Sharing Queue between two graphs in tensorflow

Question

Is it possible to share a queue between two graphs in TensorFlow? I'd like to do a kind of bootstrapping to select "hard negative" examples during training.

To speed up the process, I want separate threads for hard negative example selection, and for the training process. The hard negative selection is based on the evaluation of the current model, and it will load its graph from a checkpoint file. The training graph is run on another thread and writes the checkpoint file. The two graphs should share the same queue: the training graph will consume examples and the hard negative selection will produce them.

Answer 1

Currently there's no support for sharing state between different graphs in the open-source version of TensorFlow: each graph runs in a separate session, and each session uses an isolated set of devices.

However, it seems like it would be possible to achieve your goal using a queue in single graph. Simply construct a queue (using e.g. tf.FIFOQueue) and use tf.import_graph_def() to import the graph from the checkpoint file into the current graph. Using the return_elements argument to tf.import_graph_def() you can specify the name of the tensor that will contain the negative examples, and then add a q.enqueue_many() operation to add them to your queue. You would then fork a thread to run the enqueue_many operation in a loop. In your training graph, you can use q.dequeue_many() to get a batch of negative examples, and use them as the input to your training process.