diff --git a/examples/healthcare/Malaria_Detection/README.md b/examples/healthcare/Malaria_Detection/README.md
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+<!--
+    Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you 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.
+-->
+
+# Singa for Malaria Detection Task
+
+## Malaria
+
+Malaria is caused by parasites and could be transmitted through infected mosquitoes. There are about 200 million cases worldwide, and about 400,000 deaths per year, therefore, malaria does lots of harm to global health.
+
+Although Malaria is a curable disease, inadequate diagnostics make it harder to reduce mortality, as a result, a fast and reliable diagnostic test is a promising and effective way to fight malaria.
+
+To mitigate the problem, we use Singa to implement a machine learning model to help with Malaria diagnosis. The dataset is from Kaggle https://www.kaggle.com/datasets/miracle9to9/files1?resource=download. Please download the dataset before running the scripts.
+
+## Structure
+
+* `data` includes the scripts for preprocessing Malaria image datasets.
+
+* `model` includes the CNN model construction codes by creating
+  a subclass of `Module` to wrap the neural network operations 
+  of each model.
+
+* `train_cnn.py` is the training script, which controls the training flow by
+  doing BackPropagation and SGD update.
+
+## Command
+```bash
+python train_cnn.py cnn malaria -dir pathToDataset
+```
\ No newline at end of file
diff --git a/examples/healthcare/Malaria_Detection/data/malaria.py b/examples/healthcare/Malaria_Detection/data/malaria.py
new file mode 100644
index 000000000..46422b739
--- /dev/null
+++ b/examples/healthcare/Malaria_Detection/data/malaria.py
@@ -0,0 +1,122 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+#
+
+try:
+    import pickle
+except ImportError:
+    import cPickle as pickle
+
+import numpy as np
+import os
+import sys
+from PIL import Image
+
+
+# need to save to specific local directories
+def load_train_data(dir_path="/tmp/malaria", resize_size=(128, 128)):
+    dir_path = check_dataset_exist(dirpath=dir_path)
+    path_train_label_1 = os.path.join(dir_path, "training_set/Parasitized")
+    path_train_label_0 = os.path.join(dir_path, "training_set/Uninfected")
+    train_label_1 = load_image_path(os.listdir(path_train_label_1))
+    train_label_0 = load_image_path(os.listdir(path_train_label_0))
+    labels = []
+    Images = np.empty((len(train_label_1) + len(train_label_0),
+                       3, resize_size[0], resize_size[1]), dtype=np.uint8)
+    for i in range(len(train_label_0)):
+        image_path = os.path.join(path_train_label_0, train_label_0[i])
+        temp_image = np.array(Image.open(image_path).resize(
+            resize_size).convert("RGB")).transpose(2, 0, 1)
+        Images[i] = temp_image
+        labels.append(0)
+    for i in range(len(train_label_1)):
+        image_path = os.path.join(path_train_label_1, train_label_1[i])
+        temp_image = np.array(Image.open(image_path).resize(
+            resize_size).convert("RGB")).transpose(2, 0, 1)
+        Images[i + len(train_label_0)] = temp_image
+        labels.append(1)
+
+    Images = np.array(Images, dtype=np.float32)
+    labels = np.array(labels, dtype=np.int32)
+    return Images, labels
+
+
+# need to save to specific local directories
+def load_test_data(dir_path='/tmp/malaria', resize_size=(128, 128)):
+    dir_path = check_dataset_exist(dirpath=dir_path)
+    path_test_label_1 = os.path.join(dir_path, "testing_set/Parasitized")
+    path_test_label_0 = os.path.join(dir_path, "testing_set/Uninfected")
+    test_label_1 = load_image_path(os.listdir(path_test_label_1))
+    test_label_0 = load_image_path(os.listdir(path_test_label_0))
+    labels = []
+    Images = np.empty((len(test_label_1) + len(test_label_0),
+                       3, resize_size[0], resize_size[1]), dtype=np.uint8)
+    for i in range(len(test_label_0)):
+        image_path = os.path.join(path_test_label_0, test_label_0[i])
+        temp_image = np.array(Image.open(image_path).resize(
+            resize_size).convert("RGB")).transpose(2, 0, 1)
+        Images[i] = temp_image
+        labels.append(0)
+    for i in range(len(test_label_1)):
+        image_path = os.path.join(path_test_label_1, test_label_1[i])
+        temp_image = np.array(Image.open(image_path).resize(
+            resize_size).convert("RGB")).transpose(2, 0, 1)
+        Images[i + len(test_label_0)] = temp_image
+        labels.append(1)
+
+    Images = np.array(Images, dtype=np.float32)
+    labels = np.array(labels, dtype=np.int32)
+    return Images, labels
+
+
+def load_image_path(list):
+    new_list = []
+    for image_path in list:
+        if (image_path.endswith(".png") or image_path.endswith(".jpg")):
+            new_list.append(image_path)
+    return new_list
+
+
+def check_dataset_exist(dirpath):
+    if not os.path.exists(dirpath):
+        print(
+            'Please download the malaria dataset first'
+        )
+        sys.exit(0)
+    return dirpath
+
+
+def normalize(train_x, val_x):
+    mean = [0.5339, 0.4180, 0.4460]  # mean for malaria dataset
+    std = [0.3329, 0.2637, 0.2761]  # std for malaria dataset
+    train_x /= 255
+    val_x /= 255
+    for ch in range(0, 2):
+        train_x[:, ch, :, :] -= mean[ch]
+        train_x[:, ch, :, :] /= std[ch]
+        val_x[:, ch, :, :] -= mean[ch]
+        val_x[:, ch, :, :] /= std[ch]
+    return train_x, val_x
+
+
+def load(dir_path):
+    train_x, train_y = load_train_data(dir_path=dir_path)
+    val_x, val_y = load_test_data(dir_path=dir_path)
+    train_x, val_x = normalize(train_x, val_x)
+    train_y = train_y.flatten()
+    val_y = val_y.flatten()
+    return train_x, train_y, val_x, val_y
diff --git a/examples/healthcare/Malaria_Detection/model/cnn.py b/examples/healthcare/Malaria_Detection/model/cnn.py
new file mode 100644
index 000000000..856adb7e7
--- /dev/null
+++ b/examples/healthcare/Malaria_Detection/model/cnn.py
@@ -0,0 +1,94 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+#
+
+from singa import layer
+from singa import model
+
+
+class CNN(model.Model):
+
+    def __init__(self, num_classes=10, num_channels=1):
+        super(CNN, self).__init__()
+        self.num_classes = num_classes
+        self.input_size = 128
+        self.dimension = 4
+        self.conv1 = layer.Conv2d(num_channels, 32, 3, padding=0, activation="RELU")
+        self.conv2 = layer.Conv2d(32, 64, 3, padding=0, activation="RELU")
+        self.conv3 = layer.Conv2d(64, 64, 3, padding=0, activation="RELU")
+        self.linear1 = layer.Linear(128)
+        self.linear2 = layer.Linear(num_classes)
+        self.pooling1 = layer.MaxPool2d(2, 2, padding=0)
+        self.pooling2 = layer.MaxPool2d(2, 2, padding=0)
+        self.pooling3 = layer.MaxPool2d(2, 2, padding=0)
+        self.relu = layer.ReLU()
+        self.flatten = layer.Flatten()
+        self.softmax_cross_entropy = layer.SoftMaxCrossEntropy()
+        self.sigmoid = layer
+
+    def forward(self, x):
+        y = self.conv1(x)
+        y = self.pooling1(y)
+        y = self.conv2(y)
+        y = self.pooling2(y)
+        y = self.conv3(y)
+        y = self.pooling3(y)
+        y = self.flatten(y)
+        y = self.linear1(y)
+        y = self.relu(y)
+        y = self.linear2(y)
+        return y
+
+    def train_one_batch(self, x, y, dist_option, spars):
+        out = self.forward(x)
+        loss = self.softmax_cross_entropy(out, y)
+
+        if dist_option == 'plain':
+            self.optimizer(loss)
+        elif dist_option == 'half':
+            self.optimizer.backward_and_update_half(loss)
+        elif dist_option == 'partialUpdate':
+            self.optimizer.backward_and_partial_update(loss)
+        elif dist_option == 'sparseTopK':
+            self.optimizer.backward_and_sparse_update(loss,
+                                                      topK=True,
+                                                      spars=spars)
+        elif dist_option == 'sparseThreshold':
+            self.optimizer.backward_and_sparse_update(loss,
+                                                      topK=False,
+                                                      spars=spars)
+        return out, loss
+
+    def set_optimizer(self, optimizer):
+        self.optimizer = optimizer
+
+
+def create_model(**kwargs):
+    """Constructs a CNN model.
+
+    Args:
+        pretrained (bool): If True, returns a pre-trained model.
+
+    Returns:
+        The created CNN model.
