升级到 1.4 后的 Tensorboard 错误:尝试在解析标志之前访问标志 [英] Tensorboard error after upgrading to 1.4: trying to access flag before flags were parsed

问题描述

自从升级到 TF 1.4 后，当我尝试运行 tensorboard 时出现此错误:

Since upgrading to TF 1.4 I am getting this error when I try to run tensorboard:

Traceback (most recent call last):
  File "/opt/python/3.6.3/bin/tensorboard", line 11, in <module>
    sys.exit(main())
  File "/opt/python/3.6.3/lib/python3.6/site-packages/tensorboard/main.py", line 39, in main
    return program.main(default.get_plugins(),
  File "/opt/python/3.6.3/lib/python3.6/site-packages/tensorboard/default.py", line 71, in get_plugins
    debugger = debugger_plugin_loader.get_debugger_plugin()
  File "/opt/python/3.6.3/lib/python3.6/site-packages/tensorboard/plugins/debugger/debugger_plugin_loader.py", line 46, in get_debugger_plugin
    if FLAGS.debugger_data_server_grpc_port is None:
  File "/opt/python/3.6.3/lib/python3.6/site-packages/absl/flags/_flagvalues.py", line 509, in __getattr__
    raise _exceptions.UnparsedFlagAccessError(error_message)
absl.flags._exceptions.UnparsedFlagAccessError: Trying to access flag --debugger_data_server_grpc_port before flags were parsed.

即使只是在没有指定 --logdir 的情况下键入 tensorboard 时，我也会收到此错误，但在我指定日志目录时也是如此.我注意到这已在 5 天前在 github 中报告(https://github.com/tensorflow/nmt/issues/176)，但我很惊讶没有看到更多的人报告这个.

I am getting this error even when just typing tensorboard with no --logdir specified but also when I do specify a log dir. I notice this has been reported in github as of 5 days ago (https://github.com/tensorflow/nmt/issues/176), but I am surprised not to see more folks reporting this.

我还注意到，上周出于同样的原因，我无法运行 Tensorflow RNN 教程，错误还表明在解析之前访问了标志.有没有人遇到过这个问题，你能告诉我是否有解决办法吗?

I also noticed that I was not able to run a Tensorflow RNN tutorial for the same reason last week, with the error also indicating flags were accessed before being parsed. Has anyone run into this and can you tell me if there's a fix?

推荐答案

按照此说明 Github问题，快速解决方法是将 Tensorboard 升级到每晚构建:
pip install --upgrade tb-nightly

As instructed in this Github issue, the quick fix is to upgrade Tensorboard to the nightly build:
pip install --upgrade tb-nightly

正如所解释的，这个问题将在 TensorFlow nightly 20171122 发布后尽快解决.

As also explained this issue will be fixed as soon as soon as TensorFlow nightly 20171122 is released though.

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