• Documentation
• Develop a Wind Power Prediction Model
• Unit 4. Design a Pipeline

Unit 4. Design a Pipeline

This section describes how to design the pipeline. You can choose one of the following options to design a pipeline:

• Option 1: design a pipeline based on a sample pipeline to quickly get started

• Option 2: design a pipeline from scratch to be familiar with the overall functions of AI Pipelines

For more information on operators used in this tutorial, see Operator Reference.

Option 1: Design a Pipeline Based on a Sample Pipeline

Step 1. Export Sample Pipeline

1. Log in to EnOS Management Console and select Data Analytics > AI Studio > AI Pipelines on the left navigation pane.

2. On the Sample Pipeline tab, select power-loss-calculation > Pipeline View.

3. Select Export to export the sample pipeline configuration.

Step 2. Create an Experiment

1. Select Data Analytics > AI Studio > AI Pipelines on the left navigation pane.

2. Select New Experiment on the Custom Pipeline tab.

3. Enter winddemo as the name of the experiment in the pop-up window.

4. Select OK to create the experiment, and you can see the canvas for designing a pipeline.

5. Select Import to import the exported sample pipeline configuration.

Step 3. Update Global Parameters

For more efficient and easier pipeline design process, you can set globally applicable parameters as global parameters to avoid repetitive configuration. The global parameters used in this tutorial are included in the sample pipeline configuration file, select Workflow setting to configure the global parameters.

Number	Parameter	Description
1	resourcepool	Update the value to specify the resource pool used for the model deployment.
2	datasetname	Specify the dataset.
3	tasktype	Tasks type. prediction: run prediction tasks training: run training tasks
4	predictiontype	Prediction types. file: make predictions based on the Mlflow model files. It takes longer time to complete the predictions but consumes less resource. service: make predictions based on the published model services.
5	lenoflist	Specify the length of looplist generated. This value is a simulation of the number of wind turbines.
6	modelinstnacename	Name of the model deployment instance.
7	prediction_dataset_name	Name of the dataset.
8	sample_split_ratio	A ratio to split the dataset into a training set and a prediction set.
9	hdfs_source	Select a HDFS data source which is configured in Data Source Connection to upload the model files.
10	hive_source	Select a Hive data source which is configured in Data Source Connection to upload the prediction data.
11	ouid	ID of the current OU.
12	prediction_instance	Name of the prediction instance. It should be the same as modelinstnacename to make predictions using the trained model.

Step 4. View Operators in the Main Canvas

You can find 2 condition operators in the main canvas:

• Condition for Training: perform the tasks under Training when task type is training.

• Condition for Prediction: perform the tasks under Prediction when task type is prediction.

Step 5. View Operators for Training Tasks

Select the Expand of the Condition for Training operator to open its sub-canvas. You can find the following operators.

Operator Name	Description
Recursion for Event Trigger	This Recursion operator generates a random number between 1-10 and export via the result_number parameter. It can determine whether the condition result_number < 5 is met.
Generate Loop List	This PythonEx operator generates a loop list for ParallelFor operator follows. Make sure the workspace is wind_power_forecasting and entrypoint is generate_model_list.py.
Loop For Model Training	This ParallelFor operator performs the training tasks.

Select the Expand of the Loop For Model Training operator to open its sub-canvas. You can find the following operators.

Operator Name	Description
Prepare Training Data	This PythonEx operator is used to prepare the training data and export the data files. Make sure the workspace is wind_power_forecasting, entrypoint is prepare_train_data.py, and requirements is requirements1.txt.
Model Training	This PythonEx operator is used to train the model based on the input data and export the training model through the output parameter. Make sure the workspace is wind_power_forecasting, entrypoint is train_model.py, and requirements is requirements2.txt.
Create a Model	This Model operator is used to create a model.
Create a Model Revision	This Mlflow Model Version Register operator is used to register an Mlflow model version for a specified model.
Create a Model Instance	This Model Instance operator is used to create a model deployment instance.
Create a Model Test Operator	This Model Test operator is used to test whether the model version can be published as a valid model.
Deploy the Model Revision	This Single Model Deployment operator is used to deploy a single model version based on a published model version.

Step 6. View Operators for Prediction Tasks

Select the Expand of the Condition for Prediction operator to open its sub-canvas. You can find the following operators:

• Generate Target List for Prediction, a PythonEx operator that generates a loop list for ParallelFor operator follows. Make sure the workspace is wind_power_forecasting and entrypoint is generate_model_list.

• Loop For prediction, a ParallelFor operator that performs the prediction tasks. Double-select this operator and you can find operators as follows.

View the Operators for Service Prediction Type

This operator is used to make predictions based on the published model service. Select the Expand of the Loop for prediction operator to open its sub-canvas. You can find the following operators.

Operator Name	Description
Prepare Prediction Data	This PythonEx operator is used to prepare the prediction data. Make sure the workspace is wind_power_forecasting, entrypoint is prepare_predict_data.py, and requirements is requirements1.txt.
Predict from Service	This Service Prediction operator is used to predict based on the model services and deliver prediction results as an output.
Write results	This PythonEx operator is used to write the prediction results to the output file. Make sure the workspace is wind_power_forecasting and entrypoint is write_results.py.
Check Hive Config	This PythonCode operator is used to check if EnOS Hive is available in the current OU to save the prediction results.
Export to Hive?	This condition operator is used to save the prediction results to the Hive table. Its sub-canvas includes the following operators: Generate Variables, a PythonCode operator that contains python codes for storage directory and the SQL statement. HDFS Uploader, an HDFS Uploader operator that uploads the prediction results file to a specified HDFS directory. Hive, an Hive operator that saves the prediction results to EnOS Hive.

