Example edits (#158)

docs/guides/advanced/execute_remotely.md

@@ -3,7 +3,7 @@ title: Remote Execution
 ---
 
 The [execute_remotely_example](https://github.com/allegroai/clearml/blob/master/examples/advanced/execute_remotely_example.py)
-script demonstrates the use of the [`Task.execute_remotely`](../../references/sdk/task.md#execute_remotely/) method. 
+script demonstrates the use of the [`Task.execute_remotely`](../../references/sdk/task.md#execute_remotely) method. 
 
 :::note
 Make sure to have at least one [ClearML Agent](../../clearml_agent.md) running and assigned to listen to the `default` queue 
@@ -37,16 +37,19 @@ In the example script's `train` function, the following code explicitly reports
 
 ```python
 Logger.current_logger().report_scalar(
-    "train", "loss", iteration=(epoch * len(train_loader) + batch_idx), value=loss.item())
+    "train", "loss", iteration=(epoch * len(train_loader) + batch_idx), value=loss.item()
+)
 ```
 
 In the `test` method, the code explicitly reports `loss` and `accuracy` scalars.
 
 ```python
 Logger.current_logger().report_scalar(
-    "test", "loss", iteration=epoch, value=test_loss)
+    "test", "loss", iteration=epoch, value=test_loss
+)
 Logger.current_logger().report_scalar(
-    "test", "accuracy", iteration=epoch, value=(correct / len(test_loader.dataset)))
+    "test", "accuracy", iteration=epoch, value=(correct / len(test_loader.dataset))
+)
 ```    
 
 These scalars can be visualized in plots, which appear in the ClearML web UI, in the experiment's
@@ -68,9 +71,8 @@ Text printed to the console for training progress, as well as all other console
 
 ## Artifacts
 
-Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in 
+Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks models
-the info panel of the **MODELS** tab.  
+and any snapshots created using PyTorch. 
-
 
 ![image](../../img/examples_remote_execution_artifacts.png)
 

docs/guides/frameworks/autokeras/autokeras_imdb_example.md

@@ -2,8 +2,14 @@
 title: AutoKeras IMDB
 ---
 The [autokeras_imdb_example.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/autokeras/autokeras_imdb_example.py) example 
-script demonstrates the integration of **ClearML** into code, which uses [autokeras](https://github.com/keras-team/autokeras). 
+script demonstrates the integration of ClearML into code, which uses [autokeras](https://github.com/keras-team/autokeras). 
-It trains text classification networks on the Keras built-in [IMDB](https://keras.io/api/datasets/imdb/) dataset, using the autokeras [TextClassifier](https://autokeras.com/text_classifier/) class, and searches for the best model. It uses two TensorBoard callbacks, one for training and one for testing. **ClearML** automatically logs everything the code sends to TensorBoard. When the script runs, it creates an experiment named `autokeras imdb example with scalars`, which is associated with the `autokeras` project.
+
+The example does the following: 
+* Trains text classification networks on the Keras built-in [IMDB](https://keras.io/api/datasets/imdb/) dataset, using 
+the autokeras [TextClassifier](https://autokeras.com/text_classifier/) class, and searches for the best model. 
+* Uses two TensorBoard callbacks, one for training and one for testing. 
+* ClearML automatically logs everything the code sends to TensorBoard. 
+* Creates an experiment named `autokeras imdb example with scalars`, which is associated with the `autokeras` project.
 
 ## Scalars
 
@@ -26,12 +32,11 @@ Text printed to the console for training progress, as well as all other console
 
 ## Artifacts
 
-Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in the model info panel in the **MODELS** tab.  
+Models created by the experiment appear in the experiment’s **ARTIFACTS** tab.
-
-The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).
 
 ![image](../../../img/examples_keras_18.png)
 
-The model info panel contains the model details, including the model URL, framework, and snapshot locations.
+Clicking on the model's name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can view 
+the model’s details and access the model.
 
 ![image](../../../img/examples_keras_17.png)

docs/guides/frameworks/lightgbm/lightgbm_example.md

@@ -25,7 +25,8 @@ ClearML automatically logs the configurations applied to LightGBM. They appear i
 
 ## Artifacts
 
-Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in the info panel of the **MODELS** tab.
+Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks 
+models and any snapshots created using LightGBM. 
 
