From 143027f8bef8cf78c32a5e50d4d87d82748f1737 Mon Sep 17 00:00:00 2001
From: Willi Rath <wrath@geomar.de>
Date: Thu, 20 Jun 2019 13:45:02 +0200
Subject: [PATCH 01/20] Add binder config

Copied / adapted from <https://github.com/lesteve/test-binder>.
---
 binder/Dockerfile              | 32 ++++++++++++++++++++++++++++++++
 binder/my-docker-entrypoint.sh |  6 ++++++
 binder/start                   |  1 +
 3 files changed, 39 insertions(+)
 create mode 100644 binder/Dockerfile
 create mode 100644 binder/my-docker-entrypoint.sh
 create mode 100644 binder/start

diff --git a/binder/Dockerfile b/binder/Dockerfile
new file mode 100644
index 0000000..5f05bed
--- /dev/null
+++ b/binder/Dockerfile
@@ -0,0 +1,32 @@
+FROM giovtorres/docker-centos7-slurm
+
+RUN yum install -y sudo
+
+RUN curl -o miniconda.sh https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh && \
+    bash miniconda.sh -f -b -p /opt/anaconda && \
+    /opt/anaconda/bin/conda clean -tipy && \
+    rm -f miniconda.sh
+ENV PATH /opt/anaconda/bin:$PATH
+
+RUN conda install --yes -c conda-forge python=3.6 dask distributed dask-jobqueue docrep jupyterlab ipywidgets
+
+COPY my-docker-entrypoint.sh /usr/local/bin/my-docker-entrypoint.sh
+ENTRYPOINT ["/usr/local/bin/my-docker-entrypoint.sh"]
+
+# create user with a home directory
+ARG NB_USER
+ARG NB_UID
+ENV USER ${NB_USER}
+ENV HOME /home/${NB_USER}
+
+RUN useradd -m --home-dir ${HOME} \
+    --uid ${NB_UID} \
+    ${NB_USER}
+
+RUN usermod -aG wheel ${NB_USER}
+RUN echo '%wheel ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
+
+WORKDIR ${HOME}
+USER ${NB_USER}
+
+COPY _binder_notebooks/*.ipynb ${HOME}/ 
diff --git a/binder/my-docker-entrypoint.sh b/binder/my-docker-entrypoint.sh
new file mode 100644
index 0000000..51ff6e0
--- /dev/null
+++ b/binder/my-docker-entrypoint.sh
@@ -0,0 +1,6 @@
+#!/bin/bash
+
+sudo sed -i.bak -e "s@ernie@localhost@" /etc/slurm/slurm.conf
+sudo /usr/local/bin/docker-entrypoint.sh
+
+exec "$@"
diff --git a/binder/start b/binder/start
new file mode 100644
index 0000000..d33b12c
--- /dev/null
+++ b/binder/start
@@ -0,0 +1 @@
+sudo supervisorctl start mysqld

From 3b5a53cdc9a1e13cbce4eb30b30102e3d205239a Mon Sep 17 00:00:00 2001
From: Willi Rath <wrath@geomar.de>
Date: Thu, 20 Jun 2019 13:56:17 +0200
Subject: [PATCH 02/20] Clarify location of binder specific files

---
 binder/Dockerfile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/binder/Dockerfile b/binder/Dockerfile
index 5f05bed..6443897 100644
--- a/binder/Dockerfile
+++ b/binder/Dockerfile
@@ -10,7 +10,7 @@ ENV PATH /opt/anaconda/bin:$PATH
 
 RUN conda install --yes -c conda-forge python=3.6 dask distributed dask-jobqueue docrep jupyterlab ipywidgets
 
-COPY my-docker-entrypoint.sh /usr/local/bin/my-docker-entrypoint.sh
+COPY binder/my-docker-entrypoint.sh /usr/local/bin/my-docker-entrypoint.sh
 ENTRYPOINT ["/usr/local/bin/my-docker-entrypoint.sh"]
 
 # create user with a home directory

From 91c865d1a488617bd1840758773ac0e54bdbacab Mon Sep 17 00:00:00 2001
From: Willi Rath <wrath@geomar.de>
Date: Thu, 20 Jun 2019 13:59:31 +0200
Subject: [PATCH 03/20] Add first (too large) notebook for binder

---
 ...l_cluster_monte_carlo_estimate_of_pi.ipynb | 697 ++++++++++++++++++
 1 file changed, 697 insertions(+)
 create mode 100644 _binder_notebooks/01_local_cluster_monte_carlo_estimate_of_pi.ipynb

