tf.compat.v1.train.QueueRunner

Holds a list of enqueue operations for a queue, each to be run in a thread.

tf.compat.v1.train.QueueRunner(
    queue=None, enqueue_ops=None, close_op=None, cancel_op=None,
    queue_closed_exception_types=None, queue_runner_def=None, import_scope=None
)

Queues are a convenient TensorFlow mechanism to compute tensors asynchronously using multiple threads. For example in the canonical 'Input Reader' setup one set of threads generates filenames in a queue; a second set of threads read records from the files, processes them, and enqueues tensors on a second queue; a third set of threads dequeues these input records to construct batches and runs them through training operations.

There are several delicate issues when running multiple threads that way: closing the queues in sequence as the input is exhausted, correctly catching and reporting exceptions, etc.

The QueueRunner, combined with the Coordinator, helps handle these issues.

queue A Queue.
enqueue_ops List of enqueue ops to run in threads later.
close_op Op to close the queue. Pending enqueue ops are preserved.
cancel_op Op to close the queue and cancel pending enqueue ops.
queue_closed_exception_types Optional tuple of Exception types that indicate that the queue has been closed when raised during an enqueue operation. Defaults to (tf.errors.OutOfRangeError,). Another common case includes (tf.errors.OutOfRangeError, tf.errors.CancelledError), when some of the enqueue ops may dequeue from other Queues.
queue_runner_def Optional QueueRunnerDef protocol buffer. If specified, recreates the QueueRunner from its contents. queue_runner_def and the other arguments are mutually exclusive.
import_scope Optional string. Name scope to add. Only used when initializing from protocol buffer.

ValueError If both queue_runner_def and queue are both specified.
ValueError If queue or enqueue_ops are not provided when not restoring from queue_runner_def.
RuntimeError If eager execution is enabled.

cancel_op

close_op

enqueue_ops

exceptions_raised Exceptions raised but not handled by the QueueRunner threads.

Exceptions raised in queue runner threads are handled in one of two ways depending on whether or not a Coordinator was passed to create_threads():

  • With a Coordinator, exceptions are reported to the coordinator and forgotten by the QueueRunner.
  • Without a Coordinator, exceptions are captured by the QueueRunner and made available in this exceptions_raised property.
name The string name of the underlying Queue.
queue

queue_closed_exception_types

Methods

create_threads

View source

create_threads(
    sess, coord=None, daemon=False, start=False
)

Create threads to run the enqueue ops for the given session.

This method requires a session in which the graph was launched. It creates a list of threads, optionally starting them. There is one thread for each op passed in enqueue_ops.

The coord argument is an optional coordinator that the threads will use to terminate together and report exceptions. If a coordinator is given, this method starts an additional thread to close the queue when the coordinator requests a stop.

If previously created threads for the given session are still running, no new threads will be created.

Args
sess A Session.
coord Optional Coordinator object for reporting errors and checking stop conditions.
daemon Boolean. If True make the threads daemon threads.
start Boolean. If True starts the threads. If False the caller must call the start() method of the returned threads.

Returns
A list of threads.

from_proto

View source

@staticmethod
from_proto(
    queue_runner_def, import_scope=None
)

Returns a QueueRunner object created from queue_runner_def.

to_proto

View source

to_proto(
    export_scope=None
)

Converts this QueueRunner to a QueueRunnerDef protocol buffer.

Args
export_scope Optional string. Name scope to remove.

Returns
A QueueRunnerDef protocol buffer, or None if the Variable is not in the specified name scope.

eager compatibility

QueueRunners are not compatible with eager execution. Instead, please use tf.data to get data into your model.