 ![LightGBM model](../../../img/examples_lightgbm_model.png)
 

docs/guides/frameworks/pytorch/pytorch_mnist.md

@@ -8,10 +8,9 @@ demonstrates the integration of **ClearML** into code that uses PyTorch.
 The example script does the following:
 * Trains a simple deep neural network on the PyTorch built-in [MNIST](https://pytorch.org/vision/stable/datasets.html#mnist)
   dataset.
-* Uses **ClearML** automatic logging. 
-* Calls the [Logger.report_scalar](../../../references/sdk/logger.md#report_scalar) method to demonstrate explicit reporting, 
-  which allows adding customized reporting to the code.
 * Creates an experiment named `pytorch mnist train`, which is associated with the `examples` project.
+* ClearML automatically logs `argparse` command line options, and models (and their snapshots) created by PyTorch
+* Additional metrics are logged by calling the [Logger.report_scalar](../../../references/sdk/logger.md#report_scalar) method.
 
 ## Scalars
 
@@ -19,16 +18,19 @@ In the example script's `train` function, the following code explicitly reports
 
 ```python
 Logger.current_logger().report_scalar(
-    "train", "loss", iteration=(epoch * len(train_loader) + batch_idx), value=loss.item())
+    "train", "loss", iteration=(epoch * len(train_loader) + batch_idx), value=loss.item()
+)
 ```
 
 In the `test` method, the code explicitly reports `loss` and `accuracy` scalars.
 
 ```python
 Logger.current_logger().report_scalar(
-    "test", "loss", iteration=epoch, value=test_loss)
+    "test", "loss", iteration=epoch, value=test_loss
+)
 Logger.current_logger().report_scalar(
-    "test", "accuracy", iteration=epoch, value=(correct / len(test_loader.dataset)))
+    "test", "accuracy", iteration=epoch, value=(correct / len(test_loader.dataset))
+)
 ```    
 
 These scalars can be visualized in plots, which appear in the **ClearML web UI**, in the experiment's
@@ -49,17 +51,12 @@ Text printed to the console for training progress, as well as all other console
 
 ## Artifacts
 
-Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in 
+Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks models 
-the info panel of the **MODELS** tab.  
+and any snapshots created using PyTorch. 
-
-
-The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).
 
 ![image](../../../img/examples_pytorch_mnist_02.png)
 
-The model info panel contains the model details, including: 
+Clicking on the model name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can view 
-* Model URL
+the model’s details and access the model.
-* Framework
-* Snapshot locations.
 
 ![image](../../../img/examples_pytorch_mnist_03.png)

docs/guides/frameworks/pytorch/pytorch_tensorboard.md

@@ -3,16 +3,14 @@ title: PyTorch with TensorBoard
 ---
 
 The [pytorch_tensorboard.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/pytorch_tensorboard.py) 
-example demonstrates the integration of **ClearML** into code that uses PyTorch and TensorBoard. 
+example demonstrates the integration of ClearML into code that uses PyTorch and TensorBoard. 
 
 The example does the following:
 * Trains a simple deep neural network on the PyTorch built-in [MNIST](https://pytorch.org/vision/stable/datasets.html#mnist) 
   dataset.
-* Creates a TensorBoard `SummaryWriter` object to log: 
-  * Scalars during training.
-  * Scalars and debug samples during testing.
-  * Test text message to the console (a test message to demonstrate **ClearML**'s automatic logging).
 * Creates an experiment named `pytorch with tensorboard`, which is associated with the `examples` project.
+* ClearML automatically captures scalars and text logged using the TensorBoard `SummaryWriter` object, and 
+  the model created by PyTorch. 
 
 ## Scalars
 
@@ -23,13 +21,13 @@ These scalars, along with the resource utilization plots, which are titled **:mo
 
 ## Debug Samples
 
-**ClearML** automatically tracks images and text output to TensorFlow. They appear in **RESULTS** **>** **DEBUG SAMPLES**.
+ClearML automatically tracks images and text output to TensorFlow. They appear in **RESULTS** **>** **DEBUG SAMPLES**.
 
 ![image](../../../img/examples_pytorch_tensorboard_08.png)
 
 ## Hyperparameters
 
-**ClearML** automatically logs TensorFlow Definitions. They appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS** **>** **TF_DEFINE**.
+ClearML automatically logs TensorFlow Definitions. They appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS** **>** **TF_DEFINE**.
 
