This Jupyter notebook will walk you through the installation of a containerized Jupyter kernel (for use at Jupyter-JSC, but it should actually work with any Jupyter server on a system where Singularity is installed). Considerable performance improvements (especially with respect to kernel start-up times) over e.g. conda-based Jupyter kernels on distributed filesystems, as are typically installed on HPC systems, might be experienced. In the example below, the base-notebook from the Jupyter docker stacks is used as an IPython kernel (already having the required ipykernel package installed), the approach presented here might be extended to any other Jupyter kernel compatible programming language, though.
Requirements:
- Python environment with an installed
ipykernelpackage in a Docker (or Singularity) container containergroup access for the JSC systems as described here in the docs
Check that the Singularity container runtime is available via the JupyterLab environment,
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singularity --version
singularity --version
singularity version 3.6.4-1.el8
Specify the filesystem location that stores the Singularity container image,
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IMAGE_TARGET_DIR=/p/project/cesmtst/hoeflich1/jupyter-base-notebook
IMAGE_TARGET_DIR=/p/project/cesmtst/hoeflich1/jupyter-base-notebook
Optional, if you already have a Singularity container image available at the above location: Convert a containerized Python environment (e.g. the Jupyter base-notebook that is available via Dockerhub) into a Singularity container image to be used as an example here,
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mkdir -p ${IMAGE_TARGET_DIR}
mkdir -p ${IMAGE_TARGET_DIR}
Note that pulling and converting the Dockerhub image will take a bit of time,
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singularity pull ${IMAGE_TARGET_DIR}/jupyter-base-notebook.sif docker://jupyter/base-notebook &> singularity.log
singularity pull ${IMAGE_TARGET_DIR}/jupyter-base-notebook.sif docker://jupyter/base-notebook &> singularity.log
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cat singularity.log | grep -v warn
cat singularity.log | grep -v warn
INFO: Converting OCI blobs to SIF format INFO: Starting build... Getting image source signatures Copying blob sha256:da7391352a9bb76b292a568c066aa4c3cbae8d494e6a3c68e3c596d34f7c75f8 Copying blob sha256:14428a6d4bcdba49a64127900a0691fb00a3f329aced25eb77e3b65646638f8d Copying blob sha256:2c2d948710f21ad82dce71743b1654b45acb5c059cf5c19da491582cef6f2601 Copying blob sha256:e3cbfeece0aec396b6793a798ed1b2aed3ef8f8693cc9b3036df537c1f8e34a1 Copying blob sha256:48bd2a353bd8ed1ad4b841de108ae42bccecc44b3f05c3fcada8a2a6f5fa09cf Copying blob sha256:235d93b8ccf12e8378784dc15c5bd0cb08ff128d61b856d32026c5a533ac3c89 Copying blob sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1 Copying blob sha256:b6c06056c45bc1da74604fcf368b02794fe4e36dcae881f4c6b4fa32b37a1385 Copying blob sha256:60918bcbe6d44988e4e48db436996106cc7569a4b880488be9cac90ea6883ae0 Copying blob sha256:762f9ebe4ddc05e56e33f7aba2cdd1be62f747ecd9c8f9eadcb379debf3ebe06 Copying blob sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1 Copying blob sha256:1df9d491a0390ecc3f9fac4484c92b2a5f71a79450017f2fca1849f2d6e7f949 