Change: 139516555

Change: 138143557

Change: 137532946

Change: 135698415

1) pip install issue related to recent numpy upgrade
2) OutOfRange package issue caused by recent changes in TF Python code

Change: 133874452

Change: 132313453

Change: 130741928

That is, cover the file with update_version.sh
Also manually update the artifact URL to 0.10.0rc0 to fix the current breakage
in nightly gcs smoke test:
http://ci.tensorflow.org/view/Nightly/job/nightly-gcs-smoke/
Change: 128984024

Change: 128401884

Change: 127027579

Change: 126335170

In response to GitHub issue: https://github.com/tensorflow/tensorflow/issues/1145

Avoids unnecessary installs and reduces docker image sizes.
Change: 126312137

Change: 122571441

Change: 121586635

Version bumping for 0.8.0 final release (#1959)

0.8.0rc0 --> 0.8.0

Change: 120185825

version_test.py: patch can be alphanumeric (e.g., 0rc0)

See README.md for detailed descriptions of the usage of the tools and tests in this changeset.

Three modes of testing are supported:
1) Launch a local Kubernetes (k8s) cluster and run the test suites on it
   (See local_test.sh)
2) Launch a remote k8s cluster on Google Container Engine (GKE) and run the test suite on it
   (See remote_test.sh)
3) Run the test suite on an existing k8s TensorFlow cluster
   (Also see remote_test.sh)

Take the remote test for example, the following steps are performed:
1) Builds a Docker image with gcloud and Kubernetes tools, and the latest TensorFlow pip installed (see Dockerfile)
2) Launches a Docker container based on the said image (see test_distributed.sh)
3) From within the image, authenticate the gcloud user (with credentials files mapped from outside the container), configer the k8s cluster and launch a new k8s container cluster for TensorFlow workers
4) Generate a k8s (yaml) config file and user this yaml file to create a TensorFlow worker cluster consisting of a certian number of parameter servers (ps) and workers. The workers are exposed as external services with public IPs (see dist_test.sh)
5) Run a simple softmax MNIST model on multiple workers, with the model weights and biases located on the ps nodes. Train the models in parallel and observe the final validation cross entropy (see dist_mnist_test.sh)
Change: 117543657