def testInputContextPropertyLocal(self):
d = one_device_strategy.OneDeviceStrategy("/device:CPU:0")
input_fn = self._input_fn_to_test_input_context(
expected_num_replicas_in_sync=1,
expected_num_input_pipelines=1,
expected_input_pipeline_id=0)
d.make_input_fn_iterator(input_fn)
def testNotPassingASessionInGraph(self):
distribution = one_device_strategy.OneDeviceStrategy("/device:CPU:0")
step_function, _ = single_loss_example(
lambda: gradient_descent.GradientDescentOptimizer(0.2),
distribution)
with context.graph_mode(), ops.Graph().as_default():
with self.assertRaisesRegexp(ValueError, "Should provide"):
_ = monitor_lib.Monitor(step_function, session=None)
def testPassingASessionInEager(self):
distribution = one_device_strategy.OneDeviceStrategy("/device:CPU:0")
step_function, _ = single_loss_example(
lambda: gradient_descent.GradientDescentOptimizer(0.2),
distribution)
with session.Session() as sess, context.eager_mode():
with self.assertRaisesRegexp(ValueError, "Should not provide"):
_ = monitor_lib.Monitor(step_function, sess)
def testMakeInputFnIterator(self):
d = one_device_strategy.OneDeviceStrategy("/device:CPU:0")
dataset_fn = lambda: dataset_ops.Dataset.range(10)
expected_values = [[i] for i in range(10)]
input_fn = self._input_fn_to_test_input_context(
dataset_fn,
expected_num_replicas_in_sync=1,
expected_num_input_pipelines=1,
expected_input_pipeline_id=0)
iterator = d.make_input_fn_iterator(input_fn)
self._test_input_fn_iterator(iterator, d.extended.worker_devices,
expected_values)
@property
def required_gpus(self):
return self._required_gpus
@property
def required_tpu(self):
return self._required_tpu
# pylint: disable=g-long-lambda
default_strategy = NamedDistribution(
"Default",
distribution_strategy_context._get_default_distribution_strategy, # pylint: disable=protected-access
required_gpus=None)
one_device_strategy = NamedDistribution(
"OneDeviceCPU", lambda: one_device_lib.OneDeviceStrategy("/cpu:0"),
required_gpus=None)
tpu_strategy = NamedDistribution(
"TPU", lambda: tpu_lib.TPUStrategy(
TPUClusterResolver(""), steps_per_run=2),
required_tpu=True)
tpu_strategy_one_step = NamedDistribution(
"TPUOneStep", lambda: tpu_lib.TPUStrategy(
TPUClusterResolver(""), steps_per_run=1),
required_tpu=True)
mirrored_strategy_with_one_cpu = NamedDistribution(
"Mirrored1CPU",
lambda: mirrored_lib.MirroredStrategy(["/cpu:0"]))
mirrored_strategy_with_one_gpu = NamedDistribution(
"Mirrored1GPU",
lambda: mirrored_lib.MirroredStrategy(["/gpu:0"]),
def _get_distribution_strategy(self):
return one_device_strategy.OneDeviceStrategy("/device:CPU:0")
@property
def required_gpus(self):
return self._required_gpus
@property
def required_tpu(self):
return self._required_tpu
default_strategy = NamedDistribution(
"Default",
distribute_lib._default_distribution_strategy, # pylint: disable=protected-access
required_gpus=None)
one_device_strategy = NamedDistribution(
"OneDeviceCPU",
one_device_strategy.OneDeviceStrategy("/cpu:0"),
required_gpus=None)
tpu_strategy_single_iteration = NamedDistribution(
"TPUSingleIteration",
tpu_strategy.TPUStrategy(iterations_per_step=1),
required_tpu=True)
tpu_strategy = NamedDistribution("TPU",
tpu_strategy.TPUStrategy(),
required_tpu=True)
# Note that we disable prefetching for testing since prefetching makes
# the input non-deterministic.
mirrored_strategy_with_gpu_and_cpu = NamedDistribution(
"MirroredCPUAndGPU",
mirrored_strategy.MirroredStrategy(["/gpu:0", "/cpu:0"],
prefetch_on_device=False),
required_gpus=1)
self._required_gpus = required_gpus
def __repr__(self):
return self._name
@property
def strategy(self):
return self._distribution
@property
def required_gpus(self):
return self._required_gpus
one_device_strategy = NamedDistribution(
"OneDeviceCPU", one_device_strategy.OneDeviceStrategy("/cpu:0"), None)
mirrored_strategy_with_gpu_and_cpu = NamedDistribution(
"MirroredCPUAndGPU",
mirrored_strategy.MirroredStrategy(["/gpu:0", "/cpu:0"]), 1)
mirrored_strategy_without_prefetch = NamedDistribution(
"MirroredCPUAndGPUNoPrefetch",
mirrored_strategy.MirroredStrategy(["/gpu:0", "/cpu:0"],
prefetch_on_device=False), 1)
mirrored_strategy_with_two_gpus = NamedDistribution(
"Mirrored2GPUs", mirrored_strategy.MirroredStrategy(["/gpu:0", "/gpu:1"]),
2)
adam_optimizer_v1_fn = NamedObject("AdamV1",
lambda: adam.AdamOptimizer(0.2, epsilon=1))
gradient_descent_optimizer_v1_fn = NamedObject(
"GradientDescentV1",
