In the web UI, in the queue/worker tab, you should see a service queue and a worker available in that queue. Otherwise the service agent is not running. Refer to John c above

None

--gpus 0,1 : I believe this basically say that your code launched by the agent has access to both GPUs and that is it. Now it is up to your code to choose which GPU to use and what not and how ...

if you have 2 agent serving the same queue and then send 2 task to that queue, each agent should take one task
But if you queue sequentially one task then wait until that task to finish and queue the next: then it will be random which agent will take the task. Can be the same on from the previous task
Are you saying that you have 1 agent running task, 1 agent sitting idle while there is a task waiting in the queue and no one is processing it ??

what about having 2 agents, one on each GPU, on the same machine, serving the same queue ? So that when you enqueue, which ever agent (thus GPU) available will take the new task

with

    df = pd.DataFrame({'num_legs': [2, 4, 8, 0],
                       'num_wings': [2, 0, 0, 0],
                       'num_specimen_seen': [10, 2, 1, 8]},
                      index=['falcon', 'dog', 'spider', 'fish'])

    import clearml
    task = clearml.Task.current_task()
    task.get_logger().report_table(title='table example', series='pandas DataFrame', iteration=0, table_plot=df)
    # logger.report_table(title='table example',series='pandas DataFrame',iteration=0,tabl...

how did you deploy your clearml server ?

Can you share the agent log, in the console tab, before the error?

(wrong tab sorry :P)

Can you paste here what inside "Installed package" to double check ?

we are not using docker compose. We are deploying in Azure with each database as a standalone service

if you are on github.com , you can use Fine tune PAT token to limit access to minimum. Although the token will be tight to an account, it's quite easy to change to another one from another account.

you are forcing ssh with force_git_ssh_protocol: true
Have you setup ssh keys ?
If you are using ssh keys, why enable_git_ask_pass: true ?

you should know where your latest model is located then just call task.upload_artifact on that file ?

do you have a video showing the use case for clearml-session ? I struggle a bit about how is it used for ?

None

Onprem: User management is not "live" as you need to reboot and password are hardcoded ... No permission distinction, as everyone is admin ...

I think he means disk space usage ...

I am not familiar with autoscaler ... are you using the paid version of Clearml ?

is this mongodb type of filtering?

all good. Just wanted to know in case I missed it

I am wondering, should it be True be default ???

hard to see with your croppout here an there ...

I don;t think ClearML is designed to handle secrets other than git and storage ...

None
CLEARML_AGENT_SKIP_PIP_VENV_INSTALL
CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL

If you care about the local destination then you may want to use this None

you can upload the df as artifact.
Or the statistics as a DataFrame and upload as artifact ?

just saw that repo: who are coder ? That not the vscode developer team is it ?

I mean, depend on what do you want to report ... if you want to stick to table, I suggest earlier to gather your stats in table format ...
Otherwise, matplotlib seems to be the most user friendly way

what is the difference between vscode via clearml-session and vscode via remote ssh extension ?

while the other may need to be 1 instead of true