Can I use TensorBoard with Google Colab?

tensorboard colab pytorch
colab notebook tensorboard
google colab tensorboard pytorch
how to use tensorboard with colab
colab tensorboard magic
run tensorboard on colab
tensorboard jupyter

Is there any way to use TensorBoard when training a TensorFlow model on Google Colab?

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'
        'tensorboard --logdir {} --host --port 6006 &'
  2. Download and unzip ngrok. Replace the link passed to wget with the correct download link for your OS.

    ! wget
    ! unzip
  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'])"

Quick guide to run TensorBoard in Google Colab, Google Colab is a free to use Jupyter notebook, that allows you to use free Nvidia Tesla T4 GPUs and 12 GB of RAM which you can use it up to 12 hours in row. You learned how to run TensorBoard on a Google Colab notebook and access it on your local machine by leveraging the free ngrok tunneling service. One bonus, alternatively you can run the following code in Colab to use localtunnel instead of ngrok.

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

!pip install tensorboardcolab


from tensorboardcolab import TensorBoardColab, TensorBoardColabCallback


Assuming you are using Keras:,callbacks=[TensorBoardColabCallback(tbc)])

You can read the original post here.

How to use TensorBoard with Google Colab - kuanhoong, This can be helpful for sharing results, integrating TensorBoard into existing workflows, In case you are running a Docker image of Jupyter Notebook server using Some dashboards are not available yet in Colab (such as the profile plugin). This site uses cookies from Google to deliver its services and to analyze traffic. This article is not about tensorboard tutorial. It is about how to use tensorboard in google colab including available methods. If you want to learn more about Tensorboard See this video Hands-on…

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

  1. Install TensorBoardColab

!pip install tensorboardcolab

  1. Create a tensorboardcolab object

tbc = TensorBoardColab()

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

  1. 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.

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

You can add scalar info or graph or histogram data.


Using TensorBoard in Notebooks, Tesla K80 GPU it also gives you a total of 12 GB of ram , and you can use it up to 12 hours in row . In this post I’ll show you two ways you can visualize your PyTorch model training when using Google Colab. The first uses the new Jupyter TensorBoard magic command, and the second uses the library

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

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 =[update_prev, merged_summaries, increment_step], feed_dict=feed_dict)
    writer.add_summary(summary, global_step=step)

# Run the graph with various inputs

# Writes the summaries to disk

# Flushes the summaries to disk and closes the SummaryWriter

# Close the session

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

Begin your Deep Learning project for free (free GPU processing , is a free cloud service and now it supports free GPU! You can: improve your Python programming language coding skills. develop deep learning applications using popular libraries such as Keras, TensorFlow, PyTorch, and OpenCV. TensorBoard can be used directly within notebook experiences such as Colab and Jupyter. This can be helpful for sharing results, integrating TensorBoard into existing workflows, and using

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() 
        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 = """
          function load() {{
            document.getElementById("{id}").pbtxt = {data};
        <link rel="import" href="" onload=load()>
        <div style="height:600px">
          <tf-graph-basic id="{id}"></tf-graph-basic>
    """.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;'))

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

Deep Learning Development with Google Colab, TensorFlow, Keras , 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,​  How can i use Tensorboard in Colab, without keras? Ask Question Asked 10 months ago. Can I use TensorBoard with Google Colab? Related. 62.

Can I use TensorBoard with Google Colab?, Google Colab is a free to use Jupyter notebook, that allows you to use free Nvidia Tesla T4 GPUs and 12 GB of RAM which you can use it up to  Is it possible to use Tensorboard in Colaboratory. Running tensorboard locally shows rich information about model behaviour like loss it possible get same information when working with Colaboratory( ).

How to use TensorBoard with Google Colab –, TensorBoard can be used directly within notebook experiences such as Colab and Jupyter. This can be helpful for sharing results, integrating TensorBoard into​  I'm trying to start Tensorboard in Google Colab, by running the basic tutorial. Unfortunately, running this tutorial on my Macbook Pro in Google Chrome only gives me the message "403.

Using TensorBoard in Notebooks, In this lecture we will discuss how Tensorboard can be operated from Google Colaboratory. A Duration: 12:21 Posted: Nov 18, 2018 To use the GPU you have to change the runtime by clicking Runtime then select change runtime type and select GPU from there. Next step is to link our Google Drive with colab so that we can access files from there, we will also use google drive to save all our logs and models.To mount google drive run these two lines