Also try with
!pip3 install clearml

AlertCrow40 Hi!

How are you trying to connect to your jupyter notebook, can you provide a snippet? What version of clearml are you using?

https://clear.ml/docs/latest/docs/references/sdk/task#taskenqueue
Is this what you're looking for?
Also you can enqueue it through the API
https://clear.ml/docs/latest/docs/references/api/tasks#post-tasksenqueue

Hi @<1523703012214706176:profile|GorgeousMole24> , I'm not sure about the exact definition, but I think when the script finishes running or the thread that started Task.init() finishes.

VexedCat68 , this is really simple ! 🙂

Add from clearml import Task and task=Task.init() to your script Run the script In the UI: Clone the experiment by right clicking it and selecting clone (or from the hamburger menu) Enqueue experiment in UI: Right click on the experiment and select enqueue 🙂

Hi ConvolutedSealion94 , you should use the InputModel/OutputModel modules for working with models:
https://clear.ml/docs/latest/docs/references/sdk/model_inputmodel

This makes getting models very easily directly by their IDs (Models have unique IDs). For example:
https://clear.ml/docs/latest/docs/references/sdk/model_inputmodel#get_local_copy
Or:
https://clear.ml/docs/latest/docs/references/sdk/model_inputmodel#get_weights_package

Hi ComfortableShark77 ,

So if I understand correctly you'd like the values of the configurations hidden when viewing in the UI?

Hi @<1634001106403069952:profile|DefeatedMole42> , the Pro plan is monthly payment according to usage. You can find more information here - None

Hi @<1762286410452176896:profile|ExcitedFrog68> , if you open dev tools (F12) do you see any console errors?

Also, did you run task.init() and task.connect()

The sample script you posted runs fine on server 1.6.0. I did however comment out from machine_learning.clearml_client import Task and used from clearml import Task

Can you please try with the regular import?

Hi ThankfulHedgehong21 ,

What versions of ClearML & ClearML-Agent are you using?
Also, can you provide a small code snippet to play with?

Hi EmbarrassedSpider34 , what do you get in the log of the experiment you're trying to run? Or do you look at it at the level of the GCP console?

I mean what python version did you initially run it locally?

In the installed packages, try removing the version for imageio (Is this a private package?). This looks like the environment (OS/Python version) doesn't support the specific package OR the package is inside a private artifactory

DepressedChimpanzee34 , Damn that's a shame. Then it means that to use the endpoint you'll need to implement some network communication in python (something like curl through python)
I found another one that might help:
client.session.get_clients()
This will return the clearm and server versions. This should be validation enough if server is up or not.

However I'd suggest implementing some sort of ability to send POST api calls via your script.

Pipeline is a unique type of task, so it should detect it without issue

Hi @<1734020162731905024:profile|RattyBluewhale45> , are they running anything? Can you see machine statistics on the experiments themselves?

I think this is what you're looking for - None

DepressedChimpanzee34 , what is the url like?
The url should be something like https://<WEBSITE>.<DOMAIN>/v2.14/debug/ping

Hi ScantCrab97 , please update it it worked 🙂

That's an option This really depends on your usage - if you want those 'custom parameters' be accessible by other tasks, then save them as artifacts. If you only want visibility - then save them as scalars. You have a nice example on usage here: https://github.com/allegroai/clearml/blob/master/examples/reporting/scalar_reporting.py

I think there might be some option, let me check if I can find something I have 🙂

Hi @<1523702307240284160:profile|TeenyBeetle18> , what do you mean by dev containers?

Hi @<1582904448076746752:profile|TightGorilla98> , can you check on the status of the elastic container?

I think this is what you're looking for - the agent integration
None

Hi @<1792726992181792768:profile|CloudyWalrus66> , can you provide the full log of the ec2 instance?

GreasyPenguin14 Hi!

If I understand you correctly, you would have to change the url's of the models yourself so they would point to the now downed instances.

You can also use the following setting:
sdk.development.default_output_uri: "SOME_URL"
in your ~/clearml.conf to set it to send the models anywhere you want them to go from the get go 🙂

Is that helpful?

MelancholyElk85 , did PipelineContoller._task.set_base_docker work?

EnormousWorm79 , Hi 🙂

What do you mean by dependency structure?