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docs/integrations/catboost.md
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@@ -117,5 +117,5 @@ 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](../hpo.md)
the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../getting_started/hpo.md)
for more information.

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docs/integrations/keras.md
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@@ -129,5 +129,5 @@ 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](../hpo.md)
the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../getting_started/hpo.md)
for more information.

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docs/integrations/lightgbm.md
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@@ -118,5 +118,5 @@ 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](../hpo.md)
the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../getting_started/hpo.md)
for more information.

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docs/integrations/megengine.md
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@@ -114,5 +114,5 @@ 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](../hpo.md)
the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../getting_started/hpo.md)
for more information.

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docs/integrations/optuna.md
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@@ -2,7 +2,7 @@
title: Optuna
---
[Optuna](https://optuna.readthedocs.io/en/latest) is a [hyperparameter optimization](../hpo.md) framework,
[Optuna](https://optuna.readthedocs.io/en/latest) is a [hyperparameter optimization](../getting_started/hpo.md) framework,
which makes use of different samplers such as grid search, random, bayesian, and evolutionary algorithms. You can integrate
Optuna into ClearML's automated hyperparameter optimization.

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docs/integrations/tensorflow.md
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@@ -131,5 +131,5 @@ 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](../hpo.md)
the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../getting_started/hpo.md)
for more information.

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docs/integrations/transformers.md
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@@ -90,5 +90,5 @@ The ClearML Agent executing the task will use the new values to [override any ha
## 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](../hpo.md)
the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../getting_started/hpo.md)
for more information.

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docs/integrations/xgboost.md
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@@ -144,5 +144,5 @@ 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](../hpo.md)
the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../getting_started/hpo.md)
for more information.

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docs/integrations/yolov5.md
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@@ -7,7 +7,7 @@ built in logger:
* Track every YOLOv5 training run in ClearML
* Version and easily access your custom training data with [ClearML Data](../clearml_data/clearml_data.md)
* Remotely train and monitor your YOLOv5 training runs using [ClearML Agent](../clearml_agent.md)
* Get the very best mAP using ClearML [Hyperparameter Optimization](../hpo.md)
* Get the very best mAP using ClearML [Hyperparameter Optimization](../getting_started/hpo.md)
* Turn your newly trained YOLOv5 model into an API with just a few commands using [ClearML Serving](../clearml_serving/clearml_serving.md)
## Setup