diff --git a/_binder_notebooks/01_local_cluster_monte_carlo_estimate_of_pi.ipynb b/_binder_notebooks/01_local_cluster_monte_carlo_estimate_of_pi.ipynb
new file mode 100644
index 0000000..f754249
--- /dev/null
+++ b/_binder_notebooks/01_local_cluster_monte_carlo_estimate_of_pi.ipynb
@@ -0,0 +1,697 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Monte-Carlo Estimate of $\\pi$\n",
+    "\n",
+    "We want to estimate the number $\\pi$ using a [Monte-Carlo method](https://en.wikipedia.org/wiki/Pi#Monte_Carlo_methods) exploiting that the area of a quarter circle of unit radius is $\\pi/4$ and that hence the probability of any randomly chosen point in a unit square to lie in a unit circle centerd at a corner of the unit square is $\\pi/4$ as well.  So for N randomly chosen pairs $(x, y)$ with $x\\in[0, 1)$ and $y\\in[0, 1)$, we count the number $N_{circ}$ of pairs that also satisfy $(x^2 + y^2) < 1$ and estimage $\\pi \\approx 4 \\cdot N_{circ} / N$.\n",
+    "\n",
+    "[<img src=\"https://upload.wikimedia.org/wikipedia/commons/8/84/Pi_30K.gif\" \n",
+    "     width=\"50%\" \n",
+    "     align=top\n",
+    "     alt=\"PI monte-carlo estimate\">](https://en.wikipedia.org/wiki/Pi#Monte_Carlo_methods)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Core Lessons\n",
+    "\n",
+    "- short Dask recap (assuming that `LocalCluster`, `Client`, and `dask.array` are familiar)\n",
+    "- Scaling (local) clusters\n",
+    "- Adaptive (local) clusters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set up a local cluster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from dask.distributed import LocalCluster, Client"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table style=\"border: 2px solid white;\">\n",
+       "<tr>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3>Client</h3>\n",
+       "<ul>\n",
+       "  <li><b>Scheduler: </b>tcp://127.0.0.1:43846\n",
+       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a>\n",
+       "</ul>\n",
+       "</td>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3>Cluster</h3>\n",
+       "<ul>\n",
+       "  <li><b>Workers: </b>2</li>\n",
+       "  <li><b>Cores: </b>2</li>\n",
+       "  <li><b>Memory: </b>2.00 GB</li>\n",
+       "</ul>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Client: scheduler='tcp://127.0.0.1:43846' processes=2 cores=2>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cluster = LocalCluster(n_workers=2, threads_per_worker=1, memory_limit=1e9)\n",
+    "client = Client(cluster)\n",
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The Monte Carlo Method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import dask.array as da\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calc_pi_mc(size_in_bytes):\n",
+    "    \"\"\"Calculate PI using a Monte Carlo estimate.\"\"\"\n",
+    "    xy = da.random.uniform(0, 1,\n",
+    "                           size=(int(size_in_bytes / 8 / 2), 2),\n",
+    "                           chunks=(100e6 / 8, 2))\n",
+    "    \n",
+    "    in_circle = ((xy ** 2).sum(axis=-1) < 1)\n",
+    "    pi = 4 * in_circle.mean()\n",
+    "\n",
+    "    return pi.compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def print_pi_stats(size, pi, time_delta, num_workers):\n",
+    "    \"\"\"Print pi, calculate offset from true value, and print some stats.\"\"\"\n",
+    "    print(f\"{size / 1e9} GB\\n\"\n",
+    "          f\"\\tMC pi: {pi : 13.11f}\"\n",
+    "          f\"\\tErr: {abs(pi - np.pi) : 10.3e}\\n\"\n",
+    "          f\"\\tWorkers: {num_workers}\"\n",
+    "          f\"\\t\\tTime: {time_delta : 7.3f}s\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The actual calculations\n",
+    "\n",
+    "We loop over different volumes of double-precision random numbers and estimate $\\pi$ as described above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from time import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.0 GB\n",
+      "\tMC pi:  3.14165280000\tErr:  6.015e-05\n",
+      "\tWorkers: 2\t\tTime:   3.535s\n",
+      "4.0 GB\n",
+      "\tMC pi:  3.14143952000\tErr:  1.531e-04\n",
+      "\tWorkers: 2\t\tTime:   6.481s\n",
+      "8.0 GB\n",
+      "\tMC pi:  3.14139753600\tErr:  1.951e-04\n",
+      "\tWorkers: 2\t\tTime:  12.800s\n",
+      "16.0 GB\n",
+      "\tMC pi:  3.14158570400\tErr:  6.950e-06\n",
+      "\tWorkers: 2\t\tTime:  25.747s\n"
+     ]
+    }
+   ],
+   "source": [
+    "for size in (1e9 * n for n in (2, 4, 8, 16)):\n",
+    "    \n",
+    "    start = time()\n",
+    "    pi = calc_pi_mc(size)\n",
+    "    elaps = time() - start\n",
+    "\n",
+    "    print_pi_stats(size, pi,\n",
+    "                   time_delta=elaps,\n",
+    "                   num_workers=len(cluster.workers))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Scaling the Cluster\n",
+    "\n",
+    "We increase the number of workers by 2 and the re-run the experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from time import sleep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Scaling from 2 to 4 workers.\n"
+     ]
+    }
+   ],
+   "source": [
+    "new_num_workers = 2 * len(cluster.workers)\n",
+    "\n",
+    "print(f\"Scaling from {len(cluster.workers)} to {new_num_workers} workers.\")\n",
+    "\n",
+    "cluster.scale(new_num_workers)\n",
+    "\n",
+    "sleep(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table style=\"border: 2px solid white;\">\n",
+       "<tr>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3>Client</h3>\n",
+       "<ul>\n",
+       "  <li><b>Scheduler: </b>tcp://127.0.0.1:43846\n",
+       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a>\n",
+       "</ul>\n",
+       "</td>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3>Cluster</h3>\n",
+       "<ul>\n",
+       "  <li><b>Workers: </b>4</li>\n",
+       "  <li><b>Cores: </b>4</li>\n",
+       "  <li><b>Memory: </b>4.00 GB</li>\n",
+       "</ul>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Client: scheduler='tcp://127.0.0.1:43846' processes=4 cores=4>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "client"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.0 GB\n",
+      "\tMC pi:  3.14174988800\tErr:  1.572e-04\n",
+      "\tWorkers: 4\t\tTime:   2.168s\n",
+      "4.0 GB\n",
+      "\tMC pi:  3.14155224000\tErr:  4.041e-05\n",
+      "\tWorkers: 4\t\tTime:   3.630s\n",
+      "8.0 GB\n",
+      "\tMC pi:  3.14148824000\tErr:  1.044e-04\n",
+      "\tWorkers: 4\t\tTime:   7.018s\n",
+      "16.0 GB\n",
+      "\tMC pi:  3.14160793200\tErr:  1.528e-05\n",
+      "\tWorkers: 4\t\tTime:  13.904s\n"
+     ]
+    }
+   ],
+   "source": [
+    "for size in (1e9 * n for n in (2, 4, 8, 16)):\n",
+    "    \n",
+    "    start = time()\n",
+    "    pi = calc_pi_mc(size)\n",
+    "    elaps = time() - start\n",
+    "    print_pi_stats(size, pi, time_delta=elaps,\n",
+    "                   num_workers=len(cluster.workers))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Automatically Scaling the Cluster\n",
+    "\n",
+    "We want each calculation to take approximately the same time irrespective of the actual work load.\n",
+    "\n",
+    "_**Watch** how the cluster will scale down to the minimum a few (three!) seconds after being made adaptive._"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check docstring of distributed.Adaptive for keywords\n",
+    "ca = cluster.adapt(\n",
+    "    minimum=1, maximum=8,\n",
+    "    target_duration=\"15s\",\n",
+    "    scale_factor=1);\n",
+    "\n",
+    "sleep(4)  # Allow for scale-down"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table style=\"border: 2px solid white;\">\n",
