Hello again, AgitatedDove14 and others. I write you to let you know, what works for us in the case of optimizing hparams in DictConfig:
` hparams_dict = OmegaConf.to_object(hparams)

update hparams_dict using new hyperparameters set by the optimizer

hparams_dict = task.connect(hparams_dict, name="HPO")

ProxyDictPostWrite to dict

hparams_dict = hparams_dict._to_dict()

update hparams DictConfig which is used later in the training

hparams = OmegaConf.create(hparams_dict)

train(hparams) `Using this notation, you can set the hyperparameters in the optimization example like this:

hyper_parameters=[ DiscreteParameterRange("HPO/training/seed", values=[100, 900]), ],You can compare the hyperparameters of the experiments in the Configuration tab -> Hyperparameters -> HPO.
I hope this dummy example will help somebody in the future. Thank you AgitatedDove14 for cooperation.