Merge pull request #9 from dcos-labs/example_code

Moved example code into repo.

tf_examples/README.md

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+# TensorFlow Example Scripts
+
+These examples contains TensorFlow scripts for testing/demo purposes. These models are not intended for any other use cases.

tf_examples/benchmarks/alexnet_model.py

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+# Copyright 2017 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.
+# ==============================================================================
+
+"""Alexnet model configuration.
+
+References:
+  Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton
+  ImageNet Classification with Deep Convolutional Neural Networks
+  Advances in Neural Information Processing Systems. 2012
+"""
+
+import model
+
+
+class AlexnetModel(model.Model):
+  """Alexnet cnn model."""
+
+  def __init__(self):
+    super(AlexnetModel, self).__init__('alexnet', 224 + 3, 512, 0.005)
+
+  def add_inference(self, cnn):
+    # Note: VALID requires padding the images by 3 in width and height
+    cnn.conv(64, 11, 11, 4, 4, 'VALID')
+    cnn.mpool(3, 3, 2, 2)
+    cnn.conv(192, 5, 5)
+    cnn.mpool(3, 3, 2, 2)
+    cnn.conv(384, 3, 3)
+    cnn.conv(384, 3, 3)
+    cnn.conv(256, 3, 3)
+    cnn.mpool(3, 3, 2, 2)
+    cnn.reshape([-1, 256 * 6 * 6])
+    cnn.affine(4096)
+    cnn.dropout()
+    cnn.affine(4096)
+    cnn.dropout()

tf_examples/benchmarks/benchmark_storage.py

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+# Copyright 2017 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.
+# ==============================================================================
+"""Provides ways to store benchmark output."""
+
+
+def store_benchmark(data, storage_type=None):
+  """Store benchmark data.
+
+  Args:
+    data: Dictionary mapping from string benchmark name to
+      numeric benchmark value.
+    storage_type: (string) Specifies where to store benchmark
+      result. If storage_type is
+      'cbuild_benchmark_datastore': store outputs in our continuous
+        build datastore. gcloud must be setup in current environment
+        pointing to the project where data will be added.
+  """
+  if storage_type == 'cbuild_benchmark_datastore':
+    try:
+      # pylint: disable=g-import-not-at-top
+      import cbuild_benchmark_storage
+      # pylint: enable=g-import-not-at-top
+    except ImportError:
+      raise ImportError(
+          'Missing cbuild_benchmark_storage.py required for '
+          'benchmark_cloud_datastore option')
+    cbuild_benchmark_storage.upload_to_benchmark_datastore(data)
+  else:
+    assert False, 'unknown storage_type: ' + storage_type

tf_examples/benchmarks/cbuild_benchmark_storage.py

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+# Copyright 2017 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.
+# ==============================================================================
+"""Provides a way to store benchmark results in GCE Datastore.
+
+Datastore client is initialized from current environment.
+Data is stored using the format defined in:
+  https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/test/upload_test_benchmarks_index.yaml
+"""
+from datetime import datetime
+import json
+import os
+import sys
+from google.cloud import datastore
+
+
+_TEST_NAME_ENV_VAR = 'TF_DIST_BENCHMARK_NAME'
+
+
+def upload_to_benchmark_datastore(data, test_name=None, start_time=None):
+  """Use a new datastore.Client to upload data to datastore.
+
+  Create the datastore Entities from that data and upload them to the
+  datastore in a batch using the client connection.
+
+  Args:
+    data: Map from benchmark names to values.
+    test_name: Name of this test. If not specified, name will be set either
+      from TF_DIST_BENCHMARK_NAME environment variable or to default name
+      'TestBenchmark'.
+    start_time: (datetime) Time to record for this test.
+
+  Raises:
+    ValueError: if test_name is not passed in and TF_DIST_BENCHMARK_NAME
+      is not set.
+  """
+  client = datastore.Client()
+
+  if not test_name:
+    if _TEST_NAME_ENV_VAR in os.environ:
+      test_name = os.environ[_TEST_NAME_ENV_VAR]
+    else:
+      raise ValueError(
+          'No test name passed in for benchmarks. '
+          'Either pass a test_name to upload_to_benchmark_datastore or '
+          'set %s environment variable.' % _TEST_NAME_ENV_VAR)
+  test_name = unicode(test_name)
+
+  if not start_time:
+    start_time = datetime.now()
+
+  # Create one Entry Entity for each benchmark entry.  The wall-clock timing is
+  # the attribute to be fetched and displayed.  The full entry information is
+  # also stored as a non-indexed JSON blob.
+  entries = []
+  batch = []
+  for name, value in data.items():
+    e_key = client.key('Entry')
+    e_val = datastore.Entity(e_key, exclude_from_indexes=['info'])
+    entry_map = {'name': name, 'wallTime': value, 'iters': '1'}
+    entries.append(entry_map)
+    e_val.update({
+        'test': test_name,
+        'start': start_time,
+        'entry': unicode(name),
+        'timing': value,
+        'info': unicode(json.dumps(entry_map))
+    })
+    batch.append(e_val)
+
+  # Create the Test Entity containing all the test information as a
+  # non-indexed JSON blob.
+  test_result = json.dumps(
+      {'name': test_name,
+       'startTime': (start_time - datetime(1970, 1, 1)).total_seconds(),
+       'entries': {'entry': entries},
+       'runConfiguration': {'argument': sys.argv[1:]}})
+  t_key = client.key('Test')
+  t_val = datastore.Entity(t_key, exclude_from_indexes=['info'])
+  t_val.update({
+      'test': test_name,
+      'start': start_time,
+      'info': unicode(test_result)
+  })
+  batch.append(t_val)
+
+  # Put the whole batch of Entities in the datastore.
+  client.put_multi(batch)

