我可以在 Google Colab 中使用 TensorBoard 吗？

Here is how you can display your models inline on Google Colab. Below is a very simple example that displays a placeholder:

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)

Currently, you cannot run a Tensorboard service on Google Colab the way you run it locally. Also, you cannot export your entire log to your Drive via something like summary_writer = tf.summary.FileWriter('./logs', graph_def=sess.graph_def) so that you could then download it and look at it locally.

EDIT: You probably want to give the official %tensorboard magic a go, available from TensorFlow 1.13 onward.

Prior to the existence of the %tensorboard magic, the standard way to achieve this was to proxy network traffic to the Colab VM using ngrok. A Colab example can be found here.

These are the steps (the code snippets represent cells of type "code" in colab):

1. Get TensorBoard running in the background.
   Inspired by this answer.

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

2. Download and unzip ngrok.
   Replace the link passed to wget with the correct download link for your OS.

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

3. Launch ngrok background process...

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

   ...and retrieve public url. Source

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

Here's an easier way to do the same ngrok tunneling method on Google Colab.

!pip install tensorboardcolab

then,

from tensorboardcolab import TensorBoardColab, TensorBoardColabCallback


tbc=TensorBoardColab()

Assuming you are using Keras:

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

You can read the original post here.

TensorBoard for TensorFlow running on Google Colab using tensorboardcolab. This uses ngrok internally for tunnelling.

1. Install TensorBoardColab

!pip install tensorboardcolab

2. Create a tensorboardcolab object

tbc = TensorBoardColab()

This automatically creates a TensorBoard link that can be used. This Tensorboard is reading the data at './Graph'

3. Create a FileWriter pointing to this location

summary_writer = tbc.get_writer()

tensorboardcolab library has the method that returns FileWriter object pointing to above './Graph' location.

4. Start adding summary information to Event files at './Graph' location using summary_writer object

You can add scalar info or graph or histogram data.

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

I make use of google drive's back-up and sync https://www.google.com/drive/download/backup-and-sync/. The event files, which are prediodically saved in my google drive during training, are automatically synchronised to a folder on my own computer. Let's call this folder logs. To access the visualizations in tensorboard I open the command prompt, navigate to the synchronized google drive folder, and type: tensorboard --logdir=logs.

So, by automatically syncing my drive with my computer (using back-up and sync), I can use tensorboard as if I am training on my own computer.

Edit: Here is a notebook that might be helpful. https://colab.research.google.com/gist/MartijnCa/961c5f4c774930f4bdd32d51829da6f6/tensorboard-with-google-drive-backup-and-sync.ipynb

I tried but did not get the result but when used as below, got the results

import tensorboardcolab as tb
tbc = tb.TensorBoardColab()

after this open the link from the output.

import tensorflow as tf
import numpy as np

Explicitly create a Graph object

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

Complete example :

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'

There is an alternative solution but we have to use TFv2.0 preview. So if you don't have problems with the migration try this:

install tfv2.0 for GPU or CPU (TPU no available yet)

CPU
tf-nightly-2.0-preview
GPU
tf-nightly-gpu-2.0-preview

%%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

import TensorBoard as usual:

from tensorflow.keras.callbacks import TensorBoard

Clean or Create folder where to save the logs (run this lines before run the training fit())

# 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)

Have fun with TensorBoard! :)

%tensorboard --logdir logs/fit

Here the official colab notebook and the repo on github

New TFv2.0 alpha release:

CPU
!pip install -q tensorflow==2.0.0-alpha0 GPU
!pip install -q tensorflow-gpu==2.0.0-alpha0

To join @solver149 answer, here is a simple example how to use TensorBoard in google colab

1.Create the Graph,ex:

a = tf.constant(3.0, dtype=tf.float32)
b = tf.constant(4.0)
total = a + b

2. Install Tensorboard

!pip install tensorboardcolab # to install tensorboeadcolab if it does not it not exist

==> Result in my case :

Requirement already satisfied: tensorboardcolab in /usr/local/lib/python3.6/dist-packages (0.0.22)

3. Use it :)

Fist of all import TensorBoard from tensorboaedcolab (you can use import* to import everything at once), then create your tensorboeardcolab after that attach a writer to it like this :

from tensorboardcolab import *
tbc = TensorBoardColab() # To create a tensorboardcolab object it will automatically creat a link
writer = tbc.get_writer() # To create a FileWriter
writer.add_graph(tf.get_default_graph()) # add the graph
writer.flush()

==> Result

Using TensorFlow backend.


