Add Keras Tuner integration page (#681)

docs/guides/frameworks/tensorflow/integration_keras_tuner.md

@@ -1,10 +1,9 @@
 ---
 title: Keras Tuner
-displayed_sidebar: mainSidebar
 ---
 
 :::tip
-If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
+If you are not already using ClearML, see [Getting Started](../../../getting_started/ds/ds_first_steps.md) for setup 
 instructions.
 :::
 
@@ -41,7 +40,7 @@ When the script runs, it logs:
 
 ## Scalars
 
-ClearML logs the scalars from training each network. They appear in the project's page in the **ClearML web UI**, under
+ClearML logs the scalars from training each network. They appear in the experiment's page in the **ClearML web UI**, under
 **SCALARS**.
 
 ![image](../../../img/integration_keras_tuner_06.png)

docs/integrations/keras_tuner.md

@@ -0,0 +1,71 @@
+---
+title: Keras Tuner
+---
+
+:::tip
+If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
+instructions.
+:::
+
+[Keras Tuner](https://www.tensorflow.org/tutorials/keras/keras_tuner) is a library that helps you pick the optimal set 
+of hyperparameters for training your models. ClearML integrates seamlessly with `kerastuner` and automatically logs 
+experiment scalars, the output model, and hyperparameter optimization summary. 
+
+Integrate ClearML into your Keras Tuner optimization script by doing the following:
+* Instantiate a ClearML Task: 
+   
+  ```python
+  from clearml import Task
+  task = Task.init(task_name="<task_name>", project_name="<project_name>")
+  ```
+
+* Specify `ClearMLTunerLogger` as the Keras Tuner logger:  
+
+ ```python
+ from clearml.external.kerastuner import ClearmlTunerLogger
+ import keras_tuner as kt
+    
+ # Create tuner object
+ tuner = kt.Hyperband(  
+    build_model,
+    project_name='kt examples',
+    logger=ClearMLTunerLogger(),    # specify ClearMLTunerLogger
+    objective='val_accuracy',
+    max_epochs=10,
+    hyperband_iterations=6
+ )
+ ```
+
+And that’s it! This creates a [ClearML Task](../fundamentals/task.md) which captures: 
+* Output Keras model
+* Optimization trial scalars - scalar plot showing metrics for all runs
+* Hyperparameter optimization summary plot - Tabular summary of hyperparameters tested and their metrics by trial ID
+* Source code and uncommitted changes
+* Installed packages
+* TensorFlow definitions
+* Console output
+* General details such as machine details, runtime, creation date etc.
+* And more
+
+You can view all the task details in the [WebApp](../webapp/webapp_exp_track_visual.md). 
+
+## WebApp
+
+ClearML logs the scalars from training each network. They appear in the experiment's **SCALARS** tab in the Web UI.
+
+![Optimization scalars](../img/integration_keras_tuner_06.png)
+
+ClearML automatically logs the parameters of each experiment run in the hyperparameter search. They appear in tabular 
+form in the experiment's **PLOTS**.
+
+![Optimization plot](../img/integration_keras_tuner_07.png)
+
+ClearML automatically stores the output model. It appears in the experiment's **ARTIFACTS** **>** **Output Model**.
+
+![output model](../img/integration_keras_tuner_03.png)
+
+## Example 
+
+See Keras Tuner and ClearML in action in the [keras_tuner_cifar.py](../guides/frameworks/tensorflow/integration_keras_tuner.md)
+example script.
+

sidebars.js

@@ -62,7 +62,7 @@ module.exports = {
                 'integrations/autokeras',
                 'integrations/catboost', 'integrations/click', 'integrations/fastai',
                 'integrations/hydra',
-                'integrations/keras', 'guides/frameworks/tensorflow/integration_keras_tuner',
+                'integrations/keras', 'integrations/keras_tuner',
                 'integrations/lightgbm', 'integrations/matplotlib',
                 'integrations/megengine', 'integrations/monai', 'integrations/openmmv', 'integrations/optuna',
                 'integrations/python_fire', 'integrations/pytorch',