Merge pull request #200 from microsoft/master

merge master

docs/zh_CN/training_services.rst

@@ -2,8 +2,8 @@ NNI 支持的训练平台介绍
 =====================================
 
 ..  toctree::
-    本机<LocalMode>
-    远程<RemoteMachineMode>
-    OpenPAI<PaiMode>
-    Kubeflow<KubeflowMode>
-    FrameworkController<FrameworkControllerMode>
\ No newline at end of file
+    本机<./TrainingService/LocalMode>
+    远程<./TrainingService/RemoteMachineMode>
+    OpenPAI<./TrainingService/PaiMode>
+    Kubeflow<./TrainingService/KubeflowMode>
+    FrameworkController<./TrainingService/FrameworkControllerMode>
\ No newline at end of file

docs/zh_CN/tuners.rst

@@ -13,6 +13,6 @@ Tuner 从 Trial 接收指标结果，来评估一组超参或网络结构的性
 ..  toctree::
     :maxdepth: 2
 
-    内置 Tuner<BuiltinTuner>
-    自定义 Tuner<CustomizeTuner>
-    自定义 Advisor<CustomizeAdvisor>
\ No newline at end of file
+    内置 Tuner<builtin_tuner>
+    自定义 Tuner<Tuner/CustomizeTuner>
+    自定义 Advisor<Tuner/CustomizeAdvisor>

docs/zh_CN/tutorials.rst

@@ -5,12 +5,13 @@
 ..  toctree::
     :maxdepth: 2
 
-    安装<Installation>
-    实现 Trial<Trials>
+    安装<Tutorial/Installation>
+    实现 Trial<./TrialExample/Trials>
     Tuner<tuners>
     Assessor<assessors>
-    Web 界面<WebUI>
+    Web 界面<Tutorial/WebUI>
     训练平台<training_services>
-    如何使用 Docker <HowToUseDocker>
+    如何使用 Docker<Tutorial/HowToUseDocker>
     高级功能<advanced>
-    如何调试<HowToDebug>
\ No newline at end of file
+    如何调试<Tutorial/HowToDebug>
+    Windows 中使用 NNI<Tutorial/NniOnWindows>
\ No newline at end of file

examples/trials/auto-feature-engineering/README_zh_CN.md

+**NNI 中的自动特征工程**  
+===
+
+此[示例](https://github.com/SpongebBob/tabular_automl_NNI)在 NNI 中实现了自动特征工程。
+
+代码来自于贡献者。 谢谢可爱的贡献者！
+
+欢迎越来越多的人加入我们！
\ No newline at end of file

examples/trials/mnist-nas/config.yml → examples/trials/mnist-nas/classic_mode/config_nas.yml

@@ -8,13 +8,11 @@ trainingServicePlatform: local
 #choice: true, false
 useAnnotation: true
 tuner:
-  #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
-  #SMAC (SMAC should be installed through nnictl)
-  #codeDir: ~/nni/nni/examples/tuners/random_nas_tuner
-  codeDir: ../../tuners/random_nas_tuner
+  codeDir: ../../../tuners/random_nas_tuner
   classFileName: random_nas_tuner.py
   className: RandomNASTuner
 trial:
   command: python3 mnist.py
   codeDir: .
   gpuNum: 0
+  nasMode: classic_mode

examples/trials/mnist-nas/darts_mode/config_darts.yml

+authorName: default
+experimentName: example_mnist
+trialConcurrency: 1
+maxExecDuration: 1h
+maxTrialNum: 10
+#choice: local, remote, pai
+trainingServicePlatform: local
+#choice: true, false
+useAnnotation: true
+tuner:
+  codeDir: ../../../tuners/random_nas_tuner
+  classFileName: random_nas_tuner.py
+  className: RandomNASTuner
+trial:
+  command: python3 mnist-darts.py
+  codeDir: .
+  gpuNum: 0
+  nasMode: oneshot_mode

