Python absl.flags.DEFINE_integer() Examples
The following are 30
code examples of absl.flags.DEFINE_integer().
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absl.flags
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Example #1
| Source File: flags.py From benchmarks with Apache License 2.0 | 7 votes |
|
def define_flags(specs=None):
"""Define a command line flag for each ParamSpec in flags.param_specs."""
specs = specs or param_specs
define_flag = {
'boolean': absl_flags.DEFINE_boolean,
'float': absl_flags.DEFINE_float,
'integer': absl_flags.DEFINE_integer,
'string': absl_flags.DEFINE_string,
'enum': absl_flags.DEFINE_enum,
'list': absl_flags.DEFINE_list
}
for name, param_spec in six.iteritems(specs):
if param_spec.flag_type not in define_flag:
raise ValueError('Unknown flag_type %s' % param_spec.flag_type)
else:
define_flag[param_spec.flag_type](name, param_spec.default_value,
help=param_spec.description,
**param_spec.kwargs)
Example #2
| Source File: lamb_flags.py From lamb with Apache License 2.0 | 6 votes |
|
def _define_flags(options):
for option in _filter_options(options):
name, type_, default_ = option[:3]
if type_ == 'boolean':
flags.DEFINE_boolean(name, default_, '')
elif type_ == 'integer':
flags.DEFINE_integer(name, default_, '')
elif type_ == 'float':
flags.DEFINE_float(name, default_, '')
elif type_ == 'string':
flags.DEFINE_string(name, default_, '')
elif type_ == 'list_of_ints':
flags.DEFINE_string(name, default_, '')
else:
assert 'Unexpected option type %s' % type_
# Define command-line flags for all options (unless `internal`).
Example #3
| Source File: app.py From clgen with GNU General Public License v3.0 | 6 votes |
|
def DEFINE_integer(
name: str,
default: Optional[int],
help: str,
required: bool = False,
lower_bound: Optional[int] = None,
upper_bound: Optional[int] = None,
validator: Callable[[int], bool] = None,
):
"""Registers a flag whose value must be an integer."""
absl_flags.DEFINE_integer(
name,
default,
help,
module_name=get_calling_module_name(),
lower_bound=lower_bound,
upper_bound=upper_bound,
)
if required:
absl_flags.mark_flag_as_required(name)
if validator:
RegisterFlagValidator(name, validator)
Example #4
| Source File: argparse_flags_test.py From abseil-py with Apache License 2.0 | 6 votes |
|
def setUp(self):
self._absl_flags = flags.FlagValues()
flags.DEFINE_bool(
'absl_bool', None, 'help for --absl_bool.',
short_name='b', flag_values=self._absl_flags)
# Add a boolean flag that starts with "no", to verify it can correctly
# handle the "no" prefixes in boolean flags.
flags.DEFINE_bool(
'notice', None, 'help for --notice.',
flag_values=self._absl_flags)
flags.DEFINE_string(
'absl_string', 'default', 'help for --absl_string=%.',
short_name='s', flag_values=self._absl_flags)
flags.DEFINE_integer(
'absl_integer', 1, 'help for --absl_integer.',
flag_values=self._absl_flags)
flags.DEFINE_float(
'absl_float', 1, 'help for --absl_integer.',
flag_values=self._absl_flags)
flags.DEFINE_enum(
'absl_enum', 'apple', ['apple', 'orange'], 'help for --absl_enum.',
flag_values=self._absl_flags)
Example #5
| Source File: module_bar.py From abseil-py with Apache License 2.0 | 6 votes |
|
def define_flags(flag_values=FLAGS):
"""Defines some flags.
Args:
flag_values: The FlagValues object we want to register the flags
with.
"""