Copying blob sha256:be84c8c720e3c53037ac2c5cbc53cf9a2a674503b2c995da1351e5560f60cc12 Copying blob sha256:28807e96859dc8c00c96255dfa51a0822380638a092803e7143473d1870970fb Copying blob sha256:bcdaf848f29a8bf0efc18a5883dc65a4a7a6b2c6cf4094e5115188ed22165a00 Copying blob sha256:49777cff52f155a9ba35e58102ecec7029dddf52aa4947f2cffbd1af12848e81 Copying blob sha256:7fb3bffa2e730b052c0c7aabd715303cc5830a05b992f2d3d70afeffa0a9ed4f Copying blob sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1 Copying config sha256:79f074439b14ae0634f2f217e5debc159c4e8c3a9ff2e0119e4dc88f9c7e21a5 Writing manifest to image destination Storing signatures 2021/01/19 11:59:33 info unpack layer: sha256:da7391352a9bb76b292a568c066aa4c3cbae8d494e6a3c68e3c596d34f7c75f8 2021/01/19 11:59:34 info unpack layer: sha256:14428a6d4bcdba49a64127900a0691fb00a3f329aced25eb77e3b65646638f8d 2021/01/19 11:59:34 info unpack layer: sha256:2c2d948710f21ad82dce71743b1654b45acb5c059cf5c19da491582cef6f2601 2021/01/19 11:59:34 info unpack layer: sha256:e3cbfeece0aec396b6793a798ed1b2aed3ef8f8693cc9b3036df537c1f8e34a1 2021/01/19 11:59:34 info unpack layer: sha256:48bd2a353bd8ed1ad4b841de108ae42bccecc44b3f05c3fcada8a2a6f5fa09cf 2021/01/19 11:59:34 info unpack layer: sha256:235d93b8ccf12e8378784dc15c5bd0cb08ff128d61b856d32026c5a533ac3c89 2021/01/19 11:59:34 info unpack layer: sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1 2021/01/19 11:59:34 info unpack layer: sha256:b6c06056c45bc1da74604fcf368b02794fe4e36dcae881f4c6b4fa32b37a1385 2021/01/19 11:59:34 info unpack layer: sha256:60918bcbe6d44988e4e48db436996106cc7569a4b880488be9cac90ea6883ae0 2021/01/19 11:59:34 info unpack layer: sha256:762f9ebe4ddc05e56e33f7aba2cdd1be62f747ecd9c8f9eadcb379debf3ebe06 2021/01/19 11:59:34 info unpack layer: sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1 2021/01/19 11:59:34 info unpack layer: sha256:1df9d491a0390ecc3f9fac4484c92b2a5f71a79450017f2fca1849f2d6e7f949 2021/01/19 11:59:36 info unpack layer: sha256:be84c8c720e3c53037ac2c5cbc53cf9a2a674503b2c995da1351e5560f60cc12 2021/01/19 11:59:40 info unpack layer: sha256:28807e96859dc8c00c96255dfa51a0822380638a092803e7143473d1870970fb 2021/01/19 11:59:40 info unpack layer: sha256:bcdaf848f29a8bf0efc18a5883dc65a4a7a6b2c6cf4094e5115188ed22165a00 2021/01/19 11:59:40 info unpack layer: sha256:49777cff52f155a9ba35e58102ecec7029dddf52aa4947f2cffbd1af12848e81 2021/01/19 11:59:40 info unpack layer: sha256:7fb3bffa2e730b052c0c7aabd715303cc5830a05b992f2d3d70afeffa0a9ed4f 2021/01/19 11:59:40 info unpack layer: sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1 INFO: Creating SIF file...
Check that the Singularity image is available,
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ls -lah ${IMAGE_TARGET_DIR}
ls -lah ${IMAGE_TARGET_DIR}
total 177M drwxr-sr-x 2 hoeflich1 cesmtst 4.0K Jan 19 11:59 . drwxr-sr-x 5 hoeflich1 cesmtst 4.0K Jan 19 11:59 .. -rwxr-xr-x 1 hoeflich1 cesmtst 183M Jan 19 11:59 jupyter-base-notebook.sif
Now, setup a Jupyter kernel specification with the install-jupyter-kernel.sh script from this repository (which basically writes a kernel.json file to the home directory location that Jupyter expects for user-specific kernels),
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KERNEL_DISPLAY_NAME=Singularity-Python # don't use whitespaces here!