examples/trials/mnist-nas/darts_mode/mnist-darts.py

+"""A deep MNIST classifier using convolutional layers."""
+
+import argparse
+import logging
+import math
+import tempfile
+import time
+
+import tensorflow as tf
+from tensorflow.examples.tutorials.mnist import input_data
+
+import operators as op
+
+FLAGS = None
+
+logger = logging.getLogger('mnist_AutoML')
+
+
+class MnistNetwork(object):
+    '''
+    MnistNetwork is for initializing and building basic network for mnist.
+    '''
+    def __init__(self,
+                 channel_1_num,
+                 channel_2_num,
+                 conv_size,
+                 hidden_size,
+                 pool_size,
+                 learning_rate,
+                 x_dim=784,
+                 y_dim=10):
+        self.channel_1_num = channel_1_num
+        self.channel_2_num = channel_2_num
+        self.conv_size = conv_size
+        self.hidden_size = hidden_size
+        self.pool_size = pool_size
+        self.learning_rate = learning_rate
+        self.x_dim = x_dim
+        self.y_dim = y_dim
+
+        self.images = tf.placeholder(tf.float32, [None, self.x_dim], name='input_x')
+        self.labels = tf.placeholder(tf.float32, [None, self.y_dim], name='input_y')
+        self.keep_prob = tf.placeholder(tf.float32, name='keep_prob')
+
+        self.train_step = None
+        self.accuracy = None
+
+    def build_network(self):
+        '''
+        Building network for mnist, meanwhile specifying its neural architecture search space
+        '''
+
+        # Reshape to use within a convolutional neural net.
+        # Last dimension is for "features" - there is only one here, since images are
+        # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.
+        with tf.name_scope('reshape'):
+            try:
+                input_dim = int(math.sqrt(self.x_dim))
+            except:
+                print(
+                    'input dim cannot be sqrt and reshape. input dim: ' + str(self.x_dim))
+                logger.debug(
+                    'input dim cannot be sqrt and reshape. input dim: %s', str(self.x_dim))
+                raise
+            x_image = tf.reshape(self.images, [-1, input_dim, input_dim, 1])
+
+        """@nni.mutable_layers(
+            {
+                layer_choice: [op.conv2d(size=1, in_ch=1, out_ch=self.channel_1_num),
+                               op.conv2d(size=3, in_ch=1, out_ch=self.channel_1_num),
+                               op.twice_conv2d(size=3, in_ch=1, out_ch=self.channel_1_num),
+                               op.twice_conv2d(size=7, in_ch=1, out_ch=self.channel_1_num),
+                               op.dilated_conv(in_ch=1, out_ch=self.channel_1_num),
+                               op.separable_conv(size=3, in_ch=1, out_ch=self.channel_1_num),
+                               op.separable_conv(size=5, in_ch=1, out_ch=self.channel_1_num),
+                               op.separable_conv(size=7, in_ch=1, out_ch=self.channel_1_num)],
+                fixed_inputs: [x_image],
+                layer_output: conv1_out
+            },
+            {
+                layer_choice: [op.post_process(ch_size=self.channel_1_num)],
+                fixed_inputs: [conv1_out],
+                layer_output: post1_out
+            },
+            {
+                layer_choice: [op.max_pool(size=3),
+                               op.max_pool(size=5),
+                               op.max_pool(size=7),
+                               op.avg_pool(size=3),
+                               op.avg_pool(size=5),
+                               op.avg_pool(size=7)],
+                fixed_inputs: [post1_out],
+                layer_output: pool1_out
+            },
+            {
+                layer_choice: [op.conv2d(size=1, in_ch=self.channel_1_num, out_ch=self.channel_2_num),
+                               op.conv2d(size=3, in_ch=self.channel_1_num, out_ch=self.channel_2_num),
+                               op.twice_conv2d(size=3, in_ch=self.channel_1_num, out_ch=self.channel_2_num),
+                               op.twice_conv2d(size=7, in_ch=self.channel_1_num, out_ch=self.channel_2_num),
+                               op.dilated_conv(in_ch=self.channel_1_num, out_ch=self.channel_2_num),
+                               op.separable_conv(size=3, in_ch=self.channel_1_num, out_ch=self.channel_2_num),
+                               op.separable_conv(size=5, in_ch=self.channel_1_num, out_ch=self.channel_2_num),
+                               op.separable_conv(size=7, in_ch=self.channel_1_num, out_ch=self.channel_2_num)],
+                fixed_inputs: [pool1_out],
+                optional_inputs: [post1_out],
+                optional_input_size: [0, 1],
+                layer_output: conv2_out
+            },
+            {
+                layer_choice: [op.post_process(ch_size=self.channel_2_num)],
+                fixed_inputs: [conv2_out],
+                layer_output: post2_out
+            },
+            {
+                layer_choice: [op.max_pool(size=3),
+                               op.max_pool(size=5),
+                               op.max_pool(size=7),
+                               op.avg_pool(size=3),
+                               op.avg_pool(size=5),
+                               op.avg_pool(size=7)],
+                fixed_inputs: [post2_out],
+                optional_inputs: [post1_out, pool1_out],
+                optional_input_size: [0, 1],
+                layer_output: pool2_out
+            }
+        )"""
+
+        # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
+        # is down to 7x7x64 feature maps -- maps this to 1024 features.
+        last_dim_list = pool2_out.get_shape().as_list()
+        assert(last_dim_list[1] == last_dim_list[2])
+        last_dim = last_dim_list[1]
+        with tf.name_scope('fc1'):
+            w_fc1 = op.weight_variable(
+                [last_dim * last_dim * self.channel_2_num, self.hidden_size])
+            b_fc1 = op.bias_variable([self.hidden_size])
+
+        h_pool2_flat = tf.reshape(
+            pool2_out, [-1, last_dim * last_dim * self.channel_2_num])
+        h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1)
+
+        # Dropout - controls the complexity of the model, prevents co-adaptation of features.
+        with tf.name_scope('dropout'):
+            h_fc1_drop = tf.nn.dropout(h_fc1, self.keep_prob)
+
+        # Map the 1024 features to 10 classes, one for each digit
+        with tf.name_scope('fc2'):
+            w_fc2 = op.weight_variable([self.hidden_size, self.y_dim])
+            b_fc2 = op.bias_variable([self.y_dim])
+            y_conv = tf.matmul(h_fc1_drop, w_fc2) + b_fc2
+
+        with tf.name_scope('loss'):
+            self.cross_entropy = tf.reduce_mean(
+                tf.nn.softmax_cross_entropy_with_logits(labels=self.labels, logits=y_conv))
+        with tf.name_scope('adam_optimizer'):
+            self.train_step = tf.train.AdamOptimizer(
+                self.learning_rate).minimize(self.cross_entropy)
+
+        with tf.name_scope('accuracy'):
+            correct_prediction = tf.equal(
+                tf.argmax(y_conv, 1), tf.argmax(self.labels, 1))
+            self.accuracy = tf.reduce_mean(
+                tf.cast(correct_prediction, tf.float32))
+
+
+def download_mnist_retry(data_dir, max_num_retries=20):
+    """Try to download mnist dataset and avoid errors"""
+    for _ in range(max_num_retries):
+        try:
+            return input_data.read_data_sets(data_dir, one_hot=True)
+        except tf.errors.AlreadyExistsError:
+            time.sleep(1)
+    raise Exception("Failed to download MNIST.")
+
+def main(params):
+    '''
+    Main function, build mnist network, run and send result to NNI.
+    '''
+    # Import data
+    mnist = download_mnist_retry(params['data_dir'])
+    print('Mnist download data done.')
+    logger.debug('Mnist download data done.')
+
+    # Create the model
+    # Build the graph for the deep net
+    mnist_network = MnistNetwork(channel_1_num=params['channel_1_num'],
+                                 channel_2_num=params['channel_2_num'],
+                                 conv_size=params['conv_size'],
+                                 hidden_size=params['hidden_size'],
+                                 pool_size=params['pool_size'],
+                                 learning_rate=params['learning_rate'])
+    mnist_network.build_network()
+    logger.debug('Mnist build network done.')
+
+    # Write log
+    graph_location = tempfile.mkdtemp()
+    logger.debug('Saving graph to: %s', graph_location)
+    train_writer = tf.summary.FileWriter(graph_location)
+    train_writer.add_graph(tf.get_default_graph())
+
+    test_acc = 0.0
+    with tf.Session() as sess:
+        sess.run(tf.global_variables_initializer())
+        for i in range(params['batch_num']):
+            batch = mnist.train.next_batch(params['batch_size'])
+            feed_dict={mnist_network.images: batch[0],
+                        mnist_network.labels: batch[1],
+                        mnist_network.keep_prob: 1 - params['dropout_rate']}
+            """@nni.training_update(tf, sess, mnist_network.cross_entropy)"""
+            batch = mnist.train.next_batch(params['batch_size'])
+            feed_dict={mnist_network.images: batch[0],
+                        mnist_network.labels: batch[1],
+                        mnist_network.keep_prob: 1 - params['dropout_rate']}
+            mnist_network.train_step.run(feed_dict=feed_dict)
+
+            if i % 100 == 0:
+                test_acc = mnist_network.accuracy.eval(
+                    feed_dict={mnist_network.images: mnist.test.images,
+                               mnist_network.labels: mnist.test.labels,
+                               mnist_network.keep_prob: 1.0})
+
+                """@nni.report_intermediate_result(test_acc)"""
+                logger.debug('test accuracy %g', test_acc)
+                logger.debug('Pipe send intermediate result done.')
+
+        test_acc = mnist_network.accuracy.eval(
+            feed_dict={mnist_network.images: mnist.test.images,
+                       mnist_network.labels: mnist.test.labels,
+                       mnist_network.keep_prob: 1.0})
+
+        """@nni.report_final_result(test_acc)"""
+        logger.debug('Final result is %g', test_acc)
+        logger.debug('Send final result done.')
+
+def get_params():
+    ''' Get parameters from command line '''
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--data_dir", type=str, default='/tmp/tensorflow/mnist/input_data', help="data directory")
+    parser.add_argument("--dropout_rate", type=float, default=0.5, help="dropout rate")
+    parser.add_argument("--channel_1_num", type=int, default=32)
+    parser.add_argument("--channel_2_num", type=int, default=64)
+    parser.add_argument("--conv_size", type=int, default=5)
+    parser.add_argument("--pool_size", type=int, default=2)
+    parser.add_argument("--hidden_size", type=int, default=1024)
+    parser.add_argument("--learning_rate", type=float, default=1e-4)
+    parser.add_argument("--batch_num", type=int, default=2000)
+    parser.add_argument("--batch_size", type=int, default=32)
+
+    args, _ = parser.parse_known_args()
+    return args
+
+if __name__ == '__main__':
+    try:
+        params = vars(get_params())
+        main(params)
+    except Exception as exception:
+        logger.exception(exception)
+        raise

