在本地访问远程服务器的tensorboard以及程序展示

如何访问远程服务器上的tensorboard

1.如果你和远程服务器在同一个局域网内，那么直接在浏览器地址栏输入：$remoteserveraddress:6006$,例如：$192.168.1.200:6006$
2.你可以利用ssh端口映射来访问远程服务器的tensorboard
操作命令为：ssh -L 16006:127.0.0.1:6006 username@remote_server_ip
然后在远程执行tensorboard运行后，在浏览器输入：$127.0.0.1:16006$

遇到的问题

在远程服务器上执行之后有可能会报错：ssh tunnel refusing connections with “channel 3: open failed”.

解决方案

在/etc/ssh/sshdconfig文件中将PermitTunnel设为yes。

具体流程

1. 使用tensorboard的代码

import tensorflow as tf
import numpy as np

## prepare the original data
with tf.name_scope('data'):
    x_data = np.random.rand(100).astype(np.float32)
    y_data = 0.3*x_data+0.1


##creat parameters
with tf.name_scope('parameters'):
    with tf.name_scope('weights'):
        weight = tf.Variable(tf.random_uniform([1],-1.0,1.0))
        tf.summary.histogram('weights', weight)
    with tf.name_scope('biases'):
        bias = tf.Variable(tf.zeros([1]))
        tf.summary.histogram('biases', bias)


##get y_prediction
with tf.name_scope('y_prediction'):
    y_prediction = weight*x_data+bias

##compute the loss
with tf.name_scope('loss'):
    loss = tf.reduce_mean(tf.square(y_data-y_prediction))
    tf.summary.scalar('loss', loss)

##creat optimizer
optimizer = tf.train.GradientDescentOptimizer(0.5)

#creat train ,minimize the loss
with tf.name_scope('train'):
    train = optimizer.minimize(loss)

#creat init
with tf.name_scope('init'):
    init = tf.global_variables_initializer()

merged = tf.summary.merge_all()

with tf.Session() as sess:
    sess.run(init)
    summary_writer = tf.summary.FileWriter('logs/', sess.graph)
    ## Loop
    for step in range(101):
        sess.run(train)
    
        if step %10==0 :
            print(step ,'weight:',sess.run(weight),'bias:',sess.run(bias))
            summary_str = sess.run(merged)
            summary_writer.add_summary(summary_str, step)

2. 在当前目录下使用命令：tensorboard --logdir logs/
   注意当前目录中需要包含logs这个生成的文件夹
   3.在浏览器输入对应tensorboard的地址，前文中已经提到
   4.对应显示结果