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Jupyter kernels manipulation and in other environments (docker, Lmod, etc.)

Project description

Switch environments before running Jupyter kernels

Sometimes, one needs to execute Jupyter kernels in a different environment. Say you want to execute the kernel in a conda environment (that's easy, but actually misses setting certain environment variables). Or run it inside a Docker container. One could manually adjust the kernelspec files to set environment variables or run commands before starting the kernel, but envkernel automates this process.

envkernel is equally usable for end users (on their own systems or clusters) to easily access environments in Jupyter, or sysadmins deploying this access on systems they administer.

In general, there are two passes: First, install the kernel, e.g.: envkernel virtualenv --name=my-venv /path/to/venv. This parses some options and writes a kernelspec file with the the --name you specify. When Jupyter tries to start this kernel, it will execute the next phase. When Jupyter tries to run the kernel, the kernelspec file will re-execute envkernel in the run mode, which does whatever is needed to set up the environment (in this case, sets PATH to the /path/to/venv/bin/ that is needed). Then it starts the normal IPython kernel.

Available modes:

  • conda: Activate a conda environment first.
  • virtualenv: Activate a virtualenv first.
  • docker: Run the kernel in a Docker container.
  • singularity: Run the kernel in a singularity container.
  • Lmod: Activate Lmod modules first.


Available on the PiPI: pip install envkernel.

Or, you can install latest from Github in the usual way: pip install

This is a single-file script and can be copied directly and added to PATH as well. By design, there are no dependencies except the basic Jupyter client (not notebook or any UI), and that is only needed at kernel-setup time, not at kernel-runtime. The script must be available both when a kernel is set up, and each time the kernel is started (and currently assumes they are in the same location).

General usage and common arguments

General invocation:

envkernel [mode] [envkernel options] [mode-specific-options]

General arguments usable by all classes during the setup phase:

These options directly map to normal Jupyter kernel install options:

  • mode: singularity, docker, lmod, or whatever mode is desired.
  • --name $name: Name of kernel to install (required).
  • --user: Install kernel into user directory.
  • --sys-prefix: Install to the current Python's sys.prefix (the Python which is running envkernel).
  • --prefix: same as normal kernel install option.
  • --display-name NAME: Human-readable name.
  • --replace: Replace existing kernel (Jupyter option, unsure what this means).
  • --language: What language to tag this kernel (default python).

These are envkernel-specific options:

  • --verbose, -v: Print more debugging information when installing the kernel. It is always in verbose mode when actually running the kernel.
  • --python: Python interpreter to use when invoking inside the environment. (Default python. Unlike other kernels, this defaults to a relative path because the point of envkernel is to set up PATH properly.) If this is the special value SELF, this will be replaced with the value of sys.executable of the Python running envkernel.
  • --kernel=NAME: Auto-set --language and --kernel-cmd to that needed for these well-known kernels. Options include ipykernel (the default), ir, or imatlab. But all of these hard-code a kernel command line and could possibly be wrong some day.
  • --kernel-cmd: a string which is the kernel to start - space separated, no shell quoting, it will be split when saving. The default is python -m ipykernel_launcher -f {connection_file}, which is suitable for IPython. For example, to start an R kernel in the environment use R --slave -e IRkernel::main() --args {connection_file} as the value to this, being careful with quoting the spaces only once. To find what the strings should be, copy form some existing kernels. --kernel=NAME includes shortcut for some popular kernels.
  • --kernel-template: An already-installed kernel name which is used as a template for the new envkernel. This is searched using the normal Jupyter search paths. This kernel json file is loaded and used as a template for all kernel options (--language, --kernel-cmd, etc). Also, any other file in this directory (such as logos) are copied to the new kernel (like kernel.js in irkernel).
  • --kernel-make-path-relative removes an absolute path from the kernel command (mainly useful with --kernel-template). This would be useful, for example, where you are setting up an lmod install and the absolute path of the module might change, but you want it to always run Python relative to that module anyway.
  • --env=NAME=VALUE. Set these environment variables when running the kernel. These are actually just saved in the kernel.json file under the env key, which is used by Jupyter itself. So, this is just a shorthand for adding variables there, it is not used at the envkernel stage at all.

