993c80b427
* add initial version of hpo doc * modify the doc index * revised doc * revise docs * revise docs
263 lines
12 KiB
Markdown
263 lines
12 KiB
Markdown
# AutoML Overview
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Nano provides built-in AutoML support through hyperparameter optimization.
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By simply changing imports, you are able to search the model architecture (e.g. by specifying search spaces in layer/activation/function arguments when defining the model), or the training procedure (e.g. by specifying search spaces in `learning_rate` or `batch_size`). You can simply use `model.search` (tensorflow)/`Trainer.search`(pytorch) to launch trials, and `model.search_summary`(tensorflow)/`Trainer.search_summary`(pytorch) to review the search results.
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Under the hood, the objects (layers, activations, model, etc.) are implicitly turned into searchable objects at creation, which allows search spaces to be specified in their init arguments. Nano HPO collects those search spaces and pass them to the underlying HPO engine (i.e. Optuna) which generates hyperparameter suggestions accordingly. The instantiation and execution of the corresponding objects are delayed until the hyperparameter values are available in each trial.
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### Install
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If you have not installed BigDL-Nano yet, follow the [Nano Install Guide](../Overview/nano.md#install) to install it according to your system and framework (i.e. tensorflow or pytorch).
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Next, install a few dependencies required for Nano HPO using below commands.
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```bash
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pip install ConfigSpace
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pip install optuna
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```
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### Search Spaces
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Search spaces are value range specifications that the search engine use for sampling hyperparameters. The available search spaces in Nano HPO is defined in `bigdl.nano.automl.hpo.space`. Refer to [Search Space API doc]() for more details.
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### Tensorflow HPO
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#### Enable/Disable HPO for tensorflow
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For tensorflow training, you should call `hpo_config.enable_hpo_tf` before using Nano HPO.
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`hpo_config.enable_hpo_tf` will dynamically add searchable layers, activations, functions, optimizers, etc into the `bigdl.nano.tf` module. When importing layers, you need to change the imports from `tf.keras.layers` to `bigdl.nano.tf.keras.layers`, so that you can specify search spaces in their init arguments. Note that even if you don't need to search the model architecture, you still need to change the imports.
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```python
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import bigdl.nano.automl as nano_automl
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nano_automl.hpo_config.enable_hpo_tf()
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```
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To disable HPO, use `hpo_config.disable_hpo_tf`. This will remove the searchable objects from `bigdl.nano.tf` module.
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```python
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import bigdl.nano.automl as nano_automl
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nano_automl.hpo_config.disable_hpo_tf()
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```
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#### Search the learning rate
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To search the learning rate, specify search space in `learning_rate` argument in the optimizer in `model.compile`. Remember to import the optimizer from `bigdl.nano.tf.optimizers` instead of `tf.keras.optimizers`.
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```python
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import bigdl.nano.automl.hpo.space as space
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from bigdl.nano.tf.optimizers import RMSprop
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model.compile(
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loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
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optimizer=RMSprop(learning_rate=space.Real(0.0001, 0.01, log=True)),
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metrics=["accuracy"],
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)
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```
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#### Search the batch size
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To search the batch size, specify search space in `batch_size` argument in `model.fit`.
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```python
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import bigdl.nano.automl.hpo.space as space
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model.search(n_trials=2, target_metric='accuracy', direction="maximize",
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x=x_train, y=y_train,validation_data=(x_valid, y_valid),
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batch_size=space.Categorical(128,64))
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```
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#### Search the Model Architecture
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To search different versions of model, you can specify search spaces when defining the model using either sequential API, functional API or by subclassing `tf.keras.Model`.
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##### using Sequential API
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You can specify search spaces in layer arguments. Note that search spaces can only be specified in key-word argument (which means `Dense(space.xxx)` should be changes to `Dense(units=space.xxx)`).