 ![image](../../../img/examples_pytorch_tensorboard_01.png)
 
@@ -41,16 +39,12 @@ Text printed to the console for training progress, as well as all other console
 
 ## Artifacts
 
-Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in the info panel 
+Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks 
-of the **MODELS** tab.  
+models and any snapshots created using PyTorch. 
-
-The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).
 
 ![image](../../../img/examples_pytorch_tensorboard_02.png)
 
-The model info panel contains the model details, including: 
+Clicking on a model's name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can view 
-* Model URL
+the model’s details and access the model.
-* Framework
-* Snapshot locations.
 
 ![image](../../../img/examples_pytorch_tensorboard_03.png)

docs/guides/frameworks/pytorch/pytorch_tensorboardx.md

@@ -3,28 +3,26 @@ title: PyTorch TensorBoardX
 ---
 
 The [pytorch_tensorboardX.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/tensorboardx/pytorch_tensorboardX.py) 
-example demonstrates the integration of **ClearML** into code that uses PyTorch and TensorBoardX. 
+example demonstrates the integration of ClearML into code that uses PyTorch and TensorBoardX. 
 
 The example does the following:
 * Trains a simple deep neural network on the PyTorch built-in [MNIST](https://pytorch.org/vision/stable/datasets.html#mnist) 
   dataset. 
-* Creates a TensorBoardX `SummaryWriter` object to log: 
+* Creates an experiment named `pytorch with tensorboardX`, which is associated with the `examples` project.
-  * Scalars during training 
+* ClearML automatically captures scalars and text logged using the TensorBoardX `SummaryWriter` object, and 
-  * Scalars and debug samples during testing 
+  the model created by PyTorch. 
-  * A test text message to the console (a test message to demonstrate **ClearML** automatic logging).
-* Creates an experiment named `pytorch with tensorboardX`, which is associated with the `examples` project in the **ClearML Web UI**.
 
 ## Scalars
 
 The loss and accuracy metric scalar plots, along with the resource utilization plots, which are titled **:monitor: machine**, 
-appear in the experiment's page in the **web UI**, under **RESULTS** **>** **SCALARS**.
+appear in the experiment's page in the [web UI](../../../webapp/webapp_overview.md), under **RESULTS** **>** **SCALARS**.
-.
+
 
 ![image](../../../img/examples_pytorch_tensorboardx_03.png)
 
 ## Hyperparameters
 
-**ClearML** automatically logs command line options defined with `argparse`. They appear in **CONFIGURATIONS** **>** 
+ClearML automatically logs command line options defined with `argparse`. They appear in **CONFIGURATIONS** **>** 
 **HYPER PARAMETERS** **>** **Args**.
 
 ![image](../../../img/examples_pytorch_tensorboardx_01.png)
@@ -37,16 +35,12 @@ Text printed to the console for training progress, as well as all other console
 
 ## Artifacts
 
-Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in the info panel 
+Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks 
-of the **MODELS** tab.  
+models and any snapshots created using PyTorch. 
-
-The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).
 
 ![image](../../../img/examples_pytorch_tensorboardx_04.png)
 
-The model info panel contains the model details, including: 
+Clicking on the model name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can view 
-* Model URL 
+the model’s details and access the model.
-* Framework 
-* Snapshot locations.
 
 ![image](../../../img/examples_pytorch_tensorboardx_05.png)

docs/guides/frameworks/pytorch_lightning/pytorch_lightning_example.md

@@ -28,10 +28,13 @@ ClearML automatically logs command line options defined with argparse and Tensor
 
 ## Artifacts
 
-Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in the info panel of the **MODELS** tab.
+Models created by the experiment appear in the experiment’s **ARTIFACTS** tab.
 
