Python absl.flags.adopt_module_key_flags() Examples

def register_key_flags_in_core(f):
  """Defines a function in core.py, and registers its key flags.

  absl uses the location of a flags.declare_key_flag() to determine the context
  in which a flag is key. By making all declares in core, this allows model
  main functions to call flags.adopt_module_key_flags() on core and correctly
  chain key flags.

  Args:
    f:  The function to be wrapped

  Returns:
    The "core-defined" version of the input function.
  """

  def core_fn(*args, **kwargs):
    key_flags = f(*args, **kwargs)
    [flags.declare_key_flag(fl) for fl in key_flags]  # pylint: disable=expression-not-assigned
  return core_fn 

def define_movie_flags():
  """Define flags for movie dataset training."""
  wide_deep_run_loop.define_wide_deep_flags()
  flags.DEFINE_enum(
      name="dataset", default=movielens.ML_1M,
      enum_values=movielens.DATASETS, case_sensitive=False,
      help=flags_core.help_wrap("Dataset to be trained and evaluated."))
  flags.adopt_module_key_flags(wide_deep_run_loop)
  flags_core.set_defaults(data_dir="/tmp/movielens-data/",
                          model_dir='/tmp/movie_model',
                          model_type="deep",
                          train_epochs=50,
                          epochs_between_evals=5,
                          inter_op_parallelism_threads=0,
                          intra_op_parallelism_threads=0,
                          batch_size=256)

  @flags.validator("stop_threshold",
                   message="stop_threshold not supported for movielens model")
  def _no_stop(stop_threshold):
    return stop_threshold is None 

def define_wide_deep_flags():
  """Add supervised learning flags, as well as wide-deep model type."""
  flags_core.define_base()
  flags_core.define_benchmark()
  flags_core.define_performance(
      num_parallel_calls=False, inter_op=True, intra_op=True,
      synthetic_data=False, max_train_steps=False, dtype=False,
      all_reduce_alg=False)

  flags.adopt_module_key_flags(flags_core)

  flags.DEFINE_enum(
      name="model_type", short_name="mt", default="wide_deep",
      enum_values=['wide', 'deep', 'wide_deep'],
      help="Select model topology.")
  flags.DEFINE_boolean(
      name="download_if_missing", default=True, help=flags_core.help_wrap(
          "Download data to data_dir if it is not already present.")) 

def register_key_flags_in_core(f):
  """Defines a function in core.py, and registers its key flags.

  absl uses the location of a flags.declare_key_flag() to determine the context
  in which a flag is key. By making all declares in core, this allows model
  main functions to call flags.adopt_module_key_flags() on core and correctly
  chain key flags.

  Args:
    f:  The function to be wrapped

  Returns:
    The "core-defined" version of the input function.
  """

  def core_fn(*args, **kwargs):
    key_flags = f(*args, **kwargs)
    [flags.declare_key_flag(fl) for fl in key_flags]  # pylint: disable=expression-not-assigned
  return core_fn 

def register_key_flags_in_core(f):
  """Defines a function in core.py, and registers its key flags.

  absl uses the location of a flags.declare_key_flag() to determine the context
  in which a flag is key. By making all declares in core, this allows model
  main functions to call flags.adopt_module_key_flags() on core and correctly
  chain key flags.

  Args:
    f:  The function to be wrapped

  Returns:
    The "core-defined" version of the input function.
  """

  def core_fn(*args, **kwargs):
    key_flags = f(*args, **kwargs)
    [flags.declare_key_flag(fl) for fl in key_flags]  # pylint: disable=expression-not-assigned
  return core_fn 

def register_key_flags_in_core(f):
  """Defines a function in core.py, and registers its key flags.

  absl uses the location of a flags.declare_key_flag() to determine the context
  in which a flag is key. By making all declares in core, this allows model
  main functions to call flags.adopt_module_key_flags() on core and correctly
  chain key flags.

