jupyter_notebook_config.py

def add_home_dir(*fn):
    import os.path
    return os.path.join(os.path.expanduser('~'), *fn)

c.ServerApp.ip = '*'
c.ServerApp.port = 9999

c.ServerApp.certfile = add_home_dir('certs', 'mycert.pem')
c.ServerApp.keyfile = add_home_dir('certs', 'mykey.key')

c.NotebookApp.open_browser = False
c.LabApp.open_browser = False
c.ServerApp.open_browser = False

c.PasswordIdentityProvider.hashed_password = u'sha1:5f2cf85d2872:e9b52b2240cbcde307b5907e5c3848f3f9fd8a92' # 31KT

#------------------------------------------------------------------------------
# Configurable configuration
#------------------------------------------------------------------------------

# templates
c.JupyterLabTemplates.template_dirs = [add_home_dir('jupyter-templates')]
c.JupyterLabTemplates.include_default = False

jupyterlab-conda-create.sh

conda create --name "jupyterlab" pip wheel jupyter ipykernel
conda activate jupyterlab

conda install -c conda-forge jupyterlab
conda install -c conda-forge nodejs

conda install -c conda-forge jupyterlab_templates

#jupyter serverextension enable --py jupyterlab_templates

logging-only-template.ipynb

pydata-jupyter-template.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting start\n",
    "\n",
    "1. Given the notebook a meaningful name.\n",
    "2. Select an appropriate kernel."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "#### Standard pydata imports\n",
    "\n",
    "This also creates a default `logger` for use in the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "\n",
    "from argparse import Namespace\n",
    "\n",
    "try:\n",
    "    import pyllars.logging_utils as logging_utils\n",
    "    logger = logging_utils.get_ipython_logger()\n",
    "    logger.setLevel('INFO')\n",
    "except:\n",
    "    pass\n",
    "\n",
    "# standard pydata imports\n",
    "import joblib\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pathlib\n",
    "import seaborn as sns; sns.set(style='white', color_codes=True)\n",
    "import tqdm\n",
    "import yaml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Typing imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Mapping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Custom imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyllars.collection_utils as collection_utils\n",
    "import pyllars.mpl_utils as mpl_utils\n",
    "import pyllars.pandas_utils as pd_utils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "BASE = pathlib.Path(\"/prj/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data():\n",
    "    path = BASE / \"to\" / \"data.csv\"\n",
    "    df = pd.read_csv(path)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## Start code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "args = Namespace()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}

The template should likely be placed at $HOME/jupyter-templates/templates.

The certificate and key are self-signed using the method described here. Namely, the following command is used:

openssl req -x509 -nodes -days 365 -newkey rsa:2048 -keyout mykey.key -out mycert.pem

The config file expects these to be located in $HOME/certs.

---

Update:

The following command also specifies the certificate details from the command line and requires no user input:

openssl req -x509 -nodes -days 365 -newkey rsa:2048 -keyout mykey.key -out mycert.pem -subj "/C=DE/ST= /L= /O= /OU= /CN= "

---

The password specified in the template to access the server is 31KT.

The config file should be located in $HOME/.jupyter

The following error message (and similar) is common when using Chrome to access the server:

SSL Error on 8 ('::1', 53988, 0, 0): [SSL: SSLV3_ALERT_CERTIFICATE_UNKNOWN] ssl/tls alert certificate unknown (_ssl.c:1000)

This appears to be specific to Chrome. If it does not appear when using some other browser, then it can be safely ignored.