# The 'tmod_bar_' prefix (short for 'test_module_bar') ensures there
# is no name clash with the existing flags.
flags.DEFINE_boolean('tmod_bar_x', True, 'Boolean flag.',
flag_values=flag_values)
flags.DEFINE_string('tmod_bar_y', 'default', 'String flag.',
flag_values=flag_values)
flags.DEFINE_boolean('tmod_bar_z', False,
'Another boolean flag from module bar.',
flag_values=flag_values)
flags.DEFINE_integer('tmod_bar_t', 4, 'Sample int flag.',
flag_values=flag_values)
flags.DEFINE_integer('tmod_bar_u', 5, 'Sample int flag.',
flag_values=flag_values)
flags.DEFINE_integer('tmod_bar_v', 6, 'Sample int flag.',
flag_values=flag_values)
Example #6
| Source File: config.py From trax with Apache License 2.0 | 6 votes |
|
def config_with_absl(self):
# Run this before calling `app.run(main)` etc
import absl.flags as absl_FLAGS
from absl import app, flags as absl_flags
self.use_absl = True
self.absl_flags = absl_flags
absl_defs = { bool: absl_flags.DEFINE_bool,
int: absl_flags.DEFINE_integer,
str: absl_flags.DEFINE_string,
'enum': absl_flags.DEFINE_enum }
for name, val in self.values.items():
flag_type, meta_args, meta_kwargs = self.meta[name]
absl_defs[flag_type](name, val, *meta_args, **meta_kwargs)
app.call_after_init(lambda: self.complete_absl_config(absl_flags))
Example #7
| Source File: flag_util_test.py From PerfKitBenchmarker with Apache License 2.0 | 6 votes |
|
def testReadAndWrite(self):
flag_values = flags.FlagValues()
flags.DEFINE_integer('test_flag', 0, 'Test flag.', flag_values=flag_values)
flag_values([sys.argv[0]])
flag_values_overrides = {}
flag_values_overrides['test_flag'] = 1
self.assertFlagState(flag_values, 0, False)
self.assertEqual(flag_values_overrides['test_flag'], 1)
with flag_util.OverrideFlags(flag_values, flag_values_overrides):
self.assertFlagState(flag_values, 1, True)
self.assertEqual(flag_values_overrides['test_flag'], 1)
self.assertFlagState(flag_values, 0, False)
self.assertEqual(flag_values_overrides['test_flag'], 1)
flag_values.test_flag = 3
self.assertFlagState(flag_values, 3, False)
self.assertEqual(flag_values_overrides['test_flag'], 1)
Example #8
| Source File: flags_helpxml_test.py From abseil-py with Apache License 2.0 | 6 votes |
|
def test_flag_help_in_xml_int_with_bounds(self):
flags.DEFINE_integer('nb_iters', 17, 'An integer flag',
lower_bound=5, upper_bound=27,
flag_values=self.fv)
expected_output = (
'<flag>\n'
' <key>yes</key>\n'
' <file>module.name</file>\n'
' <name>nb_iters</name>\n'
' <meaning>An integer flag</meaning>\n'
' <default>17</default>\n'
' <current>17</current>\n'
' <type>int</type>\n'
' <lower_bound>5</lower_bound>\n'
' <upper_bound>27</upper_bound>\n'
'</flag>\n')
self._check_flag_help_in_xml('nb_iters', 'module.name', expected_output,
is_key=True)
Example #9
| Source File: flags.py From dlcookbook-dlbs with Apache License 2.0 | 6 votes |
|
def define_flags():
"""Define a command line flag for each ParamSpec in flags.param_specs."""
define_flag = {
'boolean': absl_flags.DEFINE_boolean,
'float': absl_flags.DEFINE_float,
'integer': absl_flags.DEFINE_integer,
'string': absl_flags.DEFINE_string,
'enum': absl_flags.DEFINE_enum,
'list': absl_flags.DEFINE_list
}
for name, param_spec in six.iteritems(param_specs):
if param_spec.flag_type not in define_flag:
raise ValueError('Unknown flag_type %s' % param_spec.flag_type)
else:
define_flag[param_spec.flag_type](name, param_spec.default_value,
help=param_spec.description,
**param_spec.kwargs)
Example #10
| Source File: mnist_eager.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
|
def define_mnist_eager_flags():
"""Defined flags and defaults for MNIST in eager mode."""
flags_core.define_base_eager()
flags_core.define_image()
flags.adopt_module_key_flags(flags_core)
flags.DEFINE_integer(
name='log_interval', short_name='li', default=10,
help=flags_core.help_wrap('batches between logging training status'))
flags.DEFINE_string(
name='output_dir', short_name='od', default=None,
help=flags_core.help_wrap('Directory to write TensorBoard summaries'))
flags.DEFINE_float(name='learning_rate', short_name='lr', default=0.01,
help=flags_core.help_wrap('Learning rate.'))
flags.DEFINE_float(name='momentum', short_name='m', default=0.5,
help=flags_core.help_wrap('SGD momentum.'))
flags.DEFINE_bool(name='no_gpu', short_name='nogpu', default=False,
help=flags_core.help_wrap(
'disables GPU usage even if a GPU is available'))
flags_core.set_defaults(
data_dir='/tmp/tensorflow/mnist/input_data',
model_dir='/tmp/tensorflow/mnist/checkpoints/',
batch_size=100,
train_epochs=10,
)
Example #11
| Source File: train_higgs.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
|
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")
Example #12
| Source File: _device.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
|
def define_device(tpu=True):
"""Register device specific flags.
