add structure data preprocessing and training

solutionbox/structured_data/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/structured_data/datalab_solutions/structured_data/__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, local_batch_predict, cloud_batch_predict

solutionbox/structured_data/datalab_solutions/structured_data/_package.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.
+
+
+"""Provides interface for Datalab.
+
+  Datalab will look for functions with the below names:
+     local_preprocess
+     local_train
+     local_predict
+     cloud_preprocess
+     cloud_train
+     cloud_predict
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import datetime
+import logging
+import os
+import shutil
+import subprocess
+import sys
+import tempfile
+import urllib
+
+import tensorflow as tf
+import yaml
+
+import google.cloud.ml as ml
+
+_TF_GS_URL = 'gs://cloud-datalab/deploy/tf/tensorflow-0.12.0rc0-cp27-none-linux_x86_64.whl'
+
+
+def _percent_flags(train_percent=None, eval_percent=None, test_percent=None):
+  """Convert train/eval/test percents into command line flags.
+
+  Args:
+    train_percent: Int in range [0, 100].
+    eval_percent: Int in range [0, 100].
+    train_percent: Int in range [0, 100].
+
+  Returns:
+    Array of strings encoding the command line flags.
+  """
+  train_percent = train_percent or 0
+  eval_percent = eval_percent or 0
+  test_percent = test_percent or 0
+  if train_percent == 0 and eval_percent == 0 and test_percent == 0:
+    percent_flags = []
+  else:
+    percent_flags = ['--train_percent=%s' % str(train_percent),
+                     '--eval_percent=%s' % str(eval_percent),
+                     '--test_percent=%s' % str(test_percent)]
+  return percent_flags
+
+
+def _default_project():
+  import datalab.context
+  context = datalab.context.Context.default()
+  return context.project_id
+
+def _is_in_IPython():
+  try:
+    import IPython
+    return True
+  except ImportError:
+    return False
+
+def _check_transforms_config_file(transforms_config_file):
+  """Check that the transforms file has expected values."""
+  pass
+
+
+def _run_cmd(cmd):
+  output = subprocess.Popen(cmd, shell=True, stderr=subprocess.STDOUT, stdout=subprocess.PIPE)
+
+  while True:
+    line = output.stdout.readline().rstrip()
+    print(line)
+    if line == '' and output.poll() != None:
+      break
+
+def local_preprocess(input_file_path, output_dir, transforms_config_file,
+                     train_percent=None, eval_percent=None, test_percent=None):
+  """Preprocess data locally with Beam.
+
+  Produce output that can be used by training efficiently. Will also split
+  data into three sets (training, eval, and test). {train, eval, test}_percent
+  should be nonnegative integers that sum to 100.
+
+  Args:
+    input_file_path: String. File pattern what will expand into a list of csv
+        files. Preprocessing will automatically slip the data into three sets
+        for training, evaluation, and testing. Can be local or GCS path.
+    output_dir: The output directory to use; can be local or GCS path.
+    transforms_config_file: File path to the config file.
+    train_percent: Int in range [0, 100].
+    eval_percent: Int in range [0, 100].
+    train_percent: Int in range [0, 100].
+  """
+  _check_transforms_config_file(transforms_config_file)
+
+  percent_flags = _percent_flags(train_percent, eval_percent, test_percent)
+  this_folder = os.path.dirname(os.path.abspath(__file__))
+
+  cmd = ['python',
+         os.path.join(this_folder, 'preprocess/preprocess.py'),
+         '--input_file_path=%s' % input_file_path,
+         '--output_dir=%s' % output_dir,
+         '--transforms_config_file=%s' % transforms_config_file] + percent_flags
+
+  print('Local preprocess, running command: %s' % ' '.join(cmd))
+  _run_cmd(' '.join(cmd))
+
+  print('Local preprocessing done.')
+
+
+def cloud_preprocess(input_file_path, output_dir, transforms_config_file,
+                     train_percent=None, eval_percent=None, test_percent=None,
+                     project_id=None, job_name=None):
+  """Preprocess data in the cloud with Dataflow.
+
+  Produce output that can be used by training efficiently. Will also split
+  data into three sets (training, eval, and test). {train, eval, test}_percent
+  should be nonnegative integers that sum to 100.
+
+  Args:
+    input_file_path: String. File pattern what will expand into a list of csv
+        files. Preprocessing will automatically slip the data into three sets
+        for training, evaluation, and testing. Can be local or GCS path.
+    output_dir: The output directory to use; can be local or GCS path.
+    transforms_config_file: File path to the config file.
