Add hyperparam optimization diagram + minor corrections (#22)

* add hpo diagram

* edit around hpo diagram

* small edits hpo, put tutorial under own heading

* fix incorrect wording

docs/fundamentals/hpo.md

@@ -6,26 +6,39 @@ title: Hyperparameter Optimization
 Hyperparameters are variables that directly control the behaviors of training algorithms, and have a significant effect on 
 the performance of the resulting machine learning models. Finding the hyperparameter values that yield the best 
 performing models can be complicated. Manually adjusting hyperparameters over the course of many training trials can be 
-slow and tedious. Luckily, **hyperparameter optimization** can be automated and boosted using **ClearML**'s 
-`HyperParameterOptimizer` class.
+slow and tedious. Luckily, hyperparameter optimization can be automated and boosted using ClearML's 
+**`HyperParameterOptimizer`** class.
 
 ## ClearML's HyperParameter Optimization
 
-ClearML provides the `HyperParameterOptimizer` class which takes care of the entire optimization process for users in 
-with a simple interface.
+ClearML provides the `HyperParameterOptimizer` class, which takes care of the entire optimization process for users in 
+with a simple interface.  
 
-The `HyperParameterOptimizer` class does the following:
-* Clones a base experiment that needs to be optimized.
-* Changes arguments based on the specified optimizer strategy.
-* Tries to minimize / maximize defined objectives. 
+ClearML's approach to hyperparameter optimization is scalable, easy to set up and to manage, and it makes it easy to 
+compare results.
 
+### Workflow
+
+![Hyperparameter optimization diagram](../img/hpo_diagram.png)
+
+The diagram above demonstrates the typical flow of hyperparameter optimization where the parameters of a base task are optimized: 
+
+1. Configure an Optimization Task with a base task whose parameters will be optimized, and a set of parameter values to 
+   test
+1. Clone the base task. Each clone's parameter is overridden with a value from the optimization task  
+1. Enqueue each clone for execution by a ClearML Agent
+1. The Optimization Task records and monitors the cloned tasks' configuration and execution details, and returns a 
+   summary of the optimization results 
+ 
+
+![Optimization results summary chart](../img/fundamentals_hpo_summary.png)
+
+### Supported Optimizers
 
 The `HyperParameterOptimizer` class contains **ClearML**’s hyperparameter optimization modules. Its modular design enables 
 using different optimizers, including existing software frameworks, enabling simple, accurate, and fast hyperparameter 
 optimization.
 
-### Supported Optimizers
-
 * **Optuna** - `automation.optuna.optuna.OptimizerOptuna`. Optuna is the default optimizer in ClearML. It makes use of 
   different samplers such as grid search, random, bayesian, and evolutionary algorithms. 
   For more information, see the [Optuna](https://optuna.readthedocs.io/en/latest/) 
@@ -34,29 +47,10 @@ optimization.
   at scale by combining the speed of Hyperband searches with the guidance and guarantees of convergence of Bayesian Optimization. 
   For more information about HpBandSter BOHB, see the [HpBandSter](https://automl.github.io/HpBandSter/build/html/index.html) 
   documentation.
-* **Random** uniform sampling of hyperparameters - `automation.optimization.RandomSearch`
+* **Random** uniform sampling of hyperparameters - `automation.optimization.RandomSearch`.
 * **Full grid** sampling strategy of every hyperparameter combination - `Grid search automation.optimization.GridSearch`.
-* **Custom** - `automation.optimization.SearchStrategy`. - Use a custom class and inherit from the ClearML automation base strategy class 
+* **Custom** - `automation.optimization.SearchStrategy` - Use a custom class and inherit from the ClearML automation base strategy class 
 
-## How Does it Work?
-
-**ClearML**'s approach to hyperparameter optimization is scalable, easy to set up and to manage, and it makes it easy to 
-compare results.
-
-### Workflow
-
-Make use of **ClearML**'s hyperparameter optimization capabilities by:
-* Initializing an Optimizer Task, which will record and monitor arguments, execution details, results, and more. 
-* Instantiating a `HyperParameterOptimizer`, where the following is specified:
-  * Task to optimize
-  * Hyperparameters to optimize
-  * Metric to optimize 
-  * Optimizer class (optimization strategy) where the optimization configuration and resources budget are defined
-  * And more.
-* Enqueuing the Task to be executed by a ClearML Agent (or multiple agents) in a remote machine. 
-* Monitoring the optimization process and viewing the summarized results in the **ClearML web UI**
-
-![image](../img/fundamentals_hpo_summary.png)
 
 ## Defining a hyperparameter optimization search example
 
@@ -68,7 +62,7 @@ Make use of **ClearML**'s hyperparameter optimization capabilities by:
   from clearml.automation.optuna import OptimizerOptuna
   ```
 1. Initialize the Task, which will be stored in ClearML Server when the code runs. After the code runs at least once, 
-   it can be reproduced and tuned:
+   it can be reproduced, and the parameters can be tuned:
   ```python
   from clearml import Task
   
@@ -82,7 +76,7 @@ Make use of **ClearML**'s hyperparameter optimization capabilities by:
 1. Define the optimization configuration and resources budget:
   ```python
   optimizer = HyperParameterOptimizer(
-        # specifying the Task to be optimized, Task must be in system already so it can be cloned
+        # specifying the task to be optimized, task must be in system already so it can be cloned
         base_task_id=TEMPLATE_TASK_ID,  
         # setting the hyper-parameters to optimize
         hyper_parameters=[
@@ -111,6 +105,9 @@ Make use of **ClearML**'s hyperparameter optimization capabilities by:
   ```
 <br/><br/>
 
-Check out the [Hyperparameter Optimization](../guides/optimization/hyper-parameter-optimization) tutorial for a step by step guide.
+For more information about `HyperParameterOptimizer` and supported optimization modules, see the [HyperParameterOptimizer class reference](../references/sdk/hpo_optimization_hyperparameteroptimizer.md).
+
+## Tutorial
+
+Check out the [Hyperparameter Optimization](../guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt.md) tutorial for a step-by-step guide.
 
- For further information about the `HyperParameterOptimizer` arguments, see the [Automation module reference](../references/sdk/hpo_optimization_hyperparameteroptimizer.md).

sidebars.js

@@ -14,7 +14,7 @@ module.exports = {
                 {'MLOps': ['getting_started/mlops/mlops_first_steps','getting_started/mlops/mlops_second_steps','getting_started/mlops/mlops_best_practices']}]
         }, 'getting_started/architecture']},
         {'ClearML Fundamentals': ['fundamentals/task', 'fundamentals/hyperparameters', 'fundamentals/artifacts', 'fundamentals/logger', 'fundamentals/agents_and_queues',
-            'fundamentals/pipelines']},
+            'fundamentals/hpo', 'fundamentals/pipelines']},
         'clearml_sdk',
         'clearml_agent',
         'clearml_data',