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DeliciousBluewhale87 You can havwe multiple queues for the k8s queuea in priory order:
python k8s_glue_example.py --queue glue_q_high glue_q_lowThen if someone is doing 100 experiments (say HPO), then they push into the "glie_q_low" which means it will first pop Tasks from the high priority queue and if it is empty it will pop from the low priority queue.
Does that make sense ?

It seems something is wrong with the server itself...

where is it running? could you restart all the dockers ? Is it running on your machine?

DeliciousBluewhale87 my apologies you are correct 😞
We should probably add support for that, do you feel like adding a GitHub issue, so we do not forget?

I just called exit(0) in a notebooke and it closed it (the kernel) no exception

Sure LazyTurkey38 here's a nice hack for that:
` # code here
task.execute_remotely(queue_name=None, clone=False, exit_process=False)

patch the Task and actually send it for execution

if Task.running_locally():
task.update_task(task_data={'script': {'branch': 'new_branch', 'repository': 'new_repo'}})
# now to actually enqueue the Task
Task.enqueue(task, queue_name='default') You can also clear the git diff by passing "diff": "" `
wdyt?

Just a bit of background, the execute)remotely will kill the current process (after the Task is synced) and enqueue the Task that was created for remote execution. What seems to fail is actually killing the current process. You can just pass exit_process=False

BTW:
TrickySheep9 what's the jupyter version / python version / OS ?

Last but not least - can I cancel the offline zip creation if I'm not interested in it

you can override with OS environment, would that work?

Or well, because it's not geared for tests, I'm just encountering weird shit. Just calling

task.close()

takes a long time

It actually zips the entire offline folder so you can later upload it. Maybe we can disable that part?!

` # generate the script section
script = (
"fr...

I still do not get why this leads to some 0.5 values when in my plot there should only be 0 and 1.

Smart sub-sampling (lowpass filter before, aka averaging on a window)

hit ctrl-f5 (reload the page) do you still ge the same error? Is it limited to a specific experiment?

GreasyPenguin14 let me check with the guys when is the next version .
Are you using the self-hosted server of the community server ?

But that should not mean you cannot write to them, no?!

What I try to do is that DSes have some lightweight baseclass that is independent of clearml they use and a framework have all the clearml specific code. This will allow them to experiment outside of clearml and only switch to it when they are in an OK state. This will also help not to pollute clearml spaces with half backed ideas

So you want the DS to manually tell the baseclasss what to store ?
then the base class will store it for them, for example with joblib , is this the...

I'm thinking of a few plots in my current in-house tooling which are slightly different than the standard charts we look at. For example a custom parallel coordinate chart that can use aggregations, categorical variables, etc.

This can be done by comparing experiments, then check the Hyper-Parameters tab, and select graph from the drop down at the top

So my question in general is pertaining to if I would need to get better at Javascript if I were to make those changes. My guess is ...

do I need to have the repo that I am running on my account

If it is a public repo, then no need, credentials are only needed for private repos 🙂
Am I missing something ?

(just using local server not connected to Internet), am I right?

You can if you host your own git server, Or if your code is a single file / jupyter notebook, then the entire code is stored on the Task.
btw: what is the exact setup, how come there is no git repo?

Hi @<1645597514990096384:profile|GrievingFish90>
You mean the agent itself inside a docker then the agent spins sibling dockers for the Tasks ?

Hi GiddyTurkey39
Are you referring to an already executed Task or the current running one?
(Also, what is the use case here? is it because the "installed packages are in accurate?)

It was set to true earlier, I changed it to false to see if there would be any difference but doesn’t seem like it

I would actually just add:
Task.add_requirements('google.cloud')Before the Task.init call (Notice, it has to be before the the init call)

GiddyTurkey39 my bad 😞 try this one
task._update_requirements({})

Hi GrittyCormorant73
At the end everything goes through session.send, you can add a print there?
btw: why would you print all the requests? what are we debugging here?

I'm not sure how the helm is built but do we have a "services queue" on the helm?

Hi TightElk12
Are you looking for a way to set the output_uri from environment variable ? Is this it?

Verified, you are correct there is no way to override with OS environment 😞

And if you could also update the docs with all env vars possible to set up it would awesome!

Yes, I'll pass it on, that is a good point

Thanks! Yes, this could be great !

Could you please open a GitHub issue, so we remember to update the feature ?

Let's add it!

LudicrousDeer3 when using Logger you can provide 'iteration' argument, is this what you are looking for?

TBH ClearML doesn't seem to be picking the model up so I need to do it manually

This is odd, cleamrl will pick framework level serialization, but not just any pickle call

Why do I need an output_uri for the model saving? The dataset API can figure this out on its own

So that it knows where to upload it, if your are setting True this will be the default files server, you can also set iy for shared files system, S3 GCP storage etc.
If no value is passed, it will just log th...