def benchmark_hrnn_mnist_bs_256(self):
"""Measure performance with batch_size=256."""
batch_size = 256
metrics, wall_time, extras = benchmark_util.measure_performance(
self._build_model,
x=self.x_train,
y=self.y_train,
batch_size=batch_size,
optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
metadata = benchmark_util.get_keras_examples_metadata(
'hierarchical_rnn', batch_size)
extras.update(metadata)
self.report_benchmark(wall_time=wall_time,
metrics=metrics,
extras=extras)
def benchmark_text_classification_bs_256(self):
"""Measure performance with batch_size=256."""
batch_size = 256
metrics, wall_time, extras = benchmark_util.measure_performance(
self._build_model,
x=self.imdb_x,
y=self.imdb_y,
batch_size=batch_size,
optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
metadata = benchmark_util.get_keras_examples_metadata(
'transformer', batch_size)
extras.update(metadata)
self.report_benchmark(wall_time=wall_time,
metrics=metrics,
extras=extras)
def benchmark_bidirect_lstm_imdb_bs_512(self):
"""Measure performance with batch_size=512."""
batch_size = 512
metrics, wall_time, extras = benchmark_util.measure_performance(
self._build_model,
x=self.imdb_x,
y=self.imdb_y,
batch_size=batch_size,
optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
metadata = benchmark_util.get_keras_examples_metadata(
'bidirectional_lstm', batch_size)
extras.update(metadata)
self.report_benchmark(wall_time=wall_time,
metrics=metrics,
extras=extras)
def benchmark_text_classification_bs_128(self):
"""Measure performance with batch_size=128."""
batch_size = 128
metrics, wall_time, extras = benchmark_util.measure_performance(
self._build_model,
x=self.imdb_x,
y=self.imdb_y,
batch_size=batch_size,
optimizer="adam",
loss="sparse_categorical_crossentropy",
metrics=["accuracy"],
)
metadata = benchmark_util.get_keras_examples_metadata(
"transformer", batch_size)
extras.update(metadata)
self.report_benchmark(wall_time=wall_time,
metrics=metrics,
extras=extras)
def benchmark_antirectifier_bs_512(self):
"""Measure performance with batch_size=512."""
batch_size = 512
metrics, wall_time, extras = benchmark_util.measure_performance(
self._build_model,
x=self.x_train,
y=self.y_train,
batch_size=batch_size,
optimizer="rmsprop",
loss=tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True),
metrics=["sparse_categorical_accuracy"])
metadata = benchmark_util.get_keras_examples_metadata(
"antirectifier", batch_size)
extras.update(metadata)
self.report_benchmark(wall_time=wall_time,
metrics=metrics,
extras=extras)
def benchmark_conv_mnist_bs_128(self):
"""Measure performance with batch_size=128."""
batch_size = 128
metrics, wall_time, extras = benchmark_util.measure_performance(
self._build_model,
x=self.x_train,
y=self.y_train,
batch_size=batch_size,
epochs=self.epochs,
optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
metadata = benchmark_util.get_keras_examples_metadata(
'conv', batch_size)
extras.update(metadata)
self.report_benchmark(wall_time=wall_time,
metrics=metrics,
extras=extras)
def benchmark_hrnn_mnist_bs_512(self):
"""Measure performance with batch_size=512."""
batch_size = 512
metrics, wall_time, extras = benchmark_util.measure_performance(
self._build_model,
x=self.x_train,
y=self.y_train,
batch_size=batch_size,
optimizer="rmsprop",
loss="categorical_crossentropy",
metrics=["accuracy"],
)
metadata = benchmark_util.get_keras_examples_metadata(
"hierarchical_rnn", batch_size)
extras.update(metadata)
self.report_benchmark(wall_time=wall_time,
metrics=metrics,
extras=extras)
def benchmark_irnn_mnist_bs_512(self):
"""Measure performance with batch_size=512."""
batch_size = 512
metrics, wall_time, extras = benchmark_util.measure_performance(
self._build_model,
x=self.x_train,
y=self.y_train,
batch_size=batch_size,
optimizer=tf.keras.optimizers.RMSprop(
learning_rate=self.learning_rate),
loss='categorical_crossentropy',
metrics=['accuracy'])
metadata = benchmark_util.get_keras_examples_metadata(
'irnn', batch_size)
extras.update(metadata)
self.report_benchmark(wall_time=wall_time,
metrics=metrics,
extras=extras)
def benchmark_conv_mnist_bs_256(self):
"""Measure performance with batch_size=256."""
batch_size = 256
metrics, wall_time, extras = benchmark_util.measure_performance(
self._build_model,
x=self.x_train,
y=self.y_train,
batch_size=batch_size,
epochs=self.epochs,
optimizer="adam",
loss="categorical_crossentropy",
metrics=["accuracy"],
)
metadata = benchmark_util.get_keras_examples_metadata(
"conv", batch_size)
extras.update(metadata)
self.report_benchmark(wall_time=wall_time,
metrics=metrics,
extras=extras)
def benchmark_text_classification_bs_512_gpu_2(self):
"""Measure performance with batch_size=512, gpu=1 and
distribution_strategy='mirrored'
"""
batch_size = 512
metrics, wall_time, extras = benchmark_util.measure_performance(
self._build_model,
x=self.imdb_x,
y=self.imdb_y,
batch_size=batch_size,
num_gpus=2,
distribution_strategy='mirrored',
optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
self.report_benchmark(wall_time=wall_time,
metrics=metrics,
extras=extras)
def benchmark_cnn_cifar10_bs_256(self):
"""Measure performance with batch_size=256."""