+       "<tr>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3>Client</h3>\n",
+       "<ul>\n",
+       "  <li><b>Scheduler: </b>tcp://127.0.0.1:43846\n",
+       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a>\n",
+       "</ul>\n",
+       "</td>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3>Cluster</h3>\n",
+       "<ul>\n",
+       "  <li><b>Workers: </b>1</li>\n",
+       "  <li><b>Cores: </b>1</li>\n",
+       "  <li><b>Memory: </b>1000.00 MB</li>\n",
+       "</ul>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Client: scheduler='tcp://127.0.0.1:43846' processes=4 cores=4>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Repeat the calculation from above with larger work loads.  (And watch the dash board!)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.0 GB\n",
+      "\tMC pi:  3.14140288000\tErr:  1.898e-04\n",
+      "\tWorkers: 1\t\tTime:   6.816s\n",
+      "4.0 GB\n",
+      "\tMC pi:  3.14146401600\tErr:  1.286e-04\n",
+      "\tWorkers: 1\t\tTime:  13.075s\n",
+      "8.0 GB\n",
+      "\tMC pi:  3.14165064800\tErr:  5.799e-05\n",
+      "\tWorkers: 2\t\tTime:  15.139s\n",
+      "16.0 GB\n",
+      "\tMC pi:  3.14153852000\tErr:  5.413e-05\n",
+      "\tWorkers: 4\t\tTime:  15.182s\n",
+      "32.0 GB\n",
+      "\tMC pi:  3.14157405800\tErr:  1.860e-05\n",
+      "\tWorkers: 8\t\tTime:  16.313s\n"
+     ]
+    }
+   ],
+   "source": [
+    "for size in (n * 1e9 for n in (2, 4, 8, 16, 32)):\n",
+    "    \n",
+    "    start = time()\n",
+    "    pi = calc_pi_mc(size)\n",
+    "    elaps = time() - start\n",
+    "    \n",
+    "    print_pi_stats(size, pi, time_delta=elaps,\n",
+    "                   num_workers=len(cluster.workers))\n",
+    "    \n",
+    "    sleep(4)  # allow for scale-down time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Complete listing of software used here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/usr/bin/sh: module: line 1: syntax error: unexpected end of file\n",
+      "/usr/bin/sh: error importing function definition for `BASH_FUNC_module'\n",
+      "/usr/bin/sh: jutil: line 1: syntax error: unexpected end of file\n",
+      "/usr/bin/sh: error importing function definition for `BASH_FUNC_jutil'\n",
+      "/usr/bin/sh: ml: line 1: syntax error: unexpected end of file\n",
+      "/usr/bin/sh: error importing function definition for `BASH_FUNC_ml'\n",
+      "Package          Version \n",
+      "---------------- --------\n",
+      "asciitree        0.3.3   \n",
+      "backcall         0.1.0   \n",
+      "bokeh            1.1.0   \n",
+      "certifi          2019.3.9\n",
+      "cftime           1.0.3.4 \n",
+      "Click            7.0     \n",
+      "cloudpickle      1.0.0   \n",
+      "cycler           0.10.0  \n",
+      "cytoolz          0.9.0.1 \n",
+      "dask             1.2.2   \n",
+      "dask-jobqueue    0.4.1   \n",
+      "decorator        4.4.0   \n",
+      "distributed      1.28.1  \n",
+      "docrep           0.2.5   \n",
+      "fasteners        0.14.1  \n",
+      "heapdict         1.0.0   \n",
+      "ipykernel        5.1.1   \n",
+      "ipython          7.5.0   \n",
+      "ipython-genutils 0.2.0   \n",
+      "jedi             0.13.3  \n",
+      "Jinja2           2.10.1  \n",
+      "jupyter-client   5.2.4   \n",
+      "jupyter-core     4.4.0   \n",
+      "kiwisolver       1.1.0   \n",
+      "llvmlite         0.28.0  \n",
+      "locket           0.2.0   \n",
+      "MarkupSafe       1.1.1   \n",
+      "matplotlib       3.0.3   \n",
+      "monotonic        1.5     \n",
+      "msgpack          0.6.1   \n",
+      "netCDF4          1.5.1.2 \n",
+      "numba            0.43.1  \n",
+      "numcodecs        0.6.3   \n",
+      "numpy            1.16.3  \n",
+      "olefile          0.46    \n",
+      "packaging        19.0    \n",
+      "pandas           0.24.2  \n",
+      "parso            0.4.0   \n",
+      "partd            0.3.9   \n",
+      "patsy            0.5.1   \n",
+      "pexpect          4.7.0   \n",
+      "pickleshare      0.7.5   \n",
+      "Pillow           6.0.0   \n",
+      "pip              19.1    \n",
+      "prompt-toolkit   2.0.9   \n",
+      "psutil           5.6.2   \n",
+      "ptyprocess       0.6.0   \n",
+      "Pygments         2.4.0   \n",
+      "pyparsing        2.4.0   \n",
+      "python-dateutil  2.8.0   \n",
+      "pytz             2019.1  \n",
+      "PyYAML           5.1     \n",
+      "pyzmq            18.0.1  \n",
+      "scipy            1.2.1   \n",
+      "seaborn          0.9.0   \n",
+      "setuptools       41.0.1  \n",
+      "six              1.12.0  \n",
+      "sortedcontainers 2.1.0   \n",
+      "statsmodels      0.9.0   \n",
+      "tblib            1.3.2   \n",
+      "toolz            0.9.0   \n",
+      "tornado          6.0.2   \n",
+      "traitlets        4.3.2   \n",
+      "wcwidth          0.1.7   \n",
+      "wheel            0.33.4  \n",
+      "xarray           0.12.1  \n",
+      "zarr             2.3.1   \n",
+      "zict             0.1.4   \n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/usr/bin/sh: module: line 1: syntax error: unexpected end of file\n",
+      "/usr/bin/sh: error importing function definition for `BASH_FUNC_module'\n",
+      "/usr/bin/sh: jutil: line 1: syntax error: unexpected end of file\n",
+      "/usr/bin/sh: error importing function definition for `BASH_FUNC_jutil'\n",
+      "/usr/bin/sh: ml: line 1: syntax error: unexpected end of file\n",
+      "/usr/bin/sh: error importing function definition for `BASH_FUNC_ml'\n",
+      "# This file may be used to create an environment using:\n",
+      "# $ conda create --name <env> --file <this file>\n",
+      "# platform: linux-64\n",
+      "@EXPLICIT\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/git-lfs-2.7.2-0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2019.3.9-hecc5488_0.tar.bz2\n",
+      "https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-8.2.0-hdf63c60_1.tar.bz2\n",
+      "https://repo.anaconda.com/pkgs/main/linux-64/libgfortran-ng-7.3.0-hdf63c60_0.tar.bz2\n",
+      "https://repo.anaconda.com/pkgs/main/linux-64/libstdcxx-ng-8.2.0-hdf63c60_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.6-h14c3975_1002.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/expat-2.2.5-hf484d3e_1002.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/icu-58.2-hf484d3e_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/jpeg-9c-h14c3975_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libffi-3.2.1-he1b5a44_1006.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.15-h516909a_1005.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libsodium-1.0.16-h14c3975_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.32.1-h14c3975_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.1-hf484d3e_1002.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/openblas-0.3.6-h6e990d7_2.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/openssl-1.1.1b-h14c3975_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pcre-8.41-hf484d3e_1003.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/perl-5.26.2-h516909a_1006.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h14c3975_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.9-h14c3975_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.3-h516909a_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/xz-5.2.4-h14c3975_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/yaml-0.1.7-h14c3975_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/zlib-1.2.11-h14c3975_1004.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/gettext-0.19.8.1-hc5be6a0_1002.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/hdf4-4.2.13-h9a582f1_1002.