  Args:
    f:  The function to be wrapped

  Returns:
    The "core-defined" version of the input function.
  """

  def core_fn(*args, **kwargs):
    key_flags = f(*args, **kwargs)
    [flags.declare_key_flag(fl) for fl in key_flags]  # pylint: disable=expression-not-assigned
  return core_fn 

def register_key_flags_in_core(f):
  """Defines a function in core.py, and registers its key flags.

  absl uses the location of a flags.declare_key_flag() to determine the context
  in which a flag is key. By making all declares in core, this allows model
  main functions to call flags.adopt_module_key_flags() on core and correctly
  chain key flags.

  Args:
    f:  The function to be wrapped

  Returns:
    The "core-defined" version of the input function.
  """

  def core_fn(*args, **kwargs):
    key_flags = f(*args, **kwargs)
    [flags.declare_key_flag(fl) for fl in key_flags]  # pylint: disable=expression-not-assigned
  return core_fn 

def register_key_flags_in_core(f):
  """Defines a function in core.py, and registers its key flags.

  absl uses the location of a flags.declare_key_flag() to determine the context
  in which a flag is key. By making all declares in core, this allows model
  main functions to call flags.adopt_module_key_flags() on core and correctly
  chain key flags.

  Args:
    f:  The function to be wrapped

  Returns:
    The "core-defined" version of the input function.
  """

  def core_fn(*args, **kwargs):
    key_flags = f(*args, **kwargs)
    [flags.declare_key_flag(fl) for fl in key_flags]  # pylint: disable=expression-not-assigned
  return core_fn 

def define_movie_flags():
  """Define flags for movie dataset training."""
  wide_deep_run_loop.define_wide_deep_flags()
  flags.DEFINE_enum(
      name="dataset", default=movielens.ML_1M,
      enum_values=movielens.DATASETS, case_sensitive=False,
      help=flags_core.help_wrap("Dataset to be trained and evaluated."))
  flags.adopt_module_key_flags(wide_deep_run_loop)
  flags_core.set_defaults(data_dir="/tmp/movielens-data/",
                          model_dir='/tmp/movie_model',
                          model_type="deep",
                          train_epochs=50,
                          epochs_between_evals=5,
                          inter_op_parallelism_threads=0,
                          intra_op_parallelism_threads=0,
                          batch_size=256)

  @flags.validator("stop_threshold",
                   message="stop_threshold not supported for movielens model")
  def _no_stop(stop_threshold):
    return stop_threshold is None 

def define_wide_deep_flags():
  """Add supervised learning flags, as well as wide-deep model type."""
  flags_core.define_base(clean=True, train_epochs=True,
                         epochs_between_evals=True)
  flags_core.define_benchmark()
  flags_core.define_performance(
      num_parallel_calls=False, inter_op=True, intra_op=True,
      synthetic_data=False, max_train_steps=False, dtype=False,
      all_reduce_alg=False)

  flags.adopt_module_key_flags(flags_core)

  flags.DEFINE_enum(
      name="model_type", short_name="mt", default="wide_deep",
      enum_values=['wide', 'deep', 'wide_deep'],
      help="Select model topology.")
  flags.DEFINE_boolean(
      name="download_if_missing", default=True, help=flags_core.help_wrap(
          "Download data to data_dir if it is not already present.")) 

def register_key_flags_in_core(f):
  """Defines a function in core.py, and registers its key flags.

  absl uses the location of a flags.declare_key_flag() to determine the context
  in which a flag is key. By making all declares in core, this allows model
  main functions to call flags.adopt_module_key_flags() on core and correctly
  chain key flags.