View the Operators for File Prediction Type

This operator is used to make predictions based on the Mlflow model files. Double-select the Condition for Model File Prediction to open its sub-canvas and check 6 operators as follows.

Operator Name	Description
Get Latest Model Version	This PythonEx operator is used to get the latest model version of a specific model. Make sure the workspace is wind_power_forecasting and entrypoint is get_latest_model_version.py.
Prepare Prediction Data 2	This PythonEx operator is used to prepare the prediction data. Make sure the workspace is wind_power_forecasting, entrypoint is prepare_predict_data.py, and requirements is requirements1.txt.
Predict from Mlflow Model File	This Mlflow Model Version Prediction operator is used to make predictions based on the internal Mlflow model files and deliver the prediction results as an output.
Write results 2	This PythonEx operator is used to write the prediction results to the output file. Make sure the workspace is wind_power_forecasting and entrypoint is write_results.py.
Check Hive Config 2	This PythonCode operator is used to check if EnOS Hive is available in the current OU to save the prediction results.
Export to Hive 2?	This condition operator is used to save the prediction results to the Hive table. Its sub-canvas includes the following operators: Generate Variables 2, a PythonCode operator that contains python codes for storage directory and the SQL statement. HDFS Uploader 2, an HDFS Uploader operator that uploads the prediction results file to a specified HDFS directory. Hive 2, an Hive operator that saves the prediction results to EnOS Hive.

Option 2: Design a Pipeline from Scratch

Step 1. Create an Experiment

1. Select Data Analytics > AI Studio > AI Pipelines on the left navigation pane.

2. Select New Experiment on the Custom Pipeline tab.

3. Enter winddemo as the name of the experiment in the pop-up window.

4. Select OK to create the experiment, and you can see the canvas for designing a pipeline.

Step 2. Add Global Parameters

For more efficient and easier pipeline design process, you can set globally applicable parameters as global parameters to avoid repetitive configuration by the following steps:

1. On the power-loss pipeline canvas, select Workflow Setting to open the Workflow Setting panel.

2. Select Add Parameter to add the following global parameters in the Configuration Parameters section.

Number	Name	Type	Value
1	resourcepool	resource_pool	The value specifies the resource pool used for the model deployment. Select the main resource pool from the dropdown list here.
2	dataset_name	string	The value specifies the dataset used in the pipeline. Enter sample-power-forecast to use the sample dataset.
3	tasktype	string	The value defines the task type: training for training tasks and prediction for prediction tasks. Enter training as the task type here.
4	predictiontype	string	The value defines the prediction type: file for Mlflow model file prediction and service for model service prediction. Enter service as the prediction type here.
5	lenoflist	number	The value simulates the number of wind turbines. Enter 1 here.
6	modelinstancename	string	The value defines the name of the model deployment instance. Enter a name less than 20 characters here.
7	prediction_dataset_name	string	The value specifies the dataset used for prediction. Enter sample-power-forecast to use the sample dataset for prediction.
8	sample_split_ratio	number	The value specifies the ratio to split the dataset into a training set and a prediction set. Enter 0.75 to put 75% of the data in the training set and 25% of the data in the prediction set.
9	hdfs_source	hdfs_source	The value specifies an HDFS data source to upload the model files. Select an HDFS data source from the dropdown list here.
10	hive_source	hive_source	The value specifies a Hive data source to upload the prediction data. Select a Hive data source from the dropdown list here.
11	ouid	string	The value specifies the current OU. Enter the current OU ID, which you can find by hovering over the OU name on the top bar.
12	prediction_instance	model_instance	The value specifies the name of the prediction instance. It should be the same as modelinstnacename to ensure the trained model can be used in prediction tasks.

Step 3. Configure Operators in Main Canvas

You need to add two Condition operators in the main canvas:

• Condition operator 1: perform the training tasks when the tasktype global parameter is training.

• Condition operator 2: perform the prediction tasks when the tasktype global parameter is prediction.

To create Condition operator 1:

1. Drag a Condition operator to the canvas from the operator list on the left.

2. Select the operator to configure the following operator information:

   • Enter Condition for Training as Name in the Basic Info section.

   • Configure Reference | tasktype | == | Declaration | training as Expression in the Configuration Parameter section.

To create Condition operator 2:

1. Drag a Condition operator to the canvas from the operator list on the left.

2. Select the operator to configure the following operator information:

   • Enter Condition for Prediction as Name in the Basic Info section.

   • Configure Reference | tasktype | == | Declaration | prediction as Expression in the Configuration Parameter section.

Step 4. Configure Operators for Training Tasks

The training process in this tutorial includes data preparation, model training, model version test, model deployment and so on. Double-select the Condition for Training operator to open its sub-canvas. You need to add 3 operators in the Condition for Training sub-canvas:

• A Recursion operator that determines when the raw data is ready for feature engineering and model training. In this tutorial, the data is ready when the random number generated is smaller than 5.

• A PythonEx operator that generates a loop list for a ParallelFor operator.

• A ParallelFor operator that performs the model training.

Configure a Recursion Operator as a Trigger

To create a Recursion operator:

1. In the Condition for Training sub-canvas, drag a Recursion operator from the operator list on the left.

2. Double-select the Recursion operator to open its sub-canvas.

3. In the sub-canvas, drag a PythonEx operator from the operator list on the left.

4. Select the PythonEx operator to configure the following operator information:

   • Enter Generate a Number between 1-10 as Name in the Basic Info section.

   • Select wind_power_forecasting as workspace in the Input Parameter section.

   • Select generate_random_int.py as entrypoint in the Input Parameter section.

   • Select Output Parameter > Add Parameter to add an output parameter, and configure result_number | number in the Output Parameter section.

5. Select the Condition for Training tab on the navigation bar to return to the Condition operator sub-canvas.

6. Select the Recursion operator to configure the following operator information:

   • Enter Recursion for Event Trigger as Name in the Basic Info section.