examples/trials/mnist-nas/darts_mode/operators.py

+import tensorflow as tf
+import math
+
+
+def weight_variable(shape):
+    """weight_variable generates a weight variable of a given shape."""
+    initial = tf.truncated_normal(shape, stddev=0.1)
+    return tf.Variable(initial)
+
+def bias_variable(shape):
+    """bias_variable generates a bias variable of a given shape."""
+    initial = tf.constant(0.1, shape=shape)
+    return tf.Variable(initial)
+
+def sum_op(inputs):
+    """sum_op"""
+    fixed_input = inputs[0][0]
+    optional_input = tf.concat(inputs[1], axis=3)
+    fixed_shape = fixed_input.get_shape().as_list()
+    optional_shape = optional_input.get_shape().as_list()
+    assert fixed_shape[1] == fixed_shape[2]
+    assert optional_shape[1] == optional_shape[2]
+    pool_size = math.ceil(optional_shape[1] / fixed_shape[1])
+    pool_out = tf.nn.avg_pool(optional_input, ksize=[1, pool_size, pool_size, 1], strides=[1, pool_size, pool_size, 1], padding='SAME')
+    conv_matrix = weight_variable([1, 1, optional_shape[3], fixed_shape[3]])
+    conv_out = tf.nn.conv2d(pool_out, conv_matrix, strides=[1, 1, 1, 1], padding='SAME')
+    return fixed_input + conv_out
+
+
+def conv2d(inputs, size=-1, in_ch=-1, out_ch=-1):
+    """conv2d returns a 2d convolution layer with full stride."""
+    if not inputs[1]:
+        x_input = inputs[0][0]
+    else:
+        x_input = sum_op(inputs)
+    if size in [1, 3]:
+        w_matrix = weight_variable([size, size, in_ch, out_ch])
+        return tf.nn.conv2d(x_input, w_matrix, strides=[1, 1, 1, 1], padding='SAME')
+    else:
+        raise Exception("Unknown filter size: %d." % size)
+
+def twice_conv2d(inputs, size=-1, in_ch=-1, out_ch=-1):
+    """twice_conv2d"""
+    if not inputs[1]:
+        x_input = inputs[0][0]
+    else:
+        x_input = sum_op(inputs)
+    if size in  [3, 7]:
+        w_matrix1 = weight_variable([1, size, in_ch, int(out_ch/2)])
+        out = tf.nn.conv2d(x_input, w_matrix1, strides=[1, 1, 1, 1], padding='SAME')
+        w_matrix2 = weight_variable([size, 1, int(out_ch/2), out_ch])
+        return tf.nn.conv2d(out, w_matrix2, strides=[1, 1, 1, 1], padding='SAME')
+    else:
+        raise Exception("Unknown filter size: %d." % size)
+
+def dilated_conv(inputs, size=3, in_ch=-1, out_ch=-1):
+    """dilated_conv"""
+    if not inputs[1]:
+        x_input = inputs[0][0]
+    else:
+        x_input = sum_op(inputs)
+    if size == 3:
+        w_matrix = weight_variable([size, size, in_ch, out_ch])
+        return tf.nn.atrous_conv2d(x_input, w_matrix, rate=2, padding='SAME')
+    else:
+        raise Exception("Unknown filter size: %d." % size)
+
+def separable_conv(inputs, size=-1, in_ch=-1, out_ch=-1):
+    """separable_conv"""
+    if not inputs[1]:
+        x_input = inputs[0][0]
+    else:
+        x_input = sum_op(inputs)
+    if size in [3, 5, 7]:
+        depth_matrix = weight_variable([size, size, in_ch, 1])
+        point_matrix = weight_variable([1, 1, 1*in_ch, out_ch])
+        return tf.nn.separable_conv2d(x_input, depth_matrix, point_matrix, strides=[1, 1, 1, 1], padding='SAME')
+    else:
+        raise Exception("Unknown filter size: %d." % size)
+
+
+def avg_pool(inputs, size=-1):
+    """avg_pool downsamples a feature map."""
+    if not inputs[1]:
+        x_input = inputs[0][0]
+    else:
+        x_input = sum_op(inputs)
+    if size in [3, 5, 7]:
+        return tf.nn.avg_pool(x_input, ksize=[1, size, size, 1], strides=[1, 1, 1, 1], padding='SAME')
+    else:
+        raise Exception("Unknown filter size: %d." % size)
+
+def max_pool(inputs, size=-1):
+    """max_pool downsamples a feature map."""
+    if not inputs[1]:
+        x_input = inputs[0][0]
+    else:
+        x_input = sum_op(inputs)
+    if size in [3, 5, 7]:
+        return tf.nn.max_pool(x_input, ksize=[1, size, size, 1], strides=[1, 1, 1, 1], padding='SAME')
+    else:
+        raise Exception("Unknown filter size: %d." % size)
+
+
+def post_process(inputs, ch_size=-1):
+    """post_process"""
+    x_input = inputs[0][0]
+    bias_matrix = bias_variable([ch_size])
+    return tf.nn.relu(x_input + bias_matrix)

examples/trials/mnist-nas/enas_mode/config_enas.yml

+authorName: default
+experimentName: example_mnist
+trialConcurrency: 1
+maxExecDuration: 1h
+maxTrialNum: 10
+#choice: local, remote, pai
+trainingServicePlatform: local
+#choice: true, false
+useAnnotation: true
+multiPhase: true
+tuner:
+  codeDir: ../../../tuners/random_nas_tuner
+  classFileName: random_nas_tuner.py
+  className: RandomNASTuner
+trial:
+  command: python3 mnist-enas.py
+  codeDir: .
+  gpuNum: 0
+  nasMode: enas_mode