tf_examples/benchmarks/cnn_util.py

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+# Copyright 2017 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.
+# ==============================================================================
+
+"""Utilities for CNN benchmarks."""
+
+import tensorflow as tf
+
+
+def tensorflow_version_tuple():
+  v = tf.__version__
+  major, minor, patch = v.split('.')
+  return (int(major), int(minor), patch)
+
+
+def tensorflow_version():
+  vt = tensorflow_version_tuple()
+  return vt[0] * 1000 + vt[1]
+

tf_examples/benchmarks/datasets.py

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+# Copyright 2017 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.
+# ==============================================================================
+
+"""Benchmark dataset utilities.
+"""
+
+from abc import abstractmethod
+import os
+
+import tensorflow as tf
+
+
+class Dataset(object):
+  """Abstract class for cnn benchmarks dataset."""
+
+  def __init__(self, name, data_dir=None):
+    self.name = name
+    if data_dir is None:
+      raise ValueError('Data directory not specified')
+    self.data_dir = data_dir
+
+  def tf_record_pattern(self, subset):
+    return os.path.join(self.data_dir, '%s-*-of-*' % subset)
+
+  def reader(self):
+    return tf.TFRecordReader()
+
+  @abstractmethod
+  def num_classes(self):
+    pass
+
+  @abstractmethod
+  def num_examples_per_epoch(self, subset):
+    pass
+
+  def __str__(self):
+    return self.name
+
+
+class FlowersData(Dataset):
+
+  def __init__(self, data_dir=None):
+    super(FlowersData, self).__init__('Flowers', data_dir)
+
+  def num_classes(self):
+    return 5
+
+  def num_examples_per_epoch(self, subset):
+    if subset == 'train':
+      return 3170
+    elif subset == 'validation':
+      return 500
+    else:
+      raise ValueError('Invalid data subset "%s"' % subset)
+
+
+class ImagenetData(Dataset):
+
+  def __init__(self, data_dir=None):
+    super(ImagenetData, self).__init__('ImageNet', data_dir)
+
+  def num_classes(self):
+    return 1000
+
+  def num_examples_per_epoch(self, subset='train'):
+    if subset == 'train':
+      return 1281167
+    elif subset == 'validation':
+      return 50000
+    else:
+      raise ValueError('Invalid data subset "%s"' % subset)

tf_examples/benchmarks/googlenet_model.py

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+# Copyright 2017 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.
+# ==============================================================================
+
+"""Googlenet model configuration.
+
+References:
+  Szegedy, Christian, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
+  Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich
+  Going deeper with convolutions
+  arXiv preprint arXiv:1409.4842 (2014)
+"""
+
+import model
+
+
+class GooglenetModel(model.Model):
+
+  def __init__(self):
+    super(GooglenetModel, self).__init__('googlenet', 224, 32, 0.005)
+
+  def add_inference(self, cnn):
+    def inception_v1(cnn, k, l, m, n, p, q):
+      cols = [[('conv', k, 1, 1)], [('conv', l, 1, 1), ('conv', m, 3, 3)],
+              [('conv', n, 1, 1), ('conv', p, 5, 5)],
+              [('mpool', 3, 3, 1, 1, 'SAME'), ('conv', q, 1, 1)]]
+      cnn.inception_module('incept_v1', cols)
+
+    cnn.conv(64, 7, 7, 2, 2)
+    cnn.mpool(3, 3, 2, 2, mode='SAME')
+    cnn.conv(64, 1, 1)
+    cnn.conv(192, 3, 3)
+    cnn.mpool(3, 3, 2, 2, mode='SAME')
+    inception_v1(cnn, 64, 96, 128, 16, 32, 32)
+    inception_v1(cnn, 128, 128, 192, 32, 96, 64)
+    cnn.mpool(3, 3, 2, 2, mode='SAME')
+    inception_v1(cnn, 192, 96, 208, 16, 48, 64)
+    inception_v1(cnn, 160, 112, 224, 24, 64, 64)
+    inception_v1(cnn, 128, 128, 256, 24, 64, 64)
+    inception_v1(cnn, 112, 144, 288, 32, 64, 64)
+    inception_v1(cnn, 256, 160, 320, 32, 128, 128)
+    cnn.mpool(3, 3, 2, 2, mode='SAME')
+    inception_v1(cnn, 256, 160, 320, 32, 128, 128)
+    inception_v1(cnn, 384, 192, 384, 48, 128, 128)
+    cnn.apool(7, 7, 1, 1, mode='VALID')
+    cnn.reshape([-1, 1024])