class BigDLEstimator(OrcaSparkEstimator):
def __init__(self,
*,
model,
loss,
optimizer=None,
metrics=None,
feature_preprocessing=None,
label_preprocessing=None,
model_dir=None):
self.loss = loss
self.optimizer = optimizer
self.metrics = Metrics.convert_metrics_list(metrics)
self.feature_preprocessing = feature_preprocessing
self.label_preprocessing = label_preprocessing
self.model_dir = model_dir
self.model = model
self.nn_model = NNModel(
self.model, feature_preprocessing=self.feature_preprocessing)
self.nn_estimator = NNEstimator(self.model, self.loss,
self.feature_preprocessing,
self.label_preprocessing)
if self.optimizer is None:
from bigdl.optim.optimizer import SGD
self.optimizer = SGD()
self.nn_estimator.setOptimMethod(self.optimizer)
self.estimator = SparkEstimator(self.model, self.optimizer,
self.model_dir)
self.log_dir = None
self.app_name = None
self.is_nnframe_fit = False
def fit(self,
data,
epochs,
batch_size=32,
feature_cols="features",
label_cols="label",
caching_sample=True,
validation_data=None,
validation_trigger=None,
checkpoint_trigger=None):
"""
Train this BigDL model with train data.
:param data: train data. It can be XShards or Spark DataFrame.
If data is XShards, each partition is a dictionary of {'x': feature,
'y': label}, where feature(label) is a numpy array or a list of numpy arrays.
:param epochs: Number of epochs to train the model.
:param batch_size: Batch size used for training. Default: 32.
:param feature_cols: Feature column name(s) of data. Only used when data is a Spark
DataFrame. Default: "features".
:param label_cols: Label column name(s) of data. Only used when data is a Spark DataFrame.
Default: "label".
:param caching_sample: whether to cache the Samples after preprocessing. Default: True
:param validation_data: Validation data. XShards and Spark DataFrame are supported.
If data is XShards, each partition is a dictionary of {'x': feature,
'y': label}, where feature(label) is a numpy array or a list of numpy arrays.
:param validation_trigger: Orca Trigger to trigger validation computation.
:param checkpoint_trigger: Orca Trigger to set a checkpoint.
:return:
"""
from zoo.orca.learn.trigger import Trigger
assert batch_size > 0, "batch_size should be greater than 0"
if validation_data is not None:
assert self.metrics is not None, \
"You should provide metrics when creating this estimator if you provide " \
"validation_data."
if isinstance(data, DataFrame):
if isinstance(feature_cols, list):
data, validation_data, feature_cols = \
BigDLEstimator._combine_cols(data, feature_cols, col_name="features",
val_data=validation_data)
if isinstance(label_cols, list):
data, validation_data, label_cols = \
BigDLEstimator._combine_cols(data, label_cols, col_name="label",
val_data=validation_data)
self.nn_estimator.setBatchSize(batch_size).setMaxEpoch(epochs)\
.setCachingSample(caching_sample).setFeaturesCol(feature_cols)\
.setLabelCol(label_cols)
if validation_data is not None:
assert isinstance(validation_data, DataFrame), \
"validation_data should be a spark DataFrame."
assert validation_trigger is not None, \
"You should provide validation_trigger if you provide validation_data."
validation_trigger = Trigger.convert_trigger(
validation_trigger)
self.nn_estimator.setValidation(validation_trigger,
validation_data, self.metrics,
batch_size)
if self.log_dir is not None and self.app_name is not None:
from bigdl.optim.optimizer import TrainSummary
from bigdl.optim.optimizer import ValidationSummary
train_summary = TrainSummary(log_dir=self.log_dir,
app_name=self.app_name)
self.nn_estimator.setTrainSummary(train_summary)
val_summary = ValidationSummary(log_dir=self.log_dir,
app_name=self.app_name)
self.nn_estimator.setValidationSummary(val_summary)
if self.model_dir is not None and checkpoint_trigger is not None:
checkpoint_trigger = Trigger.convert_trigger(
checkpoint_trigger)
self.nn_estimator.setCheckpoint(self.model_dir,
checkpoint_trigger)
self.nn_model = self.nn_estimator.fit(data)
self.is_nnframe_fit = True
elif isinstance(data, SparkXShards):
from zoo.orca.data.utils import xshard_to_sample
end_trigger = MaxEpoch(epochs)
checkpoint_trigger = Trigger.convert_trigger(checkpoint_trigger)
if isinstance(data, SparkXShards):
train_rdd = data.rdd.flatMap(xshard_to_sample)
train_feature_set = FeatureSet.sample_rdd(train_rdd)
if validation_data is None:
val_feature_set = None
else:
assert isinstance(validation_data, SparkXShards), \
"validation_data should be a XShards"
val_feature_set = FeatureSet.sample_rdd(
validation_data.rdd.flatMap(xshard_to_sample))
if self.log_dir is not None and self.app_name is not None:
self.estimator.set_tensorboard(self.log_dir, self.app_name)
self.estimator.train(train_feature_set, self.loss, end_trigger,
checkpoint_trigger, val_feature_set,
self.metrics, batch_size)
self.is_nnframe_fit = False
else:
raise ValueError(
"Data and validation data should be XShards, but get " +
data.__class__.__name__)
else:
raise ValueError(
"Data should be XShards or Spark DataFrame, but get " +
data.__class__.__name__)
return self
def predict(self,
data,
batch_size=4,
feature_cols="features",
sample_preprocessing=None):
"""
Predict input data
:param data: predict input data. It can be XShards or Spark DataFrame.
