def clone(self): """Generates a clone of the GraphVertexData object. Results in a splitting in the DAG. """ cloned_layers = [] for i,node in enumerate(self.layers): temp = lbann.Split(node) layers[i] = lbann.Identity(temp) cloned_layers.append(lbann.Identity(temp)) return GraphVertexData(cloned_layers, self.num_features)
help='Keep error signals (default: False)') parser.add_argument('--batch-job', action='store_true', help='Run as a batch job (default: false)') lbann.contrib.args.add_optimizer_arguments( parser, default_optimizer="adam", default_learning_rate=0.001, ) args = parser.parse_args() # Construct layer graph input = lbann.Input(io_buffer='partitioned', target_mode='regression') universes = lbann.Split(input) secrets = lbann.Split(input) statistics_group_size = 1 if args.local_batchnorm else -1 preds = CosmoFlow( input_width=args.input_width, output_size=args.num_secrets, use_bn=args.use_batchnorm, bn_statistics_group_size=statistics_group_size)(universes) mse = lbann.MeanSquaredError([preds, secrets]) obj = lbann.ObjectiveFunction([mse]) layers = list(lbann.traverse_layer_graph(input)) # Set parallel_strategy parallel_strategy = get_parallel_strategy_args( sample_groups=args.mini_batch_size, depth_groups=args.depth_groups) pooling_id = 0
parser.add_argument( '--data-reader', action='store', default='default', type=str, help= 'Data reader options: \"numpy_npz_int16\", or \"mnist\" (default: data_reader_mnist.prototext)' ) lbann.contrib.args.add_optimizer_arguments(parser, default_learning_rate=0.1) args = parser.parse_args() # Start of layers # Construct layer graph input_ = lbann.Input(name='data') image = lbann.Split(input_, name='image') dummy = lbann.Dummy(input_, name='dummy') # Encoder encode1 = lbann.FullyConnected(image, name="encode1", num_neurons=1000, has_bias=True) encode1neuron = lbann.Relu(encode1, name="encode1neuron") encode2 = lbann.FullyConnected(encode1neuron, name="encode2", num_neurons=500, has_bias=True)
parser.add_argument( '--data-reader', action='store', default='./data_readers/data_reader_candle_pilot1.prototext', type=str, help= 'scheduler job name (default: data_readers/data_reader_candle_pilot1.prototext)' ) lbann.contrib.args.add_optimizer_arguments(parser, default_learning_rate=0.1) args = parser.parse_args() # Start of layers # Construct layer graph input_ = lbann.Input(name='input', target_mode="reconstruction") data = lbann.Split(input_, name='data') dummy = lbann.Dummy(input_, name='dummy') # Encoder encode1 = lbann.FullyConnected(data, name="encode1", data_layout="model_parallel", num_neurons=2000, has_bias=True) relu1 = lbann.Relu(encode1, name="relu1", data_layout="model_parallel") encode2 = lbann.FullyConnected(relu1, name="encode2", data_layout="model_parallel", num_neurons=1000,
parser.add_argument( '--data-reader', action='store', default='data_readers/data_reader_candle_pilot1.prototext', type=str, help= 'scheduler job name (default: data_readers/data_reader_candle_pilot1.prototext)' ) lbann.contrib.args.add_optimizer_arguments(parser, default_learning_rate=0.1) args = parser.parse_args() # Start of layers # Construct layer graph input_ = lbann.Input(name='data') finetunedata = lbann.Split(input_, name='finetunedata') label = lbann.Split(input_, name='label') # Encoder encode1 = lbann.FullyConnected(finetunedata, name="encode1", data_layout="model_parallel", num_neurons=2000, has_bias=True) relu1 = lbann.Relu(encode1, name="relu1", data_layout="model_parallel") encode2 = lbann.FullyConnected(relu1, name="encode2", data_layout="model_parallel", num_neurons=1000,
help='Run as a batch job (default: false)') lbann.contrib.args.add_optimizer_arguments( parser, default_optimizer="adam", default_learning_rate=0.001, ) args = parser.parse_args() parallel_strategy = get_parallel_strategy_args( sample_groups=args.mini_batch_size, depth_groups=args.depth_groups) # Construct layer graph input = lbann.Input(io_buffer='partitioned', target_mode='label_reconstruction') volume = lbann.Split(input) output = UNet3D()(volume) segmentation = lbann.Split(input) ce = lbann.CrossEntropy([output, segmentation], use_labels=True) obj = lbann.ObjectiveFunction([ce]) layers = list(lbann.traverse_layer_graph(input)) for l in layers: l.parallel_strategy = parallel_strategy # Setup model metrics = [lbann.Metric(ce, name='CE', unit='')] callbacks = [ lbann.CallbackPrint(), lbann.CallbackTimer(), lbann.CallbackGPUMemoryUsage(), lbann.CallbackProfiler(skip_init=True),