examples/trials/mnist-nas/enas_mode/mnist-enas.py

+"""A deep MNIST classifier using convolutional layers."""
+
+import argparse
+import logging
+import math
+import tempfile
+import time
+
+import tensorflow as tf
+from tensorflow.examples.tutorials.mnist import input_data
+
+import operators as op
+
+FLAGS = None
+
+logger = logging.getLogger('mnist_AutoML')
+
+
+class MnistNetwork(object):
+    '''
+    MnistNetwork is for initializing and building basic network for mnist.
+    '''
+    def __init__(self,
+                 channel_1_num,
+                 channel_2_num,
+                 conv_size,
+                 hidden_size,
+                 pool_size,
+                 learning_rate,
+                 x_dim=784,
+                 y_dim=10):
+        self.channel_1_num = channel_1_num
+        self.channel_2_num = channel_2_num
+        self.conv_size = conv_size
+        self.hidden_size = hidden_size
+        self.pool_size = pool_size
+        self.learning_rate = learning_rate
+        self.x_dim = x_dim
+        self.y_dim = y_dim
+
+        self.images = tf.placeholder(tf.float32, [None, self.x_dim], name='input_x')
+        self.labels = tf.placeholder(tf.float32, [None, self.y_dim], name='input_y')
+        self.keep_prob = tf.placeholder(tf.float32, name='keep_prob')
+
+        self.train_step = None
+        self.accuracy = None
+
+    def build_network(self):
+        '''
+        Building network for mnist, meanwhile specifying its neural architecture search space
+        '''
+
+        # Reshape to use within a convolutional neural net.
+        # Last dimension is for "features" - there is only one here, since images are
+        # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.
+        with tf.name_scope('reshape'):
+            try:
+                input_dim = int(math.sqrt(self.x_dim))
+            except:
+                print(
+                    'input dim cannot be sqrt and reshape. input dim: ' + str(self.x_dim))
+                logger.debug(
+                    'input dim cannot be sqrt and reshape. input dim: %s', str(self.x_dim))
+                raise
+            x_image = tf.reshape(self.images, [-1, input_dim, input_dim, 1])
+
+        """@nni.mutable_layers(
+            {
+                layer_choice: [op.conv2d(size=1, in_ch=1, out_ch=self.channel_1_num),
+                               op.conv2d(size=3, in_ch=1, out_ch=self.channel_1_num),
+                               op.twice_conv2d(size=3, in_ch=1, out_ch=self.channel_1_num),
+                               op.twice_conv2d(size=7, in_ch=1, out_ch=self.channel_1_num),
+                               op.dilated_conv(in_ch=1, out_ch=self.channel_1_num),
+                               op.separable_conv(size=3, in_ch=1, out_ch=self.channel_1_num),
+                               op.separable_conv(size=5, in_ch=1, out_ch=self.channel_1_num),
+                               op.separable_conv(size=7, in_ch=1, out_ch=self.channel_1_num)],
+                fixed_inputs: [x_image],
+                layer_output: conv1_out
+            },
+            {
+                layer_choice: [op.post_process(ch_size=self.channel_1_num)],
+                fixed_inputs: [conv1_out],
+                layer_output: post1_out
+            },
+            {
+                layer_choice: [op.max_pool(size=3),
+                               op.max_pool(size=5),
+                               op.max_pool(size=7),
+                               op.avg_pool(size=3),
+                               op.avg_pool(size=5),
+                               op.avg_pool(size=7)],
+                fixed_inputs: [post1_out],
+                layer_output: pool1_out
+            },
+            {
+                layer_choice: [op.conv2d(size=1, in_ch=self.channel_1_num, out_ch=self.channel_2_num),
+                               op.conv2d(size=3, in_ch=self.channel_1_num, out_ch=self.channel_2_num),
+                               op.twice_conv2d(size=3, in_ch=self.channel_1_num, out_ch=self.channel_2_num),
+                               op.twice_conv2d(size=7, in_ch=self.channel_1_num, out_ch=self.channel_2_num),
+                               op.dilated_conv(in_ch=self.channel_1_num, out_ch=self.channel_2_num),
+                               op.separable_conv(size=3, in_ch=self.channel_1_num, out_ch=self.channel_2_num),
+                               op.separable_conv(size=5, in_ch=self.channel_1_num, out_ch=self.channel_2_num),
+                               op.separable_conv(size=7, in_ch=self.channel_1_num, out_ch=self.channel_2_num)],
+                fixed_inputs: [pool1_out],
+                optional_inputs: [post1_out],
+                optional_input_size: [0, 1],
+                layer_output: conv2_out
+            },
+            {
+                layer_choice: [op.post_process(ch_size=self.channel_2_num)],
+                fixed_inputs: [conv2_out],
+                layer_output: post2_out
+            },
+            {
+                layer_choice: [op.max_pool(size=3),
+                               op.max_pool(size=5),
+                               op.max_pool(size=7),
+                               op.avg_pool(size=3),
+                               op.avg_pool(size=5),
+                               op.avg_pool(size=7)],
+                fixed_inputs: [post2_out],
+                optional_inputs: [post1_out, pool1_out],
+                optional_input_size: [0, 1],
+                layer_output: pool2_out
+            }
+        )"""
+
+        # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
+        # is down to 7x7x64 feature maps -- maps this to 1024 features.
+        last_dim_list = pool2_out.get_shape().as_list()
+        assert(last_dim_list[1] == last_dim_list[2])
+        last_dim = last_dim_list[1]
+        with tf.name_scope('fc1'):
+            w_fc1 = op.weight_variable(
+                [last_dim * last_dim * self.channel_2_num, self.hidden_size])
+            b_fc1 = op.bias_variable([self.hidden_size])
+
+        h_pool2_flat = tf.reshape(
+            pool2_out, [-1, last_dim * last_dim * self.channel_2_num])
+        h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1)
+
+        # Dropout - controls the complexity of the model, prevents co-adaptation of features.
+        with tf.name_scope('dropout'):
+            h_fc1_drop = tf.nn.dropout(h_fc1, self.keep_prob)
+
+        # Map the 1024 features to 10 classes, one for each digit
+        with tf.name_scope('fc2'):
+            w_fc2 = op.weight_variable([self.hidden_size, self.y_dim])
+            b_fc2 = op.bias_variable([self.y_dim])
+            y_conv = tf.matmul(h_fc1_drop, w_fc2) + b_fc2
+
+        with tf.name_scope('loss'):
+            cross_entropy = tf.reduce_mean(
+                tf.nn.softmax_cross_entropy_with_logits(labels=self.labels, logits=y_conv))
+        with tf.name_scope('adam_optimizer'):
+            self.train_step = tf.train.AdamOptimizer(
+                self.learning_rate).minimize(cross_entropy)
+
+        with tf.name_scope('accuracy'):
+            correct_prediction = tf.equal(
+                tf.argmax(y_conv, 1), tf.argmax(self.labels, 1))
+            self.accuracy = tf.reduce_mean(
+                tf.cast(correct_prediction, tf.float32))
+
+
+def download_mnist_retry(data_dir, max_num_retries=20):
+    """Try to download mnist dataset and avoid errors"""
+    for _ in range(max_num_retries):
+        try:
+            return input_data.read_data_sets(data_dir, one_hot=True)
+        except tf.errors.AlreadyExistsError:
+            time.sleep(1)
+    raise Exception("Failed to download MNIST.")
+
+def main(params):
+    '''
+    Main function, build mnist network, run and send result to NNI.
+    '''
+    # Import data
+    mnist = download_mnist_retry(params['data_dir'])
+    print('Mnist download data done.')
+    logger.debug('Mnist download data done.')
+
+    # Create the model
+    # Build the graph for the deep net
+    mnist_network = MnistNetwork(channel_1_num=params['channel_1_num'],
+                                 channel_2_num=params['channel_2_num'],
+                                 conv_size=params['conv_size'],
+                                 hidden_size=params['hidden_size'],
+                                 pool_size=params['pool_size'],
+                                 learning_rate=params['learning_rate'])
+    mnist_network.build_network()
+    logger.debug('Mnist build network done.')
+
+    # Write log
+    graph_location = tempfile.mkdtemp()
+    logger.debug('Saving graph to: %s', graph_location)
+    train_writer = tf.summary.FileWriter(graph_location)
+    train_writer.add_graph(tf.get_default_graph())
+
+    test_acc = 0.0
+    with tf.Session() as sess:
+        sess.run(tf.global_variables_initializer())
+        for i in range(params['batch_num']):
+            """@nni.training_update(tf, sess)"""
+            batch = mnist.train.next_batch(params['batch_size'])
+            mnist_network.train_step.run(feed_dict={mnist_network.images: batch[0],
+                                                    mnist_network.labels: batch[1],
+                                                    mnist_network.keep_prob: 1 - params['dropout_rate']}
+                                        )
+
+            if i % 100 == 0:
+                test_acc = mnist_network.accuracy.eval(
+                    feed_dict={mnist_network.images: mnist.test.images,
+                               mnist_network.labels: mnist.test.labels,
+                               mnist_network.keep_prob: 1.0})
+
+                """@nni.report_intermediate_result(test_acc)"""
+                logger.debug('test accuracy %g', test_acc)
+                logger.debug('Pipe send intermediate result done.')
+
+        test_acc = mnist_network.accuracy.eval(
+            feed_dict={mnist_network.images: mnist.test.images,
+                       mnist_network.labels: mnist.test.labels,
+                       mnist_network.keep_prob: 1.0})
+
+        """@nni.report_final_result(test_acc)"""
+        logger.debug('Final result is %g', test_acc)
+        logger.debug('Send final result done.')
+
+def get_params():
+    ''' Get parameters from command line '''
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--data_dir", type=str, default='/tmp/tensorflow/mnist/input_data', help="data directory")
+    parser.add_argument("--dropout_rate", type=float, default=0.5, help="dropout rate")
+    parser.add_argument("--channel_1_num", type=int, default=32)
+    parser.add_argument("--channel_2_num", type=int, default=64)
+    parser.add_argument("--conv_size", type=int, default=5)
+    parser.add_argument("--pool_size", type=int, default=2)
+    parser.add_argument("--hidden_size", type=int, default=1024)
+    parser.add_argument("--learning_rate", type=float, default=1e-4)
+    parser.add_argument("--batch_num", type=int, default=2000)
+    parser.add_argument("--batch_size", type=int, default=32)
+
+    args, _ = parser.parse_known_args()
+    return args
+
+if __name__ == '__main__':
+    try:
+        params = vars(get_params())
+        main(params)
+    except Exception as exception:
+        logger.exception(exception)
+        raise

