What does global_step mean in Tensorflow?

The global_step Variable holds the total number of steps during training across the tasks (each step index will occur only on a single task).

A timeline created by global_step helps us understand know where we are in the grand scheme, from each of the tasks separately. For instance, the loss and accuracy could be plotted against global_step on Tensorboard.

global_step refers to the number of batches seen by the graph. Every time a batch is provided, the weights are updated in the direction that minimizes the loss. global_step just keeps track of the number of batches seen so far. When it is passed in the minimize() argument list, the variable is increased by one. Have a look at optimizer.minimize().

You can get the global_step value using tf.train.global_step(). Also handy are the utility methods tf.train.get_global_step or tf.train.get_or_create_global_step.

0 is the initial value of the global step in this context.