CODEWITHSUNDEEP
tensorflowtensorboardgoogle-colaboratory
ociule
ociule

Reputation: 1241

Can I use TensorBoard with Google Colab?

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

Upvotes: 113

Views: 93343

Answers (22)

JMA
JMA

Reputation: 1825

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.

Upvotes: 5

RajV
RajV

Reputation: 7180

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

Upvotes: 86

Vincent Yuan
Vincent Yuan

Reputation: 245

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

This is the best shortcut per my experiment:

NOTE: Insert this AFTER the creation of your model

# Load the TensorBoard notebook extension
%load_ext tensorboard

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

Upvotes: 4

DJ6968
DJ6968

Reputation: 109

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'

Upvotes: 7

solver149
solver149

Reputation: 425

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.

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

Upvotes: 12

Joppe Geluykens
Joppe Geluykens

Reputation: 1862

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'])"

Upvotes: 97

Hisan
Hisan

Reputation: 2655

LATEST UPDATE:

You can simply use this in colab now:

%tensorboard --logdir your_path

if you are running it again then use

%reload_ext tensorboard
%tensorboard --logdir your_path

Upvotes: 2

Isaac
Isaac

Reputation: 1536

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)

Upvotes: 1

ziyi liu
ziyi liu

Reputation: 49

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.

Upvotes: 2

szelesaron
szelesaron

Reputation: 69

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 can't post a picture yet so use the link.

Upvotes: 3

rafi
rafi

Reputation: 364

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

Upvotes: 3

Myra
Myra

Reputation: 21

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])

Upvotes: -1

virtualdvid
virtualdvid

Reputation: 2411

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

Upvotes: 2

user11530462
user11530462

Reputation:

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.

Upvotes: 4

Mridul Pandey
Mridul Pandey

Reputation: 402

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

Upvotes: 3

Abhinav Sagar
Abhinav Sagar

Reputation: 35

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.

Upvotes: 1

Naga
Naga

Reputation: 11594

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

Upvotes: 2

Muhammad Umar Amanat
Muhammad Umar Amanat

Reputation: 917

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

Upvotes: -1

mmulibra
mmulibra

Reputation: 345

TensorBoard works with Google Colab and TensorFlow 2.0

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

Upvotes: 1

DINA TAKLIT
DINA TAKLIT

Reputation: 8388

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

Tensorboard_Result_Graph_Image

This example was token from TF guide : TensorBoard.

Upvotes: 1

Martijn Cazemier
Martijn Cazemier

Reputation: 141

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

Upvotes: 4

Keshan
Keshan

Reputation: 14677

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.

Upvotes: 27

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