If data is XShards, each partition is a dictionary of {'x': feature}, where feature
is a numpy array or a list of numpy arrays.
:param batch_size: Batch size used for inference. Default: 4.
:param feature_cols: Feature column name(s) of data. Only used when data is a Spark
DataFrame. Default: "features".
:param sample_preprocessing: Used when data is a Spark DataFrame. If the user want change
the default feature_preprocessing specified in Estimator.from_bigdl, the user can pass the
new sample_preprocessing methods.
:return: predicted result.
If input data is Spark DataFrame, the predict result is a DataFrame which includes original
columns plus 'prediction' column. The 'prediction' column can be FloatType, VectorUDT
or Array of VectorUDT depending on model outputs shape.
If input data is an XShards, the predict result is a XShards, each partition of the XShards
is a dictionary of {'prediction': result}, where result is a numpy array or a list of numpy
arrays.
"""
if isinstance(data, DataFrame):
if isinstance(feature_cols, list):
data, _, feature_cols = \
BigDLEstimator._combine_cols(data, feature_cols, col_name="features")
self.nn_model.setBatchSize(batch_size).setFeaturesCol(feature_cols)
if sample_preprocessing is not None:
self.nn_model.setSamplePreprocessing(sample_preprocessing)
return self.nn_model.transform(data)
elif isinstance(data, SparkXShards):
from zoo.orca.data.utils import xshard_to_sample
from zoo.orca.learn.utils import convert_predict_rdd_to_xshard
sample_rdd = data.rdd.flatMap(xshard_to_sample)
result_rdd = self.model.predict(sample_rdd)
return convert_predict_rdd_to_xshard(data, result_rdd)
else:
raise ValueError(
"Data should be XShards or Spark DataFrame, but get " +
data.__class__.__name__)
def evaluate(self,
data,
batch_size=32,
feature_cols=None,
label_cols=None):
"""
Evaluate model.
:param data: validation data. It can be XShards, each partition is a dictionary of
{'x': feature, 'y': label}, where feature(label) is a numpy array or a list of numpy arrays.
:param batch_size: Batch size used for validation. Default: 32.
:param feature_cols: (Not supported yet) Feature column name(s) of data. Only used when
data is a Spark DataFrame. Default: None.
:param label_cols: (Not supported yet) Label column name(s) of data. Only used when data
is a Spark DataFrame. Default: None.
:return:
"""
assert data is not None, "validation data shouldn't be None"
assert self.metrics is not None, "metrics shouldn't be None, please specify the metrics" \
" argument when creating this estimator."
if isinstance(data, DataFrame):
raise NotImplementedError
elif isinstance(data, SparkXShards):
from zoo.orca.data.utils import xshard_to_sample
from zoo.orca.learn.metrics import Metrics
val_feature_set = FeatureSet.sample_rdd(
data.rdd.flatMap(xshard_to_sample))
result = self.estimator.evaluate(val_feature_set, self.metrics,
batch_size)
else:
raise ValueError(
"Data should be XShards or Spark DataFrame, but get " +
data.__class__.__name__)
return bigdl_metric_results_to_dict(result)
def get_model(self):
"""
Get the trained BigDL model
:return: The trained BigDL model
"""
return self.model
def save(self, model_path):
"""
Save the BigDL model to model_path
:param model_path: path to save the trained model.
:return:
"""
try:
model = self.get_model()
model.saveModel(model_path + ".bigdl", model_path + ".bin", True)
except ValueError:
raise ValueError("You should fit before calling save")
def load(self,
checkpoint,
optimizer=None,
loss=None,
feature_preprocessing=None,
label_preprocessing=None,
model_dir=None,
is_checkpoint=False):
"""
Load existing BigDL model or checkpoint
:param checkpoint: Path to the existing model or checkpoint.
:param optimizer: BigDL optimizer.
:param loss: BigDL criterion.
:param feature_preprocessing: Used when data in `fit` and `predict` is a Spark DataFrame.
The param converts the data in feature column to a Tensor or to a Sample directly.
