GitLab

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+)

* Refining preprocessing, part 1

* Refinements to preprocessing resulting from multi-GPU tests

* Reviving one-hot labels

* Reviving one-hot labels

* Fixing label shapes

* Adding random flip back in

* Reverting unnecessary linting of test file

* Respond to CR

* Respond to CR

* Respond to CR

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

* Refactoring input function

* Updating name of variable

The `sparse` version is more efficient anyway.

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

Add multi-GPU option to MNIST

* Using nightly TF docker

* Explicitly pull image

* Update readme

* Respond to CR

The `sparse` version is more efficient anyway.

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

This reverts commit e8726907.

Add multi-GPU flag to MNIST and allow for setting of replicated optimizer and model_fn

Opening gzipped datasets in binary, read-only mode fixes the issue

This is a step towards merging the example in
https://github.com/tensorflow/tpu-demos/tree/master/cloud_tpu/models/mnist
with this repository, so we have a single model definition for
training across CPU/GPU/eager execution/TPU.

The change to dataset.py is so that the raw data can be read
from cloud storage systems (like GCS and S3).