def _UpdateBenchmarkSpecWithFlags(benchmark_spec): """Update the benchmark_spec with supplied command line flags. Args: benchmark_spec: benchmark specification to update """ benchmark_spec.learning_rate = FLAGS.inception3_learning_rate benchmark_spec.use_data = FLAGS.inception3_use_data benchmark_spec.mode = FLAGS.inception3_mode benchmark_spec.save_checkpoints_secs = FLAGS.inception3_save_checkpoints_secs benchmark_spec.train_batch_size = FLAGS.inception3_train_batch_size benchmark_spec.eval_batch_size = FLAGS.inception3_eval_batch_size benchmark_spec.commit = cloud_tpu_models.GetCommit(benchmark_spec.vms[0]) benchmark_spec.data_dir = FLAGS.imagenet_data_dir benchmark_spec.num_train_images = FLAGS.imagenet_num_train_images benchmark_spec.num_eval_images = FLAGS.imagenet_num_eval_images benchmark_spec.num_examples_per_epoch = ( float(benchmark_spec.num_train_images) / benchmark_spec.train_batch_size) benchmark_spec.train_epochs = FLAGS.inception3_train_epochs benchmark_spec.train_steps = int(benchmark_spec.train_epochs * benchmark_spec.num_examples_per_epoch) benchmark_spec.epochs_per_eval = FLAGS.inception3_epochs_per_eval benchmark_spec.steps_per_eval = int(benchmark_spec.epochs_per_eval * benchmark_spec.num_examples_per_epoch)
def CreateMetadataDict(benchmark_spec): """Create metadata dict to be used in run results. Args: benchmark_spec: The benchmark specification. Contains all data that is required to run the benchmark. Returns: metadata dict """ return { 'data_dir': benchmark_spec.data_dir, 'use_tpu': benchmark_spec.use_tpu, 'model_dir': benchmark_spec.model_dir, 'train_steps': benchmark_spec.train_steps, 'eval_steps': benchmark_spec.eval_steps, 'tpu': benchmark_spec.tpu, 'tpu_train': benchmark_spec.tpu_train, 'tpu_eval': benchmark_spec.tpu_eval, 'commit': cloud_tpu_models.GetCommit(benchmark_spec.vms[0]), 'iterations': benchmark_spec.iterations, 'num_shards': benchmark_spec.num_shards, 'num_shards_train': benchmark_spec.num_shards_train, 'num_shards_eval': benchmark_spec.num_shards_eval, 'num_train_images': benchmark_spec.num_train_images, 'num_eval_images': benchmark_spec.num_eval_images, 'train_epochs': benchmark_spec.train_epochs, 'eval_epochs': benchmark_spec.eval_epochs, 'num_examples_per_epoch': benchmark_spec.num_examples_per_epoch, 'train_batch_size': benchmark_spec.batch_size, 'eval_batch_size': benchmark_spec.batch_size }
def CreateMetadataDict(benchmark_spec): """Create metadata dict to be used in run results. Args: benchmark_spec: The benchmark specification. Contains all data that is required to run the benchmark. Returns: metadata dict """ metadata = { 'use_tpu': bool(benchmark_spec.tpus), 'data_dir': benchmark_spec.data_dir, 'model_dir': benchmark_spec.model_dir, 'train_steps': benchmark_spec.train_steps, 'eval_steps': benchmark_spec.eval_steps, 'commit': cloud_tpu_models.GetCommit(benchmark_spec.vms[0]), 'iterations': benchmark_spec.iterations, 'num_train_images': benchmark_spec.num_train_images, 'num_eval_images': benchmark_spec.num_eval_images, 'train_epochs': benchmark_spec.train_epochs, 'eval_epochs': benchmark_spec.eval_epochs, 'num_examples_per_epoch': benchmark_spec.num_examples_per_epoch, 'train_batch_size': benchmark_spec.batch_size, 'eval_batch_size': benchmark_spec.batch_size } if benchmark_spec.tpus: metadata.update({ 'train_tpu_num_shards': benchmark_spec.tpu_groups['train'].GetNumShards(), 'train_tpu_accelerator_type': benchmark_spec.tpu_groups['train'].GetAcceleratorType() }) return metadata
def _UpdateBenchmarkSpecWithFlags(benchmark_spec): """Update the benchmark_spec with supplied command line flags. Args: benchmark_spec: benchmark specification to update """ benchmark_spec.depth = FLAGS.resnet_depth benchmark_spec.mode = FLAGS.resnet_mode benchmark_spec.train_batch_size = FLAGS.resnet_train_batch_size benchmark_spec.eval_batch_size = FLAGS.resnet_eval_batch_size benchmark_spec.data_format = FLAGS.resnet_data_format benchmark_spec.commit = cloud_tpu_models.GetCommit(benchmark_spec.vms[0]) benchmark_spec.skip_host_call = FLAGS.resnet_skip_host_call benchmark_spec.data_dir = FLAGS.imagenet_data_dir benchmark_spec.num_train_images = FLAGS.imagenet_num_train_images benchmark_spec.num_eval_images = FLAGS.imagenet_num_eval_images benchmark_spec.num_examples_per_epoch = ( float(benchmark_spec.num_train_images) / benchmark_spec.train_batch_size) benchmark_spec.train_epochs = FLAGS.resnet_train_epochs benchmark_spec.train_steps = int(benchmark_spec.train_epochs * benchmark_spec.num_examples_per_epoch) benchmark_spec.epochs_per_eval = FLAGS.resnet_epochs_per_eval benchmark_spec.steps_per_eval = int(benchmark_spec.epochs_per_eval * benchmark_spec.num_examples_per_epoch)
