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dcn_2node_8gpu.py
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import hugectr
from mpi4py import MPI
solver = hugectr.CreateSolver(max_eval_batches = 300,
batchsize_eval = 16384,
batchsize = 16384,
lr = 0.001,
vvgpu = [[0,1,2,3,4,5,6,7],[0,1,2,3,4,5,6,7]],
repeat_dataset = True)
reader = hugectr.DataReaderParams(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)
optimizer = hugectr.CreateOptimizer(optimizer_type = hugectr.Optimizer_t.Adam,
update_type = hugectr.Update_t.Global,
beta1 = 0.9,
beta2 = 0.999,
epsilon = 0.0000001)
model = hugectr.Model(solver, reader, optimizer)
model.add(hugectr.Input(label_dim = 1, label_name = "label",
dense_dim = 13, dense_name = "dense",
data_reader_sparse_param_array =
[hugectr.DataReaderSparseParam("data1", 2, False, 26)]))
model.add(hugectr.SparseEmbedding(embedding_type = hugectr.Embedding_t.LocalizedSlotSparseEmbeddingHash,
workspace_size_per_gpu_in_mb = 267,
embedding_vec_size = 16,
combiner = "sum",
sparse_embedding_name = "sparse_embedding1",
bottom_name = "data1",
optimizer = optimizer))
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.MultiCross,
bottom_names = ["concat1"],
top_names = ["multicross1"],
num_layers=6))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,
bottom_names = ["concat1"],
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.InnerProduct,
bottom_names = ["dropout2"],
top_names = ["fc3"],
num_output=1024))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.ReLU,
bottom_names = ["fc3"],
top_names = ["relu3"]))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Dropout,
bottom_names = ["relu3"],
top_names = ["dropout3"],
dropout_rate=0.5))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,
bottom_names = ["dropout3"],
top_names = ["fc4"],
num_output=1024))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.ReLU,
bottom_names = ["fc4"],
top_names = ["relu4"]))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Dropout,
bottom_names = ["relu4"],
top_names = ["dropout4"],
dropout_rate=0.5))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,
bottom_names = ["dropout4"],
top_names = ["fc5"],
num_output=1024))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.ReLU,
bottom_names = ["fc5"],
top_names = ["relu5"]))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Dropout,
bottom_names = ["relu5"],
top_names = ["dropout5"],
dropout_rate=0.5))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,
bottom_names = ["dropout5"],
top_names = ["fc6"],
num_output=1024))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.ReLU,
bottom_names = ["fc6"],
top_names = ["relu6"]))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Dropout,
bottom_names = ["relu6"],
top_names = ["dropout6"],
dropout_rate=0.5))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,
bottom_names = ["dropout6"],
top_names = ["fc7"],
num_output=1024))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.ReLU,
bottom_names = ["fc7"],
top_names = ["relu7"]))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Dropout,
bottom_names = ["relu7"],
top_names = ["dropout7"],
dropout_rate=0.5))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Concat,
bottom_names = ["dropout7", "multicross1"],
top_names = ["concat2"]))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,
bottom_names = ["concat2"],
top_names = ["fc8"],
num_output=1))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.BinaryCrossEntropyLoss,
bottom_names = ["fc8", "label"],
top_names = ["loss"]))
model.compile()
model.summary()
model.fit(max_iter = 2300, display = 200, eval_interval = 1000, snapshot = 1000000, snapshot_prefix = "dcn")