  Args:
    f:  The function to be wrapped

  Returns:
    The "core-defined" version of the input function.
  """

  def core_fn(*args, **kwargs):
    key_flags = f(*args, **kwargs)
    [flags.declare_key_flag(fl) for fl in key_flags]  # pylint: disable=expression-not-assigned
  return core_fn 

def define_wide_deep_flags():
  """Add supervised learning flags, as well as wide-deep model type."""
  flags_core.define_base()
  flags_core.define_benchmark()
  flags_core.define_performance(
      num_parallel_calls=False, inter_op=True, intra_op=True,
      synthetic_data=False, max_train_steps=False, dtype=False,
      all_reduce_alg=False)

  flags.adopt_module_key_flags(flags_core)

  flags.DEFINE_enum(
      name="model_type", short_name="mt", default="wide_deep",
      enum_values=['wide', 'deep', 'wide_deep'],
      help="Select model topology.")
  flags.DEFINE_boolean(
      name="download_if_missing", default=True, help=flags_core.help_wrap(
          "Download data to data_dir if it is not already present.")) 

def register_key_flags_in_core(f):
  """Defines a function in core.py, and registers its key flags.

  absl uses the location of a flags.declare_key_flag() to determine the context
  in which a flag is key. By making all declares in core, this allows model
  main functions to call flags.adopt_module_key_flags() on core and correctly
  chain key flags.

  Args:
    f:  The function to be wrapped

  Returns:
    The "core-defined" version of the input function.
  """

  def core_fn(*args, **kwargs):
    key_flags = f(*args, **kwargs)
    [flags.declare_key_flag(fl) for fl in key_flags]  # pylint: disable=expression-not-assigned
  return core_fn 

def register_key_flags_in_core(f):
  """Defines a function in core.py, and registers its key flags.

  absl uses the location of a flags.declare_key_flag() to determine the context
  in which a flag is key. By making all declares in core, this allows model
  main functions to call flags.adopt_module_key_flags() on core and correctly
  chain key flags.

  Args:
    f:  The function to be wrapped

  Returns:
    The "core-defined" version of the input function.
  """

  def core_fn(*args, **kwargs):
    key_flags = f(*args, **kwargs)
    [flags.declare_key_flag(fl) for fl in key_flags]  # pylint: disable=expression-not-assigned
  return core_fn 

def define_movie_flags():
  """Define flags for movie dataset training."""
  wide_deep_run_loop.define_wide_deep_flags()
  flags.DEFINE_enum(
      name="dataset", default=movielens.ML_1M,
      enum_values=movielens.DATASETS, case_sensitive=False,
      help=flags_core.help_wrap("Dataset to be trained and evaluated."))
  flags.adopt_module_key_flags(wide_deep_run_loop)
  flags_core.set_defaults(data_dir="/tmp/movielens-data/",
                          model_dir='/tmp/movie_model',
                          model_type="deep",
                          train_epochs=50,
                          epochs_between_evals=5,
                          inter_op_parallelism_threads=0,
                          intra_op_parallelism_threads=0,
                          batch_size=256)

  @flags.validator("stop_threshold",
                   message="stop_threshold not supported for movielens model")
  def _no_stop(stop_threshold):
    return stop_threshold is None 

def register_key_flags_in_core(f):
  """Defines a function in core.py, and registers its key flags.

  absl uses the location of a flags.declare_key_flag() to determine the context
  in which a flag is key. By making all declares in core, this allows model
  main functions to call flags.adopt_module_key_flags() on core and correctly
  chain key flags.

  Args:
    f:  The function to be wrapped

  Returns:
    The "core-defined" version of the input function.
  """

  def core_fn(*args, **kwargs):
    key_flags = f(*args, **kwargs)
    [flags.declare_key_flag(fl) for fl in key_flags]  # pylint: disable=expression-not-assigned
  return core_fn 

def register_key_flags_in_core(f):
  """Defines a function in core.py, and registers its key flags.

  absl uses the location of a flags.declare_key_flag() to determine the context
  in which a flag is key. By making all declares in core, this allows model
  main functions to call flags.adopt_module_key_flags() on core and correctly
  chain key flags.