batch_size = 256
metrics, wall_time, extras = benchmark_util.measure_performance(
self._build_model,
x=self.x_train,
y=self.y_train,
batch_size=batch_size,
epochs=self.epochs,
optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.0001,
decay=1e-6),
loss='categorical_crossentropy',
metrics=['accuracy'])
metadata = benchmark_util.get_keras_examples_metadata(
'cnn', batch_size)
extras.update(metadata)
self.report_benchmark(wall_time=wall_time,
metrics=metrics,
extras=extras)
def benchmark_bidirect_lstm_imdb_bs_256(self):
"""Measure performance with batch_size=256."""
batch_size = 256
metrics, wall_time, extras = benchmark_util.measure_performance(
self._build_model,
x=self.imdb_x,
y=self.imdb_y,
batch_size=batch_size,
optimizer="adam",
loss="binary_crossentropy",
metrics=["accuracy"],
)
metadata = benchmark_util.get_keras_examples_metadata(
"bidirectional_lstm", batch_size
)
extras.update(metadata)
self.report_benchmark(
wall_time=wall_time, metrics=metrics, extras=extras
)
def benchmark_bidirect_lstm_imdb_bs_512_gpu_2(self):
"""Measure performance with batch_size=512, gpu=2 and
distribution_strategy=`mirrored`.
"""
batch_size = 512
metrics, wall_time, extras = benchmark_util.measure_performance(
self._build_model,
x=self.imdb_x,
y=self.imdb_y,
batch_size=batch_size,
num_gpus=2,
distribution_strategy='mirrored',
optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
self.report_benchmark(wall_time=wall_time,
metrics=metrics,
extras=extras)
def benchmark_antirectifier_bs_512_gpu_2(self):
"""Measure performance with batch_size=512, gpu=2 and
distribution_strategy=`mirrored`.
"""
batch_size = 512
metrics, wall_time, extras = benchmark_util.measure_performance(
self._build_model,
x=self.x_train,
y=self.y_train,
batch_size=batch_size,
num_gpus=2,
distribution_strategy="mirrored",
optimizer="rmsprop",
loss=tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True),
metrics=["sparse_categorical_accuracy"])
self.report_benchmark(wall_time=wall_time,
metrics=metrics,
extras=extras)
def benchmark_conv_mnist_bs_512_gpu_2(self):
"""Measure performance with batch_size=512, gpu=2 and
distribution_strategy='mirrored'
"""
batch_size = 512
metrics, wall_time, extras = benchmark_util.measure_performance(
self._build_model,
x=self.x_train,
y=self.y_train,
batch_size=batch_size,
num_gpus=2,
distribution_strategy='mirrored',
epochs=self.epochs,
optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
self.report_benchmark(wall_time=wall_time,
metrics=metrics,
extras=extras)
def benchmark_cnn_cifar10_bs_1024_gpu_2(self):
"""Measure performance with batch_size=1024, gpu=2 and
distribution_strategy=`mirrored`.
"""
batch_size = 1024
metrics, wall_time, extras = benchmark_util.measure_performance(
self._build_model,
x=self.x_train,
y=self.y_train,
batch_size=batch_size,
num_gpus=2,
distribution_strategy='mirrored',
epochs=self.epochs,
optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.0001,
decay=1e-6),
loss='categorical_crossentropy',
metrics=['accuracy'])
self.report_benchmark(wall_time=wall_time,
metrics=metrics,
extras=extras)
def benchmark_hrnn_mnist_bs_1024_gpu_2(self):
"""Measure performance with batch_size=1024, gpu=2 and
distribution_strategy='mirrored'
"""
batch_size = 1024
metrics, wall_time, extras = benchmark_util.measure_performance(
self._build_model,
x=self.x_train,
y=self.y_train,
batch_size=batch_size,
num_gpus=2,
distribution_strategy='mirrored',
optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
metadata = benchmark_util.get_keras_examples_metadata(
'hierarchical_rnn', batch_size)
extras.update(metadata)
self.report_benchmark(wall_time=wall_time,
metrics=metrics,
extras=extras)
def benchmark_text_classification_bs_512_gpu_2(self):
"""Measure performance with batch_size=512, gpu=1 and
distribution_strategy='mirrored'
"""
batch_size = 512
metrics, wall_time, extras = benchmark_util.measure_performance(
self._build_model,
x=self.imdb_x,
y=self.imdb_y,
batch_size=batch_size,
num_gpus=2,
distribution_strategy="mirrored",
optimizer="adam",
loss="sparse_categorical_crossentropy",
metrics=["accuracy"],
)
metadata = benchmark_util.get_keras_examples_metadata(
"transformer", batch_size)
extras.update(metadata)
self.report_benchmark(wall_time=wall_time,
metrics=metrics,
extras=extras)
def benchmark_mlp_reuters_bs_512_gpu_2(self):
"""Measure performance with batch_size=512, gpu=2 and
distribution_strategy='mirrored'
"""
batch_size = 512
metrics, wall_time, extras = benchmark_util.measure_performance(
self._build_model,
x=self.x_train,
y=self.y_train,
batch_size=batch_size,
num_gpus=2,
distribution_strategy='mirrored',
epochs=self.epochs,
optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
metadata = benchmark_util.get_keras_examples_metadata(
'mlp', batch_size)
extras.update(metadata)
self.report_benchmark(wall_time=wall_time,
metrics=metrics,
extras=extras)
