diff --git a/model_zoo/official/recommend/wide_and_deep/script/run_parameter_server_cache_multigpu_train.sh b/model_zoo/official/recommend/wide_and_deep/script/run_parameter_server_cache_multigpu_train.sh
new file mode 100644
index 0000000000..355102764a
--- /dev/null
+++ b/model_zoo/official/recommend/wide_and_deep/script/run_parameter_server_cache_multigpu_train.sh
@@ -0,0 +1,58 @@
+#!/bin/bash
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+execute_path=$(pwd)
+script_self=$(readlink -f "$0")
+self_path=$(dirname "${script_self}")
+export RANK_SIZE=$1
+export EPOCH_SIZE=$2
+export DATASET=$3
+export MS_COMM_TYPE=zmq
+export MS_SCHED_NUM=1
+export MS_WORKER_NUM=$RANK_SIZE
+export MS_SERVER_NUM=$4
+export MS_SCHED_HOST=$5
+export MS_SCHED_PORT=$6
+
+export MS_ROLE=MS_SCHED
+for((i=0;i<1;i++));
+do
+  rm -rf ${execute_path}/sched_$i/
+  mkdir ${execute_path}/sched_$i/
+  cd ${execute_path}/sched_$i/ || exit
+  python -s ${self_path}/../train_and_eval_parameter_server_cache_distribute.py                 \
+         --device_target='GPU' --data_path=$DATASET --epochs=$EPOCH_SIZE --parameter_server=1   \
+         --vocab_cache_size=300000 >sched_$i.log 2>&1 &
+done
+
+export MS_ROLE=MS_PSERVER
+for((i=0;i<$MS_SERVER_NUM;i++));
+do
+  rm -rf ${execute_path}/server_$i/
+  mkdir ${execute_path}/server_$i/
+  cd ${execute_path}/server_$i/ || exit
+  python -s ${self_path}/../train_and_eval_parameter_server_cache_distribute.py                 \
+         --device_target='GPU' --data_path=$DATASET --epochs=$EPOCH_SIZE --parameter_server=1   \
+         --vocab_cache_size=300000 >server_$i.log 2>&1 &
+done
+
+export MS_ROLE=MS_WORKER
+rm -rf ${execute_path}/worker/
+mkdir ${execute_path}/worker/
+cd ${execute_path}/worker/ || exit
+mpirun --allow-run-as-root -n $RANK_SIZE python -s ${self_path}/../train_and_eval_parameter_server_cache_distribute.py               \
+                                                --device_target='GPU' --data_path=$DATASET --epochs=$EPOCH_SIZE --parameter_server=1 \
+                                                --vocab_cache_size=300000 --full_batch=1 --dropout_flag=1 >worker.log 2>&1 &
diff --git a/model_zoo/official/recommend/wide_and_deep/script/run_parameter_server_cache_multinpu_train.sh b/model_zoo/official/recommend/wide_and_deep/script/run_parameter_server_cache_multinpu_train.sh
new file mode 100644
index 0000000000..fc9f9a455b
--- /dev/null
+++ b/model_zoo/official/recommend/wide_and_deep/script/run_parameter_server_cache_multinpu_train.sh
@@ -0,0 +1,68 @@
+#!/bin/bash
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+execute_path=$(pwd)
+script_self=$(readlink -f "$0")
+self_path=$(dirname "${script_self}")
+export RANK_SIZE=$1
+export EPOCH_SIZE=$2
+export DATASET=$3
+export RANK_TABLE_FILE=$4
+export MS_COMM_TYPE=zmq
+export MS_SCHED_NUM=1
+export MS_WORKER_NUM=$RANK_SIZE
+export MS_SERVER_NUM=$5
+export MS_SCHED_HOST=$6
+export MS_SCHED_PORT=$7
+
+export MS_ROLE=MS_SCHED
+for((i=0;i<1;i++));
+do
+  rm -rf ${execute_path}/sched_$i/
+  mkdir ${execute_path}/sched_$i/
+  cd ${execute_path}/sched_$i/ || exit
+  export RANK_ID=$i
+  export DEVICE_ID=$i
+  python -s ${self_path}/../train_and_eval_parameter_server_cache_distribute.py \
+         --data_path=$DATASET --epochs=$EPOCH_SIZE --parameter_server=1         \
+         --vocab_cache_size=300000 >sched_$i.log 2>&1 &
+done
+
+export MS_ROLE=MS_PSERVER
+for((i=0;i<$MS_SERVER_NUM;i++));
+do
+  rm -rf ${execute_path}/server_$i/
+  mkdir ${execute_path}/server_$i/
+  cd ${execute_path}/server_$i/ || exit
+  export RANK_ID=$i
+  export DEVICE_ID=$i
+  python -s ${self_path}/../train_and_eval_parameter_server_cache_distribute.py \
+         --data_path=$DATASET --epochs=$EPOCH_SIZE --parameter_server=1         \
+         --vocab_cache_size=300000 >server_$i.log 2>&1 &
+done
+
