At what stage is a tensorflow graph set up?

Question

An optimizer typically run the same computation graph for many steps until convergence. Does tensorflow setup the graph at the beginning and reuse it for every step? What if

Answer 1

TensorFlow exposes only one graph that is visible to the user, namely the one specified by the user. The user can run the graph with Session.run() or by calling Tensor.eval() on some tensor. A Session.run() call can specify some tensors to be fed and others to be fetched. Depending on what needs to be fetched, the TensorFlow runtime could be internally constructing and optimizing various data structures, including a pruned version of the user visible graph. However, this internal graph is not visible to the user in anyway. No, TensorFlow doesn't 'pre-generate' all possible graphs. Yes, TensorFlow does perform extensive optimizations on the computation graph. And finally, changing the batch size of a tensor that is fed doesn't change the structure of the graph.

Answer 2

As keveman says, from the client's perspective there is a single TensorFlow graph. In the runtime, there can be multiple pruned subgraphs that contain just the nodes that are necessary to compute the values t1, t2 etc. that you fetch when calling sess.run([t1, t2, ...]).

If you call sess.run([t1, t2]) will prune the overall graph (sess.graph) down to the subgraph required to compute those values: i.e. the operations that produce t1 and t2 and all of their antecedents. If you subsequently call sess.run([t3, t4]), the runtime will prune the graph down to the subgraph required to compute t3 and t4. Each time you pass a new combination of values to fetch, TensorFlow will compute a new pruned graph and cache it—this is why the first sess.run() can be somewhat slower than subsequent ones.

If the pruned graphs overlap, TensorFlow will reuse the "kernel" for the ops that are shared. This is relevant because some ops (e.g. tf.Variable and tf.FIFOQueue) are stateful, and their contents can be used in both pruned graphs. This allows you, for example, to initialize your variables with one subgraph (e.g. sess.run(tf.initialize_all_variables())), train them with another (e.g. sess.run(train_op)), and evaluate your model with a third (e.g. sess.run(loss, feed_dict={x: ...})). It also lets you enqueue elements to a queue with one subgraph, and dequeue them with another, which is the foundation of the input pipelines.