Queues are a powerful mechanism for asynchronous computation using TensorFlow.
Like everything in TensorFlow, a queue is a node in a TensorFlow graph. It's a stateful node, like a variable: other nodes can modify its content. In particular, nodes can enqueue new items in to the queue, or dequeue existing items from the queue.
To get a feel for queues, let's consider a simple example. We will create a
"first in, first out" queue (
FIFOQueue) and fill it with zeros.
Then we'll construct a graph
that takes an item off the queue, adds one to that item, and puts it back on the
end of the queue. Slowly, the numbers on the queue increase.
Dequeue are special nodes. They take a pointer
to the queue instead of a normal value, allowing them to change it. We recommend
you think of these as being like methods of the queue. In fact, in the Python
API, they are methods of the queue object (e.g.
N.B. Queue methods (such as
q.enqueue(...)) must run on the same device
as the queue. Incompatible device placement directives will be ignored when
creating these operations.
Now that you have a bit of a feel for queues, let's dive into the details...
Queue use overview
Queues, such as
RandomShuffleQueue, are important TensorFlow
objects for computing tensors asynchronously in a graph.
For example, a typical input architecture is to use a
prepare inputs for training a model:
- Multiple threads prepare training examples and push them in the queue.
- A training thread executes a training op that dequeues mini-batches from the queue
This architecture has many benefits, as highlighted in the Reading data how to, which also gives an overview of functions that simplify the construction of input pipelines.
Session object is multithreaded, so multiple threads can
easily use the same session and run ops in parallel. However, it is not always
easy to implement a Python program that drives threads as described above. All
threads must be able to stop together, exceptions must be caught and
reported, and queues must be properly closed when stopping.
TensorFlow provides two classes to help:
tf.QueueRunner. These two classes
are designed to be used together. The
Coordinator class helps multiple threads
stop together and report exceptions to a program that waits for them to stop.
QueueRunner class is used to create a number of threads cooperating to
enqueue tensors in the same queue.
The Coordinator class helps multiple threads stop together.
Its key methods are:
should_stop(): returns True if the threads should stop.
request_stop(<exception>): requests that threads should stop.
join(<list of threads>): waits until the specified threads have stopped.
You first create a
Coordinator object, and then create a number of threads
that use the coordinator. The threads typically run loops that stop when
Any thread can decide that the computation should stop. It only has to call
request_stop() and the other threads will stop as
should_stop() will then
# Thread body: loop until the coordinator indicates a stop was requested. # If some condition becomes true, ask the coordinator to stop. def MyLoop(coord): while not coord.should_stop(): ...do something... if ...some condition...: coord.request_stop() # Main code: create a coordinator. coord = Coordinator() # Create 10 threads that run 'MyLoop()' threads = [threading.Thread(target=MyLoop, args=(coord,)) for i in xrange(10)] # Start the threads and wait for all of them to stop. for t in threads: t.start() coord.join(threads)
Obviously, the coordinator can manage threads doing very different things. They don't have to be all the same as in the example above. The coordinator also has support to capture and report exceptions. See the Coordinator class documentation for more details.
QueueRunner class creates a number of threads that repeatedly run an
enqueue op. These threads can use a coordinator to stop together. In
addition, a queue runner runs a closer thread that automatically closes the
queue if an exception is reported to the coordinator.
You can use a queue runner to implement the architecture described above.
First build a graph that uses a
Queue for input examples. Add ops that
process examples and enqueue them in the queue. Add training ops that start by
dequeueing from the queue.
example = ...ops to create one example... # Create a queue, and an op that enqueues examples one at a time in the queue. queue = tf.RandomShuffleQueue(...) enqueue_op = queue.enqueue(example) # Create a training graph that starts by dequeuing a batch of examples. inputs = queue.dequeue_many(batch_size) train_op = ...use 'inputs' to build the training part of the graph...
In the Python training program, create a
QueueRunner that will run a few
threads to process and enqueue examples. Create a
Coordinator and ask the
queue runner to start its threads with the coordinator. Write a training loop
that also uses the coordinator.
# Create a queue runner that will run 4 threads in parallel to enqueue # examples. qr = tf.train.QueueRunner(queue, [enqueue_op] * 4) # Launch the graph. sess = tf.Session() # Create a coordinator, launch the queue runner threads. coord = tf.train.Coordinator() enqueue_threads = qr.create_threads(sess, coord=coord, start=True) # Run the training loop, controlling termination with the coordinator. for step in xrange(1000000): if coord.should_stop(): break sess.run(train_op) # When done, ask the threads to stop. coord.request_stop() # And wait for them to actually do it. coord.join(enqueue_threads)
Threads started by queue runners do more than just run the enqueue ops. They
also catch and handle exceptions generated by queues, including
OutOfRangeError which is used to report that a queue was closed.
A training program that uses a coordinator must similarly catch and report exceptions in its main loop.
Here is an improved version of the training loop above.
try: for step in xrange(1000000): if coord.should_stop(): break sess.run(train_op) except Exception, e: # Report exceptions to the coordinator. coord.request_stop(e) finally: # Terminate as usual. It is innocuous to request stop twice. coord.request_stop() coord.join(threads)