Order of precedence of options (later in the list overrides earlier): --kernel-template, --kernel, --kernel-cmd, --language, --python, --display-name.


The Conda envkernel will activate Conda environments (set the PATH, CPATH, LD_LIBRARY_PATH, and LIBRARY_PATH environment variables). This is done manually, if anyone knows a better way to do this, please inform us.

Conda example

This will load the anaconda environment before invoking an IPython kernel using the name python, which will presumably be the one inside the anaconda3 environment.

envkernel conda --name=conda-anaconda3 /path/to/anaconda3

Conda mode arguments

General invocation:

envkernel conda --name=NAME [envkernel options] conda-env-full-path
  • conda-env-full-path: Full path to the conda environment to load.


This operates identically to conda mode, but with name virtualenv on virtualenvs.

Virtualenv example

envkernel virtualenv --name=conda-anaconda3 /path/to/anaconda3


Docker is a containerization system that runs as a system service.

Note: docker has not been fully tested, but has been reported to work.

Docker example

envkernel docker --name=NAME  --pwd --bind /m/jh/coursedata/:/coursedata /path/to/image.simg

Docker mode arguments

General invocation:

envkernel docker --name=NAME [envkernel options] [docker options] [image]
  • image: Required positional argument: name of docker image to run.

  • --pwd: Bind-mount the current working directory and use it as the current working directory inside the notebook. This is usually useful.

  • A few more yet-undocumented and untested arguments...

Any unknown argument is passed directly to the docker run call, and thus can be any normal Docker argument. If ,copy is included in the --mount command options, the directory will be copied before mounting. This may be useful if the directory is on a network mount which the root docker can't access. It is recommended to always use the form of options with =, such as --option=X, rather than separating them with a space, to avoid problems with argument/option detection.


Singularity is a containerization system somewhat similar to Docker, but designed for user-mode usage without root, and with a mindset of using user software instead of system services.

Singularity example

envkernel singularity --name=NAME --contain --bind /m/jh/coursedata/:/coursedata /path/to/image.simg

Singularity mode arguments

General invocation:

envkernel singularity --name=NAME [envkernel options] [singularity options] [image]
  • image: Required positional argument: name of singularity image to run.

  • --pwd: Bind-mount the current working directory and use it as the current working directory inside the notebook. This may happen by default if you don't --contain.

Any unknown argument is passed directly to the singularity exec call, and thus can be any normal Singularity arguments. It is recommended to always use the form of options with =, such as --bind=X, rather than separating them with a space, to avoid problems with argument/option detection. The most useful Singularity options are (nothing envkernel specific here):

  • --contain or -c: Don't share any filesystems by default.

  • --bind src:dest[:ro]: Bind mount src from the host to dest in the container. :ro is optional, and defaults to rw.

  • --cleanenv: Clean all environment before executing.

  • --net or -n: Run in new network namespace. This does NOT work with Jupyter kernels, because localhost must currently be shared. So don't use this unless we create proper net gateway.


The Lmod envkernel will load/unload Lmod modules before running a normal IPython kernel.

Using envkernel is better than the naive (but functional) method of modifying a kernel to invoke a particular Python binary, because that will invoke the right Python interpreter but not set relevant other environment variables (so, for example, subprocesses won't be in the right environment).

Lmod example

This will run module purge and then module load anaconda3 before invoking an IPython kernel using the name python, which will presumably be the one inside the anaconda3 environment.

envkernel lmod --name=anaconda3 --purge anaconda3

Lmod mode arguments

General invocation:

envkernel lmod --name=NAME [envkernel options] [module ...]
  • module ...: Modules to load (positional argument). Note that if the module is prefixed with -, it is actually unloaded (this is a Lmod feature).

  • --purge: Purge all modules before loading the new modules. This can be safer, because sometimes users may automatically load modules from their .bashrc which will cause failures if you try to load conflicting ones.

Other kernels

Envkernel isn't specific to the IPython kernel. It defaults to ipykernel, but by using the --kernel-template option you can make it work with any other kernel without having to understand the internals. First, you install your other kernel normally, with some name (in this case, R-3.6.1). Then, you run envkernel with --kernel-template=R-3.6.1, which clones that (with all its support files from the kernel directory, argv, and so on), and (in this case) saves it to the same name with the --name=R-3.6.1 option.

