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#### **Overview of Model Training Process**

The following graph illustrates the comprehensive process involved in training a deep neural model.

![MarineGEO circle logo](/images/training_process.png#training_process)

To train a deep neural model, several components are required:
- Dataset: The dataset is collected from various resources and serves as the foundation for model training.
- Data pre-processing: This step involves enhancing the quality of the data through techniques such as cleaning, splitting, feature engineering and normalization.
- Model training: The training set is used to train the deep neural model. During this process, the model processes the input data, calculates the loss function, and adjusts the weights of the model accordingly.
- Model evaluation: The model's performance is assessed using specific metrics to evaluate its effectiveness. This evaluation helps identify areas for improvement and facilitates hyperparameter tuning.
- Hyperparameter optimization: Different combinations of hyperparameters are explored to achieve the highest possible model performance. This process involves fine-tuning the hyperparameters to optimize the model's performance on the validation set.
- Model testing: The model's performance is assessed on a separate test set that was not used during training or hyperparameter optimization. This evaluation provides an unbiased measure of the model's effectiveness.  

#### **Step-by-step to start Training Procedure**

##### ***Access to wizard interface***

![MarineGEO circle logo](/images/wizard_access.png#wizard_access)

To access the training wizard, users can navigate to the \`\`\` \`Model Training\` \`\`\` section on the sidebar and then click on the \`\`\` \`New Training\` \`\`\` button.

##### ***Wizard interface layout***

![MarineGEO circle logo](/images/wizard_layout.png#wizard_layout)

The layout of the training wizard is organized as follows:
1. Navigation bar: This bar allows users to navigate between the training list and the new training wizard.

![MarineGEO circle logo](/images/wizard_layout_navigation_bar.png#wizard_layout_navigation_bar)

2. Step navigation: This feature assists users in tracking their progress through the various steps of the training wizard. It helps ensure that users are aware of their current step and can easily move between steps if needed.

![MarineGEO circle logo](/images/wizard_layout_step_navigation.png#wizard_layout_step_navigation)

3. Step content: The step content section displays the specific content related to the current step of the training wizard. It presents the necessary information, forms, or instructions for each step, guiding users through the process.

![MarineGEO circle logo](/images/wizard_layout_step_content.png#wizard_layout_step_content)

##### ***Create a training experiment***

There are 4 steps for users to walkthrough.

###### **1. Name your model training experiment**

In this step, users have the option to provide a custom name for their training experiment. This allows them to assign a meaningful and recognizable name to their experiment, making it easier to manage and identify among other experiments in the system.

![MarineGEO circle logo](/images/wizard_step_1.png#wizard_step_1)

Users can input the desired name for their training experiment into the provided textbox.

Once users have entered the name, they can click the \`\`\` \`Next\` \`\`\` button to advance to the subsequent steps of the training wizard.

###### **2. Choose a pipeline**

In this step, users will pick a specific pipeline and its version.

D2 offers two pipeline for two tasks:
- **Daily power demand forecast**.
- **Solar power generation forecast**.
  
![MarineGEO circle logo](/images/wizard_step_2.png#wizard_step_2)  

1. Pipeline name select box: click on the select box and make a selection from the available pipeline names.

![MarineGEO circle logo](/images/wizard_step_2_select_pipeline.png#wizard_step_2_select_pipeline)

2. Pipeline version select box: click on the select box and choose a version from the provided options.

![MarineGEO circle logo](/images/wizard_step_2_select_pipeline_version.png#wizard_step_2_select_pipeline_version)

Once users have chosen the pipeline name and its version, they can click the \`\`\` \`Next\` \`\`\` button to advance to the subsequent steps of the training wizard.

###### **3. Prepare your resource**

During this step, users will be required to provide the dataset for training. There are two available options:
- Upload their own dataset
- Use a dataset from our Amazon S3 storage.

![MarineGEO circle logo](/images/wizard_step_3_options.png#wizard_step_3_options)  

###### **3.1. Upload their own dataset**

When opting to upload their own dataset, users need to click on the "Sample" button. 
  
It is important to note that we have predefined a specific set of features for the feature engineering step in the machine pipeline. Therefore, users are required to provide values for these features by completing the provided sample data.

![MarineGEO circle logo](/images/wizard_step_3_user_dataset.png#wizard_step_3_user_dataset)  
  
After uploading the dataset, D2 will generate a comprehensive analytics report for the user.

![MarineGEO circle logo](/images/wizard_step_3_report_analysist_status.png#wizard_step_3_report_analysist_status)  

![MarineGEO circle logo](/images/wizard_step_3_report_analysist_result.png#wizard_step_3_report_analysist_result)  

Users can access the analytics report by clicking on the \`\`\` \`View Analytics Report\` \`\`\` button when the status changes from \`\`\` \`Analyzing\` \`\`\`.

The report includes:  
- An overview of the dataset
- Statistical information
- Various plots for visual analysis.

