Hi HappyLion37
It seems that you are "reusing" the Tasks. Which means the second time you open them you are essentially resetting the old run and starting all over.
Try to do:
task1 = Task.init('examples', 'step one', reuse_last_task_id=False) print('do stuff') task1.close() task2 = Task.init('examples', 'step two', reuse_last_task_id=False) print('do some more stuff') task2.close()

Task.create will create a new Task (and return an object) but it does not do any auto-magic (like logging the console, tensorboard etc.)

HappyLion37 did you check the https://github.com/allegroai/trains/tree/master/examples/services/hyper-parameter-optimization ?
You can very quickly get it distributed as well

BTW: what's the use case? Why do you need to open two Tasks in the same code/script ?

JitteryCoyote63

I agree that its name is not search-engine friendly,

LOL 😄
It was an internal joke the guys decided to call it "trains" cause you know it trains...
It was unstoppable, we should probably do a line of merch with AI 🚆 😉
Anyhow, this one definitely backfired...

Wiki edit anyone ?

Hi ReassuredTiger98

I do not want to share with the clearml-agent workstations.

Long story short, no 😞
The agent is responsible to spin all jobs, regardless of users, basically it has to have a read-only user for all the repositories. I "think" the enterprise version has a vault feature, that allows you to store these kind of secrets on the User itself.
What exactly is the use case?

Hmm I would have the docker file contain the default Azure credentials/output_uri, and then have the users clearml credentials passed as env variable in runtime. wdyt?
(I'm checking if you can pass the azure credentials as env in a minute)

Hi GreasyPenguin66
Is this for the client side ? If it is why not set them in the clearml.conf ?

The --template-yaml allows you to use foll k8s YAML template (the overrides is just overrides, which do not include most of the configuration options. we should probably deprecate it

FriendlySquid61 could you help?

With pleasure 🙂

Hmm that is odd, could it be you are changing the sys.path ?
(What I'm assuming is happening is that it detects the packages in the PYTHONPATH and for some reason the order is different so it finds the "system" package before the "venv" package, hence the incorrect version)

Hmm yes this is exactly what should not happen 🙂
Let me check it

Yey @ https://app.slack.com/team/U01CJ43KX2N this one does not work!
Give me a minute I'll

potential sources of slow down in the training code

Is there one?

I am creating this user

Please explain, I think this is the culprit ...

DilapidatedDucks58 You might be able to, check the links, they might be embedded into the docker, so you can map diff png file from the host 😛
BTW: what would you change the icons to?

Yey! BTW: what the setup you are running it with ? does it include "manual" tasks? Do you also report on completed experiments (not just failed ones)? Do you filter by iteration numbers?

However, that would mean passing back the hostname to the Autoscaler class.

Sorry my bad, the agent does that automatically in real-time when it starts, no need to pass the hostname it takes it from the VM (usually they have some random number/id)

JitteryCoyote63 could you send the log maybe ?

GiddyTurkey39 can you ping the server-address (just making sure, this should be the IP of the server not 'localhost')

For now we've monkey-patched it to our usecase:

LOL, that's a cool hack

That gives us the benefit of creating "local datasets" (confined to the scope of the project, do not appear in

Datasets

tabs, but appear as normal tasks within the project)

So what would be a "perfect" solution here?
I think I'm missing the point on why it became an issue in the first place.
Notice that in new versions Dataset will be registered on the Tasks that use them (they are already...

cleamrl sdk (i.e. python client)
The issue is that the Task.create did not add the repo, link (again as mentioned above, you need to pass the local folder or repo link to the repo argument of the Task.create function). I "think" it could automatically deduce the repo from the script entry point, but I'm not sure. hence my question on the clearml package version

Right, you need to pass "repo" and direct it to the repository path
(BTW, what's the cleaml version)

Hi SubstantialElk6 I believe you just need to use clearml 1.0.5 , and make sure you rae passing the correct OS environment to the agent

I assume this is missing:
https://github.com/allegroai/clearml/blob/b21e93272682af99fffc861224f38d65b42c2354/clearml/backend_config/bucket_config.py#L102

YEY!

Damn 😞

What do you have under the "installed packages" section? Also you can configure the agent to use poetry to restore the environment (instead of pip)