:original_name: modelarts_21_0012.html

.. _modelarts_21_0012:

Training a Model
================

After labeling the images, perform auto training to obtain an appropriate model version.

Procedure
---------

#. On the **ExeML** page, click the name of the project that is successfully created. The **Label Data** tab page is displayed.

#. On the **Label Data** tab page, click **Train** in the upper right corner. In the displayed **Training Configuration** dialog box, set related parameters. :ref:`Table 1 <modelarts_21_0012__en-us_topic_0284258841_en-us_topic_0169446261_table56110116164>` describes the parameters.


   .. figure:: /_static/images/en-us_image_0000001157080807.png
      :alt: **Figure 1** Setting training parameters

      **Figure 1** Setting training parameters

   .. _modelarts_21_0012__en-us_topic_0284258841_en-us_topic_0169446261_table56110116164:

   .. table:: **Table 1** Parameter description

      +---------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------+
      | Parameter                       | Description                                                                                                                                                                                                                                                                                                                                                                         | Default Value                   |
      +=================================+=====================================================================================================================================================================================================================================================================================================================================================================================+=================================+
      | Dataset Version                 | This version is the one when the dataset is published in **Data Management**. In an ExeML project, when a training job is started, the dataset is published as a version based on the previous data labeling.                                                                                                                                                                       | Randomly provided by the system |
      |                                 |                                                                                                                                                                                                                                                                                                                                                                                     |                                 |
      |                                 | The system automatically provides a version number. You can change it to the version number that you want.                                                                                                                                                                                                                                                                          |                                 |
      +---------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------+
      | Training and Validation Ratios  | The labeled sample is randomly divided into a training set and a validation set. By default, the ratio for the training set is 0.8, and that for the validation set is 0.2. The **usage** field in the manifest file records the set type. The value ranges from 0 to 1.                                                                                                            | 0.8                             |
      +---------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------+
      | Incremental Training Version    | Select the version with the highest precision to perform training again. This accelerates model convergence and improves training precision.                                                                                                                                                                                                                                        | None                            |
      +---------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------+
      | Max. Training Duration (Minute) | If training is not completed within the maximum training duration, the model is saved and training stops. To prevent the model from exiting before convergence, set this parameter to a large value. The value ranges from 6 to 6000. You are advised to properly extend the training duration. Set the training duration to more than 1 hour for a training set with 2,000 images. | 60                              |
      +---------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------+
      | Training Preference             | -  **performance_first**: performance first. The training duration is short and the generated model is small.                                                                                                                                                                                                                                                                       | balance                         |
      |                                 | -  **balance**: balanced performance and precision                                                                                                                                                                                                                                                                                                                                  |                                 |
      |                                 | -  **accuracy_first**: precision first. The training duration is long and the generated model is large.                                                                                                                                                                                                                                                                             |                                 |
      +---------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------+
      | Instance Flavor                 | Select the resource specifications used for training. By default, the following specifications are supported:                                                                                                                                                                                                                                                                       | **ExeML (CPU)**                 |
      |                                 |                                                                                                                                                                                                                                                                                                                                                                                     |                                 |
      |                                 | -  **Compute-intensive 1 instance (CPU)**                                                                                                                                                                                                                                                                                                                                           |                                 |
      |                                 |                                                                                                                                                                                                                                                                                                                                                                                     |                                 |
      |                                 | The compute flavors are for reference only. Obtain the flavors on the management console.                                                                                                                                                                                                                                                                                           |                                 |
      +---------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------+

#. After configuring training parameters, click **Yes** to start auto model training. The training takes a certain period of time. Wait until the training is complete. If you close or exit this page, the system still performs the training operation.

#. On the **Train Model** tab page, wait until the training status changes from **Running** to **Completed**.

#. View the training details, such as **Accuracy**, **Evaluation Result**, **Training Parameters**, and **Classification Statistics**. For details about the evaluation result parameters, see :ref:`Table 2 <modelarts_21_0012__en-us_topic_0284258841_en-us_topic_0169446261_table15870125755817>`.

   .. _modelarts_21_0012__en-us_topic_0284258841_en-us_topic_0169446261_table15870125755817:

   .. table:: **Table 2** Evaluation result parameters

      +-----------+-------------------------------------------------------------------------------------------------------------------------------------------------+
      | Parameter | Description                                                                                                                                     |
      +===========+=================================================================================================================================================+
      | Recall    | Fraction of correctly predicted samples over all samples predicted as a class. It shows the ability of a model to distinguish positive samples. |
      +-----------+-------------------------------------------------------------------------------------------------------------------------------------------------+
      | Precision | Fraction of correctly predicted samples over all samples predicted as a class. It shows the ability of a model to distinguish negative samples. |
      +-----------+-------------------------------------------------------------------------------------------------------------------------------------------------+
      | Accuracy  | Fraction of correctly predicted samples over all samples. It shows the general ability of a model to recognize samples.                         |
      +-----------+-------------------------------------------------------------------------------------------------------------------------------------------------+
      | F1 Score  | Harmonic average of the precision and recall of a model. It is used to evaluate the quality of a model. A high F1 score indicates a good model. |
      +-----------+-------------------------------------------------------------------------------------------------------------------------------------------------+

.. note::

   An ExeML project supports multiple rounds of training, and each round generates a version. For example, the first training version is **V001 (**\ *xxx*\ **)**, and the next version is **V002 (**\ *xxx*\ **)**. The trained models can be managed by training version. After the trained model meets your requirements, deploy the model as a service.