It expects a List of Int as the size of the converted Tensor, or a Preprocessing[F,
Tensor[T]]
If a List of Int is set as feature_preprocessing, it can only handle the case that
feature column contains the following data types:
Float, Double, Int, Array[Float], Array[Double], Array[Int] and MLlib Vector. The
feature data are converted to Tensors with the specified sizes before
sending to the model. Internally, a SeqToTensor is generated according to the
size, and used as the feature_preprocessing.
Alternatively, user can set feature_preprocessing as Preprocessing[F, Tensor[T]]
that transforms the feature data to a Tensor[T]. Some pre-defined Preprocessing are
provided in package zoo.feature. Multiple Preprocessing can be combined as a
ChainedPreprocessing.
The feature_preprocessing will also be copied to the generated NNModel and applied
to feature column during transform.
:param label_preprocessing: Used when data in `fit` and `predict` is a Spark DataFrame.
similar to feature_preprocessing, but applies to Label data.
:param model_dir: The path to save model. During the training, if checkpoint_trigger is
defined and triggered, the model will be saved to model_dir.
:param is_checkpoint: Whether the path is a checkpoint or a saved BigDL model.
Default: False.
:return: The loaded estimator object.
"""
if loss is not None:
self.loss = loss
if optimizer is not None:
self.optimizer = optimizer
if feature_preprocessing is not None:
self.feature_preprocessing = feature_preprocessing
if label_preprocessing is not None:
self.label_preprocessing = label_preprocessing
if model_dir is not None:
self.model_dir = model_dir
if is_checkpoint:
self.load_latest_orca_checkpoint(checkpoint)
else:
from zoo.pipeline.api.net import Net
self.model = Net.load_bigdl(checkpoint + ".bigdl",
checkpoint + ".bin")
self.nn_estimator = NNEstimator(self.model, self.loss,
self.feature_preprocessing,
self.label_preprocessing)
if self.optimizer is None:
from bigdl.optim.optimizer import SGD
self.optimizer = SGD()
self.nn_estimator.setOptimMethod(self.optimizer)
self.estimator = SparkEstimator(self.model, self.optimizer,
self.model_dir)
self.nn_model = NNModel(
self.model, feature_preprocessing=self.feature_preprocessing)
return self
def load_orca_checkpoint(self, path, version, prefix=None):
"""
Load existing checkpoint
:param path: Path to the existing checkpoint.
:param version: checkpoint version, which is the suffix of model.* file,
i.e., for modle.4 file, the version is 4.
:param prefix: optimMethod prefix, for example 'optimMethod-Sequentialf53bddcc'
:return:
"""
from bigdl.nn.layer import Model, Container
from bigdl.optim.optimizer import OptimMethod
import os
try:
self.model = Model.load(
os.path.join(path, "model.{}".format(version)))
assert isinstance(self.model, Container), \
"The loaded model should be a Container, please check your checkpoint type."
self.optimizer = OptimMethod.load(
os.path.join(path, "{}.{}".format(prefix, version)))
except Exception:
raise ValueError(
"Cannot load BigDL checkpoint, please check your checkpoint path "
"and checkpoint type.")
self.estimator = SparkEstimator(self.model, self.optimizer,
self.model_dir)
self.nn_estimator = NNEstimator(self.model, self.loss,
self.feature_preprocessing,
self.label_preprocessing)
if self.optimizer is not None:
self.nn_estimator.setOptimMethod(self.optimizer)
self.nn_model = NNModel(
self.model, feature_preprocessing=self.feature_preprocessing)
def load_latest_orca_checkpoint(self, path):
"""
Load latest Orca checkpoint under specified directory.
:param path: directory containing Orca checkpoint files.
"""
from zoo.orca.learn.utils import find_latest_checkpoint
path, prefix, version = find_latest_checkpoint(path,
model_type="bigdl")
if path is None:
raise ValueError(
"Cannot find BigDL checkpoint, please check your checkpoint path."
)
self.load_orca_checkpoint(path=path, version=version, prefix=prefix)
def clear_gradient_clipping(self):
"""
Clear gradient clipping parameters. In this case, gradient clipping will not be applied.
In order to take effect, it needs to be called before fit.
:return:
"""
self.nn_estimator.clearGradientClipping()
self.estimator.clear_gradient_clipping()
def set_constant_gradient_clipping(self, min, max):
"""
Set constant gradient clipping during the training process.
In order to take effect, it needs to be called before fit.
:param min: The minimum value to clip by.
:param max: The maximum value to clip by.
:return:
"""
self.nn_estimator.setConstantGradientClipping(min, max)
self.estimator.set_constant_gradient_clipping(min, max)
def set_l2_norm_gradient_clipping(self, clip_norm):
"""
Clip gradient to a maximum L2-Norm during the training process.
In order to take effect, it needs to be called before fit.