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```python
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from bigdl.nano.tf.keras.layers import Dense, Conv2D, Flatten
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from bigdl.nano.automl.tf.keras import Sequential
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model = Sequential()
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model.add(Conv2D(
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filters=space.Categorical(32, 64),
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kernel_size=space.Categorical(3, 5),
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strides=space.Categorical(1, 2),
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activation=space.Categorical("relu", "linear"),
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input_shape=input_shape))
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model.add(Flatten())
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model.add(Dense(10, activation="softmax"))
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```
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##### using Functional API
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You can specify search spaces in layer arguments. Note that if a layer is used more than once in the model, we strongly suggest you to specify a `prefix` for each of the search space in the layer to distinguish them, or they will share the same search space (the last space will override all previous definition), as shown in the below example.
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```python
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import bigdl.nano.automl.hpo.space as space
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from bigdl.nano.tf.keras import Input
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from bigdl.nano.tf.keras.layers import Dense, Dropout
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from bigdl.nano.automl.tf.keras import Model
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inputs = Input(shape=(784,))
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x = Dense(units=space.Categorical(8,16,prefix='dense_1'), activation="linear")(inputs)
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x = Dense(units=space.Categorical(32,64,prefix='dense_2'), activation="tanh")(x)
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x = Dropout(rate=space.Real(0.1,0.5, prefix='dropout'))(x)
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outputs = Dense(units=10)(x)
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model = Model(inputs=inputs, outputs=outputs, name="mnist_model")
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```
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##### by subclassing tf.keras.Model
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For models defined by subclassing tf.keras.Model, use the decorator `@hpo.tfmodel` to turn the model into a searchable object. Then you will able to specify either search spaces or normal values in the model init arguments.
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```python
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import bigdl.nano.automl.hpo.space as space
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import bigdl.nano.automl.hpo as hpo
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@hpo.tfmodel()
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class MyModel(tf.keras.Model):
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def __init__(self, filters, kernel_size, strides, num_classes=10):
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super().__init__()
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self.conv1 = tf.keras.layers.Conv2D(filters=filters,
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kernel_size=kernel_size,
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strides=strides,
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activation="relu")
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self.max1 = tf.keras.layers.MaxPooling2D(3)
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self.bn1 = tf.keras.layers.BatchNormalization()
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self.gap = tf.keras.layers.GlobalAveragePooling2D()
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self.dense = tf.keras.layers.Dense(num_classes)
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def call(self, inputs, training=False):
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x = self.conv1(inputs)
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x = self.max1(x)
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x = self.bn1(x)
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x = self.gap(x)
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return self.dense(x)
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model = MyModel(
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filters=hpo.space.Categorical(32, 64),
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kernel_size=hpo.space.Categorical(3, 5),
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strides=hpo.space.Categorical(1, 2)
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)
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```
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#### Launch Hyperparameter Search and Review the Results
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To launch hyperparameter search, call `model.search` after compile, as shown below. `model.search` runs the `n_trials` number of trials (meaning `n_trials` set of hyperparameter combinations are searched), and optimizes the `target_metric` in the specified `direction`. Besides search arguments, you also need to specify fit arguments in `model.search` which will be used in the fitting process in each trial. Refer to (API docs)[] for details.
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Use `model.search_summary` to retrieve the search results, which you can use to review in data frames, pick the best trial, or do visualizations. Examples of search results analysis and visualization can be found [here]().
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Finally, `model.fit` will automatically fit the model using the best set of hyper parameters found in the search. You can also use a set of hyperparameters from a trial other than the best one. Refer to [API docs]() for details.
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```python
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model = ... # define the model
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model.compile(...)
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model.search(n_trials=100, target_metric='accuracy', direction="maximize",
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x=x_train, y=y_train, batch_size=32, epochs=20, validation_split=0.2)
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study = model.search_summary()
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model.fit(...)
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```
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---
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### PyTorch HPO
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Nano-HPO now only support hyperparameter search for pytorch-lightning modules.
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#### Search the Model Architecture
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To search the model architecture, use the decorator `@hpo.plmodel()` to turn the model into a searchable object. Put the arguments that needs to be searched in the init arguments and use the arguments to construct the model. The arguments can be either space or non-space values, as shown below.