examples/trials/mnist-nas/enas_mode/operators.py

+import tensorflow as tf
+import math
+
+
+def weight_variable(shape):
+    """weight_variable generates a weight variable of a given shape."""
+    initial = tf.truncated_normal(shape, stddev=0.1)
+    return tf.Variable(initial)
+
+def bias_variable(shape):
+    """bias_variable generates a bias variable of a given shape."""
+    initial = tf.constant(0.1, shape=shape)
+    return tf.Variable(initial)
+
+def sum_op(inputs):
+    """sum_op"""
+    fixed_input = inputs[0][0]
+    optional_input = tf.reduce_sum(inputs[1], axis=0)
+    if len(optional_input.get_shape()) < 1:
+        return fixed_input
+    fixed_shape = fixed_input.get_shape().as_list()
+    optional_shape = optional_input.get_shape().as_list()
+    assert fixed_shape[1] == fixed_shape[2]
+    assert optional_shape[1] == optional_shape[2]
+    pool_size = math.ceil(optional_shape[1] / fixed_shape[1])
+    pool_out = tf.nn.avg_pool(optional_input, ksize=[1, pool_size, pool_size, 1], strides=[1, pool_size, pool_size, 1], padding='SAME')
+    conv_matrix = weight_variable([1, 1, optional_shape[3], fixed_shape[3]])
+    conv_out = tf.nn.conv2d(pool_out, conv_matrix, strides=[1, 1, 1, 1], padding='SAME')
+    return fixed_input + conv_out
+
+
+def conv2d(inputs, size=-1, in_ch=-1, out_ch=-1):
+    """conv2d returns a 2d convolution layer with full stride."""
+    x_input = sum_op(inputs)
+    if size in [1, 3]:
+        w_matrix = weight_variable([size, size, in_ch, out_ch])
+        return tf.nn.conv2d(x_input, w_matrix, strides=[1, 1, 1, 1], padding='SAME')
+    else:
+        raise Exception("Unknown filter size: %d." % size)
+
+def twice_conv2d(inputs, size=-1, in_ch=-1, out_ch=-1):
+    """twice_conv2d"""
+    x_input = sum_op(inputs)
+    if size in  [3, 7]:
+        w_matrix1 = weight_variable([1, size, in_ch, int(out_ch/2)])
+        out = tf.nn.conv2d(x_input, w_matrix1, strides=[1, 1, 1, 1], padding='SAME')
+        w_matrix2 = weight_variable([size, 1, int(out_ch/2), out_ch])
+        return tf.nn.conv2d(out, w_matrix2, strides=[1, 1, 1, 1], padding='SAME')
+    else:
+        raise Exception("Unknown filter size: %d." % size)
+
+def dilated_conv(inputs, size=3, in_ch=-1, out_ch=-1):
+    """dilated_conv"""
+    x_input = sum_op(inputs)
+    if size == 3:
+        w_matrix = weight_variable([size, size, in_ch, out_ch])
+        return tf.nn.atrous_conv2d(x_input, w_matrix, rate=2, padding='SAME')
+    else:
+        raise Exception("Unknown filter size: %d." % size)
+
+def separable_conv(inputs, size=-1, in_ch=-1, out_ch=-1):
+    """separable_conv"""
+    x_input = sum_op(inputs)
+    if size in [3, 5, 7]:
+        depth_matrix = weight_variable([size, size, in_ch, 1])
+        point_matrix = weight_variable([1, 1, 1*in_ch, out_ch])
+        return tf.nn.separable_conv2d(x_input, depth_matrix, point_matrix, strides=[1, 1, 1, 1], padding='SAME')
+    else:
+        raise Exception("Unknown filter size: %d." % size)
+
+
+def avg_pool(inputs, size=-1):
+    """avg_pool downsamples a feature map."""
+    x_input = sum_op(inputs)
+    if size in [3, 5, 7]:
+        return tf.nn.avg_pool(x_input, ksize=[1, size, size, 1], strides=[1, 1, 1, 1], padding='SAME')
+    else:
+        raise Exception("Unknown filter size: %d." % size)
+
+def max_pool(inputs, size=-1):
+    """max_pool downsamples a feature map."""
+    x_input = sum_op(inputs)
+    if size in [3, 5, 7]:
+        return tf.nn.max_pool(x_input, ksize=[1, size, size, 1], strides=[1, 1, 1, 1], padding='SAME')
+    else:
+        raise Exception("Unknown filter size: %d." % size)
+
+
+def post_process(inputs, ch_size=-1):
+    """post_process"""
+    x_input = inputs[0][0]
+    bias_matrix = bias_variable([ch_size])
+    return tf.nn.relu(x_input + bias_matrix)

examples/trials/mnist-nas/oneshot_mode/config_oneshot.yml

+authorName: default
+experimentName: example_mnist
+trialConcurrency: 1
+maxExecDuration: 1h
+maxTrialNum: 10
+#choice: local, remote, pai
+trainingServicePlatform: local
+#choice: true, false
+useAnnotation: true
+tuner:
+  codeDir: ../../../tuners/random_nas_tuner
+  classFileName: random_nas_tuner.py
+  className: RandomNASTuner
+trial:
+  command: python3 mnist-oneshot.py
+  codeDir: .
+  gpuNum: 0
+  nasMode: oneshot_mode

