cli

mlsdk-cli

Remote

To use with a remote Firefly target, add a .mlsdk-cli file to your $HOME directory with contents that look like this:

{
   "remotes":{
      "dev":{
         "url":"http://localhost:8090/hub/api",
         "username":"firefly",
         "hub_token":"TOKEN"
      }
   }
}

You should then be able to use the CLI with the --remote=<REMOTE NAME> flag. The above example will provide a remote named dev

The test-data directory in this repo contains a my_study.yaml file which is the firefly manifest. From that directory, you should be able to run

# If a manifest path isn't provided, it will look for a file name study.yaml by default
> hyper jupyter start --remote=dev --manifestPath=./my_study.yaml 

Which will start a notebook server instance on the remote using the study_name field in the manifest (log_reg_health_tracker)

Once you have a running notebook server instance, you should be able to start a training session on it by

# Currently all this does is upload the manifest and data to the _jobs folder
> hyper train --remote=dev --manifestPath=./my_study.yaml

Local

To use a local Firefly server, first create the server

# If a manifest path isn't provided, it will look for a file name study.yaml by default
> hyper jupyter 

Once you have a running notebook server instance, you should be able to start a training session on it by

> hyper train --manifestPath=./my_study.yaml

The study will be scheduled to be executed on the server, to fetch the hyperpackage from the training session run

> hyper train fetch --manifestPath=./my_study.yaml

Note: To use a local Firefly server for training, it is necessary to create the notebook server instance and execute the traning session from within the same git project.