Datalab Inception (image classification) solution.

solutionbox/inception/datalab_solutions/__init__.py

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+# Copyright 2017 Google Inc. 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.
+

solutionbox/inception/datalab_solutions/inception/__init__.py

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+# Copyright 2017 Google Inc. 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.
+
+
+from ._package import local_preprocess, cloud_preprocess, local_train, cloud_train, local_predict, cloud_predict

solutionbox/inception/datalab_solutions/inception/_cloud.py

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+# Copyright 2017 Google Inc. 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.
+
+
+"""Cloud implementation for preprocessing, training and prediction for inception model.
+"""
+
+import apache_beam as beam
+import base64
+import collections
+import datetime
+from googleapiclient import discovery
+import google.cloud.ml as ml
+import logging
+import os
+
+from . import _model
+from . import _preprocess
+from . import _trainer
+from . import _util
+
+
+_CLOUDML_DISCOVERY_URL = 'https://storage.googleapis.com/cloud-ml/discovery/' \
+                         'ml_v1beta1_discovery.json'
+_TF_GS_URL= 'gs://cloud-datalab/deploy/tf/tensorflow-0.12.0rc0-cp27-none-linux_x86_64.whl'
+
+
+class Cloud(object):
+  """Class for cloud training, preprocessing and prediction."""
+
+  def __init__(self, project, checkpoint=None):
+    self._project = project
+    self._checkpoint = checkpoint
+    if self._checkpoint is None:
+      self._checkpoint = _util._DEFAULT_CHECKPOINT_GSURL
+
+  def preprocess(self, input_csvs, labels_file, output_dir, pipeline_option=None):
+    """Cloud preprocessing with Cloud DataFlow."""
+
+    job_name = 'preprocess-inception-' + datetime.datetime.now().strftime('%y%m%d-%H%M%S')
+    options = {
+        'staging_location': os.path.join(output_dir, 'tmp', 'staging'),
+        'temp_location': os.path.join(output_dir, 'tmp'),
+        'job_name': job_name,
+        'project': self._project,
+        'extra_packages': [ml.sdk_location, _util._PACKAGE_GS_URL, _TF_GS_URL],
+        'teardown_policy': 'TEARDOWN_ALWAYS',
+        'no_save_main_session': True
+    }
+    if pipeline_option is not None:
+      options.update(pipeline_option)
+
+    opts = beam.pipeline.PipelineOptions(flags=[], **options)
+    p = beam.Pipeline('DataflowPipelineRunner', options=opts)
+    _preprocess.configure_pipeline(
+        p, self._checkpoint, input_csvs, labels_file, output_dir, job_name)
+    p.run()
+
+  def train(self, labels_file, input_dir, batch_size, max_steps, output_path, credentials,
+            region, scale_tier):
+    """Cloud training with CloudML trainer service."""
+
+    num_classes = len(_util.get_labels(labels_file))
+    job_id = 'inception_train_' + datetime.datetime.now().strftime('%y%m%d_%H%M%S')
+    job_args_dict = {
+      'input_dir': input_dir,
+      'output_path': output_path,
+      'max_steps': max_steps,
+      'batch_size': batch_size,
+      'num_classes': num_classes,
+      'checkpoint': self._checkpoint
+    }
+    # convert job_args from dict to list as service required.
+    job_args = []
+    for k,v in job_args_dict.iteritems():
+      if isinstance(v, list):
+        for item in v:
+
+          job_args.append('--' + k)
+          job_args.append(str(item))
+      else:
+        job_args.append('--' + k)
+        job_args.append(str(v))
+
+    job_request = {
+      'package_uris': _util._PACKAGE_GS_URL,
+      'python_module': 'datalab_solutions.inception.task',
+      'scale_tier': scale_tier,
+      'region': region,
+      'args': job_args
+    }
+    job = {
+      'job_id': job_id,
+      'training_input': job_request,
+    }
+    cloudml = discovery.build('ml', 'v1beta1', discoveryServiceUrl=_CLOUDML_DISCOVERY_URL,
+        credentials=credentials)
+    request = cloudml.projects().jobs().create(body=job,
+                                               parent='projects/' + self._project)
+    request.headers['user-agent'] = 'GoogleCloudDataLab/1.0'
+    job_info = request.execute()
+    return job_info
+
+  def predict(self, model_id, image_files, labels_file, credentials):
+    """Cloud prediction with CloudML prediction service."""
+
+    labels = _util.get_labels(labels_file)
+    data = []
+    for ii, img_file in enumerate(image_files):
+      with ml.util._file.open_local_or_gcs(img_file, 'rb') as f:
+        img = base64.b64encode(f.read())
+      data.append({
+        'key': str(ii),
+        'image_bytes': {'b64': img}
+      })
+    parts = model_id.split('.')
+    if len(parts) != 2:
+      raise Exception('Invalid model name for cloud prediction. Use "model.version".')    
+    full_version_name = ('projects/%s/models/%s/versions/%s' % (self._project, parts[0], parts[1]))
+    api = discovery.build('ml', 'v1beta1', credentials=credentials,
+                          discoveryServiceUrl=_CLOUDML_DISCOVERY_URL)
+    request = api.projects().predict(body={'instances': data}, name=full_version_name)
+    job_results = request.execute()
+    if 'predictions' not in job_results:
+      raise Exception('Invalid response from service. Cannot find "predictions" in response.')
+    predictions = job_results['predictions']
+    labels_and_scores = [(labels[x['prediction']], x['scores'][x['prediction']])
+                         for x in predictions]
+    return labels_and_scores