Rewrite AutoKeras integration page (#642)

docs/guides/frameworks/autokeras/autokeras_imdb_example.md

@@ -1,5 +1,5 @@
 ---
-title: AutoKeras IMDB
+title: AutoKeras
 ---
 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). 

docs/guides/frameworks/autokeras/integration_autokeras.md

@@ -1,42 +0,0 @@
----
-title: AutoKeras
-displayed_sidebar: mainSidebar
----
-Integrate ClearML into code that uses [autokeras](https://github.com/keras-team/autokeras). Initialize a ClearML 
-Task in a code, and ClearML automatically logs scalars, plots, and images reported to TensorBoard, Matplotlib, Plotly, 
-and Seaborn, and all other automatic logging, and explicit reporting added to the code (see [Logging](../../../fundamentals/logger.md)).
-
-ClearML allows to:
-
-* Visualize experiment results in the **ClearML Web UI**.
-* Track and upload models.
-* Track model performance and create tracking leaderboards.
-* Rerun experiments, reproduce experiments on any target machine, and tune experiments.
-* Compare experiments.
-
-See the [AutoKeras](autokeras_imdb_example.md) example, which shows ClearML automatically logging: 
-* Scalars
-* Hyperparameters
-* The console log 
-* Models. 
-
-Once these are logged, they can be visualized in the **ClearML Web UI**.
- 
-:::note
-If you are not already using ClearML, see [Getting Started](/getting_started/ds/best_practices.md).
-:::
-
-## Adding ClearML to Code
-
-Add two lines of code:
-```python
-from clearml import Task
-task = Task.init(project_name="myProject", task_name="myExperiment")
-```
-
-When the code runs, it initializes a Task in ClearML Server. A hyperlink to the experiment's log is output to the console.
-
-    CLEARML Task: created new task id=c1f1dc6cf2ee4ec88cd1f6184344ca4e
-    CLEARML results page: https://app.clear.ml/projects/1c7a45633c554b8294fa6dcc3b1f2d4d/experiments/c1f1dc6cf2ee4ec88cd1f6184344ca4e/output/log
-
-Later in the code, define callbacks using TensorBoard, and ClearML logs TensorBoard scalars, histograms, and images.

docs/integrations/autokeras.md

@@ -0,0 +1,117 @@
+---
+title: AutoKeras
+---
+
+:::tip
+If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
+instructions.
+:::
+
+ClearML integrates seamlessly with [AutoKeras](https://autokeras.com/), automatically logging its models and scalars. 
+
+All you have to do is simply add two lines of code to your AutoKeras 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
+* AutoKeras model files 
+* Scalars (loss, learning rates)
+* Console output
+* General details such as machine details, runtime, creation date etc.
+* Hyperparameters created with standard python packages (e.g. argparse, click, Python Fire, etc.)
+* And more
+
+You can view all the task details in the [WebApp](../webapp/webapp_exp_track_visual.md). 
+
+See an example of AutoKeras and ClearML in action [here](../guides/frameworks/autokeras/autokeras_imdb_example.md).
+
+![Experiment scalars](../img/examples_keras_14.png)
+
+## Automatic Logging Control 
+By default, when ClearML is integrated into your AutoKeras script, it captures AutoKeras 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, 'pytorch': True,
+   'xgboost': False, 'scikit': True, 'fastai': True, 'lightgbm': False,
+   'hydra': True, 'detect_repository': True, 'tfdefines': True, 'joblib': True,
+   'megengine': True, 'jsonargparse': True, 'catboost': False
+}
+```
+
+To control AutoKeras 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 models only if their paths have the `.h5` extension. The 
+unspecified frameworks' values default to true so all their models are automatically logged.
+
+```python
+auto_connect_frameworks={'tensorflow' : '*.h5'}
+```
+
+## 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).
+
+## 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)
+```

sidebars.js

@@ -59,7 +59,7 @@ module.exports = {
         {'ClearML Serving':['clearml_serving/clearml_serving', 'clearml_serving/clearml_serving_setup', 'clearml_serving/clearml_serving_cli', 'clearml_serving/clearml_serving_tutorial']},
         {'CLI Tools': ['apps/clearml_session', 'apps/clearml_task', 'apps/clearml_param_search']},
         {'Integrations': [
-                'guides/frameworks/autokeras/integration_autokeras',
+                'integrations/autokeras',
                 'integrations/catboost', 'integrations/click', 'guides/frameworks/fastai/fastai_with_tensorboard',
                 'integrations/hydra',
                 'integrations/keras', 'guides/frameworks/tensorflow/integration_keras_tuner',
@@ -147,7 +147,7 @@ module.exports = {
             {'Distributed': ['guides/distributed/distributed_pytorch_example', 'guides/distributed/subprocess_example']},
             {'Docker': ['guides/docker/extra_docker_shell_script']},
             {'Frameworks': [
-                {'Autokeras': ['guides/frameworks/autokeras/integration_autokeras', 'guides/frameworks/autokeras/autokeras_imdb_example']},
+                'guides/frameworks/autokeras/autokeras_imdb_example',
                 'guides/frameworks/catboost/catboost',
                 'guides/frameworks/fastai/fastai_with_tensorboard',
                 {'Keras': ['guides/frameworks/keras/jupyter', 'guides/frameworks/keras/keras_tensorboard']},