fix example for enas

cmd/metricscollector/v1beta1/tfevent-metricscollector/Dockerfile.ppc64le

@@ -1,5 +1,4 @@
 FROM ibmcom/tensorflow-ppc64le:2.2.0-py3
-RUN pip install rfc3339 grpcio googleapis-common-protos
 ADD . /usr/src/app/github.com/kubeflow/katib
 WORKDIR /usr/src/app/github.com/kubeflow/katib/cmd/metricscollector/v1beta1/tfevent-metricscollector/
 RUN pip install --no-cache-dir -r requirements.txt

examples/v1beta1/trial-images/enas-cnn-cifar10/Dockerfile.cpu

@@ -5,6 +5,7 @@ ENV TARGET_DIR /opt/enas-cnn-cifar10
 ADD examples/v1beta1/trial-images/enas-cnn-cifar10 ${TARGET_DIR}
 WORKDIR  ${TARGET_DIR}
 
+RUN pip3 install --no-cache-dir -r requirements.txt
 ENV PYTHONPATH ${TARGET_DIR}
 
 RUN chgrp -R 0 ${TARGET_DIR} \

examples/v1beta1/trial-images/enas-cnn-cifar10/RunTrial.py

@@ -1,12 +1,10 @@
-import keras
-import numpy as np
+from tensorflow import keras
 from keras.datasets import cifar10
 from ModelConstructor import ModelConstructor
 from tensorflow.keras.utils import to_categorical
 from tensorflow.python.keras.utils.multi_gpu_utils import multi_gpu_model
 from keras.preprocessing.image import ImageDataGenerator
 import argparse
-import time
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description='TrainingContainer')
@@ -46,7 +44,7 @@
 
     test_model.summary()
     test_model.compile(loss=keras.losses.categorical_crossentropy,
-                       optimizer=keras.optimizers.Adam(lr=1e-3, decay=1e-4),
+                       optimizer=keras.optimizers.Adam(learning_rate=1e-3, decay=1e-4),
                        metrics=['accuracy'])
 
     (x_train, y_train), (x_test, y_test) = cifar10.load_data()
@@ -67,12 +65,12 @@
 
     print(">>> Data Loaded. Training starts.")
     for e in range(num_epochs):
-        print("\nTotal Epoch {}/{}".format(e+1, num_epochs))
-        history = test_model.fit_generator(generator=aug_data_flow,
-                                           steps_per_epoch=int(len(x_train)/128)+1,
-                                           epochs=1, verbose=1,
-                                           validation_data=(x_test, y_test))
-        print("Training-Accuracy={}".format(history.history['acc'][-1]))
+        print("\nTotal Epoch {}/{}".format(e + 1, num_epochs))
+        history = test_model.fit(aug_data_flow,
+                                 steps_per_epoch=int(len(x_train) / 128) + 1,
+                                 epochs=1, verbose=1,
+                                 validation_data=(x_test, y_test))
+        print("Training-Accuracy={}".format(history.history['accuracy'][-1]))
         print("Training-Loss={}".format(history.history['loss'][-1]))
-        print("Validation-Accuracy={}".format(history.history['val_acc'][-1]))
+        print("Validation-Accuracy={}".format(history.history['val_accuracy'][-1]))
         print("Validation-Loss={}".format(history.history['val_loss'][-1]))

examples/v1beta1/trial-images/enas-cnn-cifar10/requirements.txt

@@ -0,0 +1 @@
+scipy>=1.7.2

examples/v1beta1/trial-images/tf-mnist-with-summaries/README.md

@@ -8,4 +8,4 @@ If you want to read more about this example, visit the official
 GitHub repository.
 
 Katib uses this training container in some Experiments, for instance in the
-[TF Event Metrics Collector](../../metrics-collector/tfevent-metrics-collector.yaml#L55-L64).
+[TF Event Metrics Collector](../../metrics-collector/tfevent-metrics-collector.yaml#L42-L49).

