Remove unused example (#600)

* update README in metis and update RuntimeError

* remove smart params

examples/trials/mnist-smartparam/config.yml

-authorName: default
-experimentName: example_mnist-smartparam
-trialConcurrency: 1
-maxExecDuration: 1h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: local
-#choice: true, false
-useAnnotation: true
-tuner:
-  #choice: TPE, Random, Anneal, Evolution, BatchTuner
-  #SMAC (SMAC should be installed through nnictl)
-  builtinTunerName: TPE
-  classArgs:
-    #choice: maximize, minimize
-    optimize_mode: maximize
-trial:
-  command: python3 mnist.py
-  codeDir: .
-  gpuNum: 0

examples/trials/mnist-smartparam/config_kubeflow.yml

-authorName: default
-experimentName: example_dist
-trialConcurrency: 1
-maxExecDuration: 1h
-maxTrialNum: 10
-#choice: local, remote, pai, kubeflow
-trainingServicePlatform: kubeflow
-#choice: true, false
-useAnnotation: true
-tuner:
-  #choice: TPE, Random, Anneal, Evolution
-  builtinTunerName: TPE
-  classArgs:
-    #choice: maximize, minimize
-    optimize_mode: maximize
-trial:
-  codeDir: .
-  worker:
-    replicas: 1
-    command: python3 mnist.py
-    gpuNum: 0
-    cpuNum: 1
-    memoryMB: 8192
-    image: msranni/nni:latest
-kubeflowConfig:
-  operator: tf-operator
-  apiVersion: v1alpha2
-  storage: nfs
-  nfs:
-    server: 10.10.10.10
-    path: /var/nfs/general
\ No newline at end of file

examples/trials/mnist-smartparam/config_pai.yml

-authorName: default
-experimentName: example_mnist-smartparam
-trialConcurrency: 1
-maxExecDuration: 1h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: pai
-#choice: true, false
-useAnnotation: true
-tuner:
-  #choice: TPE, Random, Anneal, Evolution, BatchTuner
-  #SMAC (SMAC should be installed through nnictl)
-  builtinTunerName: TPE
-  classArgs:
-    #choice: maximize, minimize
-    optimize_mode: maximize
-trial:
-  command: python3 mnist.py
-  codeDir: .
-  gpuNum: 0
-  cpuNum: 1
-  memoryMB: 8196
-  #The docker image to run nni job on pai
-  image: msranni/nni:latest
-  #The hdfs directory to store data on pai, format 'hdfs://host:port/directory'
-  dataDir: hdfs://10.10.10.10:9000/username/nni
-  #The hdfs directory to store output data generated by nni, format 'hdfs://host:port/directory'
-  outputDir: hdfs://10.10.10.10:9000/username/nni
-paiConfig:
-  #The username to login pai
-  userName: username
-  #The password to login pai
-  passWord: password
-  #The host of restful server of pai
-  host: 10.10.10.10
\ No newline at end of file