Args:
tpu: Create flags to specify TPU operation.
Returns:
A list of flags for core.py to marks as key flags.
"""
key_flags = []
if tpu:
flags.DEFINE_string(
name="tpu", default=None,
help=help_wrap(
"The Cloud TPU to use for training. This should be either the name "
"used when creating the Cloud TPU, or a "
"grpc://ip.address.of.tpu:8470 url. Passing `local` will use the"
"CPU of the local instance instead. (Good for debugging.)"))
key_flags.append("tpu")
flags.DEFINE_string(
name="tpu_zone", default=None,
help=help_wrap(
"[Optional] GCE zone where the Cloud TPU is located in. If not "
"specified, we will attempt to automatically detect the GCE "
"project from metadata."))
flags.DEFINE_string(
name="tpu_gcp_project", default=None,
help=help_wrap(
"[Optional] Project name for the Cloud TPU-enabled project. If not "
"specified, we will attempt to automatically detect the GCE "
"project from metadata."))
flags.DEFINE_integer(name="num_tpu_shards", default=8,
help=help_wrap("Number of shards (TPU chips)."))
return key_flags
Example #13
| Source File: train_higgs.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
|
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")
Example #14
| Source File: flag_util_test.py From PerfKitBenchmarker with Apache License 2.0 | 5 votes |
|
def testFlagChangesAreNotReflectedInConfigDict(self):
flag_values = flags.FlagValues()
flags.DEFINE_integer('test_flag', 0, 'Test flag.', flag_values=flag_values)
flag_values([sys.argv[0]])
flag_values_overrides = {}
flag_values_overrides['test_flag'] = 1
self.assertFlagState(flag_values, 0, False)
self.assertEqual(flag_values_overrides['test_flag'], 1)
with flag_util.OverrideFlags(flag_values, flag_values_overrides):
self.assertFlagState(flag_values, 1, True)
flag_values.test_flag = 2
self.assertFlagState(flag_values, 2, True)
self.assertEqual(flag_values_overrides['test_flag'], 1)
Example #15
| Source File: common.py From models with Apache License 2.0 | 5 votes |
|
def define_pruning_flags():
"""Define flags for pruning methods."""
flags.DEFINE_string('pruning_method', None,
'Pruning method.'
'None (no pruning) or polynomial_decay.')
flags.DEFINE_float('pruning_initial_sparsity', 0.0,
'Initial sparsity for pruning.')
flags.DEFINE_float('pruning_final_sparsity', 0.5,
'Final sparsity for pruning.')
flags.DEFINE_integer('pruning_begin_step', 0,
'Begin step for pruning.')
flags.DEFINE_integer('pruning_end_step', 100000,
'End step for pruning.')
flags.DEFINE_integer('pruning_frequency', 100,
'Frequency for pruning.')
Example #16
| Source File: classifier_trainer.py From models with Apache License 2.0 | 5 votes |
|
def define_classifier_flags():
"""Defines common flags for image classification."""
hyperparams_flags.initialize_common_flags()
flags.DEFINE_string(
'data_dir',
default=None,
help='The location of the input data.')
flags.DEFINE_string(
'mode',
default=None,
help='Mode to run: `train`, `eval`, `train_and_eval` or `export`.')
flags.DEFINE_bool(
'run_eagerly',
default=None,
help='Use eager execution and disable autograph for debugging.')
flags.DEFINE_string(
'model_type',
default=None,
help='The type of the model, e.g. EfficientNet, etc.')
flags.DEFINE_string(
'dataset',
default=None,
help='The name of the dataset, e.g. ImageNet, etc.')
flags.DEFINE_integer(
'log_steps',
default=100,
help='The interval of steps between logging of batch level stats.')
Example #17
| Source File: keras_common.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
|
def define_keras_flags():
flags.DEFINE_boolean(name='enable_eager', default=False, help='Enable eager?')
flags.DEFINE_boolean(name='skip_eval', default=False, help='Skip evaluation?')
flags.DEFINE_integer(
name='train_steps', default=None,
help='The number of steps to run for training. If it is larger than '
'# batches per epoch, then use # batches per epoch. When this flag is '
'set, only one epoch is going to run for training.')
flags.DEFINE_integer(
name='log_steps', default=100,
help='For every log_steps, we log the timing information such as '
'examples per second. Besides, for every log_steps, we store the '
'timestamp of a batch end.')