   • Configure Reference | Generate a Number between 1-10.result_number | < | Declaration | 5 as Expression in the Configuration Parameter section.

7. Select the save icon on the top toolbar to save your changes.

Configure a PythonEx Operator to Generate a Target List

To create a PythonEx operator:

1. In the Condition for Training sub-canvas, drag a PythonEx operator` from the operator list on the left.

2. Connect the output port of the Recursion for Event Trigger operator to the input port of this PythonEx operator.

3. Select the PythonEx operator to configure the following information:

   • Enter Generate Target List as Name in the Basic Info section.

   • Select wind_power_forecasting as workspace in the Input Parameter section.

   • Select generate_model_list.py as entrypoint in the Input Parameter section.

   • Select Input Parameter > Add Parameter to add an input parameter, and configure lenoflist | number | Reference | lenoflist in the Input Parameter section.

   • Select Output Parameter > Add Parameter to add an output parameter, and configure result_list | list in the Output Parameter section.

4. Select the save icon on the top toolbar to save your changes.

Configure a ParallelFor Operator for Model Training Loop

To create a ParallelFor operator:

1. In the Condition for Training sub-canvas, drag a ParallelFor operator from the operator list on the left.

2. Connect the output port of the Generate Target List operator to the input port of this ParallelFor operator.

3. Select the ParallelFor operator to configure the following information:

   • Enter Loop For Model Training as Name in the Basic Info section.

   • Configure Reference | Generate Target List.result_list | item as Input Parameter in the Configuration Parameter section.

The Loop For Model Training operator trains a model with model training data, and deploys the model version. You need to add 7 operators in the Loop For Model Training sub-canvas:

• A PythonEx operator that prepares the training data and exports the data files.

• A PythonEx operator that trains the model based on the input data and exports the model.

• A Model operator that creates a model.

• An Mlflow Model Version Register operator that registers an Mlflow model version and exports the model version as files.

• A Model Instance operator that creates a model deployment instance.

• A Model Test operator that checks whether the model version can be published as a valid model.

• A Single Model Deployment operator that deploys a single model version based on a published model version.

To create PythonEx operator 1:

1. In the Loop For Model Training sub-canvas, drag a PythonEx operator from the operator list on the left.

2. Select the operator to configure the following operator information:

   • Enter Prepare Training Data as Name in the Basic Info section.

   • Select wind_power_forecasting as workspace in the Input Parameter section.

   • Select prepare_training_data.py as entrypoint in the Input Parameter section.

   • Select requirements1.txt as requirements in the Input Parameter section.

   • Select Input Parameter > Add Parameter to add an input parameter, and configure datasetname | string | Reference | dataset_name in the Input Parameter section.

   • Select Input Parameter > Add Parameter to add another input parameter, and configure ratio | number | Reference | sample_split_ratio in the Input Parameter section.

   • Select Output Parameter > Add Parameter to add an output parameter, and configure result_datafile | file in the Output Parameter section.

To create PythonEx operator 2:

1. In the Loop For Model Training sub-canvas, drag a PythonEx operator from the operator list on the left.

2. Connect the output port of the Prepare Training Data operator to the input port of this PythonEx operator.

3. Select the operator to configure the following operator information:

   • Enter Model Training as Name in the Basic Info section.

   • Select wind_power_forecasting as workspace in the Input Parameter section.

   • Select train_model.py as entrypoint in the Input Parameter section.

   • Select requirements2.txt as requirements in the Input Parameter section.

   • Select Input Parameter > Add parameter to add an input parameter, and configure result_datafile | file | Reference | Prepare Training Data.result_datafile in the Input Parameter section.

To create a Model operator:

1. In the Loop For Model Training sub-canvas, drag a Model operator from the operator list on the left.

2. Select the operator to configure the following operator information:

   • Enter Create a Model as Name in the Basic Info section.

   • Select model-predictor as category in the Input Parameter section.

   • Refer to item as model_name in the Input Parameter section.

   • Select Text as input_data_type in the Input Parameter section.

   • Select private as scope in the Input Parameter section.

   • Select matching as technique in the Input Parameter section.

   • Select Light field as usecase in the Input Parameter section.

   • Select system as publisher in the Input Parameter section.

   • Enter the following as input_format in the Input Parameter section:

     [   {      "annotations": "",      "defaultValue": 300,      "dtype": "int",      "ftype": "continuous",      "name": "i.set",      "range": [0, 440],      "repeat": 0      },{      "annotations": "",      "defaultValue": 300,      "dtype": "int",      "ftype": "continuous",      "name": "X_basic.forecast_time",      "range": [0, 440],      "repeat": 0},{      "annotations": "",      "defaultValue": 300,      "dtype": "int",      "ftype": "continuous",      "name": "X_basic.horizon",      "range": [0, 440],      "repeat": 0},{      "annotations": "",      "defaultValue": 8,      "dtype": "int",      "ftype": "continuous",      "name": "X-basic.time",      "range": [0, 49],      "repeat": 0},{      "annotations": "",      "defaultValue": 10,      "dtype": "int",      "ftype": "continuous",      "name": "X-basic.hour",      "range": [0, 23],      "repeat": 0},{      "annotations": "",      "defaultValue": 10,      "dtype": "int",      "ftype": "continuous",      "name": "EC.nwp_time",      "range": [0, 23],      "repeat": 0},{      "annotations": "",      "defaultValue": 1.5,      "dtype": "float",      "ftype": "continuous",      "name": "EC.dist",      "range": [1, 2],      "repeat": 0},{      "annotations": "",      "defaultValue": 1.5,      "dtype": "float",      "ftype": "continuous",      "name": "EC.ws",      "range": [1, 2],      "repeat": 0},{      "annotations": "",      "defaultValue": 250,      "dtype": "float",      "ftype": "continuous",      "name": "EC.wd",      "range": [240, 300],      "repeat": 0},{      "annotations": "",      "defaultValue": 1,      "dtype": "float",      "ftype": "continuous",      "name": "EC.rho",      "range": [1, 2],      "repeat": 0},{      "annotations": "",      "defaultValue": 850,      "dtype": "float",      "ftype": "continuous",      "name": "EC.pres",      "range": [820, 900],      "repeat": 0},{      "annotations": "",      "defaultValue": 20,      "dtype": "float",      ftype": "continuous",      "name": "EC.tmp",      "range": [18, 30],      "repeat": 0},{      "annotations": "",      "defaultValue": 1,      "dtype": "float",      "ftype": "continuous",      "name": "GFS.nwp_time",      range": [1, 2],      "repeat": 0},{      "annotations": "",      "defaultValue": 20,      "dtype": "int",      "ftype": "continuous",      "name": "GFS.dist",      "range": [12, 100],      "repeat": 0},{      "annotations": "",      "defaultValue": 1,      "dtype": "float",      "ftype": "continuous",      "name": "GFS.ws",      range": [1, 2],      "repeat": 0      },{      "annotations": "",      "defaultValue": 50,      "dtype": "float",      "ftype": "continuous",      "name": "GFS.wd",      "range": [40, 300],      "repeat": 0      },{      "annotations": "",      "defaultValue": 1,      "dtype": "float",      "ftype": "continuous",      "name": "GFS.rho",      "range": [1, 2],      "repeat": 0},{      "annotations": "",      "defaultValue": 850,      "dtype": "float",      "ftype": "continuous",      "name": "GFS.pres",      "range": [840, 900],      "repeat": 0},{      "annotations": "",      "defaultValue": 19,      "dtype": "float",      "ftype": "continuous",      "name": "GFS.tmp",      "range": [18, 20],      "repeat": 0}      ]
     

   • Enter the following as output_format in the Input Parameter section:

     [   {      "annotations": "",      "defaultValue": 0,      "dtype": "float",      "ftype": "continuous",      "name": "power",      "range": [],      "repeat": 0}]
     

   • Select REST as interface in the Input Parameter section.

   • Select false as error_on_exit in the Input Parameter section.

To create an Mlflow Model Version Register operator:

1. In the Loop For Model Training sub-canvas, drag a Mlflow Model Version Register operator from the operator list on the left.

2. Connect the output ports of the Create a Model and Model Training operators to the input port of this Mlflow Model Version Register operator.

3. Select the operator to configure the following operator information:

   • Enter Create a Model Revision as Name in the Basic Info section.

   • Enter the following as input data in the Input Parameter section:

     {      "data": {      "names": [      "i.set",      "X_basic.forecast_time",      "X_basic.horizon",      "X-basic.time",      "X-basic.hour",      "EC.nwp_time",      "EC.dist",      "EC.ws",      "EC.wd",      "EC.rho",      "EC.pres",      "EC.tmp",      "GFS.nwp_time",      "GFS.dist",      "GFS.ws",      "GFS.wd",      "GFS.rho",      "GFS.pres",      "GFS.tmp"],      "ndarray": [      [      300,      300,      300,      8,      10,      10,      1.5,      1.5,      250,      1,      850,      20,      1,      20,      1,      50,      1,      850,      19]      ]}      }
     

   • Select time as version_rule in the Input Parameter section.

   • Select X86 as architecture in the Input Parameter section.

   • Select None as coprocessor in the Input Parameter section.

   • Select sklearn as framework in the Input Parameter section.

   • Select python3 as language in the Input Parameter section.

   • Refer to Create a Model.model_name_output as model_reference in the Input Parameter section.

   • Select system as publisher in the Input Parameter section.

   • Refer to Generate Target List.mlflow_model_file_paths as minio_paths in the Input Parameter section.

   • Select true as enforce_register in the Input Parameter section to register a model version even it fails the test.

   • Select true as serve_as_file in the Input Parameter section, which saves the model files and allows you to make predictions based on the Mlflow model files.

To create a Model Instance operator:

1. In the Loop For Model Training sub-canvas, drag a Model Instance operator from the operator list on the left.

2. Drag the output port of the Create a Model operator to the input port of this Model Instance operator.

3. Select the operator to configure the following operator information:

   • Enter Create a Model Instance as Name in the Basic Info section.

   • Refer to modelinstancename as name in the Input Parameter section.

   • Refer to resourcepool as resource_pool in the Input Parameter section.

   • Refer to Create a Model.model_name_output as model_name in the Input Parameter section.

   • Select ONLINE as deploy_mode in the Input Parameter section.

To create a Model Test operator:

1. In the Loop For Model Training sub-canvas, drag a Model Test operator from the operator list on the left.

2. Connect the output port of the Create a Model Revision operator to the input port of this Model Test operator.

3. Select the operator to configure the following operator information:

   • Enter Create a Model Test as Name in the Basic Info section.

   • Enter the following as input_dat in the Input Parameter section:

     {      "data": {      "names": [      "i.set",      "X_basic.forecast_time",      "X_basic.horizon",      "X-basic.time",      "X-basic.hour",      "EC.nwp_time",      "EC.dist",      "EC.ws",      "EC.wd",      "EC.rho",      "EC.pres",      "EC.tmp",      "GFS.nwp_time",      "GFS.dist",      "GFS.ws",      "GFS.wd",      "GFS.rho",      "GFS.pres",      "GFS.tmp"],      "ndarray": [      [      300,      300,      300,      8,      10,      10,      1.5,      1.5,      250,      1,      850,      20,      1,      20,      1,      50,      1,      850,      19      ]      ]}      }
     

   • Refer to Create a Model Revision.model_builder_name as model_builder in the Input Parameter section.

