then you can update the parameters as mentioned before:
parameters = { 'float': 2.2, 'string': 'my string', } parameters = task.connect(parameters) parameters['new_param'] = 'this is new' parameters['float'] = '9.9'
Please note that parameters['float'] = '9.9' will update the parameter specifically. I don't think you can update the parameter en masse...

Hi @<1570220858075516928:profile|SlipperySheep79> , you can use pre & post execute callback functions that run on the controller. Is that what you're looking for?

Hi, in task.init() you can define output_uri=<Fileserver>

Hi @<1523701083040387072:profile|UnevenDolphin73> , all the scalars plots etc are saved on Elastic

DepressedChimpanzee34 , the only way I see currently is to update manually each parameter

For example:
parameters = { 'float': 2.2, 'string': 'my string', } parameters = task.connect(parameters) parameters['new_param'] = 'this is new' parameters['float'] = '9.9'
Does this help?

You can do it in one API call as follows:
https://clear.ml/docs/latest/docs/references/api/tasks#post-tasksget_all

Yep 🙂

@<1523701087100473344:profile|SuccessfulKoala55> , what is the intended behavior?

Layout is saved per project

DepressedChimpanzee34 you can try naming the connected configuration differently. Let me see if there is some other more elegant solution 🙂

Hi @<1561885921379356672:profile|GorgeousPuppy74> , yes it should be possible

Hi FierceRabbit20 , I don't think there is such an option out of the box but you can simply add it to your startup of the machine or create a Cron job

I've never worked with JupyterHub and have little experience with notebooks. What does it do in relation to notebooks?