How do I add a kernel on a remote machine in IPython (Jupyter) Notebook?
Question:
Dropdown menu in the top-right of the UI on a local machine (PC):
Kernel->
Change kernel->
Python 2 (on a local PC)
Python 3 (on a local PC)
My new kernel (on a remote PC)
Answers:
IPython use kernel is a file in ~/.ipython/kernel/<name> that describe how to launch a kernel. If you create your own kernel (remote, or whatever) it’s up to you to have the program run the remote kernel and bind locally to the port the notebook is expected.
The IPython notebook talks to the kernels over predefined ports. To talk to a remote kernel, you just need to forward the ports to the remote machine as part of the kernel initialisation, the notebook doesn’t care where the kernel is as long as it can talk to it.
You could either set up a wrapper script that gets called in the kernel spec file (https://ipython.org/ipython-doc/dev/development/kernels.html#kernel-specs) or use a module that can help you set up and manage different kinds of remote kernels: (pip install remote_ikernel; https://bitbucket.org/tdaff/remote_ikernel).
If you are using remote_ikernel, and have ssh access to the machine, the following command will set up the entry in the drop down list:
remote_ikernel manage --add
--kernel_cmd="ipython kernel -f {connection_file}"
--name="Remote Python" --interface=ssh
--host=my_remote_machine
Remote jupyter kernel/kernels administration utility (the rk):
https://github.com/korniichuk/rk
-
$ sudo pip install git+git://github.com/korniichuk/rk#egg=rk
-
Setup SSH for auto login without a password:
$ rk ssh
-
Install a template of a remote jupyter kernel:
$ rk install-template
-
Change the kernel.json file:
$ sudo gedit /usr/local/share/jupyter/kernels/template/kernel.json
For example from remote_username@remote_host to [email protected].
remote_ikernel in an earlier answer hasn’t been updated in a while. It may still work fine, but as a more recent/updated option with some additional features, I’ve just tested ssh_ipykernel, and it works well for this. I’m using it to run a kernel on a remote machine with a GPU, connecting via SSH.
Dropdown menu in the top-right of the UI on a local machine (PC):
Kernel->
Change kernel->
Python 2 (on a local PC)
Python 3 (on a local PC)
My new kernel (on a remote PC)
IPython use kernel is a file in ~/.ipython/kernel/<name> that describe how to launch a kernel. If you create your own kernel (remote, or whatever) it’s up to you to have the program run the remote kernel and bind locally to the port the notebook is expected.
The IPython notebook talks to the kernels over predefined ports. To talk to a remote kernel, you just need to forward the ports to the remote machine as part of the kernel initialisation, the notebook doesn’t care where the kernel is as long as it can talk to it.
You could either set up a wrapper script that gets called in the kernel spec file (https://ipython.org/ipython-doc/dev/development/kernels.html#kernel-specs) or use a module that can help you set up and manage different kinds of remote kernels: (pip install remote_ikernel; https://bitbucket.org/tdaff/remote_ikernel).
If you are using remote_ikernel, and have ssh access to the machine, the following command will set up the entry in the drop down list:
remote_ikernel manage --add
--kernel_cmd="ipython kernel -f {connection_file}"
--name="Remote Python" --interface=ssh
--host=my_remote_machine
Remote jupyter kernel/kernels administration utility (the rk):
https://github.com/korniichuk/rk
-
$ sudo pip install git+git://github.com/korniichuk/rk#egg=rk
-
Setup SSH for auto login without a password:
$ rk ssh
-
Install a template of a remote jupyter kernel:
$ rk install-template
-
Change the
kernel.jsonfile:$ sudo gedit /usr/local/share/jupyter/kernels/template/kernel.json
For example from remote_username@remote_host to [email protected].
remote_ikernel in an earlier answer hasn’t been updated in a while. It may still work fine, but as a more recent/updated option with some additional features, I’ve just tested ssh_ipykernel, and it works well for this. I’m using it to run a kernel on a remote machine with a GPU, connecting via SSH.