环境
Python 3.5.2
tensorflow : 1.11.0
ubuntu : 16.04

1. 先确认安装tensorboard
   运行v@v:~$ pip3 show tensorflow
   可以看到tensorboard 模块
   tensorboard = tensorboard.main:run_main

v@v:~$ pip3 show tensorflow
---
Metadata-Version: 2.0
Name: tensorflow
Version: 1.11.0
Summary: TensorFlow is an open source machine learning framework for everyone.
Home-page: https://www.tensorflow.org/
Author: Google Inc.
Author-email: opensource@google.com
Installer: pip
License: Apache 2.0
Location: /home/v/.local/lib/python3.5/site-packages
Requires: astor, numpy, six, grpcio, wheel, termcolor, setuptools, protobuf, keras-preprocessing, absl-py, keras-applications, tensorboard, gast
Classifiers:
  Development Status :: 5 - Production/Stable
  Intended Audience :: Developers
  Intended Audience :: Education
  Intended Audience :: Science/Research
  License :: OSI Approved :: Apache Software License
  Programming Language :: Python :: 2
  Programming Language :: Python :: 2.7
  Programming Language :: Python :: 3
  Programming Language :: Python :: 3.4
  Programming Language :: Python :: 3.5
  Programming Language :: Python :: 3.6
  Topic :: Scientific/Engineering
  Topic :: Scientific/Engineering :: Mathematics
  Topic :: Scientific/Engineering :: Artificial Intelligence
  Topic :: Software Development
  Topic :: Software Development :: Libraries
  Topic :: Software Development :: Libraries :: Python Modules
Entry-points:
  [console_scripts]
  freeze_graph = tensorflow.python.tools.freeze_graph:run_main
  saved_model_cli = tensorflow.python.tools.saved_model_cli:main
  tensorboard = tensorboard.main:run_main
  tflite_convert = tensorflow.contrib.lite.python.tflite_convert:main
  toco = tensorflow.contrib.lite.python.tflite_convert:main
  toco_from_protos = tensorflow.contrib.lite.toco.python.toco_from_protos:main
You are using pip version 8.1.1, however version 18.1 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
v@v:~$

1. 测试代码 1_simple.py

import tensorflow as tf

# 定义一个计算图，实现两个向量的减法操作

# 定义两个输入，a为常量，b为变量

a=tf.constant([10.0, 20.0, 40.0], name='a')

b=tf.Variable(tf.random_uniform([3]), name='b')

output=tf.add_n([a,b], name='add')

# 生成一个具有写权限的日志文件操作对象，将当前命名空间的计算图写进日志中

writer=tf.summary.FileWriter('/tmp/logs', tf.get_default_graph())

writer.close()

1. 运行测试代码，日志写入到/tmp/logs
   mkdir /tmp/logs
python3 1_simple.py

2. 运行tensorboard
   python3 -m tensorboard.main --logdir=/tmp/logs
   输出

v@v:~/tutorials/python_tutorial/tensorboard$ python3 -m tensorboard.main --logdir=~/logs
TensorBoard 1.11.0 at http://v:6006 (Press CTRL+C to quit)

1. 浏览器访问http://v:6006，这个地址是第四步输出的

image.png