I didn't write this conf, but it works.

configurations:
  extra_clearml_conf: 'sdk.aws.s3.region="us-west-2"
                        agent.extra_docker_arguments=["--shm-size=90g"]
                        agent.extra_docker_shell_script=["git config --global credential.helper cache --timeout=604800",]'
  extra_trains_conf: ''
  extra_vm_bash_script: ''
  queues:
    gcp-v100:
    - - gcp-v100
      - 4
    gcp-l4:
      - - gcp-l4
        - 4
    gcp-cpu:
    - - gcp-cpu
      - 4
  resource_configurations:
    gcp-v100:
    ...

Exactly, @<1523701070390366208:profile|CostlyOstrich36>

It is an autoscaler for gcp. I think, there are unnecessary configs that were used in aws.

I see. So, is it same thing when network is slow and there is a mistake in url?

they are different tasks. I start a new task but it can be same commit sometimes.

created virtual environment CPython3.10.13.final.0-64 in 511ms
  creator CPython3Posix(dest=/root/.clearml/venvs-builds/3.10, clear=False, no_vcs_ignore=False, global=True)
  seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=/root/.local/share/virtualenv)
    added seed packages: pip==23.3.1, setuptools==69.0.2, wheel==0.42.0
  activators BashActivator,CShellActivator,FishActivator,NushellActivator,PowerShellActivator,PythonActivator
...

I don't say I am expert in this thing, but does the autoscaler have different version than the clearml?

@<1523701070390366208:profile|CostlyOstrich36>

import yaml
from clearml.automation.auto_scaler import AutoScaler, ScalerConfig
from gcp_driver import GCPDriver

with open('gcp_autoscaler.yaml') as f:
    conf = yaml.load(f, Loader=yaml.SafeLoader)

    driver = GCPDriver.from_config(conf)

    conf = ScalerConfig.from_config(conf)
    autoscaler = AutoScaler(conf, driver)
    autoscaler.start()

That is the python code.

You are right. My colleague wrote it I think starting from the aws autoscaler.

They are actually from tracked files. Actually, I get the uncommitted changes under Execution tab.

because, the param name that takes in the df is table_plot

report_table seems to be the most straight forward without matplotlib integration. Do you think it has plotting features?

I don't think I am on the pro version, is that a paid one?

That is the configuration yaml.

Yeah actually you are right. I can report the stats as a table, not the whole data.

Thanks CostlyOstrich36
But, I am not using report_media() function. The debug samples (confusion matrices) are saved from tensorboard.

You are right. But, I have to start it from draft in the UI to do that, right? I mean, clone and restart.

deployed it myself. It worked fine before I changed to ubuntu24.04 yesterday. We have been using clearml for years this way

The worker machines are on gcp

Thank you Jake. is that the same for bitbucket and other repos as well? AND is there a specific part of the doc that talks about it?

It doesn't work when I insert the credentilas individually either. I am using EC2 as clearml server.

CostlyOstrich36

I will have to go for matplotlib or seaborn options.

I know. And the very fast help 🙏 😀

clearml==1.14.1
That is the version.

Okay. thanks. But, about the overriding, I tried committing and when commited it works. So, I think that means the configuration is not overriden else where.

@<1523705004920147968:profile|CloudySwallow27>

Thank you so much.