TensorBoard is a tool that provides useful visualization of how the training is going on. Here is how to activate it with OpenNMT.
1- Activating TensorBoard
- For OpenNMT-tf, TensorBoard is enabled by default. For OpenNMT-py, you need to enable TensorBoard, and optionally customize the log directory. Add these lines to the training configuration YAML file.
tensorboard: true tensorboard_log_dir: logs
Start your OpenNMT training as usual.
Create a screen for TensorBoard:
screen -S tensorboard
Open the directory of the log files. In OpenNMT-tf, by default the log files are in the same folder as the model. In OpenNMT-py, the logs are in a directory with today’s date inside “runs/onmt” or the path you specified for
Start TensorBoard and specify the log directory:
At this point, you should see a message that TensorBoard is running on localhost
http://localhost:6006/and that’s how to access it from a local browser if you are working on the same machine.
Get out of this screen by pressing: Ctrl+A+D.
2- Accessing TensorBoard from the Internet
Disclaimer: This method should be only used for research or demonstration purposes. For corporate and security-sensitive purposes, consult with your team first. Depending on the infrastructure you are using, there might be better methods.
Unzip the downloaded ngrok archive.
Find your authentication key here and run the command:
./ngrok authtoken <your_authentication_key>
Start a new screen:
screen -S ngrok
Start ngrok on TensorBoard’s default port 6006:
./ngrok https 6006
If everything works well, you should see a black screen with “Session Status Online” and other details, including “Forwarding”.
Copy the “Forwarding” HTTP or HTTPs and run it in your browser. You should be able to see something like this: