AgitatedDove14 that worked! but i had to add:
os.environ['CLEARML_PROC_MASTER_ID'] = '' os.environ['TRAINS_PROC_MASTER_ID'] = ''or else it tought it was the parent optimizer task i was trying to run.

but now im facing new issue, the details are empty:

did i have to configure the environment first maybe? i assumed it just uses the environment where it was called

` sdk.development.store_uncommitted_code_diff: false
api.verify_certificate : false
api {

web_server: <ADDRESS>:8080
api_server: <ADDRESS>:8008
files_server: <ADDRESS>:8081
credentials {
    "access_key" = "OMF..."
    "secret_key" = "oox..."
}

} `

correct. just verified again now.

yes i can communicate with the server, i managed to put tasks in the queue and retrieve them as well as running tasks with metrics reporting

where address is our server adderss starting with https://.. etc

thanks

i can create tasks and reterive them from the queues

Nice catch! (I’m assuming you also called Task.init somewhere before, otherwise I do not think this was necessary)

i was calling task init and it still somehow tought its the parent task, until i fixed it as i said.
and yes, everything is working now! im running hyper parameter optimzation on LSF cluster where every task is an LSF job running without clearml-agent

sdk.development.store_uncommitted_code_diff: false api.verify_certificate : false api { web_server: https://<...>.com:8080 api_server: https://<...>.com:8008 files_server: https://<...>.com:8081 credentials { "access_key" = "OMF..." "secret_key" = "oox..." } }

I think i solved it by deleting the project and running the base_task one time before the hyper parameter optimzation

clearml 1.1.6 clearml-agent 1.1.2no output at all, so nothing to paste

i dont have agent configuration file if this might be the problem
all the machines share the same file system so i managed to do all the things i mentioned from different machines on the system

any idea why?

can you get the agent to execute the task on the current conda env without setting up new environment? or is there any other way to get task from the queue running locally in the current conda env?

"os": "Linux-4.18.0-348.2.1.el8_5.x86_64-x86_64-with-glibc2.28", "python": "3.9.7"

my own server

thanks it fixed it!

Im still trying to figure out what is the best way to execute task on LSF cluster, the easiest way possible would be if could just some how run task and let the lsf manage the environment, on the same filesystem it is very easy to use shared conda env etc

already reported table will be the best, otherwise any other table i can log new lines to

Im not sure what exactly your asking, someone else configured the server, im just using it

what do you mean?

thanks!

AgitatedDove14 its the same file system, so it would be better just to use the original code files and the same conda env. if possible…

python code

now i noticed clearml-agent list gets stuck as well

thanks thats exactly what i looked for!!