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update description
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mn-mikke committed Nov 13, 2023
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14 changes: 7 additions & 7 deletions h2o-py-mlflow-flavor/DESCRIPTION.rst
Original file line number Diff line number Diff line change
Expand Up @@ -19,9 +19,9 @@ The model that was trained with H2O-3 runtime can be exported to MLFlow registry
with mlflow.start_run(run_name="myrun") as run:
h2o_mlflow_flavor.log_model(h2o_model=h2o_model,
artifact_path="folder",
model_type="MOJO",
extra_prediction_args=["--predictCalibrated"])
artifact_path="folder",
model_type="MOJO",
extra_prediction_args=["--predictCalibrated"])
Compared to `log_model` functions of the other flavors being a part of MLFlow, this function has two extra arguments:
Expand Down Expand Up @@ -78,10 +78,10 @@ The functions can be utilized as follows:
mlflow.log_metrics(h2o_mlflow_flavor.get_metrics(h2o_model))
input_example = h2o_mlflow_flavor.get_input_example(h2o_model)
h2o_mlflow_flavor.log_model(h2o_model=h2o_model,
input_example=input_example,
artifact_path="folder",
model_type="MOJO",
extra_prediction_args=["--predictCalibrated"])
input_example=input_example,
artifact_path="folder",
model_type="MOJO",
extra_prediction_args=["--predictCalibrated"])
Model Scoring
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