Development Environment: ValueError: Protocol message has no non-repeated submessage field "metadata"

Hi, I cloned the latest tensorboard code, and followed development doc then I run: bazel run //tensorboard/plugins/scalar:scalars_demo to generate fake data, then I run: bazel run //tensorboard -- --logdir /tmp/scalars_demo

Unexpectedly, the terminal show:

INFO: Found 1 target… Target //tensorboard:tensorboard up-to-date: bazel-bin/tensorboard/tensorboard INFO: Elapsed time: 4.033s, Critical Path: 0.44s

INFO: Running command line: bazel-bin/tensorboard/tensorboard --logdir /tmp/scalars_demo Exception in thread Reloader: Traceback (most recent call last): File “/usr/local/Cellar/python/2.7.10_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/threading.py”, line 810, in __bootstrap_inner self.run() File “/usr/local/Cellar/python/2.7.10_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/threading.py”, line 763, in run self.__target(*self.__args, **self.__kwargs) File “/private/var/tmp/_bazel_xxx/3148572edcd824f06744282741008230/execroot/org_tensorflow_tensorboard/bazel-out/darwin_x86_64-fastbuild/bin/tensorboard/tensorboard.runfiles/org_tensorflow_tensorboard/tensorboard/backend/application.py”, line 316, in _reload_forever reload_multiplexer(multiplexer, path_to_run) File “/private/var/tmp/_bazel_xietingwen/3148572edcd824f06744282741008230/execroot/org_tensorflow_tensorboard/bazel-out/darwin_x86_64-fastbuild/bin/tensorboard/tensorboard.runfiles/org_tensorflow_tensorboard/tensorboard/backend/application.py”, line 290, in reload_multiplexer multiplexer.Reload() File “/private/var/tmp/_bazel_xxx/3148572edcd824f06744282741008230/execroot/org_tensorflow_tensorboard/bazel-out/darwin_x86_64-fastbuild/bin/tensorboard/tensorboard.runfiles/org_tensorflow_tensorboard/tensorboard/backend/event_processing/event_multiplexer.py”, line 189, in Reload accumulator.Reload() File “/private/var/tmp/_bazel_xxx/3148572edcd824f06744282741008230/execroot/org_tensorflow_tensorboard/bazel-out/darwin_x86_64-fastbuild/bin/tensorboard/tensorboard.runfiles/org_tensorflow_tensorboard/tensorboard/backend/event_processing/event_accumulator.py”, line 239, in Reload self._ProcessEvent(event) File “/private/var/tmp/_bazel_xietingwen/3148572edcd824f06744282741008230/execroot/org_tensorflow_tensorboard/bazel-out/darwin_x86_64-fastbuild/bin/tensorboard/tensorboard.runfiles/org_tensorflow_tensorboard/tensorboard/backend/event_processing/event_accumulator.py”, line 371, in _ProcessEvent if value.HasField(‘metadata’): File “/private/var/tmp/_bazel_xxx/3148572edcd824f06744282741008230/execroot/org_tensorflow_tensorboard/bazel-out/darwin_x86_64-fastbuild/bin/tensorboard/tensorboard.runfiles/protobuf/python/google/protobuf/internal/python_message.py”, line 810, in HasField raise ValueError(error_msg % field_name) ValueError: Protocol message has no non-repeated submessage field “metadata”

I wonder if it is inconsistent event format between the latest tensorboar and fake data generator. So I run bazel run //tensorboard -- --logdir /tmp/tf_1.2_event_data again, which event data was produced by tensorflow r.12, then it raised the same error.

I want to build a development environment for do some some customization needs, such as multi-tenant, Any one can help me resolve this problem?

By the way, why the DEVELOPMENT.md doc of independent Tensorboard is so simple compared with the Tensorboard of Tensorflow r1.2?

[wchargin]

TensorBoard depends on the latest nightyl version of TensorFlow; it will not work on TensorFlow r1.2. What version of TensorFlow do you have installed in your virtualenv? (Based on the error, i strongly suspect that installing the latest nightly will fix your problem.)

[wchargin]

people using 3rd party plugins will have to reinstall TensorFlow

Correct. I’m not familiar with cuDNN, but maybe?

Once tf1.3 is released (which is soon—1.3rc1 is out), TensorBoard will be able to depend on just “latest TensorFlow.” What we really use is tf.SummaryMetadata, which was introduced in 1.3 and is available in nightlies only (plus 1.3 release candidates) right now.

Would it be better to rely on the most recent stable version of TensorFlow instead that lots of people already use? Just trying to lower the barrier to entry as much as possible.

It should always be the case that the latest TensorBoard on pypi (pip install tensorflow-tensorboard) only depends on the latest version of TensorFlow. It’s just that GitHub TensorBoard master contains lots of things that you as a plugin author need (as you know), and we just haven’t released a new pip package yet.

Does this allay your fears somewhat?