+export MS_ROLE=MS_WORKER
+for((i=0;i<$MS_WORKER_NUM;i++));
+do
+  rm -rf ${execute_path}/worker_$i/
+  mkdir ${execute_path}/worker_$i/
+  cd ${execute_path}/worker_$i/ || exit
+  export RANK_ID=$i
+  export DEVICE_ID=$i
+  python -s ${self_path}/../train_and_eval_parameter_server_cache_distribute.py \
+         --data_path=$DATASET --epochs=$EPOCH_SIZE --parameter_server=1         \
+         --vocab_cache_size=300000 --full_batch=1 --dropout_flag=1 >worker_$i.log 2>&1 &
+done
diff --git a/model_zoo/official/recommend/wide_and_deep/script/run_parameter_server_cache_standalone_train.sh b/model_zoo/official/recommend/wide_and_deep/script/run_parameter_server_cache_standalone_train.sh
new file mode 100644
index 0000000000..7341cfa17b
--- /dev/null
+++ b/model_zoo/official/recommend/wide_and_deep/script/run_parameter_server_cache_standalone_train.sh
@@ -0,0 +1,56 @@
+#!/bin/bash
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+execute_path=$(pwd)
+script_self=$(readlink -f "$0")
+self_path=$(dirname "${script_self}")
+export EPOCH_SIZE=$1
+export DEVICE_TARGET=$2
+export DATASET=$3
+export MS_COMM_TYPE=zmq
+export MS_SCHED_NUM=1
+export MS_WORKER_NUM=1
+export MS_SERVER_NUM=$4
+export MS_SCHED_HOST=$5
+export MS_SCHED_PORT=$6
+
+export MS_ROLE=MS_SCHED
+rm -rf ${execute_path}/sched/
+mkdir ${execute_path}/sched/
+cd ${execute_path}/sched/ || exit
+export DEVICE_ID=$i
+python -s ${self_path}/../train_and_eval_parameter_server.py --epochs=$EPOCH_SIZE --device_target=$DEVICE_TARGET --data_path=$DATASET \
+                                                             --parameter_server=1 --vocab_cache_size=300000 >sched.log 2>&1 &
+
+export MS_ROLE=MS_PSERVER
+for((i=0;i<$MS_SERVER_NUM;i++));
+do
+  rm -rf ${execute_path}/server_$i/
+  mkdir ${execute_path}/server_$i/
+  cd ${execute_path}/server_$i/ || exit
+  export DEVICE_ID=$i
+  python -s ${self_path}/../train_and_eval_parameter_server.py --epochs=$EPOCH_SIZE --device_target=$DEVICE_TARGET --data_path=$DATASET \
+                                                               --parameter_server=1 --vocab_cache_size=300000 >server_$i.log 2>&1 &
+done
+
+export MS_ROLE=MS_WORKER
+rm -rf ${execute_path}/worker/
+mkdir ${execute_path}/worker/
+cd ${execute_path}/worker/ || exit
+export DEVICE_ID=$i
+python -s ${self_path}/../train_and_eval_parameter_server.py --epochs=$EPOCH_SIZE --device_target=$DEVICE_TARGET --data_path=$DATASET \
+                                                             --parameter_server=1 --vocab_cache_size=300000 \
+                                                             --dropout_flag=1 >worker.log 2>&1 &
diff --git a/model_zoo/official/recommend/wide_and_deep/train_and_eval_parameter_server_cache_distribute.py b/model_zoo/official/recommend/wide_and_deep/train_and_eval_parameter_server_cache_distribute.py
new file mode 100644
index 0000000000..7002dd07b5
--- /dev/null
+++ b/model_zoo/official/recommend/wide_and_deep/train_and_eval_parameter_server_cache_distribute.py
@@ -0,0 +1,141 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""train_multinpu."""
+
+
+import os
+import sys
+import mindspore.dataset.engine as de
+from mindspore import Model, context
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor
+from mindspore.context import ParallelMode
+from mindspore.communication.management import get_rank, get_group_size, init
+from mindspore.nn.wrap.cell_wrapper import VirtualDatasetCellTriple
+
+from src.wide_and_deep import PredictWithSigmoid, TrainStepWrap, NetWithLossClass, WideDeepModel
+from src.callbacks import LossCallBack, EvalCallBack
+from src.datasets import create_dataset, DataType
+from src.metrics import AUCMetric
+from src.config import WideDeepConfig
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+
+def get_WideDeep_net(config):
+    """
+    Get network of wide&deep model.
