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(