examples/trials/mnist-nas/oneshot_mode/mnist-oneshot.py

+"""A deep MNIST classifier using convolutional layers."""
+
+import argparse
+import logging
+import math
+import tempfile
+import time
+
+import tensorflow as tf
+from tensorflow.examples.tutorials.mnist import input_data
+
+import operators as op
+
+FLAGS = None
+
+logger = logging.getLogger('mnist_AutoML')
+
+
+class MnistNetwork(object):
+    '''
+    MnistNetwork is for initializing and building basic network for mnist.
+    '''
+    def __init__(self,
+                 channel_1_num,
+                 channel_2_num,
+                 conv_size,
+                 hidden_size,
+                 pool_size,
+                 learning_rate,
+                 x_dim=784,
+                 y_dim=10):
+        self.channel_1_num = channel_1_num
+        self.channel_2_num = channel_2_num
+        self.conv_size = conv_size
+        self.hidden_size = hidden_size
+        self.pool_size = pool_size
+        self.learning_rate = learning_rate
+        self.x_dim = x_dim
+        self.y_dim = y_dim
+
+        self.images = tf.placeholder(tf.float32, [None, self.x_dim], name='input_x')
+        self.labels = tf.placeholder(tf.float32, [None, self.y_dim], name='input_y')
+        self.keep_prob = tf.placeholder(tf.float32, name='keep_prob')
+
+        self.train_step = None
+        self.accuracy = None
+
+    def build_network(self):
+        '''
+        Building network for mnist, meanwhile specifying its neural architecture search space
+        '''
+
+        # Reshape to use within a convolutional neural net.
+        # Last dimension is for "features" - there is only one here, since images are
+        # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.
+        with tf.name_scope('reshape'):
+            try:
+                input_dim = int(math.sqrt(self.x_dim))
+            except:
+                print(
+                    'input dim cannot be sqrt and reshape. input dim: ' + str(self.x_dim))
+                logger.debug(
+                    'input dim cannot be sqrt and reshape. input dim: %s', str(self.x_dim))
+                raise
+            x_image = tf.reshape(self.images, [-1, input_dim, input_dim, 1])
+
+        """@nni.mutable_layers(
+            {
+                layer_choice: [op.conv2d(size=1, in_ch=1, out_ch=self.channel_1_num),
+                               op.conv2d(size=3, in_ch=1, out_ch=self.channel_1_num),
+                               op.twice_conv2d(size=3, in_ch=1, out_ch=self.channel_1_num),
+                               op.twice_conv2d(size=7, in_ch=1, out_ch=self.channel_1_num),
+                               op.dilated_conv(in_ch=1, out_ch=self.channel_1_num),
+                               op.separable_conv(size=3, in_ch=1, out_ch=self.channel_1_num),
+                               op.separable_conv(size=5, in_ch=1, out_ch=self.channel_1_num),
+                               op.separable_conv(size=7, in_ch=1, out_ch=self.channel_1_num)],
+                fixed_inputs: [x_image],
+                layer_output: conv1_out
+            },
+            {
+                layer_choice: [op.post_process(ch_size=self.channel_1_num)],
+                fixed_inputs: [conv1_out],
+                layer_output: post1_out
+            },
+            {
+                layer_choice: [op.max_pool(size=3),
+                               op.max_pool(size=5),
+                               op.max_pool(size=7),
+                               op.avg_pool(size=3),
+                               op.avg_pool(size=5),
+                               op.avg_pool(size=7)],
+                fixed_inputs: [post1_out],
+                layer_output: pool1_out
+            },
+            {
+                layer_choice: [op.conv2d(size=1, in_ch=self.channel_1_num, out_ch=self.channel_2_num),
+                               op.conv2d(size=3, in_ch=self.channel_1_num, out_ch=self.channel_2_num),
+                               op.twice_conv2d(size=3, in_ch=self.channel_1_num, out_ch=self.channel_2_num),
+                               op.twice_conv2d(size=7, in_ch=self.channel_1_num, out_ch=self.channel_2_num),
+                               op.dilated_conv(in_ch=self.channel_1_num, out_ch=self.channel_2_num),
+                               op.separable_conv(size=3, in_ch=self.channel_1_num, out_ch=self.channel_2_num),
+                               op.separable_conv(size=5, in_ch=self.channel_1_num, out_ch=self.channel_2_num),
+                               op.separable_conv(size=7, in_ch=self.channel_1_num, out_ch=self.channel_2_num)],
+                fixed_inputs: [pool1_out],
+                optional_inputs: [post1_out],
+                optional_input_size: [0, 1],
+                layer_output: conv2_out
+            },
+            {
+                layer_choice: [op.post_process(ch_size=self.channel_2_num)],
+                fixed_inputs: [conv2_out],
+                layer_output: post2_out
+            },
+            {
+                layer_choice: [op.max_pool(size=3),
+                               op.max_pool(size=5),
+                               op.max_pool(size=7),
+                               op.avg_pool(size=3),
+                               op.avg_pool(size=5),
+                               op.avg_pool(size=7)],
+                fixed_inputs: [post2_out],
+                optional_inputs: [post1_out, pool1_out],
+                optional_input_size: [0, 1],
+                layer_output: pool2_out
+            }
+        )"""
+
+        # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
+        # is down to 7x7x64 feature maps -- maps this to 1024 features.
+        last_dim_list = pool2_out.get_shape().as_list()
+        assert(last_dim_list[1] == last_dim_list[2])
+        last_dim = last_dim_list[1]
+        with tf.name_scope('fc1'):
+            w_fc1 = op.weight_variable(
+                [last_dim * last_dim * self.channel_2_num, self.hidden_size])
+            b_fc1 = op.bias_variable([self.hidden_size])
+
+        h_pool2_flat = tf.reshape(
+            pool2_out, [-1, last_dim * last_dim * self.channel_2_num])
+        h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1)
+
+        # Dropout - controls the complexity of the model, prevents co-adaptation of features.
+        with tf.name_scope('dropout'):
+            h_fc1_drop = tf.nn.dropout(h_fc1, self.keep_prob)
+
+        # Map the 1024 features to 10 classes, one for each digit
+        with tf.name_scope('fc2'):
+            w_fc2 = op.weight_variable([self.hidden_size, self.y_dim])
+            b_fc2 = op.bias_variable([self.y_dim])
+            y_conv = tf.matmul(h_fc1_drop, w_fc2) + b_fc2
+
+        with tf.name_scope('loss'):
+            cross_entropy = tf.reduce_mean(
+                tf.nn.softmax_cross_entropy_with_logits(labels=self.labels, logits=y_conv))
+        with tf.name_scope('adam_optimizer'):
+            self.train_step = tf.train.AdamOptimizer(
+                self.learning_rate).minimize(cross_entropy)
+
+        with tf.name_scope('accuracy'):
+            correct_prediction = tf.equal(
+                tf.argmax(y_conv, 1), tf.argmax(self.labels, 1))
+            self.accuracy = tf.reduce_mean(
+                tf.cast(correct_prediction, tf.float32))
+
+
+def download_mnist_retry(data_dir, max_num_retries=20):
+    """Try to download mnist dataset and avoid errors"""
+    for _ in range(max_num_retries):
+        try:
+            return input_data.read_data_sets(data_dir, one_hot=True)
+        except tf.errors.AlreadyExistsError:
+            time.sleep(1)
+    raise Exception("Failed to download MNIST.")
+
+def main(params):
+    '''
+    Main function, build mnist network, run and send result to NNI.
+    '''
+    # Import data
+    mnist = download_mnist_retry(params['data_dir'])
+    print('Mnist download data done.')
+    logger.debug('Mnist download data done.')
+
+    # Create the model
+    # Build the graph for the deep net
+    mnist_network = MnistNetwork(channel_1_num=params['channel_1_num'],
+                                 channel_2_num=params['channel_2_num'],
+                                 conv_size=params['conv_size'],
+                                 hidden_size=params['hidden_size'],
+                                 pool_size=params['pool_size'],
+                                 learning_rate=params['learning_rate'])
+    mnist_network.build_network()
+    logger.debug('Mnist build network done.')
+
+    # Write log
+    graph_location = tempfile.mkdtemp()
+    logger.debug('Saving graph to: %s', graph_location)
+    train_writer = tf.summary.FileWriter(graph_location)
+    train_writer.add_graph(tf.get_default_graph())
+
+    test_acc = 0.0
+    with tf.Session() as sess:
+        sess.run(tf.global_variables_initializer())
+        for i in range(params['batch_num']):
+            batch = mnist.train.next_batch(params['batch_size'])
+            mnist_network.train_step.run(feed_dict={mnist_network.images: batch[0],
+                                                    mnist_network.labels: batch[1],
+                                                    mnist_network.keep_prob: 1 - params['dropout_rate']}
+                                        )
+
+            if i % 100 == 0:
+                test_acc = mnist_network.accuracy.eval(
+                    feed_dict={mnist_network.images: mnist.test.images,
+                               mnist_network.labels: mnist.test.labels,
+                               mnist_network.keep_prob: 1.0})
+
+                """@nni.report_intermediate_result(test_acc)"""
+                logger.debug('test accuracy %g', test_acc)
+                logger.debug('Pipe send intermediate result done.')
+
+        test_acc = mnist_network.accuracy.eval(
+            feed_dict={mnist_network.images: mnist.test.images,
+                       mnist_network.labels: mnist.test.labels,
+                       mnist_network.keep_prob: 1.0})
+
+        """@nni.report_final_result(test_acc)"""
+        logger.debug('Final result is %g', test_acc)
+        logger.debug('Send final result done.')
+
+def get_params():
+    ''' Get parameters from command line '''
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--data_dir", type=str, default='/tmp/tensorflow/mnist/input_data', help="data directory")
+    parser.add_argument("--dropout_rate", type=float, default=0.5, help="dropout rate")
+    parser.add_argument("--channel_1_num", type=int, default=32)
+    parser.add_argument("--channel_2_num", type=int, default=64)
+    parser.add_argument("--conv_size", type=int, default=5)
+    parser.add_argument("--pool_size", type=int, default=2)
+    parser.add_argument("--hidden_size", type=int, default=1024)
+    parser.add_argument("--learning_rate", type=float, default=1e-4)
+    parser.add_argument("--batch_num", type=int, default=2000)
+    parser.add_argument("--batch_size", type=int, default=32)
+
+    args, _ = parser.parse_known_args()
+    return args
+
+if __name__ == '__main__':
+    try:
+        params = vars(get_params())
+        main(params)
+    except Exception as exception:
+        logger.exception(exception)
+        raise

