All your Jupyter kernels, on all your machines, in one place.
Launch Jupyter kernels on remote systems and through batch queues so that they can be used within a local Jupyter noteboook.
Jupyter compatible Kernels start through interactive jobs in batch queue systems (SGE, SLURM, PBS...) or through SSH connections. Once the kernel is started, SSH tunnels are created for the communication ports are so the notebook can talk to the kernel as if it was local.
Commands for managing the kernels are included. It is also possible to use
remote_ikernel
to manage kernels from different virtual environments or
different python implementations.
Install with pip install remote_ikernel
. Requires notebook
(as part
of Jupyter), version 4.0 or greater and pexpect
. Passwordless ssh
to the all the remote machines is also recommended (e.g. nodes on a cluster).
Warning
remote_ikernel
opens multiple connections across several machines
to tunnel communication ports. If you have concerns about security or
excessive use of resources, please consult your systems administrator
before using this software.
Note
When running kernels on remote machines, the notebooks themselves will
be saved onto the local filesystem, but the kernel will only have access
to filesystem of the remote machine running the kernel. If you need shared
directories, set up sshfs
between your machines.
# Install the module ('python setup.py install' also works)
pip install remote_ikernel
# Set up the kernels you'd like to use
remote_ikernel manage
# Add a new kernel running through GrideEngine
remote_ikernel manage --add \
--kernel_cmd="ipython kernel -f {connection_file}" \
--name="Python 2.7" --cpus=2 --pe=smp --interface=sge
# Add an SSH connection to a remote machine running IJulia
remote_ikernel manage --add \
--kernel_cmd="/home/me/julia-903644385b/bin/julia -i --startup-file=yes --color=yes /home/me/.julia/v0.6/IJulia/src/kernel.jl {connection_file}" \
--name="IJulia 0.6.0" --interface=ssh \
--host=me@remote.machine --workdir='/home/me/Workdir' --language=julia
# Set up kernels for your local virtual environments that can be run
# from a single notebook server.
remote_ikernel manage --add \
--kernel_cmd="/home/me/Virtualenvs/dev/bin/ipython kernel -f {connection_file}" \
--name="Python 2 (venv:dev)" --interface=local
# Connect to a SLURM cluster through a gateway machine (to get into a
# local network) and cluster frontend machine (where the sqsub runs from).
remote_ikernel manage --add \
--kernel_cmd="ipython kernel -f {connection_file}" \
--name="Python 2.7" --cpus=4 --interface=slurm \
--tunnel-hosts gateway.machine cluster.frontend
The kernel spec files will be installed so that the new kernel appears in
the drop-down list in the notebook. remote_ikernel manage
also has options
to show and delete existing kernels.
When working with remote machines, each kernel creates two ssh
connections. If you would like to reduce that, you can set up automatic
multiplexing of connections. For each machine, add a configuration to your
~/.ssh/config
:
Host myhost.ac.uk ControlMaster auto ControlPath ~/.ssh/%r@%h:%p ControlPersist 1
This will create a master connection that remains in the background when and multiplex everything through that. If you have multiple hops, this will need to be added for each hop. Note, for the security conscious, that idle kernels on multiplexed connections allow new ssh connections to be started without a password.
- Option
--tunnel-hosts
. When given, the software will try to create an ssh tunnel through all the hosts before starting the final connection. Allows using batch queues on remote systems.- Preliminary support for dealing with passwords. If a program is defined in the environment variable
SSH_ASKPASS
it will be used to ask the user for a password.--launch-cmd
can be used to override the command used to launch the interactive jobs on the cluster, e.g. to replaceqlogin
withqrsh
.- Platform LSF support.
- The kernel json files are given unique names.
- Updated pip requirements to pull in the notebook package. Use an earlier version if you need to use IPython 3.
- Remote process is polled for output which will show up when
--verbose
if used as a kernel option.
- Version 0.2.11 is the last version to support IPython notebook version 3. pip requirements enforce versions less than 4. Use a more recent version to ensure compatibility with the Jupyter split.
- Support for PBS/Torque through
qsub -I
.- Tunnels are kept alive better, if something is not responding try waiting 20 seconds to see if a tunnel had dies. (Tunnels no longer depend on pyzmq, instead they are launched through pexpect and monitored until they die.)
--remote-launch-args
can be used to setqlogin
parameters or similar.--remote-precmd
allows execution of an extra command on the remote host before launching a kernel.- Better compatibility with Python 3.
- Kernel output on terminals with
--verbose
option for debugging.- Connect to a host with ssh, slurm, or local kernels.
- Changed prefix to
rik_
.- kernel_cmd now requires the
{connection_file}
argument.remote_ikernel manage --show
command to show existing kernels.- Specify the working directory on the remote machine with
--workdir
.kernel-uuid.json
is copied to the working director for systems where there is no access to the frontend filesystem.- Added compatibility layer to get rid of Jupyter warnings.