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Commit d1855ea6 authored by Jens Henrik Goebbert's avatar Jens Henrik Goebbert
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ensure lowercase kernelnames

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# Create your own Jupyter CONDA-Kernel
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---
## Building your own Jupyter CONDA-kernel is a three step process
Download Minconda installer
1. Download/Install Miniconda
* Miniconda3.sh
2. Create Conda Environment
* conda create
2. Create/Edit launch script for the Jupyter kernel
* kernel.sh
3. Create/Edit Jupyter kernel configuration
* kernel.json
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### Settings
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Selectable **CONDA_TARGET_DIR** path for the central conda installation, should be in the project filesystem
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``` bash
export CONDA_TARGET_DIR=${HOME}/PROJECT_training2005/testdir/miniconda3
```
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Selectable **CONDA_ENV** name, will be used to specify the environment name
- must be lowercase
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``` bash
export CONDA_ENV=my_env
CONDA_ENV=my_env
export CONDA_ENV=$(echo "${CONDA_ENV}" | awk '{print tolower($0)}')
echo ${KERNEL_NAME} # double check
```
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---
## 1. Download/Install Miniconda
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Start here if you want to run the full installation.
If you want to create another environment in an existing conda setup go to **create environment**. If you want to attach yourself to an existing environment go to **create user kernel**.
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* 1.1 - Download Minconda installer
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``` bash
curl https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -o $HOME/Miniconda3.sh
```
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* 1.2 - Create target directory
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``` bash
mkdir -p ${CONDA_TARGET_DIR}
```
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* 1.3 - Install Miniconda
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``` bash
bash $HOME/Miniconda3.sh -b -u -p ${CONDA_TARGET_DIR}
```
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``` bash
${CONDA_TARGET_DIR}/bin/conda init bash
```
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* 1.4 - Disable automatic activation
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``` bash
${CONDA_TARGET_DIR}/bin/conda config --set auto_activate_base false
```
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---
## 2. Create conda environment
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Create new conda environment. The following steps can be repeated if multiple environments should be created. If the Python version differ towards the external Python version, a mix of Conda modules and external modules will not be possible
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``` bash
${CONDA_TARGET_DIR}/bin/conda create -n ${CONDA_ENV} -y python=3.6.8 ipykernel
```
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---
## 3. Create/Edit launch script for the Jupyter kernel
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* 3.1 - Create kernel to allow access to the conda environment. Adapte `module purge` and `PYTHONPATH` according to the comments.
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``` bash
echo '#!/bin/bash
# module purge # optional to disable the external environment, necessary, if python version is different
# Activate your Python virtual environment
source '"${CONDA_TARGET_DIR}"'/bin/activate '"${CONDA_ENV}"'
# Ensure python packages installed in conda are always prefered, not necessary if module purge is used
export PYTHONPATH=${CONDA_PREFIX}/lib/python3.6/site-packages:${PYTHONPATH}
exec python -m ipykernel $@' > ${CONDA_TARGET_DIR}/envs/${CONDA_ENV}/kernel.sh
```
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``` bash
chmod +x ${CONDA_TARGET_DIR}/envs/${CONDA_ENV}/kernel.sh
```
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---
## 4. Create/Edit Jupyter kernel configuration
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* 4.1 - Create user kernel, if you want to access the conda environment of a colleague, only these steps are necessary
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``` bash
mkdir -p $HOME/.local/share/jupyter/kernels/conda_${CONDA_ENV}
```
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* 4.2 - Adjust kernel.json file
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``` bash
echo '{
"argv": [
"'"${CONDA_TARGET_DIR}"'/envs/'"${CONDA_ENV}"'/kernel.sh",
"-f",
"{connection_file}"
],
"display_name": "conda_'"${CONDA_ENV}"'",
"language": "python"
}' > $HOME/.local/share/jupyter/kernels/conda_${CONDA_ENV}/kernel.json
```
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Restart of JupyterLab might be necessary to see the kernel in the kernel selection overview.
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# Create your own Jupyter Kernel
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---
## Building your own Jupyter kernel is a three step process
1. Create/Pimp new virtual Python environment
* venv
2. Create/Edit launch script for the Jupyter kernel
* kernel.sh
3. Create/Edit Jupyter kernel configuration
* kernel.json
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### Settings
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* Set kernel name
- must be lower case
- change if you like
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``` bash
# INPUT NEEDED:
export KERNEL_NAME=${USER}_kernel
KERNEL_NAME=${USER}_kernel
export KERNEL_NAME=$(echo "${KERNEL_NAME}" | awk '{print tolower($0)}')
echo ${KERNEL_NAME} # double check
```
%% Output
goebbert1_kernel
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* Set kernel type
- private kernel = "\${HOME}/.local/"
- project kernel = "\${PROJECT}/.local/"
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``` bash
# INPUT NEEDED:
export KERNEL_TYPE=private # private or project
###################
# private kernel
if [ "${KERNEL_TYPE}" == "private" ]; then
export KERNEL_SPECS_PREFIX=${HOME}/.local
echo "private kernel"
# project kernel
else
export KERNEL_SPECS_PREFIX=${PROJECT}/.local
echo "project kernel"
fi
export KERNEL_SPECS_DIR=${KERNEL_SPECS_PREFIX}/share/jupyter/kernels
# check if kernel name is unique
if [ -d "${KERNEL_SPECS_DIR}/${KERNEL_NAME}" ]; then
echo "ERROR: Kernel already exists in ${KERNEL_SPECS_DIR}/${KERNEL_NAME}"
echo " Rename kernel name or remove directory."
