def _run_and_report_benchmark(self):
start_time_sec = time.time()
stats = keras_cifar_main.run(FLAGS)
wall_time_sec = time.time() - start_time_sec
super(Resnet56KerasBenchmarkBase,
self)._report_benchmark(stats,
wall_time_sec,
total_batch_size=FLAGS.batch_size,
log_steps=FLAGS.log_steps)
def _run_and_report_benchmark(self):
start_time_sec = time.time()
stats = keras_cifar_main.run(FLAGS)
wall_time_sec = time.time() - start_time_sec
super(Resnet56KerasBenchmarkBase, self)._report_benchmark(
stats,
wall_time_sec,
total_batch_size=FLAGS.batch_size,
log_steps=FLAGS.log_steps)
def keras_resnet56_1_gpu(self):
"""Test keras based model with Keras fit and distribution strategies."""
self._setup()
flags.FLAGS.num_gpus = 1
flags.FLAGS.data_dir = DATA_DIR
flags.FLAGS.batch_size = 128
flags.FLAGS.train_epochs = 182
flags.FLAGS.model_dir = self._get_model_dir('keras_resnet56_1_gpu')
flags.FLAGS.dtype = 'fp32'
stats = keras_cifar_main.run(flags.FLAGS)
self._fill_report_object(stats)
def benchmark_graph_2_gpu(self):
"""Test keras based model with Keras fit and distribution strategies."""
self._setup()
FLAGS.num_gpus = 2
FLAGS.data_dir = DATA_DIR
FLAGS.batch_size = 128
FLAGS.train_epochs = 182
FLAGS.model_dir = self._get_model_dir('keras_resnet56_2_gpu')
FLAGS.dtype = 'fp32'
stats = keras_cifar_main.run(FLAGS)
self.fill_report_object(stats, FLAGS.batch_size)
def benchmark_1_gpu(self):
"""Test keras based model with eager and distribution strategies."""
self._setup()
FLAGS.num_gpus = 1
FLAGS.data_dir = DATA_DIR
FLAGS.batch_size = 128
FLAGS.train_epochs = 182
FLAGS.model_dir = self._get_model_dir('keras_resnet56_eager_1_gpu')
FLAGS.dtype = 'fp32'
FLAGS.enable_eager = True
stats = keras_cifar_main.run(flags.FLAGS)
self.fill_report_object(stats, FLAGS.batch_size)
def _run_and_report_benchmark(self):
start_time_sec = time.time()
stats = keras_cifar_main.run(FLAGS)
wall_time_sec = time.time() - start_time_sec
super(Resnet56KerasAccuracy,
self)._report_benchmark(stats,
wall_time_sec,
top_1_min=MIN_TOP_1_ACCURACY,
top_1_max=MAX_TOP_1_ACCURACY,
total_batch_size=FLAGS.batch_size,
log_steps=100)
def _run_and_report_benchmark(self):
start_time_sec = time.time()
stats = keras_cifar_main.run(FLAGS)
wall_time_sec = time.time() - start_time_sec
super(Resnet56KerasAccuracy, self)._report_benchmark(
stats,
wall_time_sec,
top_1_min=MIN_TOP_1_ACCURACY,
top_1_max=MAX_TOP_1_ACCURACY,
total_batch_size=FLAGS.batch_size,
log_steps=100)
def _run_benchmark(self): stats = keras_cifar_main.run(FLAGS) self.fill_report_object(stats)
