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])
