我利用了Google Drive的备份并同步https://www.google.com/drive/download/backup-and-sync/ 。这些事件文件在培训期间通常保存在我的Google驱动器中，并自动同步到我自己计算机上的文件夹中。我们将此文件夹称为logs 。要访问tensorboard中的可视化，我打开命令提示符，导航到同步的google drive文件夹，然后键入： tensorboard --logdir=logs 。

因此，通过自动将驱动器与计算机同步（使用备份和同步），我可以像使用自己的计算机进行训练一样使用tensorboard。

编辑：这是一个可能有用的笔记本。 https://colab.research.google.com/gist/MartijnCa/961c5f4c774930f4bdd32d51829da6f6/tensorboard-with-google-drive-backup-and-sync.ipynb

您可以通过以下方式在Google Colab上内嵌显示模型。下面是显示占位符的非常简单的示例：

from IPython.display import clear_output, Image, display, HTML
import tensorflow as tf
import numpy as np
from google.colab import files

def strip_consts(graph_def, max_const_size=32):
    """Strip large constant values from graph_def."""
    strip_def = tf.GraphDef()
    for n0 in graph_def.node:
        n = strip_def.node.add() 
        n.MergeFrom(n0)
        if n.op == 'Const':
            tensor = n.attr['value'].tensor
            size = len(tensor.tensor_content)
            if size > max_const_size:
                tensor.tensor_content = "<stripped %d bytes>"%size
    return strip_def

def show_graph(graph_def, max_const_size=32):
    """Visualize TensorFlow graph."""
    if hasattr(graph_def, 'as_graph_def'):
        graph_def = graph_def.as_graph_def()
    strip_def = strip_consts(graph_def, max_const_size=max_const_size)
    code = """
        <script>
          function load() {{
            document.getElementById("{id}").pbtxt = {data};
          }}
        </script>
        <link rel="import" href="https://tensorboard.appspot.com/tf-graph-basic.build.html" onload=load()>
        <div style="height:600px">
          <tf-graph-basic id="{id}"></tf-graph-basic>
        </div>
    """.format(data=repr(str(strip_def)), id='graph'+str(np.random.rand()))

    iframe = """
        <iframe seamless style="width:1200px;height:620px;border:0" srcdoc="{}"></iframe>
    """.format(code.replace('"', '&quot;'))
    display(HTML(iframe))


"""Create a sample tensor"""
sample_placeholder= tf.placeholder(dtype=tf.float32) 
"""Show it"""
graph_def = tf.get_default_graph().as_graph_def()
show_graph(graph_def)

目前，您无法像在本地运行Tensorboard服务那样在Google Colab上运行该服务。另外，您无法通过summary_writer = tf.summary.FileWriter('./logs', graph_def=sess.graph_def)类的内容将整个日志导出到云端硬盘summary_writer = tf.summary.FileWriter('./logs', graph_def=sess.graph_def)以便随后下载并在本地查看。

我今天尝试在Google colab上展示TensorBoard，

# in case of CPU, you can this line
# !pip install -q tf-nightly-2.0-preview
# in case of GPU, you can use this line
!pip install -q tf-nightly-gpu-2.0-preview

# %load_ext tensorboard.notebook  # not working on 22 Apr
%load_ext tensorboard # you need to use this line instead

import tensorflow as tf

'################
做训练
'################

# show tensorboard
%tensorboard --logdir logs/fit

这是谷歌制作的实际示例。 https://colab.research.google.com/github/tensorflow/tensorboard/blob/master/docs/r2/get_started.ipynb

这是在Google Colab上执行相同的ngrok隧道方法的更简单方法。

!pip install tensorboardcolab

然后，

from tensorboardcolab import TensorBoardColab, TensorBoardColabCallback

tbc=TensorBoardColab()

假设您正在使用Keras：

model.fit(......,callbacks=[TensorBoardColabCallback(tbc)])

您可以在此处阅读原始帖子。

使用tensorboardcolab在Google Colab上运行的TensorFlow的TensorBoard。这在内部使用ngrok进行隧道传输。

1. 安装TensorBoardColab

!pip install tensorboardcolab

2. 创建一个tensorboardcolab对象

tbc = TensorBoardColab()

这会自动创建一个可以使用的TensorBoard链接。这个Tensorboard正在读取'./Graph'中的数据

3. 创建一个指向该位置的FileWriter

summary_writer = tbc.get_writer()

tensorboardcolab库具有返回指向'./Graph'位置上方的FileWriter对象的方法。

4. 开始使用summary_writer对象将摘要信息添加到“ ./Graph”位置的事件文件中

您可以添加标量信息或图形或直方图数据。

参考： https : //github.com/taomanwai/tensorboardcolab

我尝试过但没有得到结果，但是当按如下方式使用时，得到了结果

import tensorboardcolab as tb
tbc = tb.TensorBoardColab()

之后，打开输出中的链接。

import tensorflow as tf
import numpy as np

明确创建一个Graph对象

graph = tf.Graph()
with graph.as_default()

完整的例子：

with tf.name_scope("variables"):
    # Variable to keep track of how many times the graph has been run
    global_step = tf.Variable(0, dtype=tf.int32, name="global_step")

    # Increments the above `global_step` Variable, should be run whenever the graph is run
    increment_step = global_step.assign_add(1)

    # Variable that keeps track of previous output value:
    previous_value = tf.Variable(0.0, dtype=tf.float32, name="previous_value")

# Primary transformation Operations
with tf.name_scope("exercise_transformation"):

    # Separate input layer
    with tf.name_scope("input"):
        # Create input placeholder- takes in a Vector 
        a = tf.placeholder(tf.float32, shape=[None], name="input_placeholder_a")