:param clip_norm: Gradient L2-Norm threshold.
:return:
"""
self.nn_estimator.setGradientClippingByL2Norm(clip_norm)
self.estimator.set_l2_norm_gradient_clipping(clip_norm)
def get_train_summary(self, tag=None):
"""
Get the scalar from model train summary
Return list of summary data of [iteration_number, scalar_value, timestamp]
tag: The string variable represents the scalar wanted
"""
# Exception handle
if tag != "Loss" and tag != "LearningRate" and tag != "Throughput":
raise TypeError('Only "Loss", "LearningRate", "Throughput"' +
'are supported in train summary')
if self.is_nnframe_fit:
train_summary = self.nn_estimator.getTrainSummary()
return train_summary.read_scalar(tag=tag)
else:
return self.estimator.get_train_summary(tag=tag)
def get_validation_summary(self, tag=None):
"""
Get the scalar from model validation summary
Return list of summary data of [iteration_number, scalar_value, timestamp]
Note: The metric and tag may not be consistent
Please look up following form to pass tag parameter
Left side is your metric during compile
Right side is the tag you should pass
'Accuracy' | 'Top1Accuracy'
'BinaryAccuracy' | 'Top1Accuracy'
'CategoricalAccuracy' | 'Top1Accuracy'
'SparseCategoricalAccuracy' | 'Top1Accuracy'
'AUC' | 'AucScore'
'HitRatio' | 'HitRate@k' (k is Top-k)
'Loss' | 'Loss'
'MAE' | 'MAE'
'NDCG' | 'NDCG'
'TFValidationMethod' | '${name + " " + valMethod.toString()}'
'Top5Accuracy' | 'Top5Accuracy'
'TreeNNAccuracy' | 'TreeNNAccuracy()'
'MeanAveragePrecision' | 'MAP@k' (k is Top-k) (BigDL)
'MeanAveragePrecision' | 'PascalMeanAveragePrecision' (Zoo)
'StatelessMetric' | '${name}'
tag: The string variable represents the scalar wanted
"""
if self.is_nnframe_fit:
assert tag is not None, "You should provide tag which should match the name of " \
"the ValidationMethod set into the optimizer. " \
"e.g.'MAE', 'Top1AccuracyLoss', 'Top1Accuracy' or " \
"'Top5Accuracy'."
val_summary = self.nn_estimator.getValidationSummary()
return val_summary.read_scalar(tag=tag)
else:
return self.estimator.get_validation_summary(tag=tag)
@staticmethod
def _combine_cols(data, cols, col_name="features", val_data=None):
if isinstance(cols, list):
if len(cols) == 1:
col_name = cols[0]
else:
from pyspark.ml.feature import VectorAssembler
assembler = VectorAssembler(inputCols=cols, outputCol=col_name)
data = assembler.transform(data)
if val_data is not None:
val_data = assembler.transform(val_data)
return data, val_data, col_name
class TFOptimizer:
def __init__(self,
loss,
optim_method,
sess=None,
dataset=None,
inputs=None,
grads=None,
variables=None,
graph=None,
val_outputs=None,
val_labels=None,
val_method=None,
val_split=0.0,
tensors_with_value=None,
session_config=None,
clip_norm=None,
clip_value=None,
metrics=None,
updates=None,
freeze=False,
model_dir=None):
"""
TFOptimizer is used for distributed training of TensorFlow
on Spark/BigDL.
Note that if grads and variables are not None, then they need to be sorted by name
if you want to use multiple optimization methods for a TensorFlow model according to
variable names.
:param loss: The loss tensor of the TensorFlow model, should be a scalar
:param optim_method: the optimization method to be used, such as bigdl.optim.optimizer.Adam
:param sess: the current TensorFlow Session, if you want to used a pre-trained model, you
should use the Session to load the pre-trained variables and pass it to TFOptimizer.