examples/v1beta1/trial-images/tf-mnist-with-summaries/input_data.py

@@ -0,0 +1,333 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Functions for downloading and reading MNIST data (deprecated).
+
+This module and all its submodules are deprecated.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import gzip
+import os
+
+import numpy
+from six.moves import urllib
+from six.moves import xrange  # pylint: disable=redefined-builtin
+
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import random_seed
+from tensorflow.python.platform import gfile
+from tensorflow.python.util.deprecation import deprecated
+
+_Datasets = collections.namedtuple('_Datasets', ['train', 'validation', 'test'])
+
+# CVDF mirror of http://yann.lecun.com/exdb/mnist/
+DEFAULT_SOURCE_URL = 'https://storage.googleapis.com/cvdf-datasets/mnist/'
+
+
+def _read32(bytestream):
+  dt = numpy.dtype(numpy.uint32).newbyteorder('>')
+  return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]
+
+
+@deprecated(None, 'Please use tf.data to implement this functionality.')
+def _extract_images(f):
+  """Extract the images into a 4D uint8 numpy array [index, y, x, depth].
+
+  Args:
+    f: A file object that can be passed into a gzip reader.
+
+  Returns:
+    data: A 4D uint8 numpy array [index, y, x, depth].
+
+  Raises:
+    ValueError: If the bytestream does not start with 2051.
+
+  """
+  print('Extracting', f.name)
+  with gzip.GzipFile(fileobj=f) as bytestream:
+    magic = _read32(bytestream)
+    if magic != 2051:
+      raise ValueError('Invalid magic number %d in MNIST image file: %s' %
+                       (magic, f.name))
+    num_images = _read32(bytestream)
+    rows = _read32(bytestream)
+    cols = _read32(bytestream)
+    buf = bytestream.read(rows * cols * num_images)
+    data = numpy.frombuffer(buf, dtype=numpy.uint8)
+    data = data.reshape(num_images, rows, cols, 1)
+    return data
+
+
+@deprecated(None, 'Please use tf.one_hot on tensors.')
+def _dense_to_one_hot(labels_dense, num_classes):
+  """Convert class labels from scalars to one-hot vectors."""
+  num_labels = labels_dense.shape[0]
+  index_offset = numpy.arange(num_labels) * num_classes
+  labels_one_hot = numpy.zeros((num_labels, num_classes))
+  labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
+  return labels_one_hot
+
+
+@deprecated(None, 'Please use tf.data to implement this functionality.')
+def _extract_labels(f, one_hot=False, num_classes=10):
+  """Extract the labels into a 1D uint8 numpy array [index].
+
+  Args:
+    f: A file object that can be passed into a gzip reader.
+    one_hot: Does one hot encoding for the result.
+    num_classes: Number of classes for the one hot encoding.
+
+  Returns:
+    labels: a 1D uint8 numpy array.
+
+  Raises:
+    ValueError: If the bystream doesn't start with 2049.
+  """
+  print('Extracting', f.name)
+  with gzip.GzipFile(fileobj=f) as bytestream:
+    magic = _read32(bytestream)
+    if magic != 2049:
+      raise ValueError('Invalid magic number %d in MNIST label file: %s' %
+                       (magic, f.name))
+    num_items = _read32(bytestream)
+    buf = bytestream.read(num_items)
+    labels = numpy.frombuffer(buf, dtype=numpy.uint8)
+    if one_hot:
+      return _dense_to_one_hot(labels, num_classes)
+    return labels
+
+
+class _DataSet(object):
+  """Container class for a _DataSet (deprecated).
+
+  THIS CLASS IS DEPRECATED.
+  """
+
+  @deprecated(None, 'Please use alternatives such as official/mnist/_DataSet.py'
+              ' from tensorflow/models.')
+  def __init__(self,
+               images,
+               labels,
+               fake_data=False,
+               one_hot=False,
+               dtype=dtypes.float32,
+               reshape=True,
+               seed=None):
+    """Construct a _DataSet.
+
+    one_hot arg is used only if fake_data is true.  `dtype` can be either
+    `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
+    `[0, 1]`.  Seed arg provides for convenient deterministic testing.
+
+    Args:
+      images: The images
+      labels: The labels
+      fake_data: Ignore inages and labels, use fake data.
+      one_hot: Bool, return the labels as one hot vectors (if True) or ints (if
+        False).
+      dtype: Output image dtype. One of [uint8, float32]. `uint8` output has
+        range [0,255]. float32 output has range [0,1].
+      reshape: Bool. If True returned images are returned flattened to vectors.
+      seed: The random seed to use.
+    """
+    seed1, seed2 = random_seed.get_seed(seed)
+    # If op level seed is not set, use whatever graph level seed is returned
+    numpy.random.seed(seed1 if seed is None else seed2)
+    dtype = dtypes.as_dtype(dtype).base_dtype
+    if dtype not in (dtypes.uint8, dtypes.float32):
+      raise TypeError('Invalid image dtype %r, expected uint8 or float32' %
+                      dtype)
+    if fake_data:
+      self._num_examples = 10000
+      self.one_hot = one_hot
+    else:
+      assert images.shape[0] == labels.shape[0], (
+          'images.shape: %s labels.shape: %s' % (images.shape, labels.shape))
+      self._num_examples = images.shape[0]
+
+      # Convert shape from [num examples, rows, columns, depth]
+      # to [num examples, rows*columns] (assuming depth == 1)
+      if reshape:
+        assert images.shape[3] == 1
+        images = images.reshape(images.shape[0],
+                                images.shape[1] * images.shape[2])
+      if dtype == dtypes.float32:
+        # Convert from [0, 255] -> [0.0, 1.0].
+        images = images.astype(numpy.float32)
+        images = numpy.multiply(images, 1.0 / 255.0)
+    self._images = images
