Merge branch 'main' of https://github.com/allegroai/clearml-docs

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/guides/frameworks/keras/keras_tensorboard.md

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

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)
+```

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.

docs/webapp/applications/apps_aws_autoscaler.md

@@ -42,6 +42,7 @@ For more information about how autoscalers work, see [Autoscalers Overview](../.
 * **Compute Resources**
     * Resource Name - Assign a name to the resource type. This name will appear in the Autoscaler dashboard
     * EC2 Instance Type - See [Instance Types](https://aws.amazon.com/ec2/instance-types) for full list of types
+    * Run in CPU mode - Check box to run with CPU only
     * Use Spot Instance - Check box to use a spot instance. Else, a reserved instance is used
         * Regular Instance Rollback Timeout - Controls when the autoscaler will revert to starting a regular instance after failing to start a spot instance. It will first attempt to start a spot, and then wait and retry again and again. Once the time it waited exceeded the Regular Instance Rollback Timeout, the autoscaler will try to start a regular instance instead. This is for a specific attempt, where starting a spot fails and an alternative instance needs to be started.
         * Spot Instance Blackout Period - Specifies a blackout period after failing to start a spot instance. This is related to future attempts: after failing to start a spot instance, all requests to start additional spot instances will be converted to attempts to start regular instances, as a way of "easing" the spot requests load on the cloud provider and not creating a "DOS" situation in the cloud account which might cause the provider to refuse creating spots for a longer period.
@@ -55,13 +56,14 @@ For more information about how autoscalers work, see [Autoscalers Overview](../.
       commas 
     * EBS Device (optional) - Disk mount point
     * EBS Volume Size (optional) - Disk size (GB)
-    * EBS Volume Type (optional) - See [here](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ebs-volume-types.html) 
+    * EBS Volume Type (optional) - See [Amazon EBS volume types](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ebs-volume-types.html) 
       for full list of types
     * Instance Key Pair (optional) - AWS key pair that is provided to the spun EC2 instances for connecting to them via 
-      SSH. Provide the Key Pair's name, as was created in AWS. See [here](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-key-pairs.html) 
+      SSH. Provide the Key Pair's name, as was created in AWS. See [Amazon EC2 key pairs](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-key-pairs.html) 
       for more details
     * Security Group ID (optional) - Comma separated list of AWS VPC Security Group IDs to attach to the launched 
       instance. Read more [here](https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html) 
+    * VPC Subnet ID - The subnet ID For the created instance. If more than one ID is provided, the instance will be started in the first available subnet. For more information, see [What is Amazon VPC?](https://docs.aws.amazon.com/vpc/latest/userguide/what-is-amazon-vpc.html)
     * \+ Add Item - Define another resource type
 * **IAM Instance Profile** (optional) - Set an IAM instance profile for all instances spun by the Autoscaler 
     * Arn - Amazon Resource Name specifying the instance profile

sidebars.js

@@ -59,10 +59,10 @@ 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',
-                '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',
@@ -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']},