SINGULARITY_IMAGE=${IMAGE_TARGET_DIR}/jupyter-base-notebook.sif
KERNEL_DISPLAY_NAME=Singularity-Python # don't use whitespaces here!
SINGULARITY_IMAGE=${IMAGE_TARGET_DIR}/jupyter-base-notebook.sif
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./install-singularity-jupyter-kernel.sh ${KERNEL_DISPLAY_NAME} ${SINGULARITY_IMAGE}
./install-singularity-jupyter-kernel.sh ${KERNEL_DISPLAY_NAME} ${SINGULARITY_IMAGE}
Check that the Jupyter kernel specification was written,
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cat ${HOME}/.local/share/jupyter/kernels/${KERNEL_DISPLAY_NAME}/kernel.json
cat ${HOME}/.local/share/jupyter/kernels/${KERNEL_DISPLAY_NAME}/kernel.json
{
"argv": [
"singularity",
"exec",
"--cleanenv",
"/p/project/cesmtst/hoeflich1/jupyter-base-notebook/jupyter-base-notebook.sif",
"python",
"-m",
"ipykernel",
"-f",
"{connection_file}"
],
"language": "python",
"display_name": "Singularity-Python"
}
And that the above Singularity-Python kernel is visible by the Jupyter server,
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jupyter kernelspec list
jupyter kernelspec list
Available kernels: singularity-python /p/home/jusers/hoeflich1/juwels/.local/share/jupyter/kernels/Singularity-Python ruby /p/software/juwels/stages/Devel-2019a/software/JupyterKernel-Ruby/2.6.3-gcccoremkl-8.3.0-2019.3.199-2019a.2.4/share/jupyter/kernels/ruby ir35 /p/software/juwels/stages/Devel-2019a/software/JupyterKernel-R/3.5.3-gcccoremkl-8.3.0-2019.3.199-2019a.2.4/share/jupyter/kernels/ir35 pyquantum-1.0 /p/software/juwels/stages/Devel-2019a/software/JupyterKernel-PyQuantum/1.0-gcccoremkl-8.3.0-2019.3.199-2019a.2.4/share/jupyter/kernels/pyquantum-1.0 pyparaview-5.8 /p/software/juwels/stages/Devel-2019a/software/JupyterKernel-PyParaView/5.8.0-gcccoremkl-8.3.0-2019.3.199-2019a.2.4/share/jupyter/kernels/pyparaview-5.8 octave /p/software/juwels/stages/Devel-2019a/software/JupyterKernel-Octave/5.1.0-gcccoremkl-8.3.0-2019.3.199-2019a.2.4/share/jupyter/kernels/octave julia-1.4 /p/software/juwels/stages/Devel-2019a/software/JupyterKernel-Julia/1.4.2-gcccoremkl-8.3.0-2019.3.199-2019a.2.4/share/jupyter/kernels/julia-1.4 javascript /p/software/juwels/stages/Devel-2019a/software/JupyterKernel-JavaScript/5.2.0-gcccoremkl-8.3.0-2019.3.199-2019a.2.4/share/jupyter/kernels/javascript cling-cpp17 /p/software/juwels/stages/Devel-2019a/software/JupyterKernel-Cling/0.6-gcccoremkl-8.3.0-2019.3.199-2019a.2.4/share/jupyter/kernels/cling-cpp17 bash /p/software/juwels/stages/Devel-2019a/software/JupyterKernel-Bash/0.7.1-gcccoremkl-8.3.0-2019.3.199-2019a.2.4/share/jupyter/kernels/bash python3 /p/software/juwels/stages/Devel-2019a/software/Jupyter/2019a.2.4-gcccoremkl-8.3.0-2019.3.199-Python-3.6.8/share/jupyter/kernels/python3
If so, you should be able to choose and connect to the containerized Python kernel from the drop down menu and/or the kernel launcher tab (a reload of the JupyterLab web page might be necessary).