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Add multi-objective optimization info (#844)

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@@ -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).