examples/trials/mnist-smartparam/mnist.py

-"""A deep MNIST classifier using convolutional layers."""
-
-import logging
-import math
-import tempfile
-import tensorflow as tf
-
-from tensorflow.examples.tutorials.mnist import input_data
-
-import nni
-
-FLAGS = None
-
-logger = logging.getLogger('mnist_AutoML')
-
-
-class MnistNetwork(object):
-    '''
-    MnistNetwork is for initlizing and building basic network for mnist.
-    '''
-    def __init__(self,
-                 channel_1_num,
-                 channel_2_num,
-                 pool_size,
-                 x_dim=784,
-                 y_dim=10):
-        self.channel_1_num = channel_1_num
-        self.channel_2_num = channel_2_num
-        self.conv_size = nni.choice(2, 3, 5, 7, name='conv-size')
-        self.hidden_size = nni.choice(124, 512, 1024)  # example: without name
-        self.pool_size = pool_size
-        self.learning_rate = nni.uniform(0.0001, 0.1, name='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
-        '''
-
-        # 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])
-
-        # First convolutional layer - maps one grayscale image to 32 feature maps.
-        with tf.name_scope('conv1'):
-            w_conv1 = weight_variable(
-                [self.conv_size, self.conv_size, 1, self.channel_1_num])
-            b_conv1 = bias_variable([self.channel_1_num])
-            h_conv1 = nni.function_choice(
-                lambda: tf.nn.relu(conv2d(x_image, w_conv1) + b_conv1),
-                lambda: tf.nn.sigmoid(conv2d(x_image, w_conv1) + b_conv1),
-                lambda: tf.nn.tanh(conv2d(x_image, w_conv1) + b_conv1)
-            )  # example: without name
-
-        # Pooling layer - downsamples by 2X.
-        with tf.name_scope('pool1'):
-            h_pool1 = max_pool(h_conv1, self.pool_size)
-            h_pool1 = nni.function_choice(
-                lambda: max_pool(h_conv1, self.pool_size),
-                lambda: avg_pool(h_conv1, self.pool_size),
-                name='h_pool1')
-
-
-        # Second convolutional layer -- maps 32 feature maps to 64.
-        with tf.name_scope('conv2'):
-            w_conv2 = weight_variable([self.conv_size, self.conv_size,
-                                       self.channel_1_num, self.channel_2_num])
-            b_conv2 = bias_variable([self.channel_2_num])
-            h_conv2 = tf.nn.relu(conv2d(h_pool1, w_conv2) + b_conv2)
-
-        # Second pooling layer.
-        with tf.name_scope('pool2'):  # example: another style
-            h_pool2 = max_pool(h_conv2, self.pool_size)
-
-        # 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 = int(input_dim / (self.pool_size * self.pool_size))
-        with tf.name_scope('fc1'):
-            w_fc1 = weight_variable(
-                [last_dim * last_dim * self.channel_2_num, self.hidden_size])
-            b_fc1 = bias_variable([self.hidden_size])
-
-        h_pool2_flat = tf.reshape(
-            h_pool2, [-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 = weight_variable([self.hidden_size, self.y_dim])
-            b_fc2 = 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 conv2d(x_input, w_matrix):
-    """conv2d returns a 2d convolution layer with full stride."""
-    return tf.nn.conv2d(x_input, w_matrix, strides=[1, 1, 1, 1], padding='SAME')
-
-
-def max_pool(x_input, pool_size):
-    """max_pool downsamples a feature map by 2X."""
-    return tf.nn.max_pool(x_input, ksize=[1, pool_size, pool_size, 1],
-                          strides=[1, pool_size, pool_size, 1], padding='SAME')
-
-
-def avg_pool(x_input, pool_size):
-    return tf.nn.avg_pool(x_input, ksize=[1, pool_size, pool_size, 1],
-                          strides=[1, pool_size, pool_size, 1], padding='SAME')
-
-
-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 main(params):
-    '''
-    Main function, build mnist network, run and send result to NNI.
-    '''
-    # Import data
-    mnist = input_data.read_data_sets(params['data_dir'], one_hot=True)
-    print('Mnist download data down.')
-    logger.debug('Mnist download data down.')
-
-    # 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'],
-                                 pool_size=params['pool_size'])
-    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())
-        batch_size = nni.choice(1, 4, 8, 16, 32, name='batch_size')
-        for i in range(2000):
-            batch = mnist.train.next_batch(batch_size)
-            dropout_rate = nni.choice(0.5, 0.9, name='dropout_rate')
-            mnist_network.train_step.run(feed_dict={mnist_network.images: batch[0],
-                                                    mnist_network.labels: batch[1],
-                                                    mnist_network.keep_prob: 1 - 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 generate_defualt_params():
-    '''
-    Generate default parameters for mnist network.
-    '''
-    params = {
-        'data_dir': '/tmp/tensorflow/mnist/input_data',
-        'channel_1_num': 32,
-        'channel_2_num': 64,
-        'pool_size': 2}
-    return params
-
-
-if __name__ == '__main__':
-    try:
-        nni.get_next_parameter()
-        main(generate_defualt_params())
-    except Exception as exception:
-        logger.exception(exception)
-        raise