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/hdf5-1.10.4-nompi_h3c11f04_1106.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libblas-3.8.0-10_openblas.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20170329-hf8c457e_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.37-hed695b0_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.8.2-h22169c7_2.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.0.10-h648cc4a_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.13-h14c3975_1002.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.9.9-h13577e0_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/readline-7.0-hf8c457e_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.9-h84994c4_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.1-hf484d3e_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/freetype-2.10.0-he983fc9_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/glib-2.58.3-hf63aee3_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/krb5-1.16.3-h05b26f9_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.8.0-10_openblas.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.8.0-10_openblas.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.26.0-h67949de_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/dbus-1.13.6-he372182_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.13.1-he4413a7_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.14.4-h66beb1c_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libcurl-7.64.1-hda55be3_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/python-3.7.3-h5b0a415_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/asciitree-0.3.3-py_2.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/backcall-0.1.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/certifi-2019.3.9-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/click-7.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/cloudpickle-1.0.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/curl-7.64.1-hf8cf82a_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/dask-core-1.2.2-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/decorator-4.4.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.14.4-hdf3bae2_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/heapdict-1.0.0-py37_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/ipython_genutils-0.2.0-py_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.1.0-py37hc9558a2_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/llvmlite-0.28.0-py37hdbcaa40_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/locket-0.2.0-py_2.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/markupsafe-1.1.1-py37h14c3975_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/monotonic-1.5-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/msgpack-python-0.6.1-py37h6bb024c_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/numpy-1.16.3-py37he5ce36f_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/olefile-0.46-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/parso-0.4.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pickleshare-0.7.5-py37_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/psutil-5.6.2-py37h516909a_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/ptyprocess-0.6.0-py_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/pyparsing-2.4.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/pytz-2019.1-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pyyaml-5.1-py37h14c3975_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pyzmq-18.0.1-py37hc4ba49a_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/sip-4.19.8-py37hf484d3e_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/six-1.12.0-py37_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/sortedcontainers-2.1.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/tblib-1.3.2-py_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/toolz-0.9.0-py_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/tornado-6.0.2-py37h516909a_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.1.7-py_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/cftime-1.0.3.4-py37hd352d35_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/cycler-0.10.0-py_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/cytoolz-0.9.0.1-py37h14c3975_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/docrep-0.2.5-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/fasteners-0.14.1-py_3.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/git-2.21.0-pl526h2882143_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/jedi-0.13.3-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libnetcdf-4.6.2-hbdf4f91_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/numba-0.43.1-py37hf2d7682_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/numcodecs-0.6.3-py37hf484d3e_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/packaging-19.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/partd-0.3.9-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pexpect-4.7.0-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pillow-6.0.0-py37he7afcd5_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.8.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/qt-5.9.7-h52cfd70_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/scipy-1.2.1-py37h09a28d5_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/setuptools-41.0.1-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/traitlets-4.3.2-py37_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/zict-0.1.4-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/distributed-1.28.1-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/jinja2-2.10.1-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/jupyter_core-4.4.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.0.3-py37h5f35d83_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/netcdf4-1.5.1.2-py37had58050_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pandas-0.24.2-py37hf484d3e_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/patsy-0.5.1-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/pygments-2.4.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pyqt-5.9.2-py37hcca6a23_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/wheel-0.33.4-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/zarr-2.3.1-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/bokeh-1.1.0-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/jupyter_client-5.2.4-py_3.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.0.3-py37_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pip-19.1-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/prompt_toolkit-2.0.9-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.9.0-py37h3010b51_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/xarray-0.12.1-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/dask-1.2.2-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/ipython-7.5.0-py37h24bf2e0_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/seaborn-0.9.0-py_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/dask-jobqueue-0.4.1-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/ipykernel-5.1.1-py37h24bf2e0_0.tar.bz2\n",
+      "\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%conda list --explicit"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [conda env:dask_jobqueue_workshop]",
+   "language": "python",
+   "name": "conda-env-dask_jobqueue_workshop-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 90f21901faf0335443e1189d8ba5fcd6deea8060 Mon Sep 17 00:00:00 2001
From: Willi Rath <wrath@geomar.de>
Date: Thu, 20 Jun 2019 14:20:05 +0200
Subject: [PATCH 04/20] Don't use start script for now