examples/trials/mnist-nas/oneshot_mode/operators.py

+import tensorflow as tf
+import math
+
+
+def weight_variable(shape):
+    """weight_variable generates a weight variable of a given shape."""
+    initial = tf.truncated_normal(shape, stddev=0.1)
+    return tf.Variable(initial)
+
+def bias_variable(shape):
+    """bias_variable generates a bias variable of a given shape."""
+    initial = tf.constant(0.1, shape=shape)
+    return tf.Variable(initial)
+
+def sum_op(inputs):
+    """sum_op"""
+    fixed_input = inputs[0][0]
+    optional_input = tf.concat(inputs[1], axis=3)
+    fixed_shape = fixed_input.get_shape().as_list()
+    optional_shape = optional_input.get_shape().as_list()
+    assert fixed_shape[1] == fixed_shape[2]
+    assert optional_shape[1] == optional_shape[2]
+    pool_size = math.ceil(optional_shape[1] / fixed_shape[1])
+    pool_out = tf.nn.avg_pool(optional_input, ksize=[1, pool_size, pool_size, 1], strides=[1, pool_size, pool_size, 1], padding='SAME')
+    conv_matrix = weight_variable([1, 1, optional_shape[3], fixed_shape[3]])
+    conv_out = tf.nn.conv2d(pool_out, conv_matrix, strides=[1, 1, 1, 1], padding='SAME')
+    return fixed_input + conv_out
+
+
+def conv2d(inputs, size=-1, in_ch=-1, out_ch=-1):
+    """conv2d returns a 2d convolution layer with full stride."""
+    if not inputs[1]:
+        x_input = inputs[0][0]
+    else:
+        x_input = sum_op(inputs)
+    if size in [1, 3]:
+        w_matrix = weight_variable([size, size, in_ch, out_ch])
+        return tf.nn.conv2d(x_input, w_matrix, strides=[1, 1, 1, 1], padding='SAME')
+    else:
+        raise Exception("Unknown filter size: %d." % size)
+
+def twice_conv2d(inputs, size=-1, in_ch=-1, out_ch=-1):
+    """twice_conv2d"""
+    if not inputs[1]:
+        x_input = inputs[0][0]
+    else:
+        x_input = sum_op(inputs)
+    if size in  [3, 7]:
+        w_matrix1 = weight_variable([1, size, in_ch, int(out_ch/2)])
+        out = tf.nn.conv2d(x_input, w_matrix1, strides=[1, 1, 1, 1], padding='SAME')
+        w_matrix2 = weight_variable([size, 1, int(out_ch/2), out_ch])
+        return tf.nn.conv2d(out, w_matrix2, strides=[1, 1, 1, 1], padding='SAME')
+    else:
+        raise Exception("Unknown filter size: %d." % size)
+
+def dilated_conv(inputs, size=3, in_ch=-1, out_ch=-1):
+    """dilated_conv"""
+    if not inputs[1]:
+        x_input = inputs[0][0]
+    else:
+        x_input = sum_op(inputs)
+    if size == 3:
+        w_matrix = weight_variable([size, size, in_ch, out_ch])
+        return tf.nn.atrous_conv2d(x_input, w_matrix, rate=2, padding='SAME')
+    else:
+        raise Exception("Unknown filter size: %d." % size)
+
+def separable_conv(inputs, size=-1, in_ch=-1, out_ch=-1):
+    """separable_conv"""
+    if not inputs[1]:
+        x_input = inputs[0][0]
+    else:
+        x_input = sum_op(inputs)
+    if size in [3, 5, 7]:
+        depth_matrix = weight_variable([size, size, in_ch, 1])
+        point_matrix = weight_variable([1, 1, 1*in_ch, out_ch])
+        return tf.nn.separable_conv2d(x_input, depth_matrix, point_matrix, strides=[1, 1, 1, 1], padding='SAME')
+    else:
+        raise Exception("Unknown filter size: %d." % size)
+
+
+def avg_pool(inputs, size=-1):
+    """avg_pool downsamples a feature map."""
+    if not inputs[1]:
+        x_input = inputs[0][0]
+    else:
+        x_input = sum_op(inputs)
+    if size in [3, 5, 7]:
+        return tf.nn.avg_pool(x_input, ksize=[1, size, size, 1], strides=[1, 1, 1, 1], padding='SAME')
+    else:
+        raise Exception("Unknown filter size: %d." % size)
+
+def max_pool(inputs, size=-1):
+    """max_pool downsamples a feature map."""
+    if not inputs[1]:
+        x_input = inputs[0][0]
+    else:
+        x_input = sum_op(inputs)
+    if size in [3, 5, 7]:
+        return tf.nn.max_pool(x_input, ksize=[1, size, size, 1], strides=[1, 1, 1, 1], padding='SAME')
+    else:
+        raise Exception("Unknown filter size: %d." % size)
+
+
+def post_process(inputs, ch_size=-1):
+    """post_process"""
+    x_input = inputs[0][0]
+    bias_matrix = bias_variable([ch_size])
+    return tf.nn.relu(x_input + bias_matrix)