+    """
+    WideDeep_net = WideDeepModel(config)
+    loss_net = NetWithLossClass(WideDeep_net, config)
+    loss_net = VirtualDatasetCellTriple(loss_net)
+    train_net = TrainStepWrap(loss_net, parameter_server=bool(config.parameter_server),
+                              cache_enable=bool(config.vocab_cache_size > 0))
+    eval_net = PredictWithSigmoid(WideDeep_net)
+    eval_net = VirtualDatasetCellTriple(eval_net)
+    return train_net, eval_net
+
+
+class ModelBuilder():
+    """
+    ModelBuilder
+    """
+
+    def __init__(self):
+        pass
+
+    def get_hook(self):
+        pass
+
+    def get_train_hook(self):
+        hooks = []
+        callback = LossCallBack()
+        hooks.append(callback)
+        if int(os.getenv('DEVICE_ID')) == 0:
+            pass
+        return hooks
+
+    def get_net(self, config):
+        return get_WideDeep_net(config)
+
+
+def train_and_eval(config):
+    """
+    test_train_eval
+    """
+    data_path = config.data_path
+    batch_size = config.batch_size
+    epochs = config.epochs
+    if config.dataset_type == "tfrecord":
+        dataset_type = DataType.TFRECORD
+    elif config.dataset_type == "mindrecord":
+        dataset_type = DataType.MINDRECORD
+    else:
+        dataset_type = DataType.H5
+    print("epochs is {}".format(epochs))
+    if config.full_batch:
+        context.set_auto_parallel_context(full_batch=True)
+        de.config.set_seed(1)
+        ds_train = create_dataset(data_path, train_mode=True, epochs=1,
+                                  batch_size=batch_size*get_group_size(), data_type=dataset_type)
+        ds_eval = create_dataset(data_path, train_mode=False, epochs=1,
+                                 batch_size=batch_size*get_group_size(), data_type=dataset_type)
+    else:
+        ds_train = create_dataset(data_path, train_mode=True, epochs=1,
+                                  batch_size=batch_size, rank_id=get_rank(),
+                                  rank_size=get_group_size(), data_type=dataset_type)
+        ds_eval = create_dataset(data_path, train_mode=False, epochs=1,
+                                 batch_size=batch_size, rank_id=get_rank(),
+                                 rank_size=get_group_size(), data_type=dataset_type)
+    print("ds_train.size: {}".format(ds_train.get_dataset_size()))
+    print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
+
+    net_builder = ModelBuilder()
+
+    train_net, eval_net = net_builder.get_net(config)
+    train_net.set_train()
+    auc_metric = AUCMetric()
+
+    model = Model(train_net, eval_network=eval_net,
+                  metrics={"auc": auc_metric})
+
+    eval_callback = EvalCallBack(
+        model, ds_eval, auc_metric, config)
+
+    callback = LossCallBack(config=config, per_print_times=20)
+    ckptconfig = CheckpointConfig(save_checkpoint_steps=ds_train.get_dataset_size()*epochs,
+                                  keep_checkpoint_max=5, integrated_save=False)
+    ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
+                                 directory=config.ckpt_path + '/ckpt_' + str(get_rank()) + '/', config=ckptconfig)
+    context.set_auto_parallel_context(strategy_ckpt_save_file=config.stra_ckpt)
+    callback_list = [TimeMonitor(
+        ds_train.get_dataset_size()), eval_callback, callback]
+    callback_list.append(ckpoint_cb)
+    model.train(epochs, ds_train, callbacks=callback_list, dataset_sink_mode=True)
+
+
+if __name__ == "__main__":
+    wide_deep_config = WideDeepConfig()
+    wide_deep_config.argparse_init()
+    context.set_context(mode=context.GRAPH_MODE,
+                        device_target=wide_deep_config.device_target, save_graphs=True)
+    context.set_context(variable_memory_max_size="24GB")
+    context.set_context(enable_sparse=True)
+    context.set_ps_context(enable_ps=True)
+    init()
+    context.set_context(save_graphs_path='./graphs_of_device_id_'+str(get_rank()))
+
+    context.set_auto_parallel_context(
+        parallel_mode=ParallelMode.AUTO_PARALLEL, gradients_mean=True)
+    train_and_eval(wide_deep_config)