def _run_and_report_benchmark(self): start_time_sec = time.time() stats = keras_imagenet_main.run(FLAGS) wall_time_sec = time.time() - start_time_sec super(TrivialKerasBenchmarkReal, 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_imagenet_main.run(FLAGS) wall_time_sec = time.time() - start_time_sec super(TrivialKerasBenchmarkReal, self)._report_benchmark(stats, wall_time_sec, total_batch_size=FLAGS.batch_size, log_steps=FLAGS.log_steps)
def benchmark_graph_8_gpu(self): """Test Keras model with Keras fit/dist_strat and 8 GPUs.""" self._setup() FLAGS.num_gpus = 8 FLAGS.data_dir = DATA_DIR FLAGS.batch_size = 128 * 8 FLAGS.train_epochs = 90 FLAGS.model_dir = self._get_model_dir('keras_resnet50_8_gpu') FLAGS.dtype = 'fp32' stats = keras_imagenet_main.run(FLAGS) self._fill_report_object(stats, FLAGS.batch_size)
def keras_resnet50_8_gpu(self): """Test Keras model with Keras fit/dist_strat and 8 GPUs.""" self._setup() flags.FLAGS.num_gpus = 8 flags.FLAGS.data_dir = DATA_DIR flags.FLAGS.batch_size = 64 * 8 flags.FLAGS.train_epochs = 90 flags.FLAGS.model_dir = self._get_model_dir('keras_resnet50_8_gpu') flags.FLAGS.dtype = 'fp32' stats = keras_imagenet_main.run(flags.FLAGS) self._fill_report_object(stats)
def benchmark_graph_8_gpu(self): """Test Keras model with Keras fit/dist_strat and 8 GPUs.""" self._setup() FLAGS.num_gpus = 8 FLAGS.data_dir = DATA_DIR FLAGS.batch_size = 128*8 FLAGS.train_epochs = 90 FLAGS.model_dir = self._get_model_dir('keras_resnet50_8_gpu') FLAGS.dtype = 'fp32' stats = keras_imagenet_main.run(FLAGS) self._fill_report_object(stats, FLAGS.batch_size)
def _run_and_report_benchmark(self): start_time_sec = time.time() stats = keras_imagenet_main.run(flags.FLAGS) wall_time_sec = time.time() - start_time_sec super(Resnet50KerasAccuracy, 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_imagenet_main.run(flags.FLAGS) wall_time_sec = time.time() - start_time_sec super(Resnet50KerasAccuracy, 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_imagenet_main.run(FLAGS) wall_time_sec = time.time() - start_time_sec # Number of logged step time entries that are excluded in performance # report. We keep results from last 100 batches in this case. warmup = (FLAGS.train_steps - 100) // FLAGS.log_steps super(Resnet50KerasBenchmarkBase, self)._report_benchmark(stats, wall_time_sec, total_batch_size=FLAGS.batch_size, log_steps=FLAGS.log_steps, warmup=warmup)
def _run_benchmark(self): stats = keras_imagenet_main.run(FLAGS) self.fill_report_object(stats)
def _run_benchmark(self): stats = keras_imagenet_main.run(FLAGS) self.fill_report_object(stats)