+    self._labels = labels
+    self._epochs_completed = 0
+    self._index_in_epoch = 0
+
+  @property
+  def images(self):
+    return self._images
+
+  @property
+  def labels(self):
+    return self._labels
+
+  @property
+  def num_examples(self):
+    return self._num_examples
+
+  @property
+  def epochs_completed(self):
+    return self._epochs_completed
+
+  def next_batch(self, batch_size, fake_data=False, shuffle=True):
+    """Return the next `batch_size` examples from this data set."""
+    if fake_data:
+      fake_image = [1] * 784
+      if self.one_hot:
+        fake_label = [1] + [0] * 9
+      else:
+        fake_label = 0
+      return [fake_image for _ in xrange(batch_size)
+             ], [fake_label for _ in xrange(batch_size)]
+    start = self._index_in_epoch
+    # Shuffle for the first epoch
+    if self._epochs_completed == 0 and start == 0 and shuffle:
+      perm0 = numpy.arange(self._num_examples)
+      numpy.random.shuffle(perm0)
+      self._images = self.images[perm0]
+      self._labels = self.labels[perm0]
+    # Go to the next epoch
+    if start + batch_size > self._num_examples:
+      # Finished epoch
+      self._epochs_completed += 1
+      # Get the rest examples in this epoch
+      rest_num_examples = self._num_examples - start
+      images_rest_part = self._images[start:self._num_examples]
+      labels_rest_part = self._labels[start:self._num_examples]
+      # Shuffle the data
+      if shuffle:
+        perm = numpy.arange(self._num_examples)
+        numpy.random.shuffle(perm)
+        self._images = self.images[perm]
+        self._labels = self.labels[perm]
+      # Start next epoch
+      start = 0
+      self._index_in_epoch = batch_size - rest_num_examples
+      end = self._index_in_epoch
+      images_new_part = self._images[start:end]
+      labels_new_part = self._labels[start:end]
+      return numpy.concatenate((images_rest_part, images_new_part),
+                               axis=0), numpy.concatenate(
+                                   (labels_rest_part, labels_new_part), axis=0)
+    else:
+      self._index_in_epoch += batch_size
+      end = self._index_in_epoch
+      return self._images[start:end], self._labels[start:end]
+
+
+@deprecated(None, 'Please write your own downloading logic.')
+def _maybe_download(filename, work_directory, source_url):
+  """Download the data from source url, unless it's already here.
+
+  Args:
+      filename: string, name of the file in the directory.
+      work_directory: string, path to working directory.
+      source_url: url to download from if file doesn't exist.
+
+  Returns:
+      Path to resulting file.
+  """
+  if not gfile.Exists(work_directory):
+    gfile.MakeDirs(work_directory)
+  filepath = os.path.join(work_directory, filename)
+  if not gfile.Exists(filepath):
+    urllib.request.urlretrieve(source_url, filepath)
+    with gfile.GFile(filepath) as f:
+      size = f.size()
+    print('Successfully downloaded', filename, size, 'bytes.')
+  return filepath
+
+
+@deprecated(None, 'Please use alternatives such as:'
+            ' tensorflow_datasets.load(\'mnist\')')
+def read_data_sets(train_dir,
+                   fake_data=False,
+                   one_hot=False,
+                   dtype=dtypes.float32,
+                   reshape=True,
+                   validation_size=5000,
+                   seed=None,
+                   source_url=DEFAULT_SOURCE_URL):
+  if fake_data:
+
+    def fake():
+      return _DataSet([], [],
+                      fake_data=True,
+                      one_hot=one_hot,
+                      dtype=dtype,
+                      seed=seed)
+
+    train = fake()
+    validation = fake()
+    test = fake()
+    return _Datasets(train=train, validation=validation, test=test)
+
+  if not source_url:  # empty string check
+    source_url = DEFAULT_SOURCE_URL
+
+  train_images_file = 'train-images-idx3-ubyte.gz'
+  train_labels_file = 'train-labels-idx1-ubyte.gz'
+  test_images_file = 't10k-images-idx3-ubyte.gz'
+  test_labels_file = 't10k-labels-idx1-ubyte.gz'
+
+  local_file = _maybe_download(train_images_file, train_dir,
+                               source_url + train_images_file)
+  with gfile.Open(local_file, 'rb') as f:
+    train_images = _extract_images(f)
+
+  local_file = _maybe_download(train_labels_file, train_dir,
+                               source_url + train_labels_file)
+  with gfile.Open(local_file, 'rb') as f:
+    train_labels = _extract_labels(f, one_hot=one_hot)
+
+  local_file = _maybe_download(test_images_file, train_dir,
+                               source_url + test_images_file)
+  with gfile.Open(local_file, 'rb') as f:
+    test_images = _extract_images(f)
+
+  local_file = _maybe_download(test_labels_file, train_dir,
+                               source_url + test_labels_file)
+  with gfile.Open(local_file, 'rb') as f:
+    test_labels = _extract_labels(f, one_hot=one_hot)
+
+  if not 0 <= validation_size <= len(train_images):
+    raise ValueError(
+        'Validation size should be between 0 and {}. Received: {}.'.format(
+            len(train_images), validation_size))
+
+  validation_images = train_images[:validation_size]
+  validation_labels = train_labels[:validation_size]
+  train_images = train_images[validation_size:]
+  train_labels = train_labels[validation_size:]
+
+  options = dict(dtype=dtype, reshape=reshape, seed=seed)
+
+  train = _DataSet(train_images, train_labels, **options)
+  validation = _DataSet(validation_images, validation_labels, **options)
+  test = _DataSet(test_images, test_labels, **options)
+
+  return _Datasets(train=train, validation=validation, test=test)
+