 ![PyTorch Lightning model](../../../img/examples_pytorch_lightning_model.png)
 
+Clicking on a model name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can view 
+the model’s details and access the model.
+
 ## Console
 
 All other console output appears in **RESULTS > CONSOLE**.

docs/guides/frameworks/scikit-learn/sklearn_joblib_example.md

@@ -3,28 +3,25 @@ title: scikit-learn with Joblib
 ---
 
 The [sklearn_joblib_example.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/scikit-learn/sklearn_joblib_example.py) 
-demonstrates the integration of **ClearML** into code that uses `scikit-learn` and `joblib` to store a model and model snapshots, 
+demonstrates the integration of ClearML into code that uses `scikit-learn` and `joblib` to store a model and model snapshots, 
-and `matplotlib` to create a scatter diagram. When the script runs, it creates an experiment named `scikit-learn joblib examplescikit-learn joblib example`, which is associated with the `examples` project.
+and `matplotlib` to create a scatter diagram. When the script runs, it creates an experiment named 
+`scikit-learn joblib examplescikit-learn joblib example`, which is associated with the `examples` project.
 
 ## Plots
 
-**ClearML** automatically logs the scatter plot, which appears in the experiment's page in the **ClearML web UI**, under  
+ClearML automatically logs the scatter plot, which appears in the [experiment's page](../../../webapp/webapp_exp_track_visual.md) 
-**RESULTS** **>** **PLOTS**.
+in the ClearML web UI, under **RESULTS** **>** **PLOTS**.
 
 ![image](../../../img/examples_sklearn_joblib_example_06.png)
 
 ## Artifacts
 
-Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in the info panel 
+Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. 
-of the **MODELS** tab.  
-
-The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).
 
 ![image](../../../img/examples_sklearn_joblib_example_01.png)
 
-The model info panel contains the model details, including: 
+Clicking on the model name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can 
-* Model URL 
+view the model’s details and access the model.
-* Framework 
+
-* Snapshot locations.
 
 ![image](../../../img/examples_sklearn_joblib_example_02.png)

docs/guides/frameworks/tensorboardx/tensorboardx.md

@@ -3,15 +3,13 @@ title: TensorBoardX with PyTorch
 ---
 
 The [pytorch_tensorboardX.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/tensorboardx/pytorch_tensorboardX.py) 
-example demonstrates the integration of **ClearML** into code that uses PyTorch and TensorBoardX. 
+example demonstrates the integration of ClearML into code that uses PyTorch and TensorBoardX. 
 
 The script does the following:
-1. Trains a simple deep neural network on the PyTorch built-in [MNIST](https://pytorch.org/vision/stable/datasets.html#mnist) dataset.
+* Trains a simple deep neural network on the PyTorch built-in [MNIST](https://pytorch.org/vision/stable/datasets.html#mnist) dataset.
-1. Creates a TensorBoardX `SummaryWriter` object to log: 
+* Creates an experiment named `pytorch with tensorboardX` which is associated with the `examples` project
-   * Scalars during training 
+* ClearML automatically captures scalars and text logged using the TensorBoardX `SummaryWriter` object, and 
-   * Scalars and debug samples during testing 
+  the model created by PyTorch. 
-   * A test text message to the console (a test message to demonstrate **ClearML**).
-1. Creates an experiment named `pytorch with tensorboardX` which is associated with the `examples` project in the **ClearML Web UI**.
 
 ## Scalars
 
@@ -22,7 +20,7 @@ The loss and accuracy metric scalar plots appear in the experiment's page in the
 
 ## Hyperparameters
 
-**ClearML** automatically logs command line options defined with `argparse`. They appear in **CONFIGURATIONS** **>** 
+ClearML automatically logs command line options defined with `argparse`. They appear in **CONFIGURATIONS** **>** 
 **HYPER PARAMETERS** **>** **Args**.
 
 ![image](../../../img/examples_pytorch_tensorboardx_01.png)
@@ -35,17 +33,13 @@ Text printed to the console for training progress, as well as all other console
 
 ## Artifacts
 
-Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in the info panel 
+Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks 
-of the **MODELS** tab.  
+models and any snapshots created using PyTorch. 
-
-The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).
 
 ![image](../../../img/examples_pytorch_tensorboardx_04.png)
 
-The model info panel contains the model details, including: 
+Clicking on the model’s name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can 
-* Model URL
+view the model’s details and access the model.
-* Framework 
-* Snapshot locations.
 