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```python
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import bigdl.nano.automl.hpo.space as space
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import bigdl.nano.automl.hpo as hpo
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@hpo.plmodel()
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class MyModel(pl.LightningModule):
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"""Customized Model."""
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def __init__(self,out_dim1,out_dim2,dropout_1,dropout_2):
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super().__init__()
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layers = []
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input_dim = 32
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for out_dim, dropout in [(out_dim1, dropout_1),(out_dim2,dropout_2)]:
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layers.append(torch.nn.Linear(input_dim, out_dim))
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layers.append(torch.nn.Tanh())
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layers.append(torch.nn.Dropout(dropout))
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input_dim = out_dim
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layers.append(torch.nn.Linear(input_dim, 2))
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self.layers: torch.nn.Module = torch.nn.Sequential(*layers)
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self.save_hyperparameters()
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def forward(self, x):
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return self.layers(x)
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model = MyModel(
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out_dim1=space.Categorical(16,32),
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out_dim2=space.Categorical(16,32),
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dropout_1=space.Categorical(0.1, 0.2, 0.3, 0.4, 0.5),
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dropout_2 = 0.5)
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```
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#### Search the learning rate
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`learning_rate` can be specified in the init arguments of your model. You can use `learning_rate` to construct the optimizer in `configure_optimizers()`, as shown below.
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```python
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import bigdl.nano.automl.hpo.space as space
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import bigdl.nano.automl.hpo as hpo
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@hpo.plmodel()
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class MyModel(pl.LightningModule):
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def __init__(self, ..., learning_rate=0.1):
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...
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self.save_hyperparameters()
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def configure_optimizers(self):
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# set learning rate in the optimizer
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self.optimizer = torch.optim.Adam(self.layers.parameters(),
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lr=self.hparams.learning_rate)
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return [self.optimizer], []
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model = MyModel(..., learning_rate=space.Real(0.001,0.01,log=True))
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```
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#### Search the batch size
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`batch_size` can be specified in the init arguments of your model. You can use the `batch_size` to construct the train `DataLoader` in `train_dataloader()`, as shown below.
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```python
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import bigdl.nano.automl.hpo.space as space
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import bigdl.nano.automl.hpo as hpo
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@hpo.plmodel()
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class MyModel(pl.LightningModule):
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def __init__(self, ..., batch_size=16):
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...
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self.save_hyperparameters()
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def train_dataloader(self):
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# set the batch size in train dataloader
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return DataLoader(RandomDataset(32, 64),
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batch_size=self.hparams.batch_size)
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model = MyModel(..., batch_size = space.Categorical(32,64))
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```
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#### Launch Hyperparameter Search and Review the Results
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To launch hyperparameter search, call `Trainer.search` after model is defined. Remember to set `use_hpo=True` in when initializing the `Trainer`.
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`Trainer.search` takes the decorated model as input. Similar to tensorflow, `trainer.search` runs the `n_trials` number of trials (meaning `n_trials` set of hyperparameter combinations are searched), and optimizes the `target_metric` in the specified `direction`. There's an extra argument `max_epochs` which is only used for the fitting process in search trials (do not affect the `Trainer.fit`). `Trainer.search` returns a model configured with the best set of hyper parameters.
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Use `model.search_summary` to retrieve the search results, which you can use to review in data frames, pick the best trial, or do visualizations. Examples of search results analysis and visualization can be found [here]().
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Finally you can use `Trainer.fit()` to fit the best model. You may get models constructed with hyperparameters of a specific trial. Refer to [Trainer.search API doc]() for more details.
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```python
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from bigdl.nano.pytorch import Trainer
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model = MyModel(...)
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trainer = Trainer(...,use_hpo=True)
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best_model = trainer.search(
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model,
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target_metric='val_loss',
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direction='minimize',
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n_trials=100,
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max_epochs=20,
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)
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study = trainer.search_summary()
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trainer.fit(best_model)
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```
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### Visualization
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