hugectr_dcn_pandas.py

import hugectr
from mpi4py import MPI
solver = hugectr.solver_parser_helper(num_epochs = 0,
                                    max_iter = 10000,
                                    max_eval_batches = 100,
                                    batchsize_eval = 2048,
                                    batchsize = 2048,
                                    display = 200,
                                    eval_interval = 1000,
                                    i64_input_key = False,
                                    use_mixed_precision = False,
                                    repeat_dataset = True)
optimizer = hugectr.optimizer.CreateOptimizer(optimizer_type = hugectr.Optimizer_t.Adam,
                                    use_mixed_precision = False)
model = hugectr.Model(solver, optimizer)
model.add(hugectr.Input(data_reader_type = hugectr.DataReaderType_t.Norm,
                            source = "./criteo_data/file_list.txt",
                            eval_source = "./criteo_data/file_list_test.txt",
                            check_type = hugectr.Check_t.Sum,
                            label_dim = 1, label_name = "label",
                            dense_dim = 13, dense_name = "dense",
                            data_reader_sparse_param_array = 
                            [hugectr.DataReaderSparseParam(hugectr.DataReaderSparse_t.Distributed, 30, 1, 26)],
                            sparse_names = ["data1"]))
model.add(hugectr.SparseEmbedding(embedding_type = hugectr.Embedding_t.DistributedSlotSparseEmbeddingHash, 
                            max_vocabulary_size_per_gpu = 1447751,
                            embedding_vec_size = 16,
                            combiner = 0,
                            sparse_embedding_name = "sparse_embedding1",
                            bottom_name = "data1"))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Reshape,
                            bottom_names = ["sparse_embedding1"],
                            top_names = ["reshape1"],
                            leading_dim=416))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Concat,
                            bottom_names = ["reshape1", "dense"], top_names = ["concat1"]))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Slice,
                            bottom_names = ["concat1"],
                            top_names = ["slice11", "slice12"],
                            ranges=[(0,429),(0,429)]))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.MultiCross,
                            bottom_names = ["slice11"],
                            top_names = ["multicross1"],
                            num_layers=6))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,
                            bottom_names = ["slice12"],
                            top_names = ["fc1"],
                            num_output=1024))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.ReLU,
                            bottom_names = ["fc1"],
                            top_names = ["relu1"]))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Dropout,
                            bottom_names = ["relu1"],
                            top_names = ["dropout1"],
                            dropout_rate=0.5))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,
                            bottom_names = ["dropout1"],
                            top_names = ["fc2"],
                            num_output=1024))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.ReLU,
                            bottom_names = ["fc2"],
                            top_names = ["relu2"]))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Dropout,
                            bottom_names = ["relu2"],
                            top_names = ["dropout2"],
                            dropout_rate=0.5))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Concat,
                            bottom_names = ["dropout2", "multicross1"],
                            top_names = ["concat2"]))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,
                            bottom_names = ["concat2"],
                            top_names = ["fc3"],
                            num_output=1))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.BinaryCrossEntropyLoss,
                            bottom_names = ["fc3", "label"],
                            top_names = ["loss"]))
model.compile()
model.summary()
model.fit()