get_estimator_parameters
,get_pickled_model
,create_prediction_error_chart
.Python 3.7+
in your system,pip
and conda
:neptune-client==0.9.18
, scikit-learn==0.24.1
, and neptune-sklearn==0.9.5
.project=my_workspace/my_project
: your workspace name and project name,api_token=YOUR_API_TOKEN
: your Neptune API token.api_token
argument..py
scripts for training Neptune will also log your training script automatically.base_namespace
of your choice.base_namespace
of your choice.stop()
method. This is needed only while logging from a notebook environment. While logging through a script, Neptune automatically stops tracking once the script has completed execution.get_estimator_parameters
,get_pickled_model
,create_prediction_error_chart
.gbc
object will be later used to log various metadata to the run..git
directory in your project and get the last commit information saved..py
scripts for training Neptune will also log your training script automatically.base_namespace
of your choice.stop()
method. This is needed only while logging from a notebook environment. While logging through a script, Neptune automatically stops tracking once the script has completed execution.rfr
object will be later used to log various metadata to the run..git
directory in your project and get the last commit information saved..py
scripts for training Neptune will also log your training script automatically.base_namespace
of your choice.stop()
method. This is needed only while logging from a notebook environment. While logging through a script, Neptune automatically stops tracking once the script has completed execution..git
directory in your project and get the last commit information saved..py
scripts for training Neptune will also log your training script automatically.base_namespace
of your choice.stop()
method. This is needed only while logging from a notebook environment. While logging through a script, Neptune automatically stops tracking once the script has completed execution.