+    train_percent: Int in range [0, 100].
+    eval_percent: Int in range [0, 100].
+    train_percent: Int in range [0, 100].
+    project_id: String. The GCE project to use. Defaults to the notebook's
+        default project id.
+    job_name: String. Job name as listed on the Dataflow service. If None, a
+        default job name is selected.
+  """
+  _check_transforms_config_file(transforms_config_file)
+
+  percent_flags = _percent_flags(train_percent, eval_percent, test_percent)
+  this_folder = os.path.dirname(os.path.abspath(__file__))
+  project_id = project_id or _default_project()
+  job_name = job_name or ('structured-data-' +
+                          datetime.datetime.now().strftime('%Y%m%d%H%M%S'))
+
+  cmd = ['python',
+         os.path.join(this_folder, 'preprocess/preprocess.py'),
+         '--cloud',
+         '--project_id=%s' % project_id,
+         '--job_name=%s' % job_name,
+         '--input_file_path=%s' % input_file_path,
+         '--output_dir=%s' % output_dir,
+         '--transforms_config_file=%s' % transforms_config_file] + percent_flags
+
+  print('Cloud preprocess, running command: %s' % ' '.join(cmd))
+  _run_cmd(' '.join(cmd))
+
+  print('Cloud preprocessing job submitted.')
+
+  if _is_in_IPython():
+    import IPython
+
+    dataflow_url = ('https://console.developers.google.com/dataflow?project=%s'
+                    % project_id)
+    html = ('<p>Click <a href="%s" target="_blank">here</a> to track '
+            'preprocessing job %s.</p><br/>' % (dataflow_url, job_name))
+    IPython.display.display_html(html, raw=True)
+
+
+
+def local_train(preprocessed_dir, transforms_config_file, output_dir,
+                layer_sizes=None, max_steps=None):
+  """Train model locally.
+  Args:
+    preprocessed_dir: The output directory from preprocessing. Must contain
+        files named features_train*.tfrecord.gz, features_eval*.tfrecord.gz,
+        and metadata.json. Can be local or GCS path.
+    transforms_config_file: File path to the config file.
+    output_dir: Output directory of training.
+    layer_sizes: String. Represents the layers in the connected DNN.
+        If the model type is DNN, this must be set. Example "10 3 2", this will
+        create three DNN layers where the first layer will have 10 nodes, the
+        middle layer will have 3 nodes, and the laster layer will have 2 nodes.
+    max_steps: Int. Number of training steps to perform.
+  """
+  _check_transforms_config_file(transforms_config_file)
+
+  #TODO(brandondutra): allow other flags to be set like batch size/learner rate
+  #TODO(brandondutra): doc someplace that TF>=0.12 and cloudml >-1.7 are needed.
+
+  train_filename = os.path.join(preprocessed_dir, 'features_train*')
+  eval_filename = os.path.join(preprocessed_dir, 'features_eval*')
+  metadata_filename = os.path.join(preprocessed_dir, 'metadata.json')
+  this_folder = os.path.dirname(os.path.abspath(__file__))
+
+  #TODO(brandondutra): remove the cd after b/34221856
+  cmd = ['cd %s &&' % this_folder,
+         'gcloud beta ml local train',
+         '--module-name=trainer.task',
+         '--package-path=trainer',
+         '--',
+         '--train_data_paths=%s' % train_filename,
+         '--eval_data_paths=%s' % eval_filename,
+         '--metadata_path=%s' % metadata_filename,
+         '--output_path=%s' % output_dir,
+         '--transforms_config_file=%s' % transforms_config_file,
+         '--max_steps=%s' % str(max_steps)]
+  if layer_sizes:
+    cmd += ['--layer_sizes %s' % layer_sizes]
+
+  print('Local training, running command: %s' % ' '.join(cmd))
+  _run_cmd(' '.join(cmd))
+  print('Local training done.')
+
+
+def cloud_train(preprocessed_dir, transforms_config_file, output_dir,
+                staging_bucket,
+                layer_sizes=None, max_steps=None, project_id=None,
+                job_name=None, scale_tier='BASIC'):
+  """Train model using CloudML.
+  Args:
+    preprocessed_dir: The output directory from preprocessing. Must contain
+        files named features_train*.tfrecord.gz, features_eval*.tfrecord.gz,
+        and metadata.json.
+    transforms_config_file: File path to the config file.
+    output_dir: Output directory of training.
+    staging_bucket: GCS bucket.
+    layer_sizes: String. Represents the layers in the connected DNN.
