Maybe WackyRabbit7 is a better approach as you will get a new object (instead of the runtime copy that is being used)

Ohh yes, if you deleted the token then you have to recreate the cleaml.conf
BTW: no need to generate a token, it will last 🙂

Hi @<1624579015031394304:profile|JitterySeal56>

... and credentials in clearml.conf file on client side, but I have restrictions of aws keys expiring each hour

This means that you need to configure IAM role on your client machine, the data never goes through the server it is uploaded directly from the dev machine to the S3 bucket.

You can however just store the data on your clearml-files server ...

BTW: latest PyCharm plugin with 2022 support was just released:
https://github.com/allegroai/clearml-pycharm-plugin/releases/tag/1.1.0

🙂

Hmm, so what I'm thinking is "extending" the capabilities of the "configuration" section (as it seems this is the right context). Allowing to upload a bunch of files (with the same mechanism as artifacts), as zip files, in the configuration "editable" section have the URL storing the zip, together with the target folder. wdyt?

Are they ephemeral or later used by other Tasks, execution etc ?
For example: configuration files, they are specific for an execution, and someone will edit them.
Initial weights files, are something that multiple execution might needs them, and they will be used to restore an execution. Data, even if changing, is usually used by multiple executions tasks etc.
It seems like you treat these files as "configurations", is that right ?

Hi JitteryCoyote63
I think this is the default python str() casting.
But you can specify the preview test when you call upload_artifact:
https://clear.ml/docs/latest/docs/references/sdk/task#upload_artifact
see preview argument

Hi VexedCat68
Could it be you are trying to update a committed dataset?

Hi @<1581454875005292544:profile|SuccessfulOtter28>

Why would you archive an experiment?

Because you do not want to see it any longer (i.e. not very important) but you do not want to loose the ability to later do some forensics and look into it (meaning you do not want to completely delete it)
does that make sense ?

Hi JitteryCoyote63
Do you have a specific example in mind ?

ReassuredTiger98
Okay, but you should have had the prints ...
uploading artifactand
done uploading artifactSo I suspect something is going on with the agent.
Did you manage to run any experiment on this agent ?

EDIT: Can you try with artifacts example we have on the repo:
https://github.com/allegroai/clearml/blob/master/examples/reporting/artifacts.py

Okay there should not be any difference ... 😞

Thanks you!

Should I make a new issue or just reply on the one I mentioned above?

Maybe a new issue with the merge, and then the hack+fix? what do you think?

You need to mount it to ~/clearml.conf (i.e. /root/clearml.conf)

Hi SharpHedgehog60
Task type is another way to declare the type of processing the Task performs.
Later you can filter based on the Task type (like you would with a Tag).
For example Datasets are always of a Type "data processing"

BTW: there is still the bug with the env merging, correct ?

Hmm let me check something

Yes I think you are correct

Thanks!

JitteryCoyote63 when the agent is running a job, it prints its configuration at the beginning, do you see the correct credentials there (you will not see the secret but you will see the access key)

JitteryCoyote63 see if upgrading the packages as they suggest somehow fixes it.
I have the feeling this is the same problem (the first error might be trains masking the original error)

. Can I get gpu usage over time frame via API also?

task.get_reported_scalarsBut this will get you All the scalars, I think the next version of the server supports asking a specific one as well.
How are you implementing the alert monitoring?
Is is a stateless process starting every X min, or is it a state-full process running and monitoring ?

Thanks!
No, I think we are good :)

Hi @<1523701066867150848:profile|JitteryCoyote63>
Thank you for bringing it! can you verify with the latest clearml-agent 1.5.3rc2 ?

Yes, hopefully they have a different exception type so we could differentiate ... :) I'll check

Hi QuaintPelican38 can you manually access the machine based on the IP it registered
(Look under the DevOps project, you'll see a running Task "interactive session" under the configuration tab, user properties you should find the IP

Ohh I see, makes total sense. I'm assuming the code base itself can do both 🙂

SmarmySeaurchin8 regrading (2)
I'm not sure the current visualization supports it. I mean we can put "{}", but that would imply you can edit it, which then we have to support, possible but weird, and this is why:
task.connect({'a':{},'b': {'nested': 'value}}will become
'a' = '{}'
'b/nested' = 'value'
But then if you edit to:
'a' = '{'nested': 'value'}'
'b/nested' = 'value'
you have two different ways of presenting the same type of structure...