   • Enter 300 as test_timeout in the Input Parameter section.

To add a Single Model Deployment operator:

1. In the Loop For Model Training sub-canvas, drag a Single Model Deployment operator from the operator list on the left.

2. Connect the output ports of the Create a Model Instance, Create a Model Revision, and Create a Model Test operators to the input port of this Single Model Deployment operator.

3. Select the operator to configure the following operator information:

   • Enter Deploy the Model Revision as Name in the Basic Info section.

   • Refer to Create a Model Revision.model_revision_name as model_revision in the Input Parameter section.

   • Refer to Create a Model Instance.instance_name_output as instance_name in the Input Parameter section.

   • Enter 0.5 for request_cpu in the Input Parameter section.

   • Enter 0.5 for request_memory in the Input Parameter section.

   • Enter 0.8 for limit_cpu in the Input Parameter section.

   • Enter 0.8 for limit_memory in the Input Parameter section.

4. Select the save icon on the top toolbar to save your changes.

The Loop for Model Training sub-canvas should look like this:

Step 5. Configure Operators for Prediction Tasks

In the main canvas, double-select the Condition for Prediction operator to open its sub-canvas. You need to add 2 operators in the Condition for Prediction sub-canvas:

• A PythonEx operator that generates a loop list for a ParallelFor operator.

• A ParallelFor operator that performs the prediction tasks.

Configure a PythonEx Operator to Generate a Target List

To create a PythonEx operator:

1. In the Condition for Prediction sub-canvas, drag a PythonEx operator from the operator list on the left.

2. Select the PythonEx operator to configure the following information:

   • Enter Generate Target List for Prediction as Name in the Basic Info section.

   • Select wind_power_forecasting as workspace in the Input Parameter section.

   • Select generate_model_list.py as entrypoint in the Input Parameter section.

   • Select Input Parameter > Add Parameter to add an input parameter, and configure lenoflist | number | Reference | lenoflist in the Input Parameter section.

   • Select Output Parameter > Add Parameter to add an output parameter, and configure result_list | list in the Output Parameter section.

3. Select the save icon on the top toolbar to save your changes.

Configure a ParallelFor Operator for Model Prediction Loop

To create a ParallelFor operator:

1. In the Condition for prediction sub-canvas, drag a ParallelFor operator from the operator list on the left.

2. Connect the output port of Generate Target List for Prediction operator to the input port of this ParallelFor operator.

3. Select the ParallelFor operator to configure the following information:

   • Enter Loop For prediction as Name in the Basic Info section.

   • Configure Reference | Generate Target List.result_list | item1 as Input Parameter in the Configuration Parameter section.

4. Select the save icon on the top toolbar to save your changes.

You can make predictions based on Mlflow model files or published model services. Both types are included in this tutorial. You need to add 2 Condition operators in the Loop For prediction sub-canvas:

• Condition operator 1: make predictions based on the published model service, if you enter service as the predictiontype global parameter when you run the pipeline.

• Condition operator 2: make predictions based on the internal Mlflow model files, if you enter file as the predictiontype global parameter when you run the pipeline.

Configure a Condition Operator for Model Service Prediction

To create a Condition operator for model service prediction:

1. In the Loop for prediction sub-canvas, drag a Condition operator from the operator list on the left.

2. Select the Condition operator to configure the following information:

   • Enter Condition for Service Prediction Type as Name in the Basic Info section.

   • Configure Reference | predictiontype | == | Declaration | service as Expression in the Configuration Parameter section.

The Condition for Service Prediction Type operator performs model service prediction, then writes the prediction results and the actual data into a result file. When EnOS Hive is available in the current OU, you can export the result file to EnOS Hive. You need to add 5 operators in the Condition for Service Prediction Type sub-canvas:

• A PythonEx operator that prepares the prediction data.

• A Service Prediction operator that makes predictions based on the model service and deliver prediction results as an output.

• A PythonEx operator that writes the prediction results and the actual data to the output file.

• A PythonCode operator that checks if EnOS Hive is available in the current OU to save the prediction results.

• A Condition operator that saves the prediction results to the Hive table.

To create PythonEx operator 1:

1. In the Condition for Service Prediction Type sub-canvas, drag a PythonEx operator from the operator list on the left.

2. Select the operator to configure the following operator information:

   • Enter Prepare Prediction Data as Name in the Basic Info section.

   • Select wind_power_forecasting as workspace in the Input Parameter section.

   • Select prepare_predict_data.py as entrypoint in the Input Parameter section.

   • Select requirements1.txt as requirements in the Input Parameter section.

   • Select Input Parameter > Add Parameter to add an input parameter, and configure datasetname | string | Reference | dataset_name in the Input Parameter section.

   • Select Input Parameter > Add Parameter to add another input parameter, and configure ratio | number | Reference | sample_split_ratio in the Input Parameter section.

   • Select Output Parameter > Add Parameter to add an output parameter, and configure features | file in the Output Parameter section.

   • Select Output Parameter > Add Parameter to add another output parameter, and configure raw_data | file in the Output Parameter section.

To create a Service Prediction operator:

1. In the Condition for Service Prediction Type sub-canvas, drag a Service Prediction operator from the operator list on the left.

2. Connect the output port of Prepare Prediction Data operator to the input port of this Service Prediction operator.

3. Select the operator to configure the following operator information:

   • Enter Predict from Service as Name in the Basic Info section.

   • Refer to item1 as model in the Input Parameter section.

   • Refer to modelinstancename as instance in the Input Parameter section.

   • Refer to resourcepool as namespace in the Input Parameter section.

   • Select csv as data_type in the Input Parameter section.