    # Separate middle layer
    with tf.name_scope("intermediate_layer"):
        b = tf.reduce_prod(a, name="product_b")
        c = tf.reduce_sum(a, name="sum_c")

    # Separate output layer
    with tf.name_scope("output"):
        d = tf.add(b, c, name="add_d")
        output = tf.subtract(d, previous_value, name="output")
        update_prev = previous_value.assign(output)

# Summary Operations
with tf.name_scope("summaries"):
    tf.summary.scalar('output', output)  # Creates summary for output node
    tf.summary.scalar('product of inputs', b, )
    tf.summary.scalar('sum of inputs', c)

# Global Variables and Operations
with tf.name_scope("global_ops"):
    # Initialization Op
    init = tf.initialize_all_variables()
    # Collect all summary Ops in graph
    merged_summaries = tf.summary.merge_all()

# Start a Session, using the explicitly created Graph
sess = tf.Session(graph=graph)

# Open a SummaryWriter to save summaries
writer = tf.summary.FileWriter('./Graph', sess.graph)

# Initialize Variables
sess.run(init)

def run_graph(input_tensor):
    """
    Helper function; runs the graph with given input tensor and saves summaries
    """
    feed_dict = {a: input_tensor}
    output, summary, step = sess.run([update_prev, merged_summaries, increment_step], feed_dict=feed_dict)
    writer.add_summary(summary, global_step=step)


# Run the graph with various inputs
run_graph([2,8])
run_graph([3,1,3,3])
run_graph([8])
run_graph([1,2,3])
run_graph([11,4])
run_graph([4,1])
run_graph([7,3,1])
run_graph([6,3])
run_graph([0,2])
run_graph([4,5,6])

# Writes the summaries to disk
writer.flush()

# Flushes the summaries to disk and closes the SummaryWriter
writer.close()

# Close the session
sess.close()

# To start TensorBoard after running this file, execute the following command:
# $ tensorboard --logdir='./improved_graph'

编辑：您可能想%tensorboard一下从TensorFlow 1.13开始提供的官方%tensorboard魔术 。

在存在%tensorboard魔术之前，实现此目的的标准方法是使用ngrok将网络流量代理到Colab VM。可以在此处找到Colab示例。

这些是步骤（代码段代表colab中类型为“代码”的单元格）：

1. 使TensorBoard在后台运行。
   受到这个答案的启发。

    LOG_DIR = '/tmp/log' get_ipython().system_raw( 'tensorboard --logdir {} --host 0.0.0.0 --port 6006 &' .format(LOG_DIR) ) 

2. 下载并解压缩ngrok 。
   将传递给wget的链接替换为适合您操作系统的正确下载链接。

    ! wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip ! unzip ngrok-stable-linux-amd64.zip 

3. 启动ngrok后台进程...

    get_ipython().system_raw('./ngrok http 6006 &') 

   ...并检索公共网址。 资源

    ! curl -s http://localhost:4040/api/tunnels | python3 -c \ "import sys, json; print(json.load(sys.stdin)['tunnels'][0]['public_url'])" 

2.0兼容答案 ：是的，您可以在Google Colab中使用Tensorboard。请找到以下显示完整示例的代码。

!pip install tensorflow==2.0

import tensorflow as tf
# The function to be traced.
@tf.function
def my_func(x, y):
  # A simple hand-rolled layer.
  return tf.nn.relu(tf.matmul(x, y))

# Set up logging.
logdir = './logs/func'
writer = tf.summary.create_file_writer(logdir)

# Sample data for your function.
x = tf.random.uniform((3, 3))
y = tf.random.uniform((3, 3))

# Bracket the function call with
# tf.summary.trace_on() and tf.summary.trace_export().
tf.summary.trace_on(graph=True, profiler=True)
# Call only one tf.function when tracing.
z = my_func(x, y)
with writer.as_default():
  tf.summary.trace_export(
      name="my_func_trace",
      step=0,
      profiler_outdir=logdir)

%load_ext tensorboard
%tensorboard --logdir ./logs/func

有关Google Colab的工作副本，请参阅此链接 。有关更多信息，请通过此链接 。

现在，这里的许多答案已经过时了。我肯定会在几周后成为我的。但是在撰写本文时，我所要做的就是从colab运行这些代码行。张量板打开就好了。

%load_ext tensorboard
%tensorboard --logdir logs

有一个替代解决方案，但我们必须使用TFv2.0预览。因此，如果您在迁移时没有问题，请尝试以下操作：

为GPU或CPU安装tfv2.0（尚未提供TPU）

中央处理器
tf-nightly-2.0-预览
显卡
tf-nightly-gpu-2.0-预览

%%capture
!pip install -q tf-nightly-gpu-2.0-preview
# Load the TensorBoard notebook extension
# %load_ext tensorboard.notebook # For older versions
%load_ext tensorboard

照常导入TensorBoard：

from tensorflow.keras.callbacks import TensorBoard

清理或创建用于保存日志的文件夹（在运行training fit()之前运行此行）

# Clear any logs from previous runs
import time

!rm -R ./logs/ # rf
log_dir="logs/fit/{}".format(time.strftime("%Y%m%d-%H%M%S", time.gmtime()))
tensorboard = TensorBoard(log_dir=log_dir, histogram_freq=1)

与TensorBoard玩得开心！ :)

%tensorboard --logdir logs/fit

在这里正式colab笔记本和回购在GitHub上

新的TFv2.0 alpha版本：

中央处理器
!pip install -q tensorflow==2.0.0-alpha0 GPU
!pip install -q tensorflow-gpu==2.0.0-alpha0