FlatStarfish45

In the parent task, the libs appear installed.

What do you mean by "parent Task"? Is this the base task we are optimizing (i.e. the experiment / model we are optimizing) ?
Or is it the "Optimization Task" itself?

Where do you have your Task.init ?

Thanks GrievingTurkey78 !
It seems that under the hood they user argparser
See here:
https://github.com/google/python-fire/blob/c507c093fa6622ab5efee21709ffbf25974e4cf7/fire/parser.py

Which means it might just work?!
What do you think?

Hi ReassuredTiger98
Basically assuming Linux, init.d will do the trick
https://unix.stackexchange.com/questions/20357/how-can-i-make-a-script-in-etc-init-d-start-at-boot

CrookedWalrus33 any chance you can think of a sample code to reproduce?

Hi JitteryCoyote63

Is it possible to rollback from 1.2.0 to 1.1.0?

Not really there was a DB migration so out of the box downgrade is not really supported.
That said, v1.3.1 is already out, with what seems like a fix:
As a quick fix, can you test with auto refresh (see top right button with the pause sign you have on the video)

CloudyHamster42
RC probably in a few days, but notice that it will just remove the warnings, I still can't reproduce the double axis issue.

It will be helpful if you could send a small script to reproduce the problem.

Maybe this example code can help ? https://github.com/allegroai/trains/blob/master/examples/manual_reporting.py

Hi ConvincingSwan15

For the train.py do I need a setup.py file in my repo to work corerctly with the agent ? For now it is just the path to train,py

I'm assuming the train.py is part of the repository, no?
If it is, how come the agent after cloning the repository cannot find it ?
Could it be it was accidentally not added to the git repo ?

In regards to the YAML how would you pass data? Like the pipeline from tasks example?

Thanks JitteryCoyote63 , once we have a reproducible example the fix should be very quick to push (with these things reproducing it is the challenge)

🤔

WackyRabbit7 I might be missing something here, but the pipeline itself should be launched on the "pipelines" queue, is the pipeline itself running? or is it the step itself that is stuck in ""queued" state?

I could improve the cost-efficiency of my provisionned GCP A100 instances

But their pricing is linear, if you do not need a100 get a cheaper instance ?! no?

Yep it is the scale 🙂 and yes it should appear once you upgrade

try:

import os

...

dataset_path = Dataset.get(
    dataset_name=dataset_name,
    dataset_project=dataset_project,
    alias="0013_Dataset"
).get_local_copy()
dataset_path = os.path.join(dataset_path, "data.yaml")

...

Thanks! Let me check something

But only 1 node will copy it.

they can only copy it after the first is finished, and they are not aware it is trying to set the exact venv, hence the race

I appended python path with /code/app/flair in my base image and execute

the python path is changing since it installs a new venv into the system.
Let me check what's going on with the pythonpath, because it is definitely is changed when running the code (the code base root folder is added to it). Maybe we need to make sure that if you had PYTHON PATH pre-defined we restore it.

If you choose between skipping or logging like nan, then here I find it difficult, it seems that it is better to log than skip, but you need to think.

So I "think" the issue is plotly (UI), doesn't like NaN and also elastic (storing the scalar) is not a NaN fan. We need to check if they both agree on the representation, that it should be easy to fix...
Maybe you could open a github issue, so we do not forget?

this results at the end of an experiment in an object to be saved under a given name regardless if it was dynamic or not?

Yes, at the end the name of the artifact is what it will be stored under (obviously if you reuse the name you basically overwrites the artifact)

Okay there now:
https://github.com/allegroai/trains/tree/0.15.1rc0

RipeGoose2 yes that will work 🙂
That said, we should probably fix the S3 credentials popup 😉

Do you have to have a value there ?

but I was wondering if there's any limitation in creating an image with a non root user to use as the actual worker?

SarcasticSquirrel56 non-root pods (containers) are fully supported,
I would recommend using the latest agent RC (that simplified a few things)
clearml-agent==1.4.0rc3

I see... because the problem it would be with permissions when creating artifacts to store in the "/shared" folder

You mean as output target for artifacts ?

especially for datasets (for th...

Is it still running ?

Hi RoundMosquito25
What do you mean by "local commits" ?

DepressedChimpanzee34 <character> will almost always be converted into \ because otherwise it will not support \t or \n etc.
What I'm looking here is some logic that will allow us not to break backwards compatibility on the one hand, but still will allow you to have something like "first\second" entry.
WDYT? any ideas? (I really want to make sure we fix it as soon as possible)

In that case I suggest you turn on the venv cache, it will accelerate the conda environment building because it will cache the entire conda env.

Hi @<1664079296102141952:profile|DangerousStarfish38>
You mean spin the agent on multiple Windows machines? Yes that is supported, I think that it is limited to venv (i.e. not docker) mode, but other than that should work out of the box

odd message though ... it should have said something about boto3