Example #18
| Source File: _device.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
|
def define_device(tpu=True):
"""Register device specific flags.
Args:
tpu: Create flags to specify TPU operation.
Returns:
A list of flags for core.py to marks as key flags.
"""
key_flags = []
if tpu:
flags.DEFINE_string(
name="tpu", default=None,
help=help_wrap(
"The Cloud TPU to use for training. This should be either the name "
"used when creating the Cloud TPU, or a "
"grpc://ip.address.of.tpu:8470 url. Passing `local` will use the"
"CPU of the local instance instead. (Good for debugging.)"))
key_flags.append("tpu")
flags.DEFINE_string(
name="tpu_zone", default=None,
help=help_wrap(
"[Optional] GCE zone where the Cloud TPU is located in. If not "
"specified, we will attempt to automatically detect the GCE "
"project from metadata."))
flags.DEFINE_string(
name="tpu_gcp_project", default=None,
help=help_wrap(
"[Optional] Project name for the Cloud TPU-enabled project. If not "
"specified, we will attempt to automatically detect the GCE "
"project from metadata."))
flags.DEFINE_integer(name="num_tpu_shards", default=8,
help=help_wrap("Number of shards (TPU chips)."))
return key_flags
Example #19
| Source File: mnist_eager.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
|
def define_mnist_eager_flags():
"""Defined flags and defaults for MNIST in eager mode."""
flags_core.define_base_eager()
flags_core.define_image()
flags.adopt_module_key_flags(flags_core)
flags.DEFINE_integer(
name='log_interval', short_name='li', default=10,
help=flags_core.help_wrap('batches between logging training status'))
flags.DEFINE_string(
name='output_dir', short_name='od', default=None,
help=flags_core.help_wrap('Directory to write TensorBoard summaries'))
flags.DEFINE_float(name='learning_rate', short_name='lr', default=0.01,
help=flags_core.help_wrap('Learning rate.'))
flags.DEFINE_float(name='momentum', short_name='m', default=0.5,
help=flags_core.help_wrap('SGD momentum.'))
flags.DEFINE_bool(name='no_gpu', short_name='nogpu', default=False,
help=flags_core.help_wrap(
'disables GPU usage even if a GPU is available'))
flags_core.set_defaults(
data_dir='/tmp/tensorflow/mnist/input_data',
model_dir='/tmp/tensorflow/mnist/checkpoints/',
batch_size=100,
train_epochs=10,
)
Example #20
| Source File: mnist_eager.py From models with Apache License 2.0 | 5 votes |
|
def define_mnist_eager_flags():
"""Defined flags and defaults for MNIST in eager mode."""
flags_core.define_base_eager()
flags_core.define_image()
flags.adopt_module_key_flags(flags_core)
flags.DEFINE_integer(
name='log_interval', short_name='li', default=10,
help=flags_core.help_wrap('batches between logging training status'))
flags.DEFINE_string(
name='output_dir', short_name='od', default=None,
help=flags_core.help_wrap('Directory to write TensorBoard summaries'))
flags.DEFINE_float(name='learning_rate', short_name='lr', default=0.01,
help=flags_core.help_wrap('Learning rate.'))
flags.DEFINE_float(name='momentum', short_name='m', default=0.5,
help=flags_core.help_wrap('SGD momentum.'))
flags.DEFINE_bool(name='no_gpu', short_name='nogpu', default=False,
help=flags_core.help_wrap(
'disables GPU usage even if a GPU is available'))
flags_core.set_defaults(
data_dir='/tmp/tensorflow/mnist/input_data',
model_dir='/tmp/tensorflow/mnist/checkpoints/',
batch_size=100,
train_epochs=10,
)
Example #21
| Source File: _distribution.py From models with Apache License 2.0 | 5 votes |
|
def define_distribution(worker_hosts=True, task_index=True):
"""Register distributed execution flags.
Args:
worker_hosts: Create a flag for specifying comma-separated list of workers.
task_index: Create a flag for specifying index of task.
Returns:
A list of flags for core.py to marks as key flags.