# Load modules and install the IRKernel normally, without envkernel
module load r-irkernel/1.1-python3
module load jupyterhub/live
Rscript -e "library(IRkernel); IRkernel::installspec(name='R-3.6.1', displayname='R 3.6 module')"

# Use envkernel --kernel-template
#  - Do the normal Lmod envkernel setup
#  - copy the existing kernel, incuding argv, kernel.js, icon, and display name
#  - Save it again, to the same name, with envkernel wrapper.
envkernel lmod --user --kernel-template=R-3.6.1 --name=R-3.6.1  r-irkernel/1.1-python3

This way, you can wrap any arbitrary kernel to run under envkernel. Also, you can always use --kernel-cmd to explicitly set your kernel command to whatever is needed for any other kernel (but you have to figure out that command yourself...).

How it works

When envkernel first runs, it sets up a kernelspec that will re-invoke envkernel when it runs. Some options are when firs run (kernelspec name and options), while usually most are passed through straight to the kernelspec. When the kernel is started, envkernel is re-invoked

Example envkernel setup command. This makes a new Jupyter kernel (envkernel singularity means singularity create mode) named testcourse-0.5.9 out of the image /l/simg/0.5.9.simg with the Singularity options --contain (contain, on default mounts) and --bind (bind a dir).`

envkernel singularity --sys-prefix --name=testcourse-0.5.9 /l/simg/0.5.9.simg --contain --bind /m/jh/coursedata/:/coursedata

That will create this kernelspec. Note that most of the arguments are passed through:

    "argv": [
    "display_name": "Singularity with /l/simg/0.5.9.simg",
    "language": "python"

When this runs, it runs singularity --contain --bind /m/jh/coursedata/:/coursedata /l/simg/0.5.9.simg. Inside the image, it runs python -m ipykernel_launcher -f {connection_file}. envkernel parses and manipulates these arguments however is needed.

Running multiple modes

envkernel doesn't support running multiple modes - for example, conda and lmod at the same time. But, because of the general nature, you should be able to layer it yourself. The following example uses the conda mode to create an envkernel. Then, it uses --kernel-template to re-read that kernel and wrap it in lmod:

envkernel conda --name=test1 conda_path
envkernel lmod --name=test1 --kernel-template=test1 lmod_module

There is nothing really special here, it is layering one envkernel execution on top of another. If you notice problems with this, please try to debug a bit and then send feedback/improvements, this is a relatively new feature.

Use with nbgrader

envkernel was orginally inspired by the need for nbgrader to securely contain student's code while autograding. To do this, set up a contained kernel as above - it's up to you to figure out how to do this properly with your chosen method (docker or singularity). Then autograde like normal, but add the --ExecutePreprocessor.kernel_name option.

Set up a kernel:

envkernel docker --user --name=testcourse-0.5.9 --pwd aaltoscienceit/notebook-server:0.5.9 --bind /mnt/jupyter/course/testcourse/data/:/coursedata

Run the autograding:

nbgrader autograde --ExecutePreprocessor.kernel_name=testcourse-0.5.9 R1_Introduction

Kernel quick reference

  • jupyter kernelspec list
  • jupyter kernelspec remove NAME

See also

  • General

    • a2km, "Assistant to the kernel manager" is a command line tool for dealing with kernels, including making kernels which activate conda/venv kernels. And some other handy kernel manipulations stuff. Unfortunately written in Ruby.
    • - automatically create kernels from conda environments. Uses a KernelSpecManager so possibly overrides everything at once, and also defaults to all kernels.
    • The direct way to make a conda/virtualenv available in Jupyter is to activate the environment, then run python -m ipykernel install [--user|--prefix=/path/to/other/env/]. But this does not set up PATH, so calling other executables doesn't work... thus the benefit of envkernel.
    • This thread was the clue to getting a kernel inside Docker working.
  • The following commands are essential for kernel management

    • jupyter kernelspec list
    • jupyter --paths - each $data_path/kernels dir is searched for kernels.

Development and contributions

Developed at Aalto University Science-IT. Primary contact: Richard Darst. Contributions welcome from anyone. As of early 2019, it is mid 2019, it's usable but there may be bugs as it gets used in more sites.

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