def _UpdateBenchmarkSpecWithFlags(benchmark_spec): """Update the benchmark_spec with supplied command line flags. Args: benchmark_spec: benchmark specification to update """ benchmark_spec.depth = FLAGS.resnet_depth benchmark_spec.mode = FLAGS.resnet_mode benchmark_spec.train_steps = FLAGS.resnet_train_steps benchmark_spec.train_batch_size = FLAGS.resnet_train_batch_size benchmark_spec.eval_batch_size = FLAGS.resnet_eval_batch_size benchmark_spec.num_cores = FLAGS.resnet_num_cores benchmark_spec.data_format = FLAGS.resnet_data_format benchmark_spec.precision = FLAGS.resnet_precision benchmark_spec.commit = cloud_tpu_models.GetCommit(benchmark_spec.vms[0])
def _UpdateBenchmarkSpecWithFlags(benchmark_spec): """Update the benchmark_spec with supplied command line flags. Args: benchmark_spec: benchmark specification to update """ benchmark_spec.learning_rate = FLAGS.inception3_learning_rate benchmark_spec.train_steps = FLAGS.inception3_train_steps benchmark_spec.use_data = FLAGS.inception3_use_data benchmark_spec.mode = FLAGS.inception3_mode benchmark_spec.train_steps_per_eval = FLAGS.inception3_train_steps_per_eval benchmark_spec.save_checkpoints_secs = FLAGS.inception3_save_checkpoints_secs benchmark_spec.train_batch_size = FLAGS.inception3_train_batch_size benchmark_spec.eval_batch_size = FLAGS.inception3_eval_batch_size benchmark_spec.commit = cloud_tpu_models.GetCommit(benchmark_spec.vms[0])
def _CreateMetadataDict(benchmark_spec): """Create metadata dict to be used in run results. Args: benchmark_spec: The benchmark specification. Contains all data that is required to run the benchmark. Returns: metadata dict """ metadata = dict() metadata['train_file'] = benchmark_spec.train_file metadata['use_tpu'] = benchmark_spec.use_tpu metadata['model_dir'] = benchmark_spec.model_dir metadata['train_steps'] = benchmark_spec.train_steps metadata['master'] = benchmark_spec.master vm = benchmark_spec.vms[0] metadata['commit'] = cloud_tpu_models.GetCommit(vm) return metadata
def _CreateMetadataDict(benchmark_spec): """Create metadata dict to be used in run results. Args: benchmark_spec: The benchmark specification. Contains all data that is required to run the benchmark. Returns: metadata dict """ return { 'train_file': benchmark_spec.train_file, 'use_tpu': benchmark_spec.use_tpu, 'model_dir': benchmark_spec.model_dir, 'train_steps': benchmark_spec.train_steps, 'master': benchmark_spec.master, 'commit': cloud_tpu_models.GetCommit(benchmark_spec.vms[0]), 'iterations': benchmark_spec.iterations }
def _UpdateBenchmarkSpecWithFlags(benchmark_spec): """Update the benchmark_spec with supplied command line flags. Args: benchmark_spec: benchmark specification to update """ benchmark_spec.learning_rate = FLAGS.inception3_learning_rate benchmark_spec.train_steps = FLAGS.inception3_train_steps benchmark_spec.iterations = FLAGS.inception3_iterations benchmark_spec.use_tpu = benchmark_spec.cloud_tpu is not None benchmark_spec.use_data = FLAGS.inception3_use_data benchmark_spec.mode = FLAGS.inception3_mode benchmark_spec.train_steps_per_eval = FLAGS.inception3_train_steps_per_eval benchmark_spec.data_dir = FLAGS.inception3_data_dir benchmark_spec.save_checkpoints_secs = FLAGS.inception3_save_checkpoints_secs benchmark_spec.train_batch_size = FLAGS.inception3_train_batch_size benchmark_spec.eval_batch_size = FLAGS.inception3_eval_batch_size benchmark_spec.master = 'grpc://{ip}:{port}'.format( ip=benchmark_spec.cloud_tpu.GetCloudTpuIp(), port=benchmark_spec.cloud_tpu.GetCloudTpuPort() ) if benchmark_spec.use_tpu else '' benchmark_spec.commit = cloud_tpu_models.GetCommit(benchmark_spec.vms[0])