fi
echo ${KERNEL_SPECS_DIR}/${KERNEL_NAME} # double check
```
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* Set directory for kernels virtual environment
- change if you like
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``` bash
# INPUT NEEDED:
export KERNEL_VENVS_DIR=${PROJECT}/${USER}/jupyter/kernels
###################
mkdir -p ${KERNEL_VENVS_DIR}
if [ "${KERNEL_TYPE}" != "private" ]; then
echo "Please check the permissions and ensure your project partners have read/execute permissions:"
namei -l ${KERNEL_VENVS_DIR}
fi
echo ${KERNEL_VENVS_DIR} # double check
```
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---
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## 1. Create/Pimp new virual Python environment
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* 1.1 - Load required modules
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``` bash
module -q purge
module -q use $OTHERSTAGES
module -q load Stages/Devel-2019a 2> /dev/null # any stage can be used
module -q load GCCcore/.8.3.0 2> /dev/null
module -q load Python/3.6.8 # only Python is required
module list # double check
```
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* 1.2 - Load extra modules you need for your kernel
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``` bash
# module load <module you need>
```
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* 1.3 - Create and activate a virtual environment for the kernel
and ensure python packages installed in the virtual environment are always prefered
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``` bash
python -m venv --system-site-packages ${KERNEL_VENVS_DIR}/${KERNEL_NAME}
source ${KERNEL_VENVS_DIR}/${KERNEL_NAME}/bin/activate
export PYTHONPATH=${VIRTUAL_ENV}/lib/python3.6/site-packages:${PYTHONPATH}
echo ${VIRTUAL_ENV} # double check
```
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* 1.4 - Install Python libraries required for communication with Jupyter
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``` bash
which pip
pip install --ignore-installed ipykernel
ls ${VIRTUAL_ENV}/lib/python3.6/site-packages/ # double check
```
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* 1.5 - Install whatever else you need in your Python virtual environment (using pip)
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``` bash
#pip install <python-package you need>
```
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---
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## 2. Create/Edit launch script for the Jupyter kernel
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* 2.1 - Create launch script, which loads your Python virtual environment and starts the ipykernel process inside:
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``` bash
echo "#!/bin/bash
# Load required modules
module purge
module use "'$OTHERSTAGES'"
module load Stages/Devel-2019a
module load GCCcore/.8.3.0
module load Python/3.6.8
# Load extra modules you need for your kernel (as you did in step 1.2)
#module load <module you need>
# Activate your Python virtual environment
source ${KERNEL_VENVS_DIR}/${KERNEL_NAME}/bin/activate
# Ensure python packages installed in the virtual environment are always prefered
export PYTHONPATH=${VIRTUAL_ENV}/lib/python3.6/site-packages:"'${PYTHONPATH}'"
exec python -m ipykernel "'$@' > ${VIRTUAL_ENV}/kernel.sh
chmod +x ${VIRTUAL_ENV}/kernel.sh
```
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---
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## 3. Create/Edit Jupyter kernel configuration
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* 3.1 - Create Jupyter kernel configuration directory and files
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``` bash
python -m ipykernel install --name=${KERNEL_NAME} --prefix ${VIRTUAL_ENV}
export VIRTUAL_ENV_KERNELS=${VIRTUAL_ENV}/share/jupyter/kernels
```
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* 3.2 - Adjust kernel.json file
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``` bash
mv ${VIRTUAL_ENV_KERNELS}/${KERNEL_NAME}/kernel.json ${VIRTUAL_ENV_KERNELS}/${KERNEL_NAME}/kernel.json.orig
echo '{
"argv": [
"'${KERNEL_VENVS_DIR}/${KERNEL_NAME}/kernel.sh'",
"-m",
"ipykernel_launcher",
"-f",
"{connection_file}"
],
"display_name": "'${KERNEL_NAME}'",
"language": "python"
}' > ${VIRTUAL_ENV_KERNELS}/${KERNEL_NAME}/kernel.json
```
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* 3.3 - Create link to kernel specs
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``` bash
cd ${KERNEL_SPECS_DIR}
ln -s ${VIRTUAL_ENV_KERNELS}/${KERNEL_NAME} .
ls ${KERNEL_SPECS_DIR} # double check
```
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---
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## 4. Cleanup
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``` bash
deactivate
```
......
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