"""
if dataset is None:
args = TFOptimizer._get_arguments_from_loss(
loss, optim_method, sess, val_outputs, val_labels, val_method)
loss, optim_method, sess, dataset, inputs = args[:5]
grads, variables, graph, val_outputs, val_labels, val_method = args[
5:]
self.optim_method = optim_method
self.sess = sess
self.dataset = dataset
self.graph = graph
self.clip_norm = clip_norm
if clip_value is not None and not isinstance(clip_value, tuple):
raise ValueError(
"The clip_value argument should be a tuple (min_value, max_value)"
)
self.clip_constant = clip_value
if self.dataset.batch_size <= 0:
raise ValueError(
"You should set batch_size instead of batch_per_thread for training"
)
if val_method is not None:
val_methods = to_list(val_method)
if metrics is None:
metrics = {}
for i, method in enumerate(val_methods):
metrics['bigdl_metirc_' + str(i)] = BigDLMetric(
method, val_outputs, val_labels)
if model_dir is None:
model_dir = tempfile.mkdtemp()
else:
if not os.path.isdir(model_dir):
os.makedirs(model_dir)
self.model_dir = model_dir
if freeze:
self.tf_model = TFModel.create(loss, sess, inputs, grads,
variables, graph,
tensors_with_value, session_config,
metrics, updates, model_dir)
else:
self.tf_model = TFModel.create_for_unfreeze(
loss, sess, inputs, grads, variables, graph,
tensors_with_value, session_config, metrics, updates,
model_dir)
batch_size = self.dataset.batch_size
sample_rdd = self.dataset.get_training_data()
if val_split != 0.0:
training_rdd, val_rdd = sample_rdd.randomSplit(
[1 - val_split, val_split])
else:
training_rdd = sample_rdd
val_rdd = self.dataset.get_validation_data()
self.training_rdd = training_rdd
self.val_rdd = val_rdd
self.batch_size = batch_size
self.estimator = Estimator(self.tf_model.training_helper_layer,
self.optim_method, model_dir)
if self.clip_norm:
self.estimator.set_l2_norm_gradient_clipping(self.clip_norm)
if self.clip_constant:
min_value, max_value = self.clip_constant
self.estimator.set_constant_gradient_clipping(min_value, max_value)
@staticmethod
def _get_arguments_from_loss(loss, optim_method, session, val_outputs,
val_labels, val_method):
import tensorflow as tf
if session is None:
sess = tf.Session()
sess.run(tf.global_variables_initializer())
else:
sess = session
grads_vars = tf.train.GradientDescentOptimizer(0).compute_gradients(
loss)
grads_vars.sort(key=lambda grad_var: grad_var[1].name)
variables = []
grads = []
for (grad, var) in grads_vars:
if grad is not None:
variables.append(var)
grads.append(grad)
all_required_inputs = _find_placeholders([loss])
dataset = tf.get_collection(all_required_inputs[0].name)[0]
inputs = nest.flatten(dataset._original_tensors)
return [
loss, optim_method, sess, dataset, inputs, grads, variables,
loss.graph, val_outputs, val_labels, val_method
]
@classmethod
def from_loss(cls,
loss,
optim_method,
session=None,
val_outputs=None,
val_labels=None,
val_method=None,
val_split=0.0,
clip_norm=None,
clip_value=None,
metrics=None,
tensor_with_value=None,
**kwargs):
"""
Create a TFOptimizer from a TensorFlow loss tensor.
The loss tensor must come from a TensorFlow graph that only takes TFDataset.tensors and
the tensors in `tensor_with_value` as inputs.
:param loss: The loss tensor of the TensorFlow model, should be a scalar
:param optim_method: the optimization method to be used, such as bigdl.optim.optimizer.Adam
:param session: the current TensorFlow Session, if you want to used a pre-trained model,
you should use the Session to load the pre-trained variables and pass it to TFOptimizer.
:param val_outputs: the validation output TensorFlow tensor to be used by val_methods
:param val_labels: the validation label TensorFlow tensor to be used by val_methods
:param val_method: the BigDL val_method(s) to be used.
:param val_split: Float between 0 and 1. Fraction of the training data to be used as
validation data.
:param clip_norm: float >= 0. Gradients will be clipped when their L2 norm exceeds
this value.
:param clip_value: float >= 0. Gradients will be clipped when their absolute value
exceeds this value.
:param metrics: a dictionary. The key should be a string representing the metric's name
and the value should be the corresponding TensorFlow tensor, which should be a scalar.
:param tensor_with_value: a dictionary. The key is TensorFlow tensor, usually a
placeholder, the value of the dictionary is a tuple of two elements. The first one of
the tuple is the value to feed to the tensor in training phase and the second one
is the value to feed to the tensor in validation phase.
:return: a TFOptimizer
"""
args = TFOptimizer._get_arguments_from_loss(loss, optim_method,
session, val_outputs,
val_labels, val_method)
if clip_value is not None:
if isinstance(clip_value, float) or isinstance(clip_value, int):
if clip_value <= 0:
ValueError(
"The clip_value argument should be positive number")
clip_value = (-float(clip_value), float(clip_value))
if not isinstance(clip_value, tuple):
raise ValueError(
"The clip_value argument should be" +
" a positive float/int which clips to" +
" (-clip_value, clip_value); " +
"or a tuple which clips to (min_value, max_value)")
return cls(*(args + [val_split]),
tensors_with_value=tensor_with_value,
clip_norm=clip_norm,
clip_value=clip_value,
metrics=metrics,
**kwargs)
@classmethod
def from_keras(cls,
keras_model,
dataset,
optim_method=None,
val_spilt=0.0,
**kwargs):
"""
Create a TFOptimizer from a tensorflow.keras model. The model must be compiled.
