GitLab

* begin transfer from contrib fork

more changes to resnet_run_loop

use AUTOTUNE in prefetch

first pass at resnet with functional distribution strategies

fix syntax error

delint

aesthetic tweaks

delint and fix typos

rip multi_gpu flag out of resnet entirely. Subject to saved model load verification

update cifar10 and imagenet tests to reflect that the model function no longer need to know about multi_gpu

fix imagenet test

start addressing PR comments

more PR response work

* misc tweaks

* add a comment

* final pr tweaks

* fix parsers

* Adding tests

* Adding tests

* Repackaging

* Adding logging

* Linting

* Updating name of logging package to avoid overwriting Python builtin logging.

* Updating name of logging package to avoid overwriting Python builtin logging.

* Fix MNIST to allow master=local to work again.

* Update mnist_tpu.py

* Adding export_dir and model saving for Resnet

* Moving to utils for tests

* Adding batch_size

* Adding multi-gpu export warning

* Responding to CR

* Py3 compliance

* move wide_deep parser

* move mnist parsers

* Glint everything

* Adding rcfile and pylinting

* Extra newline

* Few last lints

Use util functions hooks_helper and parser in mnist and wide_deep, and rename epochs_between_eval (from epochs_per_eval) (#3650)

* Adding logging utils

* restore utils

* delete old file

* update inputs and docstrings

* make /official a python module

* remove /utils directory

* Update readme for python path setting

* Change readme texts

In TensorFlow 1.7, the TPUClusterResolver and the TPU RunConfig are changed
to reduce the amount of boilerplate required.

Add an example showing how to train the MNIST model with eager execution
enabled.

(This change requires changes to TensorFlow made after
the 1.6 release branch was cut, i.e., will require a build
from source or TensorFlow 1.7+)

The long path doesn't work for TensorFlow 1.6 because the .python package under cluster_resolver doesn't exist.

The `sparse` version is more efficient anyway.

I'm returning the labels shape [1] instead of []
because tf.accuracy fails otherwise.