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Hello

Hello 😊
I have a local computer, a storage server and a training server. I want to create a mature algorithm code locally and upload it to the storage server, and then let my partner clone execute the tasks in the storage server on the training server. Which server should I deploy clearml on and how do I operate the process?

  
  
Posted 2 years ago
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Answers 6


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?

  
  
Posted 2 years ago

Hi WearyChicken64 ,
I'm not sure why you refer to the server as storage server - it sounds like you have a server machine and a training machine - the most simple solution is to install the ClearML Server on the server ("Storage server") and install ClearML Agent on the training machine ("Training server")

  
  
Posted 2 years ago

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

  
  
Posted 2 years ago

Sounds right 🙂

  
  
Posted 2 years ago

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

They are autodetected if you are using Argparse / Hydra / python-fire / etc.
The first time you are running the code (either locally or with an agent), it will add the hyper parameter section for you.
That said you can also provide it as part of the clearml-task command with --args
(btw: clearml-task --help will list all the options, https://clear.ml/docs/latest/docs/apps/clearml_task#command-line-options )

  
  
Posted 2 years ago

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

  
  
Posted 2 years ago
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