Wait for 8 seconds...
TensorBoard link:
http://cf426c39.ngrok.io

4.Check the given link :D

This example was token from TF guide : TensorBoard.

I tried to show TensorBoard on google colab today,

# 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

'################
do training
'################

# show tensorboard
%tensorboard --logdir logs/fit

here is actual example made by google. https://colab.research.google.com/github/tensorflow/tensorboard/blob/master/docs/r2/get_started.ipynb

TensorBoard works with Google Colab and TensorFlow 2.0

!pip install tensorflow==2.0.0-alpha0
%load_ext tensorboard.notebook

Simple and easiest way I have found so far:

Get setup_google_colab.py file using wget

!wget https://raw.githubusercontent.com/hse-aml/intro-to- dl/master/setup_google_colab.py -O setup_google_colab.py
import setup_google_colab

To run tensorboard in background, expose port and click on the link.
I am assuming that you have proper added value to visualize in your summary and then merge all summaries.

import os
os.system("tensorboard --logdir=./logs --host 0.0.0.0 --port 6006 &")
setup_google_colab.expose_port_on_colab(6006)

After running above statements you will prompted with a link like:

Open https://a1b2c34d5.ngrok.io to access your 6006 port

Refer following git for further help:

https://github.com/MUmarAmanat/MLWithTensorflow/blob/master/colab_tensorboard.ipynb

Yes definitely, using tensorboard in google colab is quite easy. Follow the following steps-

1) Load the tensorboard extension

%load_ext tensorboard.notebook

2) Add it to keras callback

tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)

3) Start tensorboard

%tensorboard — logdir logs

Hope it helps.

Many of the answers here are now obsolete. So will be mine I'm sure in a few weeks. But at the time of this writing all I had to do is run these lines of code from colab. And tensorboard opened up just fine.

%load_ext tensorboard
%tensorboard --logdir logs

Using summary_writer to write log at every epoch in a folder then running the following magic worked for me.

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

2.0 Compatible Answer: Yes, you can use Tensorboard in Google Colab. Please find the below code which shows the complete example.

!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

For the working copy of Google Colab, please refer this link. For more information, please go through this link.

Try this, it's working for me

%load_ext tensorboard
import datetime
logdir = os.path.join("logs", datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)


model.fit(x=x_train,
y=y_train,
epochs=5,
validation_data=(x_test, y_test),
callbacks=[tensorboard_callback])

You can directly connect to tensorboard in google colab using the recent upgrade from google colab.

https://medium.com/@today.rafi/tensorboard-in-google-colab-bd49fa554f9b

According to the documentation all you need to do is this:

%load_ext tensorboard
!rm -rf ./logs/ #to delete previous runs
%tensorboard --logdir logs/
tensorboard = TensorBoard(log_dir="./logs")

And just call it in the fit method:

model.fit(X_train, y_train, epochs = 1000,
callbacks=[tensorboard], validation_data=(X_test, y_test))

And that should give you something like this:

I am using tensorflow==1.15.

%load_ext tensorboard
%tensorboard --logdir /content/logs

works for me.

/content/logs

is the path of my logs in google drive.

There's also another way for a bit more control.

From the official tutorial, you could also use:

from tensorboard import notebook
notebook.list() # View open TensorBoard instances. This is not required

Then in the next cell:

# Control TensorBoard display. If no port is provided,
# the most recently launched TensorBoard is used
notebook.display(port=6006, height=1000)

As of 5/22/2022, in the latest version of Colab.

This is the best shortcut per my experiment:

# Load the TensorBoard notebook extension
%load_ext tensorboard


# Directly put the log path in the logdir param
%tensorboard --logdir="vision_model/Xception/20220522-120512/"