:param keras_model: the tensorflow.keras model, which must be compiled.
:param dataset: a TFDataset
:param optim_method: the optimization method to be used, such as bigdl.optim.optimizer.Adam
:param val_spilt: Float between 0 and 1. Fraction of the training data to be used as
validation data.
:return:
"""
import tensorflow.keras.backend as K
loss = keras_model.total_loss
model_inputs = keras_model.inputs
if hasattr(keras_model, "targets"):
model_targets = keras_model.targets
else:
model_targets = keras_model._targets
inputs = model_inputs + model_targets
variables = keras_model._collected_trainable_weights
variables.sort(key=lambda variable: variable.name)
keras_optimizer = keras_model.optimizer
grads = K.gradients(loss, variables)
if None in grads:
raise ValueError('An operation has `None` for gradient. '
'Please make sure that all of your ops have a '
'gradient defined (i.e. are differentiable). '
'Common ops without gradient: '
'K.argmax, K.round, K.eval.')
clip_norm = None
clip_value = None
if hasattr(keras_optimizer, 'clipnorm'):
clip_norm = keras_optimizer.clipnorm
if hasattr(keras_optimizer, 'clipvalue'):
clip_value = (-keras_optimizer.clipvalue,
keras_optimizer.clipvalue)
sess = K.get_session()
if optim_method is None:
optim_method = keras_optimizer
optim_method = to_bigdl_optim_method(optim_method)
if keras_model.metrics and (dataset.get_validation_data() is not None
or val_spilt != 0.0):
if isinstance(keras_model.metrics, dict):
raise ValueError(
"different metrics for different outputs are not supported right now"
)
if dataset.get_validation_data() is None and val_spilt == 0.0:
raise ValueError(
"Validation data is not specified. Please set " +
"val_rdd in TFDataset, or set val_split larger than zero")
if len(keras_model.outputs) > 1:
if not all([
name.endswith("loss")
for name in keras_model.metrics_names
]):
raise ValueError(
"metrics (except loss) for multi-head model is not supported"
)
else:
bigdl_val_methods = [Loss()]
val_outputs = keras_model.outputs
val_labels = model_targets
else:
bigdl_val_methods = \
[to_bigdl_metric(m, keras_model.loss) for m in keras_model.metrics_names]
val_outputs = keras_model.outputs
val_labels = model_targets
else:
val_outputs = None
val_labels = None
bigdl_val_methods = None
tensor_with_value = {K.learning_phase(): [True, False]}
updates = keras_model.updates
return cls(loss,
optim_method,
sess,
dataset,
inputs,
grads,
variables,
loss.graph,
val_outputs,
val_labels,
bigdl_val_methods,
val_spilt,
tensors_with_value=tensor_with_value,
clip_norm=clip_norm,
clip_value=clip_value,
updates=updates,
**kwargs)
def set_constant_gradient_clipping(self, min_value, max_value):
"""
Configure constant clipping settings.
:param min_value: the minimum value to clip by
:param max_value: the maxmimum value to clip by
"""
self.estimator.set_constant_gradient_clipping(min_value, max_value)
def set_gradient_clipping_by_l2_norm(self, clip_norm):
"""
Configure L2 norm clipping settings.
:param clip_norm: gradient L2-Norm threshold
"""
self.estimator.set_l2_norm_gradient_clipping(clip_norm)
def optimize(self, end_trigger=None, checkpoint_trigger=None):
"""
Run the training loop of the this optimizer
:param end_trigger: BigDL's Trigger to indicate when to stop the training.
:param checkpoint_trigger: When to save a checkpoint and evaluate model.
