Run Tensorboard (or Jupyter) in a Remote Docker Container
When creating a container, forward two ports 22 (for ssh) and 6006 (for Tensorboard).
docker run -ti --runtime=nvidia -p 8082:22 -p 8083:6006 nvidia/cuda:10.0-cudnn7-devel-ubuntu16.04 /bin/bash
We can access a container with ssh by,
ssh <user>@<host> -p 8082
Access through localhost
In a remote container, run Tensorboard with 6006 (the default port).
tensorboard --logdir lightning_logs
TensorFlow installation not found - running with reduced feature set. Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_all TensorBoard 2.2.2 at http://localhost:6006/ (Press CTRL+C to quit)
In a local machine, bind local’s 8083 port to remote’s 6006 port.
ssh -L 8083:127.0.0.1:6006 <user>@<host> -p 8082
Now, we can access the Tensorboard web interface using the address
localhost:8083 in a local machine.
You can use Jupyter in the same way. Just change 6006 to 8888 (the default port in Jupyter).
Access through IP address or domain name
If you want to access the Tensorboard through the IP address or domain name of the server, add
--host 0.0.0.0 to tensorboard command.
tensorboard --logdir lightning_logs --host 0.0.0.0
You can access the Tensorboard page with
http://<host>:8083 on any kind of machine connected to the internet.
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