Is there any way to use TensorBoard when training a TensorFlow model on Google Colab?
90
votes
19 Answers
89
votes
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):
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) )Download and unzip ngrok.
Replace the link passed towgetwith the correct download link for your OS.! wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip ! unzip ngrok-stable-linux-amd64.zipLaunch 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'])"
56
votes
25
votes
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.
12
votes
TensorBoard for TensorFlow running on Google Colab using tensorboardcolab. This uses ngrok internally for tunnelling.
- Install TensorBoardColab
!pip install tensorboardcolab
- Create a tensorboardcolab object
tbc = TensorBoardColab()
This automatically creates a TensorBoard link that can be used. This Tensorboard is reading the data at './Graph'
- 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.
- Start adding summary information to Event files at './Graph' location using summary_writer object
You can add scalar info or graph or histogram data.
6
votes
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'
5
votes
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('"', '"'))
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.
4
votes
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
4
votes
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.
2
votes
2
votes
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
2
votes
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.
2
votes
2
votes
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
2
votes
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
2
votes
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:
1
votes
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.
0
votes
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
0
votes
-1
votes
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])