   • Refer to Prepare Prediction Data.features as data in the Input Parameter section.

To create PythonEx operator 2:

1. In the Condition for Service Prediction Type sub-canvas, drag a PythonEx operator from the operator list on the left.

2. Connect the output ports of Prepare Prediction Data and Prediction from Service operators to the input port of this PythonEx operator.

3. Select the operator to configure the following operator information:

   • Enter Write results as Name in the Basic Info section.

   • Select wind_power_forecasting as workspace in the Input Parameter section.

   • Select write_results.py as entrypoint in the Input Parameter section.

   • Select Input Parameter > Add Parameter to add an input parameter, and configure predicted_data | file | Reference | Predict from Service.predictions in the Input Parameter section.

   • Select Input Parameter > Add Parameter to add another input parameter, and configure actual_data | file | Reference | Prepare Prediction Data.raw_data in the Input Parameter section.

   • Select Output Parameter > Add Parameter to add an output parameter, and configure outputfile | file in the Output Parameter section.

To create a PythonCode operator:

1. In the Condition for Service Prediction Type sub-canvas, drag a PythonCode operator from the operator list on the left.

2. Select the operator to configure the following operator information:

   • Enter Check Hive Config as Name in the Basic Info section

   • Enter the following codes as code in the Input Parameter section

     import json
     import argparse
     from pathlib import Path
     
     # Define an ArgumentParser
     parser = argparse.ArgumentParser()
     parser.add_argument("--hive_source", type=str, required=True)
     parser.add_argument("--export_to_hive", type=str, required=True)
     
     # Parse arguments from command
     args = parser.parse_args()
     
     Path(args.export_to_hive).parent.mkdir(parents=True, exist_ok=True)
     with open(args.export_to_hive, 'w') as f:
       f.write('True' if args.hive_source else 'False')
     

   • Select Input Parameter > Add Parameter to add an input parameter and configure hive_source | hive_source | Reference | hive_source in the Input Parameter section.

   • Select Output Parameter > Add Parameter to add an output parameter, and configure export_to_hive | Boolean in the Output Parameter section.

To create a Condition operator:

1. In the Condition for Service Prediction Type sub-canvas, drag a Condition operator from the operator list on the left.

2. Connect the output ports of Write results and Check Hive Config operators to the input port of this Condition operator.

3. Select the operator to configure the following operator information:

   • Enter Export to Hive? as Name in the Basic Info section.

   • Configure Reference | Check Hive Config.export_to_hive | == | Declaration | true as Expression in the Configuration Parameter section.

The Export to Hive? operator exports the result file of model service prediction to EnOS Hive. You need to add 3 operators in the Export to Hive? sub-canvas:

• A PythonCode operator that delivers an HDFS directory path and generates an SQL statement for EnOS Hive.

• An HDFS Uploader operator that uploads the prediction results to the HDFS directory.

• A Hive operator that saves the prediction results to EnOS Hive.

To create a PythonCode operator:

1. In the Export to Hive? sub-canvas, drag a PythonCode operator from the operator list on the left.

2. Select the operator to configure the following operator information:

   • Enter Generate variables as Name in the Basic Info section.

   • Enter the following codes as code in the Input Parameter section.

     import json
     import argparse
     from pathlib import Path
     
     # Define an ArgumentParser
     parser = argparse.ArgumentParser()
     parser.add_argument("--ouid", type=str, required=True)
     parser.add_argument("--sql_statements", type=str, required=True)
     parser.add_argument("--hdfs_dest", type=str, required=True)
     
     args = parser.parse_args()
     
     target_sqls = [f"""create external table if not exists data_{args.ouid}.kmmldsdemo(  `i_set` int,  `X_basic_forecast_time` timestamp,  `X_basic_horizon` int,  `X_basic_time` timestamp,  `X_basic_hour` int,  `EC_nwp_time` timestamp,  `EC_dist` int,  `EC_ws` double,  `EC_wd` double,  `EC_rho` double,  `EC_press` double,  `EC_tmp` double,  `GFS_nwp_time` timestamp,  `GFS_dist` int,  `GFS_ws` double,  `GFS_wd` double,  `GFS_rho` double,  `GFS_press` double,  `GFS_tmp` double,  `r_speed` double,  `r_power` double,  `f_speed` double,  `f_power` double  )  row format delimited fields terminated by ','   lines terminated by '\n'  location '/user/hive/warehouse/data_{args.ouid}.db/kmmldsdemo'  tblproperties ('skip.header.line.count'='1')"""]
     
     Path(args.sql_statements).parent.mkdir(parents=True, exist_ok=True)
     with open(args.sql_statements, 'w') as f:
       json.dump(target_sqls, f)
     
     Path(args.hdfs_dest).parent.mkdir(parents=True, exist_ok=True)
     with open(args.hdfs_dest, 'w') as f:
       f.write(f"/user/hive/warehouse/data_{args.ouid}.db/kmmldsdemo")
     

   • Select Input Parameter > Add Parameter to add an input parameter and configure ouid | string | Reference | ouid in the Input Parameter section.

   • Select Output Parameter > Add Parameter to add an output parameter, and configure sql_statements | List in the Output Parameter section.

   • Select Output Parameter > Add Parameter to add an output parameter, and configure hdfs_dest | String in the Output Parameter section.

To create an HDFS Uploader operator:

1. In the Export to Hive? sub-canvas, drag an HDFS Uploader operator from the operator list on the left.

2. Connect the output port of the Generate variables operator to the input port of this HDFS Uploader operator.

3. Select the operator to configure the following operator information:

   • Enter HDFS Uploader as Name in the Basic Info section.

   • Refer to hdfs_source as data_source in the Input Parameter section.

   • Refer to Write results.outputfile as file in the Input Parameter section.

   • Refer to item1 as filename in the Input Parameter section.

