I host the code on my Github

Do we even have an option to assign id to each agent? https://clear.ml/docs/latest/docs/clearml_agent/clearml_agent_daemon

docker mode

when is server created on my local machine, when I do clearml-init ?

more or less

Great to hear that, thanks! 🙂

I'll try that...

AgitatedDove14 do I need to have the repo that I am running on my account? Even if it is public repo, like repo with your (clearml) examples:

SOURCE CODE
REPOSITORY
https://github.com/allegroai/clearml.git
BRANCH NAME
Latest in branch master
SCRIPT PATH
pytorch_matplotlib.py
WORKING DIRECTORY
examples/frameworks/pytorch

?

Do I need to push the needed code to github if it needs to be cloned?

So there is no way to use Agent without use of remote repo (just using local server not connected to Internet), am I right?

Thanks! 🙂

So seems like this dictionary works with strings

Ill try...

I haven't change any port mapping

SuccessfulKoala55 should I make an issue on Github?

Ok, thanks!

The use case was that server with repo wasn't responding for a while and I was thinking how to solve that. Thanks for the answer!

Actually I am still struggling with a problem of agent running on docker (message on starting at 10:54)

The problem is that we have a a complex configuration of pipeline. Configuration changes quite frequently and we would not like to run the pipeline every time configuration changes, but we would like to have it scheduled in some defined periods.

Do you have an idea of some workaround / alternative solution for that problem?

Can I do this to specify which worker should execute that task?
CLEARML_WORKER_NAME=<worker_name> clearml-agent execute --id <task_id>

no

SuccessfulKoala55 we did it through default Docker-compose file.

If there a way to give more resources for server to help it somehow?

SuccessfulKoala55 We are encountering some strange problem. We are spinning N agents using script, in a loop

But not all agents are visible as workers (we check it both in UI, but also running workers_list = client.workers.get_all() ).

Do you think that is it possibility that too much of them are connecting at once and we can solve that by setting a delay between running subsequent agents?

there is no such option

SuccessfulKoala55 Thank you for the response! Let me elaborate a bit to check if I understand this correctly.
We have a time-consuming task T based on optimization for parameters. We want to run hyperparameter optimization for T, suppose that we want to run it for 100 sets of parameters.
We want to leverage the fact that we have n machines to make the work parallel.

So for that we use https://clear.ml/docs/latest/docs/references/sdk/hpo_optimization_hyperparameteroptimizer/ , we run Agent...

Yes, it is a good reason 🙂
Do you maybe know a tool that measures that during execution (to avoid looking on nvidia-smi during all training)?

So, suppose, that a task T uses 27% of GPU, means, that we can spawn 3 agents on this GPU (suppose that we will give them only task T). Does it make sense?

Hi SuccessfulKoala55 . Do you have any updates, especially on #829 which is more critical for us?

version:
1.8.1hack line:
#scheduler._schedule_jobs[0]._last_executed = datetime.utcnow() - relativedelta(days=1)

SuccessfulKoala55 How should I pass this variable? Do I need to create a file apiserver.conf in folder /opt/clearml/config and write there just CLEARML_USE_GUNICORN=1 . Do I need to restart a server after that?

SuccessfulKoala55 could we run a server with some verbose logging?