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Update distributed-tuning.md (#8324)

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ZehuaCao 2023-06-15 11:29:37 +08:00 committed by GitHub
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docs/readthedocs/source/doc/Orca/Overview/distributed-tuning.md
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@ -93,7 +93,7 @@ Finally, user can get the best learned model and the best hyper-parameters for f
best_model = auto_est.get_best_model() # a `torch.nn.Module` object best_model = auto_est.get_best_model() # a `torch.nn.Module` object
best_config = auto_est.get_best_config() # a dictionary of hyper-parameter names and values. best_config = auto_est.get_best_config() # a dictionary of hyper-parameter names and values.
``` ```
View the related [Python API doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/AutoML/automl.html#orca-automl-auto-estimator) for more details. View the related [Python API doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Orca/automl.html#orca-automl-auto-estimator) for more details.
### 3. TensorFlow/Keras AutoEstimator ### 3. TensorFlow/Keras AutoEstimator
Users can create an `AutoEstimator` for TensorFlow Keras from a `tf.keras` model (using a *Model Creator Function*). For example: Users can create an `AutoEstimator` for TensorFlow Keras from a `tf.keras` model (using a *Model Creator Function*). For example:
@ -130,14 +130,14 @@ Finally, user can get the best learned model and the best hyper-parameters for f
best_model = auto_est.get_best_model() # a `torch.nn.Module` object best_model = auto_est.get_best_model() # a `torch.nn.Module` object
best_config = auto_est.get_best_config() # a dictionary of hyper-parameter names and values. best_config = auto_est.get_best_config() # a dictionary of hyper-parameter names and values.
``` ```
View the related [Python API doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/AutoML/automl.html#orca-automl-auto-estimator) for more details. View the related [Python API doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Orca/automl.html#orca-automl-auto-estimator) for more details.
### 4. Search Space and Search Algorithms ### 4. Search Space and Search Algorithms
For Hyper-parameter Optimization, user should define the search space of various hyper-parameter values for neural network training, as well as how to search through the chosen hyper-parameter space. For Hyper-parameter Optimization, user should define the search space of various hyper-parameter values for neural network training, as well as how to search through the chosen hyper-parameter space.
#### 4.1 Basic Search Algorithms #### 4.1 Basic Search Algorithms
For basic search algorithms like **Grid Search** and **Random Search**, we provide several sampling functions with `automl.hp`. See [API doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/AutoML/automl.html#orca-automl-hp) for more details. For basic search algorithms like **Grid Search** and **Random Search**, we provide several sampling functions with `automl.hp`. See [API doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Orca/automl.html#orca-automl-hp) for more details.
`AutoEstimator` requires a dictionary for the `search_space` argument in `fit`. `AutoEstimator` requires a dictionary for the `search_space` argument in `fit`.
In the dictionary, the keys are the hyper-parameter names, and the values specify how to sample the search spaces for the hyper-parameters. In the dictionary, the keys are the hyper-parameter names, and the values specify how to sample the search spaces for the hyper-parameters.
@ -180,7 +180,7 @@ auto_estimator.fit(
search_alg="bayesopt", search_alg="bayesopt",
) )
``` ```
See [API Doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/AutoML/automl.html#orca-automl-auto-estimator) for more details. See [API Doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Orca/automl.html#orca-automl-auto-estimator) for more details.
### 5. Scheduler ### 5. Scheduler
*Scheduler* can stop/pause/tweak the hyper-parameters of running trials, making the hyper-parameter tuning process much efficient. *Scheduler* can stop/pause/tweak the hyper-parameters of running trials, making the hyper-parameter tuning process much efficient.