---
 binder/start | 1 -
 1 file changed, 1 deletion(-)
 delete mode 100644 binder/start

diff --git a/binder/start b/binder/start
deleted file mode 100644
index d33b12c..0000000
--- a/binder/start
+++ /dev/null
@@ -1 +0,0 @@
-sudo supervisorctl start mysqld

From 15d1a69994962466d274174d2edcb10892f08e87 Mon Sep 17 00:00:00 2001
From: Willi Rath <wrath@geomar.de>
Date: Thu, 20 Jun 2019 15:26:36 +0200
Subject: [PATCH 05/20] Remove env file

---
 environment.yml | 24 ------------------------
 1 file changed, 24 deletions(-)
 delete mode 100644 environment.yml

diff --git a/environment.yml b/environment.yml
deleted file mode 100644
index b1d8c0e..0000000
--- a/environment.yml
+++ /dev/null
@@ -1,24 +0,0 @@
-name: dask_jobqueue_workshop
-channels:
-  - conda-forge
-dependencies:
-  - python=3
-  - bokeh
-  - dask
-  - distributed
-  - git
-  - git-lfs
-  - hdf5
-  - ipykernel
-  - matplotlib
-  - netCDF4
-  - numba
-  - numpy
-  - pandas
-  - pip
-  - seaborn
-  - tornado
-  - xarray
-  - zarr
-  - pip:
-    - git+https://github.com/dask/dask-jobqueue@9c3371d17d943dd44103225c814b8ee042513a93

