The way that community server works, yes. All your experiments are connected to a specific workspace/user

You can send the request with empty payload I think (Just send {}).

Hi HappyDove3 , you mean when using app.clear.ml?

Strange, I'm not familiar with tensorboard_logger package. I see it's latest package on pypi is also 0.1.0 with latest supported python 3.5.

Scalers are usually reported and auto captured through SummaryWriter if I'm not mistaken. I found an example here:
https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/pytorch_tensorboard.py

Anyhow I'll take a look into it 🙂

I think that the problem is with missing region definition. You need to set region in the config file.

But it looks like that for the existing version it will not work since there still appears to be a bug related to this. The hotfix is already on the way from my understanding

So, in short, you need to set the region in the config file + wait for the hotfix that is pending for 1.14

Hi @<1523701295830011904:profile|CluelessFlamingo93> , when running remotely the agent assumes it will be a different machine. I think the best way to solve this is to add utils to your repository and import it from there during code execution.

What do you think?

SmugTurtle78 , regarding the CPU only mode - How are you running. Are you using the application in PRO version or are you running through one of the examples?

AbruptCow41 , you can already do this, just add the entire folder 🙂

I think I misunderstood your problem at the start. let me take another look 🙂

When looking at the base task, do you have that metric there?

FrothyShrimp23 , I think this is more of a product design - The idea of a published task is one that cannot be easily changed afterwards. What is your use case for wanting to often unpublish tasks? Why publish them to begin with? And why manually?

I suggest reading all of them, starting with pipeline from tasks 🙂

This should be a good started, after googling more on how ssh works will give you the right direction 🙂

Hi @<1529271098653282304:profile|WorriedRabbit94> , you can sign up with a new email

'CLEARML_CONFIG_FILE': '/home/ubuntu/clearml.conf'

PanickyMoth78 , let me check on that 🙂

Hi @<1603560525352931328:profile|BeefyOwl35> , can you please elaborate on what you mean by running the build command?

Can you add a full log?

Getting the following error when I try to run this code:

Traceback (most recent call last): File "plots-issue.py", line 9, in <module> fig=px.pie(df, names='a', facet_col='b') TypeError: pie() got an unexpected keyword argument 'facet_col'

MotionlessCoral18 , I think there is a new version out - 1.4, can you try upgrading to that?

And also exactly what command line you used to run the agent?
You will need to find the appropriate docker image with the python version you're looking for.

CluelessElephant89 , Hi!

In the UI, under the execution tab there is a 'Container' section.

There you can configure all of those 🙂

You need the docker to be available on dockerhub so the agent will be able to pull the docker

Yes and yes 🙂

Hi UnevenDolphin73 , maybe JuicyFox94 or SuccessfulKoala55 can assist

Hi @<1533159639040921600:profile|JoyousReindeer30> , is it possible that the wheel it's trying to install is internal and not possible to install via regular pip install?

Unrelated 😛

Hi @<1749602841338580992:profile|ImpressionableSparrow64> , the S3 configuration (Credentials) is always done on the client side. You don't need to configure anything server side. Also good that you configured the agent.

Hi @<1585441179091079168:profile|ColossalArcticwolf5> , can you provide a log of the run?

SubstantialMonkey63 , Hi! What exactly are you looking for ? I think you might find some relevant things here https://github.com/allegroai/clearml/tree/master/examples

The server usually takes about 2-3 minutes to start up (ES takes time to warm up), does the issue still affect you?

Hi @<1546665634195050496:profile|SolidGoose91> , I think this capability exists when running pipelines. The pipeline controller will detect spot instances that failed and will retry running them.

Are you using the PRO or the open source auto scaler?