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.