From eb194b03de974abcfc0b952dc5f65d3d98f7e57d Mon Sep 17 00:00:00 2001
From: Willi Rath <wrath@geomar.de>
Date: Thu, 20 Jun 2019 15:45:34 +0200
Subject: [PATCH 06/20] Change entrypoint name to trigger rebuild

---
 binder/Dockerfile | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/binder/Dockerfile b/binder/Dockerfile
index 6443897..bcd1ee8 100644
--- a/binder/Dockerfile
+++ b/binder/Dockerfile
@@ -10,8 +10,8 @@ ENV PATH /opt/anaconda/bin:$PATH
 
 RUN conda install --yes -c conda-forge python=3.6 dask distributed dask-jobqueue docrep jupyterlab ipywidgets
 
-COPY binder/my-docker-entrypoint.sh /usr/local/bin/my-docker-entrypoint.sh
-ENTRYPOINT ["/usr/local/bin/my-docker-entrypoint.sh"]
+COPY binder/my-docker-entrypoint.sh /usr/local/bin/my_docker_entrypoint.sh
+ENTRYPOINT ["/usr/local/bin/my_docker_entrypoint.sh"]
 
 # create user with a home directory
 ARG NB_USER
@@ -29,4 +29,4 @@ RUN echo '%wheel ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
 WORKDIR ${HOME}
 USER ${NB_USER}
 
-COPY _binder_notebooks/*.ipynb ${HOME}/ 
+COPY _binder_notebooks/*.ipynb ${HOME}/

From 3eaedc354e5bdfaa20f54e7551e0d58de49fbf30 Mon Sep 17 00:00:00 2001
From: Willi Rath <wrath@geomar.de>
Date: Thu, 20 Jun 2019 15:48:50 +0200
Subject: [PATCH 07/20] Revert "Change entrypoint name to trigger rebuild"

This reverts commit eb194b03de974abcfc0b952dc5f65d3d98f7e57d.
---
 binder/Dockerfile | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/binder/Dockerfile b/binder/Dockerfile
index bcd1ee8..6443897 100644
--- a/binder/Dockerfile
+++ b/binder/Dockerfile
@@ -10,8 +10,8 @@ ENV PATH /opt/anaconda/bin:$PATH
 
 RUN conda install --yes -c conda-forge python=3.6 dask distributed dask-jobqueue docrep jupyterlab ipywidgets
 
-COPY binder/my-docker-entrypoint.sh /usr/local/bin/my_docker_entrypoint.sh
-ENTRYPOINT ["/usr/local/bin/my_docker_entrypoint.sh"]
+COPY binder/my-docker-entrypoint.sh /usr/local/bin/my-docker-entrypoint.sh
+ENTRYPOINT ["/usr/local/bin/my-docker-entrypoint.sh"]
 
 # create user with a home directory
 ARG NB_USER
@@ -29,4 +29,4 @@ RUN echo '%wheel ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
 WORKDIR ${HOME}
 USER ${NB_USER}
 
-COPY _binder_notebooks/*.ipynb ${HOME}/
+COPY _binder_notebooks/*.ipynb ${HOME}/ 

From 7bcf3c989a9fe0492406b76489fed090331ab859 Mon Sep 17 00:00:00 2001
From: Willi Rath <wrath@geomar.de>
Date: Thu, 20 Jun 2019 15:49:19 +0200
Subject: [PATCH 08/20] Make entry point executable

---
 binder/my-docker-entrypoint.sh | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 mode change 100644 => 100755 binder/my-docker-entrypoint.sh

diff --git a/binder/my-docker-entrypoint.sh b/binder/my-docker-entrypoint.sh
old mode 100644
new mode 100755

From eca1bcc70a4a797e4f70b195bd425314eb41f69a Mon Sep 17 00:00:00 2001
From: Willi Rath <wrath@geomar.de>
Date: Thu, 20 Jun 2019 15:53:41 +0200
Subject: [PATCH 09/20] Copy all notebooks for now

---
 ...m_cluster_monte_carlo_estimate_of_pi.ipynb | 822 ++++++++++++++++
 .../03_tuning_adaptive_clusters.ipynb         | 391 ++++++++
 _binder_notebooks/04_resilient_clusters.ipynb | 893 ++++++++++++++++++
 .../05_cluster_size_and_distributed_IO.ipynb  | 793 ++++++++++++++++
 4 files changed, 2899 insertions(+)
 create mode 100644 _binder_notebooks/02_slurm_cluster_monte_carlo_estimate_of_pi.ipynb
 create mode 100644 _binder_notebooks/03_tuning_adaptive_clusters.ipynb
 create mode 100644 _binder_notebooks/04_resilient_clusters.ipynb
 create mode 100644 _binder_notebooks/05_cluster_size_and_distributed_IO.ipynb