src/nni_manager/rest_server/restValidationSchemas.ts

@@ -52,7 +52,7 @@ export namespace ValidationSchemas {
                 virtualCluster: joi.string(),
                 shmMB: joi.number(),
                 authFile: joi.string(),
-                nasMode: joi.string().valid('classic_mode', 'enas_mode', 'oneshot_mode'),
+                nasMode: joi.string().valid('classic_mode', 'enas_mode', 'oneshot_mode', 'darts_mode'),
                 worker: joi.object({
                     replicas: joi.number().min(1).required(),
                     image: joi.string().min(1),

src/nni_manager/training_service/pai/paiTrainingService.ts

@@ -77,8 +77,6 @@ class PAITrainingService implements TrainingService {
     private versionCheck: boolean = true;
     private logCollection: string;
     private isMultiPhase: boolean = false;
-    private hdfsCodeDir?: string;
-    private hdfsOutputDir?: string;
 
     constructor() {
         this.log = getLogger();
@@ -154,13 +152,13 @@ class PAITrainingService implements TrainingService {
         //TODO: use HDFS working folder instead
         const trialWorkingFolder: string = path.join(this.expRootDir, 'trials', trialJobId);
         const paiJobName: string = `nni_exp_${this.experimentId}_trial_${trialJobId}`;
-        this.hdfsCodeDir = HDFSClientUtility.getHdfsTrialWorkDir(this.paiClusterConfig.userName, trialJobId);
-        this.hdfsOutputDir = unixPathJoin(this.hdfsCodeDir, 'nnioutput');
+        const hdfsCodeDir: string = HDFSClientUtility.getHdfsTrialWorkDir(this.paiClusterConfig.userName, trialJobId);
+        const hdfsOutputDir: string = unixPathJoin(hdfsCodeDir, 'nnioutput');
 
         const hdfsLogPath : string = String.Format(
             PAI_LOG_PATH_FORMAT,
             this.paiClusterConfig.host,
-            this.hdfsOutputDir
+            hdfsOutputDir
             );
 
         const trialJobDetail: PAITrialJobDetail = new PAITrialJobDetail(
@@ -365,14 +363,6 @@ class PAITrainingService implements TrainingService {
             throw new Error('PAI token is not initialized');
         }
 
-        if (this.hdfsCodeDir === undefined) {
-            throw new Error('hdfsCodeDir is not initialized');
-        }
-
-        if (this.hdfsOutputDir === undefined) {
-            throw new Error('hdfsOutputDir is not initialized');
-        }
-
         if (this.paiRestServerPort === undefined) {
             const restServer: PAIJobRestServer = component.get(PAIJobRestServer);
             this.paiRestServerPort = restServer.clusterRestServerPort;
@@ -401,7 +391,8 @@ class PAITrainingService implements TrainingService {
                 trialForm.hyperParameters.value, { encoding: 'utf8' }
             );
         }
-
+        const hdfsCodeDir: string = HDFSClientUtility.getHdfsTrialWorkDir(this.paiClusterConfig.userName, trialJobId);
+        const hdfsOutputDir: string = unixPathJoin(hdfsCodeDir, 'nnioutput');
         // tslint:disable-next-line: strict-boolean-expressions
         const nniManagerIp: string = this.nniManagerIpConfig ? this.nniManagerIpConfig.nniManagerIp : getIPV4Address();
         const version: string = this.versionCheck ? await getVersion() : '';
@@ -417,7 +408,7 @@ class PAITrainingService implements TrainingService {
             this.paiTrialConfig.command,
             nniManagerIp,
             this.paiRestServerPort,
-            this.hdfsOutputDir,
+            hdfsOutputDir,
             this.paiClusterConfig.host,
             this.paiClusterConfig.userName,
             HDFSClientUtility.getHdfsExpCodeDir(this.paiClusterConfig.userName),
@@ -452,7 +443,7 @@ class PAITrainingService implements TrainingService {
             // Docker image
             this.paiTrialConfig.image,
             // codeDir
-            `$PAI_DEFAULT_FS_URI${this.hdfsCodeDir}`,
+            `$PAI_DEFAULT_FS_URI${hdfsCodeDir}`,
             // PAI Task roles
             paiTaskRoles,
             // Add Virutal Cluster
@@ -463,9 +454,9 @@ class PAITrainingService implements TrainingService {
 
         // Step 2. Upload code files in codeDir onto HDFS
         try {
-            await HDFSClientUtility.copyDirectoryToHdfs(trialLocalTempFolder, this.hdfsCodeDir, this.hdfsClient);
+            await HDFSClientUtility.copyDirectoryToHdfs(trialLocalTempFolder, hdfsCodeDir, this.hdfsClient);
         } catch (error) {
-            this.log.error(`PAI Training service: copy ${this.paiTrialConfig.codeDir} to HDFS ${this.hdfsCodeDir} failed, error is ${error}`);
+            this.log.error(`PAI Training service: copy ${this.paiTrialConfig.codeDir} to HDFS ${hdfsCodeDir} failed, error is ${error}`);
             trialJobDetail.status = 'FAILED';
             deferred.resolve(true);
 

src/sdk/pynni/nni/__init__.py

@@ -23,7 +23,7 @@
 
 from .trial import *
 from .smartparam import *
-from .nas_utils import reload_tensorflow_variables
+from .nas_utils import training_update
 
 class NoMoreTrialError(Exception):
     def __init__(self,ErrorInfo):

src/sdk/pynni/nni/gridsearch_tuner/gridsearch_tuner.py

@@ -97,9 +97,8 @@ class GridSearchTuner(Tuner):
 
     def _parse_quniform(self, param_value):
         '''parse type of quniform parameter and return a list'''
-        low, high, interval = param_value[0], param_value[1], param_value[2]
-        count = int(np.floor((high - low) / interval)) + 1
-        return [low + interval * i for i in range(count)]
+        low, high, q = param_value[0], param_value[1], param_value[2]
+        return np.clip(np.arange(np.round(low/q), np.round(high/q)+1) * q, low, high)
 
     def _parse_randint(self, param_value):
         '''parse type of randint parameter and return a list'''