Hi @<1554275779167129600:profile|ProudCrocodile47>
Do you mean @ clearml.io ?
If so, then this is the same domain (.ml is sometimes flagged as spam, I'm assuming this is why they use it)

SubstantialElk6
The ~<package name with first name dropped> == a.b.c is a known conda/pip temporary install issue. (Some left over from previous package install)
The easiest way is to find the site-packages folder and delete the package, or create a new virtual environment
BTW:
pip freeze will also list these broken packages

No should be fine... Let me see if I can get a windows box 🙂

Yes! Thanks so much for the quick turnaround

My pleasure 🙂

BTW: did you see this (it seems like the same bug?!)
https://github.com/allegroai/clearml-helm-charts/blob/0871e7383130411694482468c228c987b0f47753/charts/clearml-agent/templates/agentk8sglue-configmap.yaml#L14

but it is not possible to write to a private channel in which the bot is added.

Is this a Slack limitation ?

None of them is problematic, this is what I'm trying to say 🙂
I think the minio browser gets confused.
if you want to test the upload time on the client you can try:
task.flush(wait_for_uploads=True) tic = time() task.upload_artifact('test', '/tmp/localfile') task.flush(wait_for_uploads=True) print(time() - tic)

MysteriousBee56 okay look for the folder ~/.trains/vcs_cache you will find the git repo there, just overwrite the content with your local copy

Sure :) https://allegro.ai/docs/examples/services/aws_autoscaler/aws_autoscaler/

upload_artifact will actually do two things:
upload the file to the trains-server register it as an artifact on the experiment
What did you mean by "register the artifact manually"? You still need to upload the file to the trains-server (so it is later accessible )

I was unable to reproduce, but I added a few safety checks. I'll make sure they are available on the master in a few minutes, could maybe rerun after?

Hi @<1601023807399661568:profile|PompousSpider11>
Yes "activating" a conda/python environment in a docker is more complicated then it should be ...
To debug, what are you getting when you do:

docker run -it <docker name here> bash -c "set"

Hi JitteryCoyote63 ,
These properties are usually not available on the UI and are used internal, hence the lack of documentation. Regrading parent property, it will hold a parent Task.id (str) , that said it has no real effect on the Task itself. You can however search for Tasks with a specific parent ID (For examples, this is how the the hyper parameter class is using this property)

(It would be nice to have all the Pypi releases tagged in github btw)

I wanted to say, we listen ... and point to the tag , but for some reason it was not pushed LOL.

We actually added a specific call to stop the local execution and continue remotely , see it here: https://github.com/allegroai/trains/blob/master/trains/task.py#L2409

Hi JitteryCoyote63 , I have to admit, we have not thought of this scenario... what's the exact use case to clone a Task and change the type?

Obviously you can always change the task type, a bit of a hack but should work:
task._edit(type='testing')

Great, you can test directly from the master 🙂
pip3 install -U git+

Please do, just so it wont be forgotten (it won't but for the sake of transparency )

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()

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

JitteryCoyote63 could you send the log maybe ?

okay we are good 🙂

Hmmm that sounds like a good direction to follow, I'll see if I can come up with something as well. Let me know if you have a better handle on the issue...

it is using the wrong URL - an https:// instead of an s3:// so it can't upload the preprocess.py file

What do you see as the link in the UI for that specific model ?
What is the full model add command ?

and I've made a script to edit it to our needs as part of the installation process 

 Thanks Martin!

My pleasure, btw: there is no actual need to configure all the clearml.conf values. It will actually take the defaults from the clearml package itself. This means you only need something like:
` api {

server config here

}
sdk.aws.s3{

minio config here

} `

yes 🙂

I think I found something, let me dig deeper 🙂

Hi LovelyHamster1
You mean when as a section name or a variable?
Could you change this example to include a variable that breaks the support ?
https://github.com/allegroai/clearml/blob/master/examples/frameworks/hydra/hydra_example.py

So one can override the queue ID but not the worker

apparently ... I can't think of a good reason for that actually ...

For future readers, see discussion here:
https://clearml.slack.com/archives/CTK20V944/p1629840257158900?thread_ts=1629091260.446400&cid=CTK20V944

can I mount the s3 bucket as file system on place where

you need to mount it where the file server is storing it's files, correct (notice, not the DBs, just the files server)