+        If the model type is DNN, this must be set. Example "10 3 2", this will
+        create three DNN layers where the first layer will have 10 nodes, the
+        middle layer will have 3 nodes, and the laster layer will have 2 nodes.
+    max_steps: Int. Number of training steps to perform.
+    project_id: String. The GCE project to use. Defaults to the notebook's
+        default project id.
+    job_name: String. Job name as listed on the Dataflow service. If None, a
+        default job name is selected.
+    scale_tier: The CloudML scale tier. CUSTOM tiers are currently not supported
+        in this package. See https://cloud.google.com/ml/reference/rest/v1beta1/projects.jobs#ScaleTier
+  """
+  _check_transforms_config_file(transforms_config_file)
+
+  #TODO(brandondutra): allow other flags to be set like batch size,
+  #   learner rate, custom scale tiers, etc
+  #TODO(brandondutra): doc someplace that TF>=0.12 and cloudml >-1.7 are needed.
+
+  if (not preprocessed_dir.startswith('gs://')
+      or not transforms_config_file.startswith('gs://')
+      or not output_dir.startswith('gs://')):
+    print('ERROR: preprocessed_dir, transforms_config_file, and output_dir '
+          'must all be in GCS.')
+    return
+
+  # Training will fail if there are files in the output folder. Check now and
+  # fail fast.
+  if ml.util._file.glob_files(os.path.join(output_dir, '*')):
+    print('ERROR: output_dir should be empty. Use another folder')
+    return
+
+  #TODO(brandondutra): remove the tf stuff once the cloudml service is past 0.11
+  temp_dir = tempfile.mkdtemp()
+  subprocess.check_call(['gsutil', 'cp', _TF_GS_URL, temp_dir])
+  tf_local_package = os.path.join(temp_dir, os.path.basename(_TF_GS_URL))
+
+  # Buld the training config file.
+  training_config_file_path = tempfile.mkstemp(dir=temp_dir)[1]
+  training_config = {'trainingInput': {'scaleTier': scale_tier}}
+  with open(training_config_file_path, 'w') as f:
+    f.write(yaml.dump(training_config, default_flow_style=False))
+
+  train_filename = os.path.join(preprocessed_dir, 'features_train*')
+  eval_filename = os.path.join(preprocessed_dir, 'features_eval*')
+  metadata_filename = os.path.join(preprocessed_dir, 'metadata.json')
+  this_folder = os.path.dirname(os.path.abspath(__file__))
+  project_id = project_id or _default_project()
+  job_name = job_name or ('structured_data_train_' +
+                          datetime.datetime.now().strftime('%Y%m%d%H%M%S'))
+
+  # TODO(brandondutra): remove the cd after b/34221856
+  cmd = ['cd %s &&' % this_folder,
+         'gcloud beta ml jobs submit training %s' % job_name,
+         '--module-name=trainer.task',
+         '--staging-bucket=%s' % staging_bucket,
+         '--async',
+         '--package-path=%s' % 'trainer', #os.path.join(this_folder, 'trainer'),
+         '--packages=%s' % tf_local_package,
+         '--config=%s' % training_config_file_path,
+         '--',
+         '--train_data_paths=%s' % train_filename,
+         '--eval_data_paths=%s' % eval_filename,
+         '--metadata_path=%s' % metadata_filename,
+         '--output_path=%s' % output_dir,
+         '--transforms_config_file=%s' % transforms_config_file,
+         '--max_steps=%s' % str(max_steps)]
+  if layer_sizes:
+    cmd += ['--layer_sizes %s' % layer_sizes]
+
+  print('CloudML training, running command: %s' % ' '.join(cmd))
+  _run_cmd(' '.join(cmd))
+
+  print('CloudML training job submitted.')
+
+  if _is_in_IPython():
+    import IPython
+
+    dataflow_url = ('https://console.developers.google.com/ml/jobs?project=%s'
+                    % project_id)
+    html = ('<p>Click <a href="%s" target="_blank">here</a> to track '
+            'the training job %s.</p><br/>' % (dataflow_url, job_name))
+    IPython.display.display_html(html, raw=True)
+
+  # Delete the temp files made
+  shutil.rmtree(temp_dir)
+
+
+def local_predict():
+  """Not Implemented Yet."""
+  print('local_predict')
+
+
+def cloud_predict():
+  """Not Implemented Yet."""
+  print('cloud_predict')
+
+
+def local_batch_predict():
+  """Not Implemented Yet."""
+  print('local_batch_predict')
+
+
+def cloud_batch_predict():
+  """Not Implemented Yet."""
+  print('cloud_batch_predict')
+