+    """
+    model = CNN(**kwargs)
+
+    return model
+
+
+__all__ = ['CNN', 'create_model']
diff --git a/examples/healthcare/Malaria_Detection/model/mlp.py b/examples/healthcare/Malaria_Detection/model/mlp.py
new file mode 100644
index 000000000..5f46bc321
--- /dev/null
+++ b/examples/healthcare/Malaria_Detection/model/mlp.py
@@ -0,0 +1,85 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+#
+
+from singa import layer
+from singa import model
+from singa import tensor
+from singa import opt
+from singa import device
+import argparse
+import numpy as np
+
+np_dtype = {"float16": np.float16, "float32": np.float32}
+
+singa_dtype = {"float16": tensor.float16, "float32": tensor.float32}
+
+
+class MLP(model.Model):
+
+    def __init__(self, perceptron_size=100, num_classes=10):
+        super(MLP, self).__init__()
+        self.num_classes = num_classes
+        self.dimension = 2
+
+        self.relu = layer.ReLU()
+        self.linear1 = layer.Linear(perceptron_size)
+        self.linear2 = layer.Linear(num_classes)
+        self.softmax_cross_entropy = layer.SoftMaxCrossEntropy()
+
+    def forward(self, inputs):
+        y = self.linear1(inputs)
+        y = self.relu(y)
+        y = self.linear2(y)
+        return y
+
+    def train_one_batch(self, x, y, dist_option, spars):
+        out = self.forward(x)
+        loss = self.softmax_cross_entropy(out, y)
+
+        if dist_option == 'plain':
+            self.optimizer(loss)
+        elif dist_option == 'half':
+            self.optimizer.backward_and_update_half(loss)
+        elif dist_option == 'partialUpdate':
+            self.optimizer.backward_and_partial_update(loss)
+        elif dist_option == 'sparseTopK':
+            self.optimizer.backward_and_sparse_update(loss,
+                                                      topK=True,
+                                                      spars=spars)
+        elif dist_option == 'sparseThreshold':
+            self.optimizer.backward_and_sparse_update(loss,
+                                                      topK=False,
+                                                      spars=spars)
+        return out, loss
+
+    def set_optimizer(self, optimizer):
+        self.optimizer = optimizer
+
+
+def create_model(**kwargs):
+    """Constructs a CNN model.
+
+    Returns:
+        The created CNN model.
+    """
+    model = MLP(**kwargs)
+
+    return model
+
+
+__all__ = ['MLP', 'create_model']
diff --git a/examples/healthcare/Malaria_Detection/run.sh b/examples/healthcare/Malaria_Detection/run.sh
new file mode 100644
index 000000000..14718208b
--- /dev/null
+++ b/examples/healthcare/Malaria_Detection/run.sh
@@ -0,0 +1,20 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+#
+
+### malaria dataset
+python train_cnn.py cnn malaria -dir pathToDataset
diff --git a/examples/healthcare/Malaria_Detection/train_cnn.py b/examples/healthcare/Malaria_Detection/train_cnn.py
new file mode 100644
index 000000000..bfe810d4f
--- /dev/null
+++ b/examples/healthcare/Malaria_Detection/train_cnn.py
@@ -0,0 +1,294 @@
+from singa import singa_wrap as singa
+from singa import device
+from singa import tensor
+from singa import opt
+import numpy as np
+import time
+import argparse
+import sys
+from PIL import Image
+
+np_dtype = {"float16": np.float16, "float32": np.float32}
+
+singa_dtype = {"float16": tensor.float16, "float32": tensor.float32}
+
+
+# Data augmentation
+def augmentation(x, batch_size):
+    xpad = np.pad(x, [[0, 0], [0, 0], [4, 4], [4, 4]], 'symmetric')
+    for data_num in range(0, batch_size):
+        offset = np.random.randint(8, size=2)
+        x[data_num, :, :, :] = xpad[data_num, :,
+                               offset[0]:offset[0] + x.shape[2],
+                               offset[1]:offset[1] + x.shape[2]]
+        if_flip = np.random.randint(2)
+        if (if_flip):
+            x[data_num, :, :, :] = x[data_num, :, :, ::-1]
+    return x
+
+
+# Calculate accuracy
+def accuracy(pred, target):
+    # y is network output to be compared with ground truth (int)
+    y = np.argmax(pred, axis=1)
+    a = y == target
+    correct = np.array(a, "int").sum()
+    return correct
+
+
+# Data partition according to the rank
+def partition(global_rank, world_size, train_x, train_y, val_x, val_y):
+    # Partition training data
+    data_per_rank = train_x.shape[0] // world_size
+    idx_start = global_rank * data_per_rank
+    idx_end = (global_rank + 1) * data_per_rank
