Can I use TensorBoard with Google Colab?

Question

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

Answer 1

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

Answer 2

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 

Answer 3

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])

Answer 4

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.

Answer 5

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

Answer 6

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'])"