   • Refer to Generate variables.hdfs_dest as dest in the Input Parameter section.

   • Select ture as overwrite in the Input Parameter section.

To create a Hive operator:

1. In the Export to Hive? sub-canvas, drag a Hive operator from the operator list on the left.

2. Connect the output ports of Generate variables and HDFS Uploader operators to the input port of this Hive operator.

3. Select the operator to configure the following operator information:

   • Enter Hive as Name in the Basic Info section.

   • Refer to hive_source as data_source in the Input Parameter section.

   • Refer to Generate variables.sql_statements as sqls in the Input Parameter section.

4. Select the save icon on the top toolbar to save your changes.

The Condition for Service Prediction sub-canvas should look like this:

Configure Condition for Model File Prediction

To create a Condition operator for model file prediction:

1. In the Loop for prediction sub-canvas, drag a Condition operator from the operator list on the left.

2. Select the Condition operator to configure the following information:

   • Enter Condition for Model File Prediction as Name in the Basic Info section.

   • Configure Reference | predictiontype | == | Declaration | file as Expression in the Configuration Parameter section.

The Condition for Model File Prediction operator performs Mlflow model file prediction, then writes the prediction results and the actual data into a result file. When EnOS Hive is available in the current OU, you can export the result file to EnOS Hive. You need to add 6 operators in the Condition for Model File Prediction sub-canvas:

• A PythonCode operator that gets the latest model version.

• A PythonEx operator that prepare the prediction data.

• An Mlflow Model Version Prediction operator that makes predictions based on the internal Mlflow model files and deliver the prediction results as an output.

• A PythonEx operator that writes the prediction results and the actual data to the output file.

• A PythonCode operator that checks if EnOS Hive is available in the current OU to save the prediction results.

• A Condition operator that saves the prediction results to the Hive table.

To create PythonCode operator 1:

1. In the Condition for Model File Prediction sub-canvas, drag a PythonCode operator from the operator list on the left.

2. Select the operator to configure the following operator information:

   • Enter Get Latest Model Version as Name in the Basic Info section.

   • Enter the following codes as code in the Input Parameter section.

     import json
     import argparse
     from pathlib import Path
     import requests
     
     # Define an ArgumentParser
     parser = argparse.ArgumentParser()
     parser.add_argument("--model_name", type=str, required=True)
     parser.add_argument("--latest_version", type=str, required=True)
     
     # Parse arguments from command
     args = parser.parse_args()
     
     r = requests.get(url = f'http://eap-modelhub-server:8080/apis/v1beta1/modelRevisions?modelName={args.model_name}')
     data = r.json()
     
     latest_version = data['data'][0]['resourceVersion'] if 'data' in data and len(data['data']) > 0 else ""
     
     Path(args.latest_version).parent.mkdir(parents=True, exist_ok=True)
     with open(args.latest_version, 'w') as f:
       f.write(latest_version)
     

   • Select Input Parameter > Add Parameter to add an input parameter and configure model_name | model_name | Reference | item1 in the Input Parameter section.

   • Select Output Parameter > Add Parameter to add an output parameter, and configure latest_version | model_version in the Output Parameter section.

To create PythonEx operator 1:

1. In the Condition for Model File Prediction sub-canvas, drag a PythonEx operator from the operator list on the left.

2. Select the operator to configure the following operator information:

   • Enter Prepare Prediction Data 2 as Name in the Basic Info section.

   • Select wind_power_forecasting as workspace in the Input Parameter section.

   • Select prepare_predict_data.py as entrypoint in the Input Parameter section.

   • Select requirements1.txt as requirements in the Input Parameter section.

   • Select Input Parameter > Add Parameter to add an input parameter, and configure datasetname | string | Reference | prediction_dataset_name in the Input Parameter section.

   • Select Input Parameter > Add Parameter to add another input parameter, and configure ratio | number | Reference | sample_split_ratio in the Input Parameter section.

   • Select Output Parameter > Add Parameter to add an output parameter, and configure features | file in the Output Parameter section.

   • Select Output Parameter > Add Parameter to add another output parameter, and configure raw_data | file in the Output Parameter section.

To create an Mlflow Model Version Prediction operator:

1. In the Condition for Model File Prediction sub-canvas, drag an Mlflow Model Version Prediction operator from the operator list on the left.

2. Connect the output ports of Prepare Prediction Data 2 and Get Latest Model Version operators to the input port of this Mlflow Model Version Prediction operator.

3. Select the operator to configure the following operator information:

   • Enter Predict from Mlflow Model File as Name in the Basic Info section.

   • Refer to item1 as model_name in the Input Parameter section.

   • Refer to Get Latest Model Version.latest_version as model_version in the Input Parameter section.

   • Refer to Prepare Prediction Data 2.features as data in the Input Parameter section.

   • Select csv as data_type in the Input Parameter section.

To create PythonEx operator 2:

1. In the Condition for Model File Prediction sub-canvas, drag a PythonEx operator from the operator list on the left.

2. Connect the output ports of Prepare Prediction Data 2 and Predict from Mlflow Model File operators to the input port of this PythonEx operator.

3. Select the operator to configure the following operator information:

   • Enter Write results 2 as Name in the Basic Info section.

   • Select wind_power_forecasting as workspace in the Input Parameter section.

   • Select write_results.py as entrypoint in the Input Parameter section.

   • Select Input Parameter > Add Parameter to add an input parameter, and configure predicted_data | file | Reference | Predict from Mlflow Model File.predictions in the Input Parameter section.

   • Select Input Parameter > Add Parameter to add another input parameter, and configure actual_data | file | Reference | Prepare Prediction Data 2.raw_data in the Input Parameter section.

   • Select Output Parameter > Add Parameter to add an output parameter, and configure outputfile | file in the Output Parameter section.

