clearml-task --project keras_examples --name remote_test --repo --branch master --script /webinar-0620/keras_mnist.py --args batch_size=64 epochs=1 --queue default

Thank you for your solution! I have an idea to deploy ClearML Server on the storage server, and then upload multiple networks such as UNet(including code, running environment, hyperparameters, weights, etc.) that have been modified and tested locally to ClearML Server. Then install ClearML Agent on the training server to perform network training in the ClearML Server. Is this feasible?

But how do you specify the data hyperparameter input and output models to use when the agent runs the experiment