def forecast_power(model_name, model_stage):
from mlflow.tracking.client import MlflowClient
client = MlflowClient()
model_version = client.get_latest_versions(model_name,
stages=[model_stage])[0].version
model_uri = "models:/{model_name}/{model_stage}".format(
model_name=model_name, model_stage=model_stage)
model = mlflow.pyfunc.load_model(model_uri)
weather_data, past_power_output = get_weather_and_forecast()
power_predictions = pd.DataFrame(model.predict(weather_data))
power_predictions.index = pd.to_datetime(weather_data.index)
print(power_predictions)
plot(model_name, model_stage, int(model_version), power_predictions,
past_power_output)
from mlflow.tracking.client import MlflowClient
client = MlflowClient()
client.transition_model_version_stage(
name=modelDetails.name,
version=modelDetails.version,
stage='Production',
)
# COMMAND ----------
# MAGIC %md The MLflow Model Registry allows multiple model versions to share the same stage. When referencing a model by stage, the Model Registry will use the latest model version (the model version with the largest version ID). The `MlflowClient.get_latest_versions()` function fetches the latest model version for a given stage or set of stages. The following cell uses this function to print the latest version of the power forecasting model that is in the `Production` stage.
# COMMAND ----------
latestVersionInfo = client.get_latest_versions(modelName,
stages=["Production"])
latestVersion = latestVersionInfo[0].version
print("The latest production version of the model '%s' is '%s'." %
(modelName, latestVersion))
# COMMAND ----------
# MAGIC %md
# MAGIC ## Next Steps
# MAGIC
# MAGIC [Model Serving]($./06-Model-Serving)
# COMMAND ----------
# MAGIC %md-sandbox
# MAGIC © 2020 Databricks, Inc. All rights reserved.<br/>
description="This model forecasts the wine quality based on the characteristics."
)
client.update_model_version(
name=model_name,
version=model_version,
description="This model version was built using sklearn."
)
# COMMAND ----------
client.transition_model_version_stage(
name=model_name,
version=model_version,
stage=stage,
archive_existing_versions=True
)
model_version_details = client.get_model_version(
name=model_name,
version=model_version,
)
print("The current model stage is: '{stage}'".format(stage=model_version_details.current_stage))
latest_version_info = client.get_latest_versions(model_name, stages=[stage])
latest_production_version = latest_version_info[0].version
print("The latest production version of the model '%s' is '%s'." % (model_name, latest_production_version))
# COMMAND ----------
dbutils.notebook.exit()
# COMMAND ----------
model_version_details = client.get_model_version(
name=model_details.name,
version=model_details.version,
)
print("The current model stage is: '{stage}'".format(stage=model_version_details.current_stage))
# COMMAND ----------
# MAGIC %md The MLflow Model Registry allows multiple model versions to share the same stage. When referencing a model by stage, the Model Registry will use the latest model version (the model version with the largest version ID). The `MlflowClient.get_latest_versions()` function fetches the latest model version for a given stage or set of stages. The following cell uses this function to print the latest version of the power forecasting model that is in the `Production` stage.
# COMMAND ----------
latest_version_info = client.get_latest_versions(model_name, stages=["Production"])
latest_production_version = latest_version_info[0].version
print("The latest production version of the model '%s' is '%s'." % (model_name, latest_production_version))
# COMMAND ----------
# MAGIC %md # Integrate the model with the forecasting application
# MAGIC
# MAGIC Now that you have trained and registered a power forecasting model with the MLflow Model Registry, the next step is to integrate it with an application. This application fetches a weather forecast for the wind farm over the next five days and uses the model to produce power forecasts. For example purposes, the application consists of a simple `forecast_power()` function (defined below) that is executed within this notebook. In practice, you may want to execute this function as a recurring batch inference job using the Databricks Jobs service.
# MAGIC
# MAGIC The following section demonstrates how to load model versions from the MLflow Model Registry for use in applications. The **Forecast power output with the production model** section uses the **Production** model to forecast power output for the next five days.
# COMMAND ----------
# MAGIC %md ## Load versions of the registered model
# MAGIC