"""
if end_trigger is None:
end_trigger = MaxEpoch(1)
if checkpoint_trigger is None:
checkpoint_trigger = EveryEpoch()
if self.tf_model.val_methods is not None and self.val_rdd is not None:
self.estimator.train(train_set=self.training_rdd,
criterion=IdentityCriterion(),
end_trigger=end_trigger,
checkpoint_trigger=checkpoint_trigger,
validation_set=self.val_rdd,
validation_method=self.tf_model.val_methods,
batch_size=self.batch_size)
else:
self.estimator.train(train_set=self.training_rdd,
criterion=IdentityCriterion(),
end_trigger=end_trigger,
batch_size=self.batch_size)
self.tf_model.training_helper_layer.get_weights_to_python()
class BigDLEstimatorWrapper(OrcaSparkEstimator):
def __init__(self,
*,
model,
loss,
optimizer=None,
feature_preprocessing=None,
label_preprocessing=None,
model_dir=None):
self.loss = loss
self.optimizer = optimizer
self.feature_preprocessing = feature_preprocessing
self.label_preprocessing = label_preprocessing
self.model_dir = model_dir
self.model = model
self.nn_model = NNModel(
self.model, feature_preprocessing=self.feature_preprocessing)
self.nn_estimator = NNEstimator(self.model, self.loss,
self.feature_preprocessing,
self.label_preprocessing)
if self.optimizer is None:
from bigdl.optim.optimizer import SGD
self.optimizer = SGD()
self.nn_estimator.setOptimMethod(self.optimizer)
self.estimator = SparkEstimator(self.model, self.optimizer,
self.model_dir)
self.log_dir = None
self.app_name = None
self.is_nnframe_fit = False
def fit(self,
data,
epochs,
feature_cols="features",
labels_cols="label",
batch_size=32,
caching_sample=True,
val_data=None,
val_trigger=None,
val_methods=None,
checkpoint_trigger=None):
from zoo.orca.learn.metrics import Metrics
from zoo.orca.learn.trigger import Trigger
assert batch_size > 0, "batch_size should be greater than 0"
if isinstance(data, DataFrame):
if isinstance(feature_cols, list):
data, val_data, feature_cols = \
BigDLEstimatorWrapper._combine_cols(data, feature_cols, col_name="features",
val_data=val_data)
if isinstance(labels_cols, list):
data, val_data, labels_cols = \
BigDLEstimatorWrapper._combine_cols(data, labels_cols, col_name="label",
val_data=val_data)
self.nn_estimator.setBatchSize(batch_size).setMaxEpoch(epochs)\
.setCachingSample(caching_sample).setFeaturesCol(feature_cols)\
.setLabelCol(labels_cols)
if val_data is not None:
assert isinstance(
val_data,
DataFrame), "val_data should be a spark DataFrame."
assert val_trigger is not None and val_methods is not None, \
"You should provide val_trigger and val_methods if you provide val_data."
val_trigger = Trigger.convert_trigger(val_trigger)
val_methods = Metrics.convert_metrics_list(val_methods)
self.nn_estimator.setValidation(val_trigger, val_data,
val_methods, batch_size)
if self.log_dir is not None and self.app_name is not None:
from bigdl.optim.optimizer import TrainSummary
from bigdl.optim.optimizer import ValidationSummary
train_summary = TrainSummary(log_dir=self.log_dir,
app_name=self.app_name)
self.nn_estimator.setTrainSummary(train_summary)
val_summary = ValidationSummary(log_dir=self.log_dir,
app_name=self.log_dir)
self.nn_estimator.setValidationSummary(val_summary)
if self.model_dir is not None and checkpoint_trigger is not None:
checkpoint_trigger = Trigger.convert_trigger(
checkpoint_trigger)
self.nn_estimator.setCheckpoint(self.model_dir,
checkpoint_trigger)
self.nn_model = self.nn_estimator.fit(data)
self.is_nnframe_fit = True
elif isinstance(data, SparkXShards):
from zoo.orca.data.utils import to_sample
end_trigger = MaxEpoch(epochs)
val_methods = Metrics.convert_metrics_list(val_methods)
checkpoint_trigger = Trigger.convert_trigger(checkpoint_trigger)
if isinstance(data, SparkXShards):
train_rdd = data.rdd.flatMap(to_sample)
train_feature_set = FeatureSet.sample_rdd(train_rdd)
if val_data is None:
val_feature_set = None
else:
assert isinstance(
val_data, SparkXShards), "val_data should be a XShards"
val_feature_set = FeatureSet.sample_rdd(
val_data.rdd.flatMap(to_sample))
if self.log_dir is not None and self.app_name is not None:
self.estimator.set_tensorboard(self.log_dir, self.app_name)
self.estimator.train(train_feature_set, self.loss, end_trigger,
checkpoint_trigger, val_feature_set,
val_methods, batch_size)
self.is_nnframe_fit = False
else:
raise ValueError(
"Data and validation data should be XShards, but get " +
data.__class__.__name__)
else:
raise ValueError(
"Data should be XShards or Spark DataFrame, but get " +
data.__class__.__name__)
return self
def predict(self,
data,
batch_size=8,
feature_cols="features",
sample_preprocessing=None):
if isinstance(data, DataFrame):
if isinstance(feature_cols, list):
data, _, feature_cols = \
BigDLEstimatorWrapper._combine_cols(data, feature_cols, col_name="features")
self.nn_model.setBatchSize(batch_size).setFeaturesCol(feature_cols)
if sample_preprocessing is not None:
