it should still fix some issue Probably because the team didn't know of its existence when they started it. FlatBuffers is a relatively new technology, whereas Protocol Buffers has been in use at Google almost since the start, and is used for everything by default.
this will help If you want to have more than 1 version of Tensorflow on your system, you should create virtual environments for each version. You can use virtualenv or anaconda environments.If you want to just use one version, just pip uninstall the other. EDIT Im using a guide from here. If using anaconda virtual environment, create a new environment with a python version x.x of your choice. It will prompt you with all the default python dependencies anaconda will install in your environment.
should help you out Tensorflow uses every available GPU on your system. If you have 2 GPUs and Tensorflow sees both, it will allocate the same memory on both devices and it will use some scheduling algorithm to swap elements from one GPU memory to the other when it's needed.
will help you Technically, there's no global variable scope for all variables. If you run
x = tf.Variable(0.0, name='x')
x = tf.get_variable(name='x')
"""Returns the current variable scope."""
scope = ops.get_collection(_VARSCOPE_KEY)
if scope: # This collection has at most 1 element, the default scope at .
scope = VariableScope(False)
scope = tf.get_variable_scope()
What is the difference between tensorflow on spark with the default distributed tensorflow 1.0?