 ![image](../../../img/examples_pytorch_tensorboardx_05.png)
 

docs/guides/frameworks/tensorflow/tensorflow_mnist.md

@@ -3,7 +3,7 @@ title: TensorFlow MNIST
 ---
 
 The [tensorflow_mnist.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/tensorflow/tensorflow_mnist.py) 
-example demonstrates the integration of **ClearML** into code that uses TensorFlow and Keras to train a neural network on 
+example demonstrates the integration of ClearML into code that uses TensorFlow and Keras to train a neural network on 
 the Keras built-in [MNIST](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/mnist) handwritten digits dataset. 
 
 The script builds a TensorFlow Keras model, and trains and tests it with the following:
@@ -24,7 +24,7 @@ The loss and accuracy metric scalar plots appear in the experiment's page in the
 
 ## Hyperparameters
 
-**ClearML** automatically logs TensorFlow Definitions. They appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS** 
+ClearML automatically logs TensorFlow Definitions. They appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS** 
 **>** **TF_DEFINE**.
 
 ![image](../../../img/examples_tensorflow_mnist_01.png)
@@ -37,18 +37,13 @@ All console output appears in **RESULTS** **>** **CONSOLE**.
 
 ## Artifacts
 
-Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in the info panel 
+Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks 
-of the **MODELS** tab.  
+models and any snapshots created using TensorFlow. 
-
-The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).
 
 ![image](../../../img/examples_tensorflow_mnist_03.png)
 
-The model info panel contains the model details, including: 
+Clicking on a model’s name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can 
-* Model design 
+view the model’s details and access the model.
-* Label enumeration 
+
-* Model URL 
-* Framework
-* Snapshot locations.
 
 ![image](../../../img/examples_tensorflow_mnist_10.png)

docs/guides/frameworks/xgboost/xgboost_sample.md

@@ -3,26 +3,15 @@ title: XGBoost and scikit-learn
 ---
 
 The [xgboost_sample.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/xgboost/xgboost_sample.py) 
-example demonstrates integrating ClearML into code that trains a network on the scikit-learn [iris](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris) 
+example demonstrates integrating ClearML into code that uses [XGBoost](https://xgboost.readthedocs.io/en/stable/). 
-classification dataset, using XGBoost to do the following:
 
-* Load a model ([xgboost.Booster.load_model](https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.Booster.load_model))
+The example does the following:
-* Save a model ([xgboost.Booster.save_model](https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.Booster.save_model))
+* Trains a network on the scikit-learn [iris](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris) 
-* Dump a model to JSON or text file ([xgboost.Booster.dump_model](https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.Booster.dump_model))
+classification dataset using XGBoost
-* Plot feature importance ([xgboost.plot_importance](https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.plot_importance))
+* Scores accuracy using scikit-learn
-* Plot a tree ([xgboost.plot_tree](https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.plot_tree))
+* ClearML automatically logs the input model registered by XGBoost, and the output model (and its checkpoints), 
-
+  feature importance plot, and tree plot created with XGBoost. 
-And using scikit-learn to score accuracy ([sklearn.metrics.accuracy_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html)).
+* Creates an experiment named `XGBoost simple example`, which is associated with the `examples` project.
-
-ClearML automatically logs:
-* Input model
-* Output model 
-* Model checkpoints (snapshots) 
-* Feature importance plot 
-* Tree plot
-* Output to console.
-
-When the script runs, it creates an experiment named `XGBoost simple example`, which is associated with the `examples` project.
 
 ## Plots
 
@@ -39,17 +28,12 @@ All other console output appear in **RESULTS** **>** **CONSOLE**.
 
 ## Artifacts
 
-Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in the info panel 
+Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks 
-of the **MODELS** tab.  
+models and any snapshots created using XGBoost. 
-
-The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).
 
 ![image](../../../img/examples_xgboost_sample_10.png)
 
-The model info panel contains the model details, including: 
+Clicking on the model's name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can 
-* Model design
+view the model’s details and access the model.
-* Label enumeration
-* Model URL
-* Framework.
 
 ![image](../../../img/examples_xgboost_sample_03.png)

docs/webapp/webapp_exp_comparing.md

@@ -40,8 +40,8 @@ The **DETAILS** tab includes deep comparisons of the following:
 
 ### Artifacts
 
-   * Input model and model design.
+   * Input model and model configuration.
-   * Output model and model design.
+   * Output model and model configuration.
    * Other artifacts, if any.
 
 ### Execution Details