Hi @<1635088270469632000:profile|LividReindeer58> , you should do a separation. The pipeline controller should run on the services queue. Pipeline steps should run on different queues. This is why they are sitting in pending - there is no free worker to pick them up.
Hey everyone, i'm new to clearml pipelines and my tasks are always in pending.
- I have created clearml agent and it is connected to the main clearml server.
- Agent configured to use docker with mounted volumes, does listen to the "services" queue and all tasks are displayed in this queue in webui.
- Pipeline starts and launches all tasks, but they all just pending.
Could somebody please help me understand what i might be doing wrong here.
Here is my code, i'm trying to track a bunch of existing triton models in specific folders.
I replaced path to models with "path_to_model".
from clearml import OutputModel, InputModel, Task from clearml import PipelineDecorator @PipelineDecorator.component(cache=True, execution_queue="services") def load_model(abs_path: str, path_to_model: str): from os import path, listdir from clearml import InputModel name = path_to_model.strip("/").split("/") model_file = listdir(path.join(abs_path, path_to_model, "1")) input_model = InputModel.import_model( name=name, weights_url=path.join(abs_path, path_to_model, "1", model_file), # Set label enumeration values framework="PyTorch", create_as_published=True, # Set model tags tags=path_to_model.split("-"), ) input_model.set_metadata("version", "1.1", "str") @PipelineDecorator.pipeline( name="Load Models to Registry", project="Model Registry", version="1.0" ) def pipeline_logic(path_to_model_repo: str): from os import listdir for elem in listdir(path_to_model_repo): load_model(path_to_model_repo, elem) if __name__ == "__main__": pipeline_logic( path_to_model_repo="path_to_model" )