gcs_config_ops.py

# 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.
# ==============================================================================
"""GCS file system configuration for TensorFlow."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import json
import os

import tensorflow as tf
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.training import training
from tensorflow_io.core.python.ops import core_ops

# Some GCS operations may be pre-defined and available via tf.contrib in
# earlier TF versions. Because these ops are pre-registered, they will not be
# visible from the _gcs_config_ops library. In this case we use the tf.contrib
# version instead.
tf_v1 = tf.version.VERSION.startswith('1')

if not tf_v1:
  gcs_configure_credentials = core_ops.io_gcs_configure_credentials
  gcs_configure_block_cache = core_ops.io_gcs_configure_block_cache

class BlockCacheParams(object):  # pylint: disable=useless-object-inheritance
  """BlockCacheParams is a struct used for configuring the GCS Block Cache."""

  def __init__(self, block_size=None, max_bytes=None, max_staleness=None):
    self._block_size = block_size or 128 * 1024 * 1024
    self._max_bytes = max_bytes or 2 * self._block_size
    self._max_staleness = max_staleness or 0

  @property
  def block_size(self):
    return self._block_size

  @property
  def max_bytes(self):
    return self._max_bytes

  @property
  def max_staleness(self):
    return self._max_staleness


class ConfigureGcsHook(training.SessionRunHook):
  """ConfigureGcsHook configures GCS when used with Estimator/TPUEstimator.

  Warning: GCS `credentials` may be transmitted over the network unencrypted.
  Please ensure that the network is trusted before using this function. For
  users running code entirely within Google Cloud, your data is protected by
  encryption in between data centers. For more information, please take a look
  at https://cloud.google.com/security/encryption-in-transit/.

  Example:

  ```
  sess = tf.Session()
  refresh_token = raw_input("Refresh token: ")
  client_secret = raw_input("Client secret: ")
  client_id = "<REDACTED>"
  creds = {
      "client_id": client_id,
      "refresh_token": refresh_token,
      "client_secret": client_secret,
      "type": "authorized_user",
  }
  tf.contrib.cloud.configure_gcs(sess, credentials=creds)
  ```

  """

  def _verify_dictionary(self, creds_dict):
    if 'refresh_token' in creds_dict or 'private_key' in creds_dict:
      return True
    return False

  def __init__(self, credentials=None, block_cache=None):
    """Constructs a ConfigureGcsHook.

    Args:
      credentials: A json-formatted string.
      block_cache: A `BlockCacheParams`

    Raises:
      ValueError: If credentials is improperly formatted or block_cache is not a
        BlockCacheParams.
    """
    if credentials is not None:
      if isinstance(credentials, str):
        try:
          data = json.loads(credentials)
        except ValueError as e:
          raise ValueError('credentials was not a well formed JSON string.', e)
        if not self._verify_dictionary(data):
          raise ValueError(
              'credentials has neither a "refresh_token" nor a "private_key" '
              'field.')
      elif isinstance(credentials, dict):
        if not self._verify_dictionary(credentials):
          raise ValueError('credentials has neither a "refresh_token" nor a '
                           '"private_key" field.')
        credentials = json.dumps(credentials)
      else:
        raise ValueError('credentials is of an unknown type')

    self._credentials = credentials

    if block_cache and not isinstance(block_cache, BlockCacheParams):
      raise ValueError('block_cache must be an instance of BlockCacheParams.')
    self._block_cache = block_cache

  def begin(self):
    """Called once before using the session.

    When called, the default graph is the one that will be launched in the
    session.  The hook can modify the graph by adding new operations to it.
    After the `begin()` call the graph will be finalized and the other callbacks
    can not modify the graph anymore. Second call of `begin()` on the same
    graph, should not change the graph.
    """
    if self._credentials:
      self._credentials_placeholder = array_ops.placeholder(dtypes.string)
      self._credentials_op = gcs_configure_credentials(
          self._credentials_placeholder)
    else:
      self._credentials_op = None

    if self._block_cache:
      self._block_cache_op = gcs_configure_block_cache(
          max_cache_size=self._block_cache.max_bytes,
          block_size=self._block_cache.block_size,
          max_staleness=self._block_cache.max_staleness)
    else:
      self._block_cache_op = None

  def after_create_session(self, session, coord):
    """Called when new TensorFlow session is created.

    This is called to signal the hooks that a new session has been created. This
    has two essential differences with the situation in which `begin` is called:

    * When this is called, the graph is finalized and ops can no longer be added
        to the graph.
    * This method will also be called as a result of recovering a wrapped
        session, not only at the beginning of the overall session.

    Args:
      session: A TensorFlow Session that has been created.
      coord: A Coordinator object which keeps track of all threads.
    """
    del coord
    if self._credentials_op:
      session.run(
          self._credentials_op,
          feed_dict={self._credentials_placeholder: self._credentials})
    if self._block_cache_op:
      session.run(self._block_cache_op)

def _configure_gcs_tfv2(credentials=None, block_cache=None, device=None):
  """Configures the GCS file system for a given a session.

  Warning: GCS `credentials` may be transmitted over the network unencrypted.
  Please ensure that the network is trusted before using this function. For
  users running code entirely within Google Cloud, your data is protected by
  encryption in between data centers. For more information, please take a look
  at https://cloud.google.com/security/encryption-in-transit/.

  Args:
    credentials: [Optional.] A JSON string
    block_cache: [Optional.] A BlockCacheParams to configure the block cache .
    device: [Optional.] The device to place the configure ops.
  """
  def configure(credentials, block_cache):
    """Helper function to actually configure GCS."""
    if credentials:
      if isinstance(credentials, dict):
        credentials = json.dumps(credentials)
      gcs_configure_credentials(credentials)

    if block_cache:
      gcs_configure_block_cache(
          max_cache_size=block_cache.max_bytes,
          block_size=block_cache.block_size,
          max_staleness=block_cache.max_staleness)

  if device:
    with ops.device(device):
      return configure(credentials, block_cache)
  return configure(credentials, block_cache)

def _configure_colab_session_tfv2():
  """ConfigureColabSession configures the GCS file system in Colab.

  Args:
  """
  # Read from the application default credentials (adc).
  adc_filename = os.environ.get(
      'GOOGLE_APPLICATION_CREDENTIALS', '/content/adc.json')
  with open(adc_filename) as f:
    data = json.load(f)
  configure_gcs(credentials=data)


if tf_v1:
  configure_gcs = tf.contrib.cloud.configure_gcs
  configure_colab_session = tf.contrib.cloud.configure_colab_session
else:
  configure_gcs = _configure_gcs_tfv2
  configure_colab_session = _configure_colab_session_tfv2