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tf.contrib.cloud.ConfigureGcsHook

Class ConfigureGcsHook

ConfigureGcsHook configures GCS when used with Estimator/TPUEstimator.

Inherits From: SessionRunHook

View source on GitHub

Example:

sess = tf.compat.v1.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)

__init__

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__init__(
    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.

Methods

after_create_session

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after_create_session(
    session,
    coord
)

after_run

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after_run(
    run_context,
    run_values
)

Called after each call to run().

The run_values argument contains results of requested ops/tensors by before_run().

The run_context argument is the same one send to before_run call. run_context.request_stop() can be called to stop the iteration.

If session.run() raises any exceptions then after_run() is not called.

Args:

  • run_context: A SessionRunContext object.
  • run_values: A SessionRunValues object.

before_run

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before_run(run_context)

Called before each call to run().

You can return from this call a SessionRunArgs object indicating ops or tensors to add to the upcoming run() call. These ops/tensors will be run together with the ops/tensors originally passed to the original run() call. The run args you return can also contain feeds to be added to the run() call.

The run_context argument is a SessionRunContext that provides information about the upcoming run() call: the originally requested op/tensors, the TensorFlow Session.

At this point graph is finalized and you can not add ops.

Args:

  • run_context: A SessionRunContext object.

Returns:

None or a SessionRunArgs object.

begin

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begin()

end

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end(session)

Called at the end of session.

The session argument can be used in case the hook wants to run final ops, such as saving a last checkpoint.

If session.run() raises exception other than OutOfRangeError or StopIteration then end() is not called. Note the difference between end() and after_run() behavior when session.run() raises OutOfRangeError or StopIteration. In that case end() is called but after_run() is not called.

Args:

  • session: A TensorFlow Session that will be soon closed.