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):
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 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
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'])"