![MarineGEO circle logo](/images/wizard_step_3_report_overview.png#wizard_step_3_report_overview)  

![MarineGEO circle logo](/images/wizard_step_3_report_statistical.png#wizard_step_3_report_statistical)  

![MarineGEO circle logo](/images/wizard_step_3_report_plot.png#wizard_step_3_report_plot.png)  

###### **3.2. Use a dataset from Amazon S3 storage**

**T.B.D**

###### **4. Setting parameters and configurations**

In this section, users will customize the training experiment by setting various parameters for the training process.

There are 3 different groups of parameters:
- Hyperparameters
- Katib tunning experiemnt configuration.
- Other parameters

###### **4.1. Hyperparameters**

To know more about the definition of hyperparameters, please visit [Introduction of Hyperparameter Optimization section](/docs/components/hpo/introduction).

Currently, for two pipeline, the hyperparameters including:
- Batch size
- Number of layers
- Hidden Units
- Optimizer
- Number of look-back steps (window)

The following table displays information about the available hyperparameters specific to the chosen pipeline.

![MarineGEO circle logo](/images/wizard_step_4_hyperparameters.png#wizard_step_4_hyperparameters)

When users click on the \`\`\` \`Edit\` \`\`\` button, they will be able to modify the value for the selected hyperparameter. 

The edit dialog will appear as follows:

![MarineGEO circle logo](/images/wizard_step_4_hyperparameters_edit.png#wizard_step_4_hyperparameters_edit)

1. Input box: Users will input a desired value to here.

![MarineGEO circle logo](/images/wizard_step_4_hyperparameters_input_box.png#wizard_step_4_hyperparameters_input_box)

2. \`\`\` \`Add\` \`\`\` button:  add the user input value to the current value space.

![MarineGEO circle logo](/images/wizard_step_4_hyperparameters_add.png#wizard_step_4_hyperparameters_add)

3. \`\`\` \`Delete\` \`\`\` button: remove a specific value from the value space.

![MarineGEO circle logo](/images/wizard_step_4_hyperparameters_remove.png#wizard_step_4_hyperparameters_remove)

###### **4.2. Katib tunning experiemnt configuration**

The parameters including:
- Maximum trial count: 
The maximum number of trials to run. This is equivalent to the number of hyperparameter sets that the system should generate to test the model.
- Maximum failed trial count:
The maximum number of trials allowed to fail. This is equivalent to the number of failed hyperparameter sets that the system should test.

![MarineGEO circle logo](/images/wizard_step_4_katib_param_config.png#wizard_step_4_katib_param_config)

###### **4.3. Others parameters**

Others parameters including:

- Data splitting ratio
    - *Description:* The ratio used to split a given dataset into three disjoint sets, namely the training set, validation set, and testing set.
    - *Data type:* Float, range between 0.0 - 1.0. 
    - *Default value:* 0.8 / 0.1 / 0.1 (training set / validation set / test set).
- Using GPU
    - *Description:* This option allows users to select whether the training experiment will be executed on CPU or GPU.
    - *Data type:* Options.
        - CPU
        - GPU
    - *Default value:* GPU
- Normalization options
    - *Description:* This option allows users to select the method for normalizing the dataset.
    - *Data type:* Options.
        - No normalize.
        - Using MinMax normalization in range [ 0, 1 ].
        - Using MinMax normalization in range [ -1, 1].
        - Using Standardize normalization.
    - *Default value:* 1. Using MinMax normalization in range [ 0, 1 ].
- Data sampling rate options
    - *Description:* This option allows users to adjust the sampling rate of the dataset.
    - *Data type:* Options.
        - 15 minute interval.
        - 30 minute interval.
        - 60 minute interval.
    - *Default value:* 15 minute interval.
- Using addtional features
    - *Description:* This option allows users to determine whether pre-defined covariates can be used to enrich the dataset.
    - *Data type:* Options (True or False)
    - *Default value:* True.
- Loss function options
    - *Description:* This option allows users to select specific loss function for training expriment.
    - *Data type:* Options.
        - MAE: Mean Absolute Error.
        - MSE: Mean Squared Error.
    - *Default value:* MSE.
- Number of epochs
    - *Description:* This option allows users to select the number of epochs, which represents the number of times the entire training dataset is passed through the algorithm during training.
    - *Data type:* Integer.
    - *Default value:* 30.
- Learning rate:
    - *Description:* This option allows users to control how much to change the model in response to the estimated error each time the model weights are updated.
    - *Data type:* Float.
    - *Default value:* \`\`\`9e-5\`\`\`
- Number of forecasting steps (horizon)
    - *Description:* This option allows users to select how many steps the model will forecast.
    - *Data type:* Options.
        - 24 steps.
        - 48 steps.
        - 96 steps.
    - *Default value:* 24 steps.
    
![MarineGEO circle logo](/images/wizard_step_4_other_parameters.png#wizard_step_4_other_parameters.png)

###### **5. Start training experiment**

To initiate the training experiment, click on the \`\`\` \`Start Training\` \`\`\` located below the \`\`\` \`Other Parameters\` \`\`\` section or on the left side column.

![MarineGEO circle logo](/images/wizard_step_4_start_training_1.png#wizard_step_4_start_training_1)

![MarineGEO circle logo](/images/wizard_step_4_start_training_2.png#wizard_step_4_start_training_2)

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