def testAllReduceMeanGradientTape(self):
distribution = parameter_server_strategy.ParameterServerStrategy(
num_gpus_per_worker=2)
self._test_all_reduce_mean_gradient_tape(distribution)
def testAllReduceSum(self):
distribution = parameter_server_strategy.ParameterServerStrategy(
num_gpus_per_worker=2)
self._test_all_reduce_sum(distribution)
def testGlobalStepUpdate(self):
strategy = parameter_server_strategy.ParameterServerStrategy(
num_gpus_per_worker=context.num_gpus())
self._test_global_step_update(strategy)
def testDeviceAssignmentLocalTwoGPUs(self):
distribution = parameter_server_strategy.ParameterServerStrategy(
num_gpus_per_worker=2)
self._test_device_assignment_local(
distribution, compute_device='GPU', variable_device='CPU', num_gpus=2)
def test_num_replicas_in_sync(self):
distribution = parameter_server_strategy.ParameterServerStrategy(
num_gpus_per_worker=2)
# All the devices on a given worker are in sync which in this case is the
# number of gpus on each worker.
self.assertEqual(2, distribution.num_replicas_in_sync)
def testTrainableVariables(self):
distribution = parameter_server_strategy.ParameterServerStrategy()
self._test_trainable_variable(distribution)
"CoreMirrored1CPU", lambda: mirrored_lib.CoreMirroredStrategy(["/cpu:0"]))
core_mirrored_strategy_with_one_gpu = NamedDistribution(
"CoreMirrored1GPU",
lambda: mirrored_lib.CoreMirroredStrategy(["/gpu:0"]),
required_gpus=1)
core_mirrored_strategy_with_gpu_and_cpu = NamedDistribution(
"CoreMirroredCPUAndGPU",
lambda: mirrored_lib.CoreMirroredStrategy(["/gpu:0", "/cpu:0"]),
required_gpus=1)
core_mirrored_strategy_with_two_gpus = NamedDistribution(
"CoreMirrored2GPUs",
lambda: mirrored_lib.CoreMirroredStrategy(["/gpu:0", "/gpu:1"]),
required_gpus=2)
parameter_server_strategy_with_two_gpus = NamedDistribution(
"ParameterServer2GPUs",
lambda: parameter_server_strategy.ParameterServerStrategy(
num_gpus_per_worker=2),
required_gpus=2)
gradient_descent_optimizer_v1_fn = NamedObject(
"GradientDescentV1",
lambda: gradient_descent.GradientDescentOptimizer(0.2))
adagrad_optimizer_v1_fn = NamedObject("AdagradV1",
lambda: adagrad.AdagradOptimizer(0.001))
adam_optimizer_v1_fn = NamedObject(
"AdamV1", lambda: adam.AdamOptimizer(0.001, epsilon=1))
rmsprop_optimizer_v1_fn = NamedObject("RmsPropV1",
lambda: rmsprop.RMSPropOptimizer(0.001))
optimizers_v1 = [gradient_descent_optimizer_v1_fn, adagrad_optimizer_v1_fn]
gradient_descent_optimizer_v2_fn = NamedObject(
