yes, TrickySheep9 use the k8s glue from here:
https://github.com/allegroai/clearml-agent/blob/master/examples/k8s_glue_example.py

EnviousPanda91 please feel free to PR if it works 🙂
https://github.com/allegroai/clearml/blob/86586fbf35d6bdfbf96b6ee3e0068eac3e6c0979/clearml/binding/frameworks/catboost_bind.py#L114

Yep, automatically moving a tag

No, but you can get the last created/updated one with that tag (so I guess the same?)

meant like the best artifacts.

So artifacts get be retrieved like a dict:
https://github.com/allegroai/clearml/blob/master/examples/reporting/artifacts_retrieval.py
Task.get_task(project_name='examples', task_name='artifacts example').artifacts['name']

Is this the same?
https://github.com/allegroai/clearml/issues/411

I want to use services queue for running services, and I want to do it on k8s

So yes, as a standalone pod with the agent in venv mode (as opposed to docker mode)
Does that make sense to you?

The thing I don't understand is how come this DOES work on our linux setups

I do not think it actually works... I could not have find a code that will convert the ENV in the config string ...

I'll be happy to test it out if there's any commit available?

Please do, and feel free to PR it 😍
https://github.com/allegroai/clearml/blob/d3e986393ac8d1a1ea48302224962570ab8e6f9e/clearml/backend_api/session/session.py#L576
https://github.com/allegroai/clearml/blob/d3e98639...

Hmmm that is odd... based on the reply "'Task' object has no attribute 'hyperparams'", I would assume API version is lower then 2.9. But you specifically said you see Session.api_version == 2.9 is that correct?

And you have the exact same folder structure / content, and server A/B give a different set of experiments ?
(is serverB empty, meaning no experiments at all?)

Looks good!

Correct 🙂

OutrageousGiraffe8 this sounds like a bug, how can we reproduce it?
Maybe a add another layer here?
https://github.com/allegroai/clearml/blob/a47f127679ebf5912690f7c3e60791a2daa5c984/examples/frameworks/tensorflow/tensorflow_mnist.py#L40

No idea, I just remember it is relatively old 😞

Hmm, conda_freeze in the clearml.conf on the development machine ?

Hi JitteryCoyote63
Signal 9 is killed signal, could it be someone killed the process ? Do you have other logs to share ? Is this reproducible ?

I mean clone the Task in the UI (right click Clone), then go to the execution Tab, to the "installed packages" section, then click on Edit -> go to the torchvision http link, and replace it with torchvision == 0.7.0 and save.
Then right enqueue the Task (to the default queue) and see if the Agent can run it,
DeterminedToad86 Make sense ?

I think CostlyOstrich36 managed to reproduce?!

WickedElephant66 this seems like a general network issue, like the docker service is missing your companies firewall certificate.
Can you pull any container from docker hub ?

Hi SarcasticSparrow10
You will need to habe multiple trains-agent s but they will be sharing the same queue (i.e. pulling jobs from the same queue the HPO process is pushing to)
Make sense ?

(So it re-reads the configuration file)

Could not find a version that satisfies the requirement pytorch~=1.7.1

Seems like pytorch 1.7.1 has no package for python 3.7 ?

What will I do to fix my problem?

What is the problem? we just proved the upload speed is just fine?

That said, it might be different backend, I'll test with the demoserver

Hi HollowDolphin18
Sure just use:
Task.set_credentials( api_host=None, web_host=None, files_host=None, key=None, secret=None, store_conf_file=False )https://github.com/allegroai/clearml/blob/912f6f5ba2328b26de042de03f02de5802df360f/clearml/task.py#L2153

Hi JitteryCoyote63 , let me check, this backwards compatibility might only apply for API version mismatch between the client and server.

Not really sure that's easily done ... I mean you could query the data, but I'm not sure how you would import it. Btw why would you move from pro to self hosted?

JitteryCoyote63 are you calling to:
my_task.output_uri = " s3://my-bucket
in the code itself ?
Why not with Task.init output_uri=...
Also this is running remotely there is no need fo r that, use the Execution -> Output -> Destination and put it there, it will do everything for you 🙂

JitteryCoyote63 Is this an Ignite feature ? what is the expectation ? (I guess the ClearML Logger just inherits from the base ignite logger)

How about a git Issue? It sounds like it could be useful 🙂

It is configured as CPU (i.e. no CUDA)

🤔 maybe we should have "sub nodes" as just visual functions running inside the same actual pipeline component ?