add wide&deep stanalone training script for gpu in model zoo

model_zoo/wide_and_deep/README.md

@@ -37,6 +37,7 @@ To train and evaluate the model, command as follows:
 python train_and_eval.py
 ```
 Arguments:
+  * `--device_target`: Device where the code will be implemented (Default: Ascend).
   * `--data_path`: This should be set to the same directory given to the data_download's data_dir argument.
   * `--epochs`: Total train epochs.
   * `--batch_size`: Training batch size.
@@ -57,6 +58,7 @@ To train the model in one device, command as follows:
 python train.py
 ```
 Arguments:
+  * `--device_target`: Device where the code will be implemented (Default: Ascend).
   * `--data_path`: This should be set to the same directory given to the data_download's data_dir argument.
   * `--epochs`: Total train epochs.
   * `--batch_size`: Training batch size.
@@ -87,6 +89,7 @@ To evaluate the model, command as follows:
 python eval.py
 ```
 Arguments:
+  * `--device_target`: Device where the code will be implemented (Default: Ascend).
   * `--data_path`: This should be set to the same directory given to the data_download's data_dir argument.
   * `--epochs`: Total train epochs.
   * `--batch_size`: Training batch size.

model_zoo/wide_and_deep/eval.py

@@ -26,11 +26,11 @@ from src.datasets import create_dataset
 from src.metrics import AUCMetric
 from src.config import WideDeepConfig
 
-context.set_context(mode=context.GRAPH_MODE, device_target="Davinci",
-                    save_graphs=True)
-
 
 def get_WideDeep_net(config):
+    """
+    Get network of wide&deep model.
+    """
     WideDeep_net = WideDeepModel(config)
 
     loss_net = NetWithLossClass(WideDeep_net, config)
@@ -91,4 +91,5 @@ if __name__ == "__main__":
     widedeep_config = WideDeepConfig()
     widedeep_config.argparse_init()
 
+    context.set_context(mode=context.GRAPH_MODE, device_target=widedeep_config.device_target)
     test_eval(widedeep_config)

model_zoo/wide_and_deep/script/run_multigpu_train.sh

@@ -14,7 +14,7 @@
 # limitations under the License.
 # ============================================================================
 
-# bash run_multigpu_train.sh
+# bash run_multigpu_train.sh RANK_SIZE EPOCH_SIZE DATASET
 script_self=$(readlink -f "$0")
 self_path=$(dirname "${script_self}")
 RANK_SIZE=$1
@@ -25,4 +25,5 @@ mpirun --allow-run-as-root -n $RANK_SIZE                    \
     python -s ${self_path}/../train_and_eval_distribute.py  \
         --device_target="GPU"                               \
         --data_path=$DATASET                                \
+        --batch_size=8000                                   \
         --epochs=$EPOCH_SIZE > log.txt 2>&1 &

model_zoo/wide_and_deep/script/run_standalone_train_for_gpu.sh

+#!/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.
+# ============================================================================
+
+# bash run_standalone_train_for_gpu.sh EPOCH_SIZE DATASET
+script_self=$(readlink -f "$0")
+self_path=$(dirname "${script_self}")
+EPOCH_SIZE=$1
+DATASET=$2
+
+python -s ${self_path}/../train_and_eval.py             \
+    --device_target="GPU"                               \
+    --data_path=$DATASET                                \
+    --batch_size=16000                                  \
+    --epochs=$EPOCH_SIZE > log.txt 2>&1 &

model_zoo/wide_and_deep/train.py

@@ -15,16 +15,16 @@
 import os
 from mindspore import Model, context
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor
-
 from src.wide_and_deep import PredictWithSigmoid, TrainStepWrap, NetWithLossClass, WideDeepModel
 from src.callbacks import LossCallBack
 from src.datasets import create_dataset
 from src.config import WideDeepConfig
 
-context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=True)
-
 
 def get_WideDeep_net(configure):
+    """
+    Get network of wide&deep model.
+    """
     WideDeep_net = WideDeepModel(configure)
 
     loss_net = NetWithLossClass(WideDeep_net, configure)
@@ -72,7 +72,7 @@ def test_train(configure):
 
     model = Model(train_net)
     callback = LossCallBack(config=configure)
-    ckptconfig = CheckpointConfig(save_checkpoint_steps=1,
+    ckptconfig = CheckpointConfig(save_checkpoint_steps=ds_train.get_dataset_size(),
                                   keep_checkpoint_max=5)
     ckpoint_cb = ModelCheckpoint(prefix='widedeep_train', directory=configure.ckpt_path, config=ckptconfig)
     model.train(epochs, ds_train, callbacks=[TimeMonitor(ds_train.get_dataset_size()), callback, ckpoint_cb])
@@ -82,4 +82,5 @@ if __name__ == "__main__":
     config = WideDeepConfig()
     config.argparse_init()
 
+    context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
     test_train(config)

model_zoo/wide_and_deep/train_and_eval.py

@@ -15,7 +15,7 @@
 import os
 
 from mindspore import Model, context
-from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor
 
 from src.wide_and_deep import PredictWithSigmoid, TrainStepWrap, NetWithLossClass, WideDeepModel
 from src.callbacks import LossCallBack, EvalCallBack
@@ -23,10 +23,11 @@ from src.datasets import create_dataset
 from src.metrics import AUCMetric
 from src.config import WideDeepConfig
 
-context.set_context(mode=context.GRAPH_MODE, device_target="Davinci")
-
 
 def get_WideDeep_net(config):
+    """
+    Get network of wide&deep model.
+    """
     WideDeep_net = WideDeepModel(config)
 
     loss_net = NetWithLossClass(WideDeep_net, config)
@@ -87,11 +88,13 @@ def test_train_eval(config):
 
     out = model.eval(ds_eval)
     print("=====" * 5 + "model.eval() initialized: {}".format(out))
-    model.train(epochs, ds_train, callbacks=[eval_callback, callback, ckpoint_cb])
+    model.train(epochs, ds_train,
+                callbacks=[TimeMonitor(ds_train.get_dataset_size()), eval_callback, callback, ckpoint_cb])
 
 
 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)
     test_train_eval(wide_deep_config)

model_zoo/wide_and_deep/train_and_eval_auto_parallel.py

@@ -40,6 +40,9 @@ init()
 
 
 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)

model_zoo/wide_and_deep/train_and_eval_distribute.py

@@ -33,6 +33,9 @@ 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)
     train_net = TrainStepWrap(loss_net)
@@ -90,8 +93,12 @@ def train_and_eval(config):
 
     callback = LossCallBack(config=config)
     ckptconfig = CheckpointConfig(save_checkpoint_steps=ds_train.get_dataset_size(), keep_checkpoint_max=5)
-    ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
-                                 directory=config.ckpt_path, config=ckptconfig)
+    if config.device_target == "Ascend":
+        ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
+                                     directory=config.ckpt_path, config=ckptconfig)
+    elif config.device_target == "GPU":
+        ckpoint_cb = ModelCheckpoint(prefix='widedeep_train_' + str(get_rank()),
+                                     directory=config.ckpt_path, config=ckptconfig)
     out = model.eval(ds_eval)
     print("=====" * 5 + "model.eval() initialized: {}".format(out))
     model.train(epochs, ds_train,