yep

As a matter of fact, all my tasks are "running" state although some of them have failed

Nope. It gives me errors.
Just like the guy that replied in the thread I linked in my previous reply here.

I got the same issue as well last night.

I do have the configuration vault feature.
I managed to make it work.
Seems like I have been using it wrong.
In order to facilitate the multiple credentials one must use the Clearml SDK obviously.
So I just started using StorageManager and it works.

Thanks.

Ok, seems like the problem is solved.
These uncommited changes were already applied to the local branch, but the git apply error wasn't very informative.

Thanks!

My task runs just fine.
But no GPU.
(When it demands GPU it collapses).
Looking at the VM features on GCP UI it seems no GPU was defined for the VM.

So anyway,
you can pickle the above object (pickle the study).
But you can't actually pickle the optimizer itself as you said/

Using an autoscaler service(on 24/7 EC2 machine)
that triggers EC2 workers (with an AMI I saved prior to activation)

Hope that helps

Still no good, managed to apply with errors only

folder is rather small.
3.5MB
Am able to see it in the artifacts.
but can't download it (the address is wrong)

Important to notice I am running my instances on GCP, but the container is on ECR (AWS)

e.g.
my_optimizer = an_optimizer.get_optimizer() plot_optimization_history(my_optimizer._study)Since my_optimizer._study is an optuna object

You are right Idan,
I consulted our Private ClearML channel.
you cannot insert these environment variables any other place,
only in init script.
Here is the full quote:

I don't think it's related to the region.
I do have the log of the autoscaler.
We also have an autoscaler that was implemented from scarch before ClearML had the autoscaler application.
I wouldn't want to share the autoscaler log with this channel.

Update:
Manged to make the credentials attached to the configuration when the task is spinned,
Although boto3 in the script still uses the "default" access keys instead of the newly added keys

Adding the flags he added also didn't help

It's a private image (based off of this image).
` ======================================
Welcome to the Google Deep Learning VM

Version: pytorch-gpu.1-11.m91
Based on: Debian GNU/Linux 10 (buster) (GNU/Linux 4.19.0-21-cloud-amd64 x86_64\n) `I am leaving the docker line empty, so I assume there's no docker spun up for my agent,

That helps a lot!
Thanks Martin.
Although I didn't understand why you mentioned torch in my case?
Since I don't use it directly, I guess somewhere along the way multiprocessing does get activated (in HPO)
I would guess it relates to parallelization of Tasks execution of the HyperParameterOptimizer class?

clearml 1.2.0 pypi_0 pypi

CostlyOstrich36
Thank you,
Solved,
I messaged with Alon from your team and he will upload an update to the old repository.

1.2.0

I am not a staff member. But it seems like something quite trivial with not much effort.
if you can avoid conda and don't need the c++ dependencies that conda takes care of
(and since you can convert to pip fomat , you probably can).

It's generated automatically by HPO script.
So it might be added inside the report completion section

TimelyPenguin76 Maybe you were able to find the problem ?
I don't remember what was the solution.
Might just updated my ClearML version...

no, it is AWS EC2

Should note that it works when i run the container locally (with no external env variables).

The thing is this.
My optimizer works a bit different.
my "optimized task" is actually a task that gets a specific
Hyper parameters and then enqueus more tasks (each one on different object)

+1 for this 🙂