"""
key_flags = []
if worker_hosts:
flags.DEFINE_string(
name='worker_hosts', default=None,
help=help_wrap(
'Comma-separated list of worker ip:port pairs for running '
'multi-worker models with DistributionStrategy. The user would '
'start the program on each host with identical value for this '
'flag.'))
if task_index:
flags.DEFINE_integer(
name='task_index', default=-1,
help=help_wrap('If multi-worker training, the task_index of this '
'worker.'))
return key_flags
Example #22
| Source File: flags.py From dlcookbook-dlbs with Apache License 2.0 | 5 votes |
|
def DEFINE_integer(name, default, help, lower_bound=None, upper_bound=None): # pylint: disable=invalid-name,redefined-builtin
kwargs = {'lower_bound': lower_bound, 'upper_bound': upper_bound}
param_specs[name] = ParamSpec('integer', default, help, kwargs)
Example #23
| Source File: train_higgs.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 5 votes |
|
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")
Example #24
| Source File: mnist_eager.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 5 votes |
|
def define_mnist_eager_flags():
"""Defined flags and defaults for MNIST in eager mode."""
flags_core.define_base_eager(clean=True, train_epochs=True)
flags_core.define_image()
flags.adopt_module_key_flags(flags_core)
flags.DEFINE_integer(
name='log_interval', short_name='li', default=10,
help=flags_core.help_wrap('batches between logging training status'))
flags.DEFINE_string(
name='output_dir', short_name='od', default=None,
help=flags_core.help_wrap('Directory to write TensorBoard summaries'))
flags.DEFINE_float(name='learning_rate', short_name='lr', default=0.01,
help=flags_core.help_wrap('Learning rate.'))
flags.DEFINE_float(name='momentum', short_name='m', default=0.5,
help=flags_core.help_wrap('SGD momentum.'))
flags.DEFINE_bool(name='no_gpu', short_name='nogpu', default=False,
help=flags_core.help_wrap(
'disables GPU usage even if a GPU is available'))
flags_core.set_defaults(
data_dir='/tmp/tensorflow/mnist/input_data',
model_dir='/tmp/tensorflow/mnist/checkpoints/',
batch_size=100,
train_epochs=10,
)
Example #25
| Source File: _distribution.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 5 votes |
|
def define_distribution(worker_hosts=True, task_index=True):
"""Register distributed execution flags.
Args:
worker_hosts: Create a flag for specifying comma-separated list of workers.
task_index: Create a flag for specifying index of task.
Returns:
A list of flags for core.py to marks as key flags.
"""
key_flags = []
if worker_hosts:
flags.DEFINE_string(
name='worker_hosts', default=None,
help=help_wrap(
'Comma-separated list of worker ip:port pairs for running '
'multi-worker models with DistributionStrategy. The user would '
'start the program on each host with identical value for this '
'flag.'))
if task_index:
flags.DEFINE_integer(
name='task_index', default=-1,
help=help_wrap('If multi-worker training, the task_index of this '
'worker.'))
return key_flags
Example #26
| Source File: _device.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 5 votes |
|
def define_device(tpu=True):
"""Register device specific flags.
Args:
tpu: Create flags to specify TPU operation.
Returns:
A list of flags for core.py to marks as key flags.
"""
key_flags = []
if tpu:
flags.DEFINE_string(
name="tpu", default=None,
help=help_wrap(
"The Cloud TPU to use for training. This should be either the name "
"used when creating the Cloud TPU, or a "
"grpc://ip.address.of.tpu:8470 url. Passing `local` will use the"
"CPU of the local instance instead. (Good for debugging.)"))
key_flags.append("tpu")
flags.DEFINE_string(
name="tpu_zone", default=None,
help=help_wrap(
"[Optional] GCE zone where the Cloud TPU is located in. If not "
"specified, we will attempt to automatically detect the GCE "
"project from metadata."))
flags.DEFINE_string(
name="tpu_gcp_project", default=None,
help=help_wrap(
"[Optional] Project name for the Cloud TPU-enabled project. If not "
"specified, we will attempt to automatically detect the GCE "
"project from metadata."))
flags.DEFINE_integer(name="num_tpu_shards", default=8,
help=help_wrap("Number of shards (TPU chips)."))
return key_flags
Example #27
| Source File: shakespeare_main.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 5 votes |
|
def define_flags():
"""Define the flags for the Shakespeare character LSTM."""
flags_core.define_base(data_dir=False,
clean=False,
train_epochs=True,
epochs_between_evals=False,
stop_threshold=False,
hooks=False,
export_dir=False,
run_eagerly=True)
flags_core.define_performance(num_parallel_calls=False,
inter_op=False,
intra_op=False,
synthetic_data=False,
max_train_steps=False,
dtype=False,
enable_xla=True,
force_v2_in_keras_compile=True)
flags_core.set_defaults(train_epochs=43,
batch_size=64)
flags.DEFINE_boolean(name='enable_eager', default=True, help='Enable eager?')
