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# Hyperparameter Search using Trainer API
🤗 Transformers provides a `Trainer` class optimized for training 🤗 Transformers models, making it easier to start training without manually writing your own training loop. The `Trainer` provides API for hyperparameter search. This doc shows how to enable it in example.
## Hyperparameter Search backend
`Trainer` supports four hyperparameter search backends currently:
[optuna](https://optuna.org/), [sigopt](https://sigopt.com/), [raytune](https://docs.ray.io/en/latest/tune/index.html) and [wandb](https://wandb.ai/site/sweeps).
you should install them before using them as the hyperparameter search backend
```bash
pip install optuna/sigopt/wandb/ray[tune]
```
## How to enable Hyperparameter search in example
Define the hyperparameter search space, different backends need different format.
For sigopt, see sigopt [object_parameter](https://docs.sigopt.com/ai-module-api-references/api_reference/objects/object_parameter), it's like following:
```py
>>> def sigopt_hp_space(trial):
... return [
... {"bounds": {"min": 1e-6, "max": 1e-4}, "name": "learning_rate", "type": "double"},
... {
... "categorical_values": ["16", "32", "64", "128"],
... "name": "per_device_train_batch_size",
... "type": "categorical",
... },
... ]
```
For optuna, see optuna [object_parameter](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/002_configurations.html#sphx-glr-tutorial-
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