self.nn_model.setSamplePreprocessing(sample_preprocessing)
return self.nn_model.transform(data)
elif isinstance(data, SparkXShards):
from zoo.orca.data.utils import to_sample
from zoo.orca.learn.utils import convert_predict_to_xshard
sample_rdd = data.rdd.flatMap(to_sample)
result_rdd = self.model.predict(sample_rdd)
return convert_predict_to_xshard(result_rdd)
else:
raise ValueError(
"Data should be XShards or Spark DataFrame, but get " +
data.__class__.__name__)
def evaluate(self, data, validation_methods=None, batch_size=32):
assert data is not None, "validation data shouldn't be None"
if isinstance(data, DataFrame):
raise NotImplementedError
elif isinstance(data, SparkXShards):
from zoo.orca.data.utils import to_sample
from zoo.orca.learn.metrics import Metrics
validation_methods = Metrics.convert_metrics_list(
validation_methods)
val_feature_set = FeatureSet.sample_rdd(
data.rdd.flatMap(to_sample))
return self.estimator.evaluate(val_feature_set, validation_methods,
batch_size)
else:
raise ValueError(
"Data should be XShards or Spark DataFrame, but get " +
data.__class__.__name__)
def get_model(self):
return self.model
def save(self, model_path):
try:
model = self.get_model()
model.saveModel(model_path + ".bigdl", model_path + ".bin", True)
except ValueError:
raise ValueError("You should fit before calling save")
def load(self,
checkpoint,
optimizer=None,
loss=None,
feature_preprocessing=None,
label_preprocessing=None,
model_dir=None,
is_checkpoint=False):
if loss is not None:
self.loss = loss
if optimizer is not None:
self.optimizer = optimizer
if feature_preprocessing is not None:
self.feature_preprocessing = feature_preprocessing
if label_preprocessing is not None:
self.label_preprocessing = label_preprocessing
if model_dir is not None:
self.model_dir = model_dir
if is_checkpoint:
self.load_latest_orca_checkpoint(checkpoint)
else:
from zoo.pipeline.api.net import Net
self.model = Net.load_bigdl(checkpoint + ".bigdl",
checkpoint + ".bin")
self.nn_estimator = NNEstimator(self.model, self.loss,
self.feature_preprocessing,
self.label_preprocessing)
if self.optimizer is None:
from bigdl.optim.optimizer import SGD
self.optimizer = SGD()
self.nn_estimator.setOptimMethod(self.optimizer)
self.estimator = SparkEstimator(self.model, self.optimizer,
self.model_dir)
self.nn_model = NNModel(
self.model, feature_preprocessing=self.feature_preprocessing)
return self
def load_orca_checkpoint(self, path, version, prefix=None):
from bigdl.nn.layer import Model, Container
from bigdl.optim.optimizer import OptimMethod
import os
try:
self.model = Model.load(
os.path.join(path, "model.{}".format(version)))
assert isinstance(self.model, Container), \
"The loaded model should be a Container, please check your checkpoint type."
self.optimizer = OptimMethod.load(
os.path.join(path, "{}.{}".format(prefix, version)))
except Exception:
raise ValueError(
"Cannot load BigDL checkpoint, please check your checkpoint path "
"and checkpoint type.")
self.estimator = SparkEstimator(self.model, self.optimizer,
self.model_dir)
self.nn_estimator = NNEstimator(self.model, self.loss,
self.feature_preprocessing,
self.label_preprocessing)
if self.optimizer is not None:
self.nn_estimator.setOptimMethod(self.optimizer)
self.nn_model = NNModel(
self.model, feature_preprocessing=self.feature_preprocessing)
def load_latest_orca_checkpoint(self, path):
from zoo.orca.learn.utils import find_latest_checkpoint
path, prefix, version = find_latest_checkpoint(path,
model_type="bigdl")
if path is None:
raise ValueError(
"Cannot find BigDL checkpoint, please check your checkpoint path."
)
self.load_orca_checkpoint(path=path, version=version, prefix=prefix)
def clear_gradient_clipping(self):
self.nn_estimator.clearGradientClipping()
self.estimator.clear_gradient_clipping()
def set_constant_gradient_clipping(self, min, max):
self.nn_estimator.setConstantGradientClipping(min, max)
self.estimator.set_constant_gradient_clipping(min, max)
def set_l2_norm_gradient_clipping(self, clip_norm):
self.nn_estimator.setGradientClippingByL2Norm(clip_norm)
self.estimator.set_l2_norm_gradient_clipping(clip_norm)
def get_train_summary(self, tag=None):
if self.is_nnframe_fit:
return self.nn_estimator.getTrainSummary()
else:
return self.estimator.get_train_summary(tag=tag)
def get_validation_summary(self, tag=None):
if self.is_nnframe_fit:
return self.nn_estimator.getValidationSummary()
else:
return self.estimator.get_validation_summary(tag=tag)
@staticmethod
def _combine_cols(data, cols, col_name="features", val_data=None):
if isinstance(cols, list):
if len(cols) == 1:
col_name = cols[0]
else:
from pyspark.ml.feature import VectorAssembler
assembler = VectorAssembler(inputCols=cols, outputCol=col_name)
data = assembler.transform(data)
if val_data is not None:
val_data = assembler.transform(val_data)
return data, val_data, col_name