Issue 1:
System information

Linux Ubuntu 18.04.2 LTS:

TensorFlow installed from tar.gz source file

TensorFlow version: 2.0.0.alpha0

Python version: 3.5.2 (provided in Docker image)

Build inside Docker container (https://www.tensorflow.org/install/source#gpu_support_2)

Bazel version - 0.19.2 (provided in docker image)

GCC/Compiler version (if compiling from source): gcc version 5.4.0 20160609 (Ubuntu 5.4.0-6ubuntu1~16.04.11) (provided in docker image)

CUDA/cuDNN version: /usr/local/cuda-10.0 (provided in docker image)

GPU model and memory: i7 980x - no AVX instruction set so building locally with GPU support

Describe the problem

The bazel build step fails with the following messages:

root@67e84911cbdf:/tensorflow# bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package

Starting local Bazel server and connecting to it...

WARNING: The following configs were expanded more than once: [cuda]. For repeatable flags, repeats are counted twice and may lead to unexpected behavior.

INFO: Repository rule 'build_bazel_rules_swift' returned: {"remote": "https://github.com/bazelbuild/rules_swift.git", "commit": "001736d056d7eae20f1f4da41bc9e6f036857296", "shallow_since": "2019-01-18", "init_submodules": False, "verbose": False, "strip_prefix": "", "patches": [], "patch_tool": "patch", "patch_args": ["-p0"], "patch_cmds": [], "name": "build_bazel_rules_swift"}

ERROR: error loading package '': Extension file not found. Unable to load package for '//tensorflow:version_check.bzl': BUILD file not found on package path

ERROR: error loading package '': Extension file not found. Unable to load package for '//tensorflow:version_check.bzl': BUILD file not found on package path

INFO: Elapsed time: 8.103s

INFO: 0 processes.

FAILED: Build did NOT complete successfully (0 packages loaded)

root@67e84911cbdf:

Provide the exact sequence of commands / steps that you executed before running into the problem

Following the instructions here: https://www.tensorflow.org/install/source#gpu_support_2

NOTE: These instructions work fine for the included tf-1.12 source and I've used them with a different Docker image for tf-1.13/

Start a Docker container using this command to mount a tf-2.0.0 directory within Docker

docker run --runtime=nvidia -it -w /tensorflow -v /home/mark/Docker_Run:/mnt -v /home/mark/CODE/tensorflow-2.0.0-alpha0:/tensorflow_source -e HOST_PERMS="$(id -u):$(id -g)" tensorflow/tensorflow:devel-gpu-py3 bash

This image provides python-3.5.2, bazel-0.19.2 & cuda-10. It has a /tensorflow_src directory with tf-1.12 and nothing in the /tensorflow directory.

Copy the tf-2.0.0-alpha0 code from the external mounted /tensorflow_source directory into the internal /tensorflow directory:

rm -rf /tensorflow

mkdir /tensorflow

cp -R /tensorflow_source/. /tensorflow

Run the configure step accepting all default values

cd /tensorflow

./configure

Run the bazel build step which fails with the message above.

NOTE: I've looked at a lot of Docker images and felt this one had the right stuff other than the alpha0 tensorflow codebase. If there's a better docker image or a better way to get the alpha0 code into the Docker image than mounting it outside of Docker please advise.

Issue 2:
Describe the current behavior

Option values of java_outer_classname in tensorflow/compiler/tf2xla/host_compute_metadata.proto and tensorflow/compiler/tf2xla/tf2xla.proto are the same, result in protoc failure for Java:

> protoc: stdout: . stderr: tensorflow/compiler/xla/rpc/xla_service.proto: warning: Import tensorflow/compiler/xla/xla_data.proto but not used.
tensorflow/core/protobuf/replay_log.proto: warning: Import tensorflow/core/framework/graph.proto but not used.
tensorflow/core/protobuf/replay_log.proto: warning: Import tensorflow/core/protobuf/cluster.proto but not used.
org/tensorflow/tf2xla/Tf2XlaProtos.java: Tried to write the same file twice.

Describe the expected behavior

protoc succeeds without error.

java_outer_classname in tensorflow/compiler/tf2xla/host_compute_metadata.proto may be renamed to HostComputeMetadataProtos

Code to reproduce the issue

Just protoc

Other info / logs