Add multi-objective optimization info (#844)

docs/fundamentals/hpo.md

@@ -23,7 +23,7 @@ compare results.
 
 The preceding diagram 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 
+1. Configure an Optimization Task with a base task whose parameters will be optimized, optimization targets, 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
@@ -118,6 +118,19 @@ optimization.
   in the task header.
   :::
 
+  :::tip Multi-objective Optimization
+  If you are using the Optuna framework (see [Supported Optimizers](#supported-optimizers)), you can list multiple optimization objectives. 
+  When doing so, make sure the `objective_metric_title`, `objective_metric_series`, and `objective_metric_sign` lists 
+  are the same length. Each title will be matched to its respective series and sign. 
+
+  For example, the code below sets two objectives: to minimize the `validation/loss` metric and to maximize the `validation/accuracy` metric. 
+  ```python
+  objective_metric_title=["validation", "validation"]
+  objective_metric_series=["loss", "accuracy"]
+  objective_metric_sign=["min", "max"]
+  ```
+  :::
+
 
 ## Optimizer Execution Options
 The `HyperParameterOptimizer` provides options to launch the optimization tasks locally or through a ClearML [queue](agents_and_queues.md#what-is-a-queue).

docs/guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt.md

@@ -114,7 +114,9 @@ if not args['template_task_id']:
 ## Creating the Optimizer Object
 
 Initialize an [`automation.HyperParameterOptimizer`](../../../references/sdk/hpo_optimization_hyperparameteroptimizer.md) 
-object, setting the optimization parameters, beginning with the ID of the experiment to optimize.
+object, setting the following optimization parameters: 
+
+* ID of a ClearML task to optimize. This task will be cloned, and each clone will sample a different set of hyperparameters values:
 
   ```python
   an_optimizer = HyperParameterOptimizer(
@@ -122,8 +124,8 @@ an_optimizer = HyperParameterOptimizer(
       base_task_id=args['template_task_id'],
   ```
   
-Set the hyperparameter ranges to sample, instantiating them as ClearML automation objects using [`automation.UniformIntegerParameterRange`](../../../references/sdk/hpo_parameters_uniformintegerparameterrange.md) 
+* Hyperparameter ranges to sample, instantiating them as ClearML automation objects using [`automation.UniformIntegerParameterRange`](../../../references/sdk/hpo_parameters_uniformintegerparameterrange.md) 
-and [`automation.DiscreteParameterRange`](../../../references/sdk/hpo_parameters_discreteparameterrange.md).
+  and [`automation.DiscreteParameterRange`](../../../references/sdk/hpo_parameters_discreteparameterrange.md):
   
   ```python
       hyper_parameters=[
@@ -134,7 +136,7 @@ and [`automation.DiscreteParameterRange`](../../../references/sdk/hpo_parameters
           ],
   ```
         
-Set the metric to optimize and the optimization objective.
+* Metric to optimize and the optimization objective:
   
   ```python
       objective_metric_title='val_acc',
@@ -142,19 +144,35 @@ Set the metric to optimize and the optimization objective.
       objective_metric_sign='max',
   ```
   
-Set the number of concurrent Tasks.
+  :::tip Multi-objective Optimization
+  If you are using the Optuna framework (see [Set the Search Strategy for Optimization](#set-the-search-strategy-for-optimization)), 
+  you can list multiple optimization objectives. When doing so, make sure the `objective_metric_title`, 
+  `objective_metric_series`, and `objective_metric_sign` lists are 
+  the same length. Each title will be matched to its respective series and sign. 
+  
+  For example, the code below sets two objectives: to minimize the `validation/loss` metric and to maximize the 
+  `validation/accuracy` metric: 
+  ```python
+  objective_metric_title=["validation", "validation"]
+  objective_metric_series=["loss", "accuracy"]
+  objective_metric_sign=["min", "max"]
+  ```
+  :::
+  
+  * Number of concurrent Tasks:
   ```python
       max_number_of_concurrent_tasks=2,
   ```
-Set the optimization strategy, see [Set the search strategy for optimization](#set-the-search-strategy-for-optimization).
+  * Optimization strategy (see [Set the search strategy for optimization](#set-the-search-strategy-for-optimization)):
   ```python
       optimizer_class=aSearchStrategy,
   ```
-Specify the queue to use for remote execution. This is overridden if the optimizer runs as a service.
+  * Queue to use for remote execution. This is overridden if the optimizer runs as a service.
   ```python
       execution_queue='1xGPU',
   ```
-Specify the remaining parameters, including the time limit per Task (minutes), period for checking the optimization (minutes), maximum number of jobs to launch, minimum and maximum number of iterations for each Task.
+  * Remaining parameters, including the time limit per Task (minutes), period for checking the optimization (minutes), 
+  maximum number of jobs to launch, minimum and maximum number of iterations for each Task:
   ```python
       # Optional: Limit the execution time of a single experiment, in minutes.
       # (this is optional, and if using OptimizerBOHB, it is ignored)