its working now, thanks that was the problem.

yeah i see it now in the requirements of the task, that's weird, I'll create a new environment and check it again, thanks

Just upgraded to clearml-agent==1.5.1 and I still get this error.

Yes, here is the log file.

@<1523701070390366208:profile|CostlyOstrich36> my repo is like this and both the files are located at the same dir so its weird that they cannot find train:

.
├── pytorch
├── tensorflow
│   ├── Project A
│   │   └── src
│   ├── Project B
│   │   ├── data
│   │   ├── model
│   │   ├── reports
│   │   └── utils
│   ├── hand_validator_boxes
│   │   ├── src
│   │   ├── train.py (the module i need)
│   │   └── clearml_pipeline.py (where the pipeline is initilizied
└── utils

Hi @<1523701070390366208:profile|CostlyOstrich36> , it is part of the repository, do pipelines run differently then normal tasks? what I mean is when i run a task it has a working directory do pipelines also have that or are their working directory the root of the repo?

@<1523701087100473344:profile|SuccessfulKoala55> and @<1523701070390366208:profile|CostlyOstrich36> , in the end I've found the problem, it was due to me running the pipeline locally and when running the pipeline locally it, doesn't copy all the dir but only the script that is running None

Oh so in that case I'll need to change every agent's pip config file.

@<1523701087100473344:profile|SuccessfulKoala55> yes the working dir is set to the correct path and yet it cannot import the train module

yes sometimes I suffer from small network issues, is there a way to make clearml have a bigger timeout when installing packages?
and if not is there a way to point it to a local package for installation or a local virtual enviroment?

Thanks John, I read the one about the pip timeout, the problem is that I'm assume clearml runs the following command :
"pip install -r requirments.txt" and I want to know if I make clearml add the timeout flag.

Thanks John.

@<1523701070390366208:profile|CostlyOstrich36> After discussing with my TL, we think the plan we are subscribed to might not be for us, can you point me to a person who we can have a meeting with and advice us the best plan for my team?

@<1523701087100473344:profile|SuccessfulKoala55> and @<1523701070390366208:profile|CostlyOstrich36> Ok so I found the problem but its weird,
when the agent is setting up the enviorment its installing torch=1.11.0 and not installing the one in the requirements which is torch=1.11.0+cu113,
I've checked the clearml.conf and i do have this flag set:

force_repo_requirements_txt: true

and I have a local whl of torch=1.11.0+cu113 with a path set to its location in the requirements.txt ...

Yes, same one

yes it is

@<1523701087100473344:profile|SuccessfulKoala55> But when i use this setting it the packages download only from the torch repo and not a local repo correct? or does it use the url-extra-link? and is there a way to cancel the auto cuda detect?

Yes it does, thank you @<1523701070390366208:profile|CostlyOstrich36>

yep, just a string which is a path but not to upload the folder

Btw in pipelines is there a way to get the pipelines main task id? for example <step_name>.id gets me the stages id but I need the main pipeline that's running all the tasks

Solved it by doing clearml.Task.current_task().id but thank you

@<1523701087100473344:profile|SuccessfulKoala55> in the file example here there is no reference to console_cr_flush_period

Thanks @<1523701070390366208:profile|CostlyOstrich36> , but doesn’t the agent create/caches an environment from the requirements.txt when running? I’m reproducing an old project that used to work like that, and also my ClearML.conf set to work that way

Hi @<1523701070390366208:profile|CostlyOstrich36> , I am using the community server, what happens if i change to a self hosting server?
I'm using Tensorboard to report everything, nothing special besides that.

@<1523701087100473344:profile|SuccessfulKoala55> What I'm trying to do is connect 3 different tasks into 1 pipeline but still being able to run each task as an individual when needed but without changing the tasks code. for example i have a training.py file which runs task.init in the start and creates a task in the server for training a new model, but i want also to create a pipeline that will run that training.py and other tasks together, is that more clear now?

when i tried doing with the decorators it threw me an error that it cannot run task init in side a working task (the pipe lines task)

Ok cool, I'll try that, Thanks

The flow is: Training.py (which creates and runs a training task) -> conversion_task.py (converts the outputs of the models into a format of our choosing) -> testing.py (testing the model after conversion).
I tried using the decorators and fucntions but they both threw me errors that i cannot do task init in side a running task.

Wow, thanks a lot @<1523701070390366208:profile|CostlyOstrich36> for pointing me in the right direction. I also see that i can use sdk.development.worker.log_stdout if i really need to kill my api calls before I'll Host my own server.
BTW what does suppress_update_message do? I mean which kind of messages does it suppress?

Also looked at it but its only supported registered artifact object type is a pandas.DataFrame and not strings.
I think I'll keep it with ':' in the start of the string and that way it won't upload the folder