flags.DEFINE_boolean(
name='train', default=True,
help='If true trains the model.')
flags.DEFINE_string(
name='predict_context', default=None,
help='If set, makes a prediction with the given context.')
flags.DEFINE_integer(
name='predict_length', default=1000,
help='Length of the predicted text including the context.')
flags.DEFINE_integer(
name='log_steps', default=100,
help='For every log_steps, we log the timing information such as '
'examples per second.')
flags.DEFINE_string(
name='training_data', default=None,
help='Path to file containing the training data.')
flags.DEFINE_boolean(name='cudnn', default=True, help='Use CuDNN LSTM.')
Example #28
| Source File: mnist_eager.py From models with Apache License 2.0 | 5 votes |
|
def define_mnist_eager_flags():
"""Defined flags and defaults for MNIST in eager mode."""
flags_core.define_base_eager()
flags_core.define_image()
flags.adopt_module_key_flags(flags_core)
flags.DEFINE_integer(
name='log_interval', short_name='li', default=10,
help=flags_core.help_wrap('batches between logging training status'))
flags.DEFINE_string(
name='output_dir', short_name='od', default=None,
help=flags_core.help_wrap('Directory to write TensorBoard summaries'))
flags.DEFINE_float(name='learning_rate', short_name='lr', default=0.01,
help=flags_core.help_wrap('Learning rate.'))
flags.DEFINE_float(name='momentum', short_name='m', default=0.5,
help=flags_core.help_wrap('SGD momentum.'))
flags.DEFINE_bool(name='no_gpu', short_name='nogpu', default=False,
help=flags_core.help_wrap(
'disables GPU usage even if a GPU is available'))
flags_core.set_defaults(
data_dir='/tmp/tensorflow/mnist/input_data',
model_dir='/tmp/tensorflow/mnist/checkpoints/',
batch_size=100,
train_epochs=10,
)
Example #29
| Source File: _device.py From models with Apache License 2.0 | 5 votes |
|
def define_device(tpu=True):
"""Register device specific flags.
Args:
tpu: Create flags to specify TPU operation.
Returns:
A list of flags for core.py to marks as key flags.
"""
key_flags = []
if tpu:
flags.DEFINE_string(
name="tpu", default=None,
help=help_wrap(
"The Cloud TPU to use for training. This should be either the name "
"used when creating the Cloud TPU, or a "
"grpc://ip.address.of.tpu:8470 url. Passing `local` will use the"
"CPU of the local instance instead. (Good for debugging.)"))
key_flags.append("tpu")
flags.DEFINE_string(
name="tpu_zone", default=None,
help=help_wrap(
"[Optional] GCE zone where the Cloud TPU is located in. If not "
"specified, we will attempt to automatically detect the GCE "
"project from metadata."))
flags.DEFINE_string(
name="tpu_gcp_project", default=None,
help=help_wrap(
"[Optional] Project name for the Cloud TPU-enabled project. If not "
"specified, we will attempt to automatically detect the GCE "
"project from metadata."))
flags.DEFINE_integer(name="num_tpu_shards", default=8,
help=help_wrap("Number of shards (TPU chips)."))
return key_flags
Example #30
| Source File: _device.py From models with Apache License 2.0 | 5 votes |
|
def define_device(tpu=True):
"""Register device specific flags.
Args:
tpu: Create flags to specify TPU operation.
Returns:
A list of flags for core.py to marks as key flags.
"""
key_flags = []
if tpu:
flags.DEFINE_string(
name="tpu", default=None,
help=help_wrap(
"The Cloud TPU to use for training. This should be either the name "
"used when creating the Cloud TPU, or a "
"grpc://ip.address.of.tpu:8470 url. Passing `local` will use the"
"CPU of the local instance instead. (Good for debugging.)"))
key_flags.append("tpu")
flags.DEFINE_string(
name="tpu_zone", default=None,
help=help_wrap(
"[Optional] GCE zone where the Cloud TPU is located in. If not "
"specified, we will attempt to automatically detect the GCE "
"project from metadata."))
flags.DEFINE_string(
name="tpu_gcp_project", default=None,
help=help_wrap(
"[Optional] Project name for the Cloud TPU-enabled project. If not "
"specified, we will attempt to automatically detect the GCE "
"project from metadata."))
flags.DEFINE_integer(name="num_tpu_shards", default=8,
help=help_wrap("Number of shards (TPU chips)."))
return key_flags