  Args:
    f:  The function to be wrapped

  Returns:
    The "core-defined" version of the input function.
  """

  def core_fn(*args, **kwargs):
    key_flags = f(*args, **kwargs)
    [flags.declare_key_flag(fl) for fl in key_flags]  # pylint: disable=expression-not-assigned
  return core_fn 

def register_key_flags_in_core(f):
  """Defines a function in core.py, and registers its key flags.

  absl uses the location of a flags.declare_key_flag() to determine the context
  in which a flag is key. By making all declares in core, this allows model
  main functions to call flags.adopt_module_key_flags() on core and correctly
  chain key flags.

  Args:
    f:  The function to be wrapped

  Returns:
    The "core-defined" version of the input function.
  """

  def core_fn(*args, **kwargs):
    key_flags = f(*args, **kwargs)
    [flags.declare_key_flag(fl) for fl in key_flags]  # pylint: disable=expression-not-assigned
  return core_fn 

def define_imagenet_flags():
  resnet_run_loop.define_resnet_flags(
      resnet_size_choices=['18', '34', '50', '101', '152', '200'])
  flags.adopt_module_key_flags(resnet_run_loop)
  flags_core.set_defaults(train_epochs=90) 

def define_census_flags():
  wide_deep_run_loop.define_wide_deep_flags()
  flags.adopt_module_key_flags(wide_deep_run_loop)
  flags_core.set_defaults(data_dir='/tmp/census_data',
                          model_dir='/tmp/census_model',
                          train_epochs=40,
                          epochs_between_evals=2,
                          inter_op_parallelism_threads=0,
                          intra_op_parallelism_threads=0,
                          batch_size=40) 

def define_mnist_flags():
  flags_core.define_base()
  flags_core.define_performance(num_parallel_calls=False)
  flags_core.define_image()
  flags.adopt_module_key_flags(flags_core)
  flags_core.set_defaults(data_dir='/tmp/mnist_data',
                          model_dir='/tmp/mnist_model',
                          batch_size=100,
                          train_epochs=40) 

def define_cifar_flags():
  resnet_run_loop.define_resnet_flags()
  flags.adopt_module_key_flags(resnet_run_loop)
  flags_core.set_defaults(data_dir='/tmp/cifar10_data',
                          model_dir='/tmp/cifar10_model',
                          resnet_size='56',
                          train_epochs=182,
                          epochs_between_evals=10,
                          batch_size=128,
                          image_bytes_as_serving_input=False) 

def define_train_higgs_flags():
  """Add tree related flags as well as training/eval configuration."""
  flags_core.define_base(clean=False, stop_threshold=False, batch_size=False,
                         num_gpu=False)
  flags_core.define_benchmark()
  flags.adopt_module_key_flags(flags_core)

  flags.DEFINE_integer(
      name="train_start", default=0,
      help=help_wrap("Start index of train examples within the data."))
  flags.DEFINE_integer(
      name="train_count", default=1000000,
      help=help_wrap("Number of train examples within the data."))
  flags.DEFINE_integer(
      name="eval_start", default=10000000,
      help=help_wrap("Start index of eval examples within the data."))
  flags.DEFINE_integer(
      name="eval_count", default=1000000,
      help=help_wrap("Number of eval examples within the data."))

  flags.DEFINE_integer(
      "n_trees", default=100, help=help_wrap("Number of trees to build."))
  flags.DEFINE_integer(
      "max_depth", default=6, help=help_wrap("Maximum depths of each tree."))
  flags.DEFINE_float(
      "learning_rate", default=0.1,
      help=help_wrap("The learning rate."))