diff --git a/_binder_notebooks/02_slurm_cluster_monte_carlo_estimate_of_pi.ipynb b/_binder_notebooks/02_slurm_cluster_monte_carlo_estimate_of_pi.ipynb
new file mode 100644
index 0000000..b0ef414
--- /dev/null
+++ b/_binder_notebooks/02_slurm_cluster_monte_carlo_estimate_of_pi.ipynb
@@ -0,0 +1,822 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Monte-Carlo Estimate of $\\pi$\n",
+    "\n",
+    "We want to estimate the number $\\pi$ using a [Monte-Carlo method](https://en.wikipedia.org/wiki/Pi#Monte_Carlo_methods) exploiting that the area of a quarter circle of unit radius is $\\pi/4$ and that hence the probability of any randomly chosen point in a unit square to lie in a unit circle centerd at a corner of the unit square is $\\pi/4$ as well.  So for N randomly chosen pairs $(x, y)$ with $x\\in[0, 1)$ and $y\\in[0, 1)$, we count the number $N_{circ}$ of pairs that also satisfy $(x^2 + y^2) < 1$ and estimage $\\pi \\approx 4 \\cdot N_{circ} / N$.\n",
+    "\n",
+    "[<img src=\"https://upload.wikimedia.org/wikipedia/commons/8/84/Pi_30K.gif\" \n",
+    "     width=\"50%\" \n",
+    "     align=top\n",
+    "     alt=\"PI monte-carlo estimate\">](https://en.wikipedia.org/wiki/Pi#Monte_Carlo_methods)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Core Lessons\n",
+    "\n",
+    "- setting up SLURM (and other jobqueue) clusters\n",
+    "- Scaling clusters\n",
+    "- Adaptive clusters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set up a Slurm cluster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/p/project/cecam/rath1/miniconda3_20190521/envs/dask_jobqueue_workshop/lib/python3.7/site-packages/docrep/__init__.py:341: MatplotlibDeprecationWarning: \n",
+      "The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.\n",
+      "  s = dedents('\\n' + '\\n'.join(lines[first:]))\n"
+     ]
+    }
+   ],
+   "source": [
+    "from dask.distributed import Client\n",
+    "from dask_jobqueue import SLURMCluster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cluster = SLURMCluster(\n",
+    "    cores=24,\n",
+    "    processes=2,\n",
+    "    memory=\"100GB\",\n",
+    "    shebang='#!/usr/bin/env bash',\n",
+    "    queue=\"batch\",\n",
+    "    walltime=\"00:30:00\",\n",
+    "    local_directory='/tmp',\n",
+    "    death_timeout=\"15s\",\n",
+    "    interface=\"ib0\",\n",
+    "    log_directory=\"$SCRATCH_cecam/$USER/dask_jobqueue_logs/\",\n",
+    "    project=\"ecam\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table style=\"border: 2px solid white;\">\n",
+       "<tr>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3>Client</h3>\n",
+       "<ul>\n",
+       "  <li><b>Scheduler: </b>tcp://10.80.32.36:46416\n",
+       "  <li><b>Dashboard: </b><a href='http://10.80.32.36:8787/status' target='_blank'>http://10.80.32.36:8787/status</a>\n",
+       "</ul>\n",
+       "</td>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3>Cluster</h3>\n",
+       "<ul>\n",
+       "  <li><b>Workers: </b>0</li>\n",
+       "  <li><b>Cores: </b>0</li>\n",
+       "  <li><b>Memory: </b>0 B</li>\n",
+       "</ul>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Client: scheduler='tcp://10.80.32.36:46416' processes=0 cores=0>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "client = Client(cluster)\n",
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The job scripts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#!/usr/bin/env bash\n",
+      "\n",
+      "#SBATCH -J dask-worker\n",
+      "#SBATCH -e $SCRATCH_cecam/$USER/dask_jobqueue_logs//dask-worker-%J.err\n",
+      "#SBATCH -o $SCRATCH_cecam/$USER/dask_jobqueue_logs//dask-worker-%J.out\n",
+      "#SBATCH -p batch\n",
+      "#SBATCH -A ecam\n",
+      "#SBATCH -n 1\n",
+      "#SBATCH --cpus-per-task=24\n",
+      "#SBATCH --mem=94G\n",
+      "#SBATCH -t 00:30:00\n",
+      "JOB_ID=${SLURM_JOB_ID%;*}\n",
+      "\n",
+      "\n",
+      "\n",
+      "/p/project/cecam/rath1/miniconda3_20190521/envs/dask_jobqueue_workshop/bin/python -m distributed.cli.dask_worker tcp://10.80.32.36:46416 --nthreads 12 --nprocs 2 --memory-limit 50.00GB --name dask-worker--${JOB_ID}-- --death-timeout 15s --local-directory /tmp --interface ib0\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(cluster.job_script())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Scale the cluster to two nodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cluster.scale(4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The Monte Carlo Method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import dask.array as da\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calc_pi_mc(size_in_bytes, chunksize_in_bytes=200e6):\n",
+    "    \"\"\"Calculate PI using a Monte Carlo estimate.\"\"\"\n",
+    "    \n",
+    "    size = int(size_in_bytes / 8)\n",