+    train_x = train_x[idx_start:idx_end]
+    train_y = train_y[idx_start:idx_end]
+
+    # Partition evaluation data
+    data_per_rank = val_x.shape[0] // world_size
+    idx_start = global_rank * data_per_rank
+    idx_end = (global_rank + 1) * data_per_rank
+    val_x = val_x[idx_start:idx_end]
+    val_y = val_y[idx_start:idx_end]
+    return train_x, train_y, val_x, val_y
+
+
+# Function to all reduce NUMPY accuracy and loss from multiple devices
+def reduce_variable(variable, dist_opt, reducer):
+    reducer.copy_from_numpy(variable)
+    dist_opt.all_reduce(reducer.data)
+    dist_opt.wait()
+    output = tensor.to_numpy(reducer)
+    return output
+
+
+def resize_dataset(x, image_size):
+    num_data = x.shape[0]
+    dim = x.shape[1]
+    X = np.zeros(shape=(num_data, dim, image_size, image_size),
+                 dtype=np.float32)
+    for n in range(0, num_data):
+        for d in range(0, dim):
+            X[n, d, :, :] = np.array(Image.fromarray(x[n, d, :, :]).resize(
+                (image_size, image_size), Image.BILINEAR),
+                dtype=np.float32)
+    return X
+
+
+def run(global_rank,
+        world_size,
+        dir_path,
+        max_epoch,
+        batch_size,
+        model,
+        data,
+        sgd,
+        graph,
+        verbosity,
+        dist_option='plain',
+        spars=None,
+        precision='float32'):
+    # now CPU version only, could change to GPU device for GPU-support machines
+    dev = device.get_default_device()
+    dev.SetRandSeed(0)
+    np.random.seed(0)
+    if data == 'malaria':
+        from data import malaria
+        train_x, train_y, val_x, val_y = malaria.load(dir_path=dir_path)
+    else:
+        print(
+            'Wrong dataset!'
+        )
+        sys.exit(0)
+
+    num_channels = train_x.shape[1]
+    image_size = train_x.shape[2]
+    data_size = np.prod(train_x.shape[1:train_x.ndim]).item()
+    num_classes = (np.max(train_y) + 1).item()
+
+    if model == 'cnn':
+        from model import cnn
+        model = cnn.create_model(num_channels=num_channels,
+                                 num_classes=num_classes)
+    else:
+        print(
+            'Wrong model!'
+        )
+        sys.exit(0)
+
+    # For distributed training, sequential has better performance
+    if hasattr(sgd, "communicator"):
+        DIST = True
+        sequential = True
+    else:
+        DIST = False
+        sequential = False
+
+    if DIST:
+        train_x, train_y, val_x, val_y = partition(global_rank, world_size,
+                                                   train_x, train_y, val_x,
+                                                   val_y)
+
+    if model.dimension == 4:
+        tx = tensor.Tensor(
+            (batch_size, num_channels, model.input_size, model.input_size), dev,
+            singa_dtype[precision])
+    elif model.dimension == 2:
+        tx = tensor.Tensor((batch_size, data_size),
+                           dev, singa_dtype[precision])
+        np.reshape(train_x, (train_x.shape[0], -1))
+        np.reshape(val_x, (val_x.shape[0], -1))
+
+    ty = tensor.Tensor((batch_size,), dev, tensor.int32)
+    num_train_batch = train_x.shape[0] // batch_size
+    num_val_batch = val_x.shape[0] // batch_size
+    idx = np.arange(train_x.shape[0], dtype=np.int32)
+
+    # Attach model to graph
+    model.set_optimizer(sgd)
+    model.compile([tx], is_train=True, use_graph=graph, sequential=sequential)
+    dev.SetVerbosity(verbosity)
+
+    # Training and evaluation loop
+    for epoch in range(max_epoch):
+        start_time = time.time()
+        np.random.shuffle(idx)
+
+        if global_rank == 0:
+            print('Starting Epoch %d:' % (epoch))
+
+        # Training phase
+        train_correct = np.zeros(shape=[1], dtype=np.float32)
+        test_correct = np.zeros(shape=[1], dtype=np.float32)
+        train_loss = np.zeros(shape=[1], dtype=np.float32)
+
+        model.train()
+        for b in range(num_train_batch):
+            # if b % 100 == 0:
+            #     print ("b: \n", b)
+            # Generate the patch data in this iteration
+            x = train_x[idx[b * batch_size:(b + 1) * batch_size]]
+            if model.dimension == 4:
+                x = augmentation(x, batch_size)
+                if (image_size != model.input_size):
+                    x = resize_dataset(x, model.input_size)
+            x = x.astype(np_dtype[precision])
+            y = train_y[idx[b * batch_size:(b + 1) * batch_size]]