To create PythonCode operator 2:

1. In the Condition for Model File Prediction sub-canvas, drag a PythonCode operator from the operator list on the left.

2. Select the operator to configure the following operator information:

   • Enter Check Hive Config 2 as Name in the Basic Info section.

   • Enter the following codes as code in the Input Parameter section.

     import json
     import argparse
     from pathlib import Path
     
     # Define an ArgumentParser
     parser = argparse.ArgumentParser()
     parser.add_argument("--hive_source", type=str, required=True)
     parser.add_argument("--export_to_hive", type=str, required=True)
     
     # Parse arguments from command
     args = parser.parse_args()
     
     Path(args.export_to_hive).parent.mkdir(parents=True, exist_ok=True)
     with open(args.export_to_hive, 'w') as f:
       f.write('True' if args.hive_source else 'False')
     

   • Select Input Parameter > Add Parameter to add an input parameter and configure hive_source | hive_source | Reference | hive_source in the Input Parameter section.

   • Select Output Parameter > Add Parameter to add an output parameter, and configure export_to_hive | boolean in the Output Parameter section.

To create a Condition operator:

1. In the Condition for Model File Prediction sub-canvas, drag a Condition operator to the pipeline canvas from operator list on the left.

2. Connect the output ports of Write results 2 and Check Hive Config 2 operators to the input port of this Condition operator.

3. Select the operator to configure the following operator information:

   • Enter Export to Hive 2? as Name in the Basic Info section.

   • Configure Reference | Check Hive Config 2.export_to_hive | == | Declaration | true as Expression in the Configuration Parameter section.

The Export to Hive 2? operator exports the result file of Mlflow model file prediction to EnOS Hive. You need to add 3 operators in the Export to Hive 2? sub-canvas:

• A PythonCode operator that delivers an HDFS directory path and generates an SQL statement for EnOS Hive.

• An HDFS Uploader operator that uploads the prediction results to the HDFS directory.

• A Hive operator that saves the prediction results to EnOS Hive.

To create a PythonCode operator:

1. In the Export to Hive 2? sub-canvas, drag a PythonCode operator from the operator list on the left.

2. Select the operator to configure the following operator information:

   • Enter Generate variables 2 as Name in the Basic Info section.

   • Enter the following codes as code in the Input Parameter section.

     import json
     import argparse
     from pathlib import Path
     
     # Define an ArgumentParser
     parser = argparse.ArgumentParser()
     parser.add_argument("--ouid", type=str, required=True)
     parser.add_argument("--sql_statements", type=str, required=True)
     parser.add_argument("--hdfs_dest", type=str, required=True)
     
     args = parser.parse_args()
     
     target_sqls = [f"""create external table if not exists data_{args.ouid}.kmmldsdemo(  `i_set` int,  `X_basic_forecast_time` timestamp,  `X_basic_horizon` int,  `X_basic_time` timestamp,  `X_basic_hour` int,  `EC_nwp_time` timestamp,  `EC_dist` int,  `EC_ws` double,  `EC_wd` double,  `EC_rho` double,  `EC_press` double,  `EC_tmp` double,  `GFS_nwp_time` timestamp,  `GFS_dist` int,  `GFS_ws` double,  `GFS_wd` double,  `GFS_rho` double,  `GFS_press` double,  `GFS_tmp` double,  `r_speed` double,  `r_power` double,  `f_speed` double,  `f_power` double  )  row format delimited fields terminated by ','   lines terminated by '\n'  location '/user/hive/warehouse/data_{args.ouid}.db/kmmldsdemo'  tblproperties ('skip.header.line.count'='1')"""]
     
     Path(args.sql_statements).parent.mkdir(parents=True, exist_ok=True)
     with open(args.sql_statements, 'w') as f:
       json.dump(target_sqls, f)
     
     Path(args.hdfs_dest).parent.mkdir(parents=True, exist_ok=True)
     with open(args.hdfs_dest, 'w') as f:
       f.write(f"/user/hive/warehouse/data_{args.ouid}.db/kmmldsdemo")
     

   • Select Input Parameter > Add Parameter to add an input parameter and configure ouid | string | Reference | ouid in the Input Parameter section.

   • Select Output Parameter > Add Parameter to add an output parameter, and configure sql_statements | list in the Output Parameter section.

   • Select Output Parameter > Add Parameter to add another output parameter, and configure hdfs_dest | string in the Output Parameter section.

To create a Hive operator:

1. In the Export to Hive 2? sub-canvas, drag a Hive operator from the operator list on the left.

2. Connect the output port of the Generate variables operator to the input port of this Hive operator.

3. Select the operator to configure the following operator information:

   • Enter Hive 2 as Name in the Basic Info section.

   • Refer to hive_source as data_source in the Input Parameter section.

   • Refer to Generate variables 2.sql_statements as sqls in the Input Parameter section.

To create an HDFS Uploader operator:

1. In the Export to Hive 2? sub-canvas, drag an HDFS Uploader operator from the operator list on the left.

2. Connect the output ports of Generate variables 2 and Hive 2 operators to the input port of this HDFS Uploader operator.

3. Select the operator to configure the following operator information:

   • Enter HDFS Uploader 2 as Name in the Basic Info section.

   • Refer to hdfs_source as data_source in the Input Parameter section.

   • Refer to Write results 2.outputfile as file in the Input Parameter section.

   • Refer to item1 as filename in the Input Parameter section.

   • Refer to Generate variables 2.hdfs_dest as dest in the Input Parameter section.

   • Select ture as overwrite in the Input Parameter section.

4. Select the save icon on the top toolbar to save your changes.

The Condition for Model File Prediction sub-canvas should look like this:

Next Unit

Unit 5. Run the Pipeline and View Pipeline Running Results