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docs/integrations/optuna.md
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@@ -9,11 +9,11 @@ Optuna into ClearML's automated hyperparameter optimization.
The [HyperParameterOptimizer](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class contains ClearML's
hyperparameter optimization modules. Its modular design enables using different optimizers, including existing software
frameworks, like Optuna, enabling simple,
accurate, and fast hyperparameter optimization. The Optuna ([`automation.optuna.OptimizerOptuna`](../references/sdk/hpo_optuna_optuna_optimizeroptuna.md)),
accurate, and fast hyperparameter optimization. The Optuna ([`automation.optuna.OptimizerOptuna`](../references/sdk/hpo_optuna_optuna_optimizeroptuna.md))
optimizer lets you simultaneously optimize many hyperparameters efficiently by relying on early stopping (pruning)
and smart resource allocation.
To use optuna in ClearML's hyperparameter optimization, you must first install it. When you instantiate `HyperParameterOptimizer`,
To use Optuna in ClearML's hyperparameter optimization, you must first install it. When you instantiate `HyperParameterOptimizer`,
pass `OptimizerOptuna` as the `optimizer_class` argument:
```python

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docs/integrations/transformers.md
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@@ -56,6 +56,7 @@ Additionally, you can view all of your Transformers runs tracked by ClearML in t
Add custom columns to the table, such as mAP values, so you can easily sort and see what is the best performing model.
You can also select multiple experiments and directly [compare](../webapp/webapp_exp_comparing.md) them.
See an example of Transformers and ClearML in action [here](../guides/frameworks/huggingface/transformers.md).
## Remote Execution
ClearML logs all the information required to reproduce an experiment on a different machine (installed packages,