UnevenDolphin73 I have a suspicion we have a few terms mixed:
hyperparameters :
These are essentially key/value.
when you call Task. connect (dict_with_params), clearml will flatten the dict and you end up with key/value
configuration objects :
These are actually blobs of text, the UI will show as is
When you call my_local_file=Task. connect_configuration (name, "path/to/config/file")
The entire Content of the config file is stored on the Task object itself.

Back to the use case, instead of:
python train.py --config_file path/to/local/file.yaml
then inside the code:
` my_param_file = args.config_file
with open(my_param_file, 'rt'):

read parse etc. `You could bake both into the same line:

` my_param_file = task.connect_configuration("config_file", "path/to/local/file.yaml")
with open(my_param_file, 'rt'):

read parse etc. `When the "connect_configuration" is used, it actually combines the need to have both an argument pointing to the config file, and the content of the config file. It was designed to solve this exact use case. Am I making sense ?

EDIT:

"then our yaml file contains 

!include

"

Is this the point where connect_configuration breaks ?