  flags_core.set_defaults(data_dir="/tmp/higgs_data",
                          model_dir="/tmp/higgs_model") 

def define_imagenet_flags():
  resnet_run_loop.define_resnet_flags(
      resnet_size_choices=['18', '34', '50', '101', '152', '200'])
  flags.adopt_module_key_flags(resnet_run_loop)
  flags_core.set_defaults(train_epochs=90) 

def define_train_higgs_flags():
  """Add tree related flags as well as training/eval configuration."""
  flags_core.define_base(clean=False, stop_threshold=False, batch_size=False,
                         num_gpu=False)
  flags_core.define_benchmark()
  flags.adopt_module_key_flags(flags_core)

  flags.DEFINE_integer(
      name="train_start", default=0,
      help=help_wrap("Start index of train examples within the data."))
  flags.DEFINE_integer(
      name="train_count", default=1000000,
      help=help_wrap("Number of train examples within the data."))
  flags.DEFINE_integer(
      name="eval_start", default=10000000,
      help=help_wrap("Start index of eval examples within the data."))
  flags.DEFINE_integer(
      name="eval_count", default=1000000,
      help=help_wrap("Number of eval examples within the data."))

  flags.DEFINE_integer(
      "n_trees", default=100, help=help_wrap("Number of trees to build."))
  flags.DEFINE_integer(
      "max_depth", default=6, help=help_wrap("Maximum depths of each tree."))
  flags.DEFINE_float(
      "learning_rate", default=0.1,
      help=help_wrap("The learning rate."))

  flags_core.set_defaults(data_dir="/tmp/higgs_data",
                          model_dir="/tmp/higgs_model") 

def define_train_higgs_flags():
  """Add tree related flags as well as training/eval configuration."""
  flags_core.define_base(clean=False, stop_threshold=False, batch_size=False,
                         num_gpu=False)
  flags_core.define_benchmark()
  flags.adopt_module_key_flags(flags_core)

  flags.DEFINE_integer(
      name="train_start", default=0,
      help=help_wrap("Start index of train examples within the data."))
  flags.DEFINE_integer(
      name="train_count", default=1000000,
      help=help_wrap("Number of train examples within the data."))
  flags.DEFINE_integer(
      name="eval_start", default=10000000,
      help=help_wrap("Start index of eval examples within the data."))
  flags.DEFINE_integer(
      name="eval_count", default=1000000,
      help=help_wrap("Number of eval examples within the data."))

  flags.DEFINE_integer(
      "n_trees", default=100, help=help_wrap("Number of trees to build."))
  flags.DEFINE_integer(
      "max_depth", default=6, help=help_wrap("Maximum depths of each tree."))
  flags.DEFINE_float(
      "learning_rate", default=0.1,
      help=help_wrap("The learning rate."))

  flags_core.set_defaults(data_dir="/tmp/higgs_data",
                          model_dir="/tmp/higgs_model") 

def define_imagenet_keras_flags():
  common.define_keras_flags()
  flags_core.set_defaults(train_epochs=90)
  flags.adopt_module_key_flags(common) 

def define_imagenet_flags():
  resnet_run_loop.define_resnet_flags(
      resnet_size_choices=['18', '34', '50', '101', '152', '200'],
      dynamic_loss_scale=True,
      fp16_implementation=True)
  flags.adopt_module_key_flags(resnet_run_loop)
  flags_core.set_defaults(train_epochs=90) 

def define_cifar_flags():
  resnet_run_loop.define_resnet_flags()
  flags.adopt_module_key_flags(resnet_run_loop)
  flags_core.set_defaults(data_dir='/tmp/cifar10_data/cifar-10-batches-bin',
                          model_dir='/tmp/cifar10_model',
                          resnet_size='56',
                          train_epochs=182,
                          epochs_between_evals=10,
                          batch_size=128,
                          image_bytes_as_serving_input=False) 

def define_mnist_flags():
  flags_core.define_base()
  flags_core.define_image()
  flags.adopt_module_key_flags(flags_core)
  flags_core.set_defaults(data_dir='/tmp/mnist_data',
                          model_dir='/tmp/mnist_model',
                          batch_size=100,
                          train_epochs=40)