+
+            # Copy the patch data into input tensors
+            tx.copy_from_numpy(x)
+            ty.copy_from_numpy(y)
+
+            # Train the model
+            out, loss = model(tx, ty, dist_option, spars)
+            train_correct += accuracy(tensor.to_numpy(out), y)
+            train_loss += tensor.to_numpy(loss)[0]
+
+        if DIST:
+            # Reduce the evaluation accuracy and loss from multiple devices
+            reducer = tensor.Tensor((1,), dev, tensor.float32)
+            train_correct = reduce_variable(train_correct, sgd, reducer)
+            train_loss = reduce_variable(train_loss, sgd, reducer)
+
+        if global_rank == 0:
+            print('Training loss = %f, training accuracy = %f' %
+                  (train_loss, train_correct /
+                   (num_train_batch * batch_size * world_size)),
+                  flush=True)
+
+        # Evaluation phase
+        model.eval()
+        for b in range(num_val_batch):
+            x = val_x[b * batch_size:(b + 1) * batch_size]
+            if model.dimension == 4:
+                if (image_size != model.input_size):
+                    x = resize_dataset(x, model.input_size)
+            x = x.astype(np_dtype[precision])
+            y = val_y[b * batch_size:(b + 1) * batch_size]
+            tx.copy_from_numpy(x)
+            ty.copy_from_numpy(y)
+            out_test = model(tx)
+            test_correct += accuracy(tensor.to_numpy(out_test), y)
+
+        if DIST:
+            # Reduce the evaulation accuracy from multiple devices
+            test_correct = reduce_variable(test_correct, sgd, reducer)
+
+        # Output the evaluation accuracy
+        if global_rank == 0:
+            print('Evaluation accuracy = %f, Elapsed Time = %fs' %
+                  (test_correct / (num_val_batch * batch_size * world_size),
+                   time.time() - start_time),
+                  flush=True)
+
+    dev.PrintTimeProfiling()
+
+
+if __name__ == '__main__':
+    # Use argparse to get command config: max_epoch, model, data, etc., for single gpu training
+    parser = argparse.ArgumentParser(
+        description='Training using the autograd and graph.')
+    parser.add_argument(
+        'model',
+        choices=['cnn'],
+        default='cnn')
+    parser.add_argument('data',
+                        choices=['malaria'],
+                        default='malaria')
+    parser.add_argument('-p',
+                        choices=['float32', 'float16'],
+                        default='float32',
+                        dest='precision')
+    parser.add_argument('-dir',
+                        '--dir-path',
+                        default="/tmp/malaria",
+                        type=str,
+                        help='the directory to store the malaria dataset',
+                        dest='dir_path')
+    parser.add_argument('-m',
+                        '--max-epoch',
+                        default=100,
+                        type=int,
+                        help='maximum epochs',
+                        dest='max_epoch')
+    parser.add_argument('-b',
+                        '--batch-size',
+                        default=64,
+                        type=int,
+                        help='batch size',
+                        dest='batch_size')
+    parser.add_argument('-l',
+                        '--learning-rate',
+                        default=0.005,
+                        type=float,
+                        help='initial learning rate',
+                        dest='lr')
+    parser.add_argument('-g',
+                        '--disable-graph',
+                        default='True',
+                        action='store_false',
+                        help='disable graph',
+                        dest='graph')
+    parser.add_argument('-v',
+                        '--log-verbosity',
+                        default=0,
+                        type=int,
+                        help='logging verbosity',
+                        dest='verbosity')
+
+    args = parser.parse_args()
+
+    sgd = opt.SGD(lr=args.lr, momentum=0.9, weight_decay=1e-5,
+                  dtype=singa_dtype[args.precision])
+    run(0,
+        1,
+        args.dir_path,
+        args.max_epoch,
+        args.batch_size,
+        args.model,
+        args.data,
+        sgd,
+        args.graph,
+        args.verbosity,
+        precision=args.precision)