Rewrite Keras integration page (#641)

docs/guides/frameworks/keras/keras_tensorboard.md

@@ -1,6 +1,5 @@
 ---
-title: Keras
-displayed_sidebar: mainSidebar
+title: Keras with TensorBoard
 ---
 
 The example below demonstrates the integration of ClearML into code which uses Keras and TensorBoard.

docs/integrations/keras.md

@@ -0,0 +1,132 @@
+---
+title: Keras
+---
+
+:::tip
+If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
+instructions.
+:::
+
+ClearML integrates with [Keras](https://keras.io/) out-of-the-box, automatically logging its models, scalars, 
+TensorFlow definitions, and TensorBoard outputs. 
+
+All you have to do is simply add two lines of code to your Keras script:
+
+```python
+from clearml import Task
+task = Task.init(task_name="<task_name>", project_name="<project_name>")
+```
+
+And that’s it! This creates a [ClearML Task](../fundamentals/task.md) which captures: 
+* Source code and uncommitted changes
+* Installed packages
+* Keras models
+* Scalars (e.g. accuracy, loss)
+* TensorFlow definitions
+* [TensorBoard](https://www.tensorflow.org/tensorboard) outputs 
+* 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 Gif](../img/gif/tensorflow.gif)
+
+## Automatic Logging Control 
+By default, when ClearML is integrated into your Keras script, it captures Keras models and scalars, as well as TensorFlow 
+definitions and TensorBoard outputs. But, you may want to have more control over what your experiment logs.
+
+To control a task's framework logging, use the `auto_connect_frameworks` parameter of [`Task.init()`](../references/sdk/task.md#taskinit). 
+Completely disable all automatic logging by setting the parameter to `False`. For finer grained control of logged 
+frameworks, input a dictionary, with framework-boolean pairs.
+
+For example:
+
+```python
+auto_connect_frameworks={
+   'tensorflow': False, 'tensorboard': False, 'matplotlib': True, 'pytorch': True,
+   'xgboost': False, 'scikit': True, 'fastai': True, 'lightgbm': False,
+   'hydra': True, 'detect_repository': True, 'tfdefines': True, 'joblib': True,
+   'megengine': True, 'jsonargparse': True, 'catboost': True
+}
+```
+
+To control Keras logging, use the `tensorflow` and `tensorboard` keys.
+
+You can also input wildcards as dictionary values, so ClearML will log a model created by a framework only if its local 
+path matches at least one wildcard. 
+
+For example, in the code below, ClearML will log TensorFlow (and/or keras) models only if their paths have the `.keras` extension. The 
+unspecified frameworks' values default to true so all their models are automatically logged.
+
+```python
+auto_connect_frameworks={'tensorflow' : '*.keras'}
+```
+
+## Manual Logging
+To augment its automatic logging, ClearML also provides an explicit logging interface.
+
+See more information about explicitly logging information to a ClearML Task:
+* [Models](../clearml_sdk/model_sdk.md#manually-logging-models)
+* [Configuration](../clearml_sdk/task_sdk.md#configuration) (e.g. parameters, configuration files)
+* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or python objects created by a task)
+* [Scalars](../clearml_sdk/task_sdk.md#scalars) 
+* [Text/Plots/Debug Samples](../fundamentals/logger.md#manual-reporting)
+
+See [Explicit Reporting Tutorial](../guides/reporting/explicit_reporting.md).
+
+## Examples
+
+Take a look at ClearML’s Keras examples. The examples use Keras and ClearML in different configurations with 
+additional tools like TensorBoard and Matplotlib: 
+* [Keras with Tensorboard](../guides/frameworks/keras/keras_tensorboard.md) - Demonstrates ClearML logging a Keras model, 
+and plots and scalars logged to TensorBoard 
+* [Keras with Matplotlib](../guides/frameworks/keras/jupyter.md) - Demonstrates ClearML logging a Keras model, Matplotlib plots, 
+and debug samples, plots, and scalars logged to TensorBoard 
+
+
+## Remote Execution
+ClearML logs all the information required to reproduce an experiment on a different machine (installed packages, 
+uncommitted changes etc.). The [ClearML Agent](../clearml_agent) listens to designated queues and when a task is enqueued, 
+the agent pulls it, recreates its execution environment, and runs it, reporting its scalars, plots, etc. to the 
+experiment manager.
+
+Deploy a ClearML Agent onto any machine (e.g. a cloud VM, a local GPU machine, your own laptop) by simply running the 
+following command on it:
+
+```commandline
+clearml-agent daemon --queue <queues_to_listen_to> [--docker]
+```
+
+Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md), to help you manage cloud workloads in the 
+cloud of your choice (AWS, GCP, Azure) and automatically deploy ClearML agents: the autoscaler automatically spins up 
+and shuts down instances as needed, according to a resource budget that you set.
+
+### Cloning, Editing, and Enqueuing
+
+![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif)
+
+Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task 
+with the new configuration on a remote machine:
+
+* Clone the experiment
+* Edit the hyperparameters and/or other details
+* Enqueue the task
+
+The ClearML Agent executing the task will use the new values to [override any hard coded values](../clearml_agent).
+
+### Executing a Task Remotely
+
+You can set a task to be executed remotely programmatically by adding [`Task.execute_remotely()`](../references/sdk/task.md#execute_remotely) 
+to your script. This method stops the current local execution of the task, and then enqueues it to a specified queue to 
+re-run it on a remote machine.
+
+```python
+# If executed locally, process will terminate, and a copy will be executed by an agent instead
+task.execute_remotely(queue_name='default', exit_process=True)
+```
+
+## Hyperparameter Optimization
+Use ClearML’s [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find 
+the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../fundamentals/hpo.md) 
+for more information.

sidebars.js

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