+    "    chunksize = int(chunksize_in_bytes / 8)\n",
+    "    \n",
+    "    xy = da.random.uniform(0, 1,\n",
+    "                           size=(size / 2, 2),\n",
+    "                           chunks=(chunksize / 2, 2))\n",
+    "    \n",
+    "    in_circle = ((xy ** 2).sum(axis=-1) < 1)\n",
+    "    pi = 4 * in_circle.mean()\n",
+    "\n",
+    "    return pi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def print_pi_stats(size, pi, time_delta, num_workers):\n",
+    "    \"\"\"Print pi, calculate offset from true value, and print some stats.\"\"\"\n",
+    "    print(f\"{size / 1e9} GB\\n\"\n",
+    "          f\"\\tMC pi: {pi : 13.11f}\"\n",
+    "          f\"\\tErr: {abs(pi - np.pi) : 10.3e}\\n\"\n",
+    "          f\"\\tWorkers: {num_workers}\"\n",
+    "          f\"\\t\\tTime: {time_delta : 7.3f}s\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The actual calculations\n",
+    "\n",
+    "We loop over different volumes of double-precision random numbers and estimate $\\pi$ as described above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from time import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0 GB\n",
+      "\tMC pi:  3.14171686400\tErr:  1.242e-04\n",
+      "\tWorkers: 4\t\tTime:  10.157s\n",
+      "10.0 GB\n",
+      "\tMC pi:  3.14160633600\tErr:  1.368e-05\n",
+      "\tWorkers: 4\t\tTime:   1.617s\n",
+      "100.0 GB\n",
+      "\tMC pi:  3.14161142848\tErr:  1.877e-05\n",
+      "\tWorkers: 4\t\tTime:   9.231s\n"
+     ]
+    }
+   ],
+   "source": [
+    "for size in (1e9 * n for n in (1, 10, 100)):\n",
+    "    \n",
+    "    start = time()\n",
+    "    pi = calc_pi_mc(size).compute()\n",
+    "    elaps = time() - start\n",
+    "\n",
+    "    print_pi_stats(size, pi, time_delta=elaps,\n",
+    "                   num_workers=len(cluster.scheduler.workers))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Scaling the Cluster to twice its size\n",
+    "\n",
+    "We increase the number of workers by 2 and the re-run the experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from time import sleep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Scaling from 4 to 8 workers.\n"
+     ]
+    }
+   ],
+   "source": [
+    "new_num_workers = 2 * len(cluster.scheduler.workers)\n",
+    "\n",
+    "print(f\"Scaling from {len(cluster.scheduler.workers)} to {new_num_workers} workers.\")\n",
+    "\n",
+    "cluster.scale(new_num_workers)\n",
+    "\n",
+    "sleep(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table style=\"border: 2px solid white;\">\n",
+       "<tr>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3>Client</h3>\n",
+       "<ul>\n",
+       "  <li><b>Scheduler: </b>tcp://10.80.32.36:46416\n",
+       "  <li><b>Dashboard: </b><a href='http://10.80.32.36:8787/status' target='_blank'>http://10.80.32.36:8787/status</a>\n",
+       "</ul>\n",
+       "</td>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3>Cluster</h3>\n",
+       "<ul>\n",
+       "  <li><b>Workers: </b>4</li>\n",
+       "  <li><b>Cores: </b>48</li>\n",
+       "  <li><b>Memory: </b>200.00 GB</li>\n",
+       "</ul>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Client: scheduler='tcp://10.80.32.36:46416' processes=4 cores=48>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Re-run same experiments with doubled cluster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0 GB\n",
+      "\tMC pi:  3.14218988800\tErr:  5.972e-04\n",
+      "\tWorkers: 4\t\tTime:   0.724s\n",
+      "10.0 GB\n",
+      "\tMC pi:  3.14164865280\tErr:  5.600e-05\n",
+      "\tWorkers: 4\t\tTime:   1.627s\n",
+      "100.0 GB\n",
+      "\tMC pi:  3.14161009536\tErr:  1.744e-05\n",
+      "\tWorkers: 8\t\tTime:   9.199s\n"
+     ]
+    }
+   ],
+   "source": [
+    "for size in (1e9 * n for n in (1, 10, 100)):\n",
+    "    \n",
+    "        \n",
+    "    start = time()\n",
+    "    pi = calc_pi_mc(size).compute()\n",
+    "    elaps = time() - start\n",
+    "\n",
+    "    print_pi_stats(size, pi,\n",
+    "                   time_delta=elaps,\n",
+    "                   num_workers=len(cluster.scheduler.workers))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Automatically Scaling the Cluster\n",
+    "\n",
+    "We want each calculation to take only a few seconds.  Dask will try to add more workers to the cluster when workloads are high and remove workers when idling.\n",
+    "\n",
+    "_**Watch** how the cluster will scale down to the minimum a few seconds after being made adaptive._"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ca = cluster.adapt(\n",
+    "    minimum=4, maximum=100);\n",
+    "\n",
+    "sleep(4)  # Allow for scale-down"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table style=\"border: 2px solid white;\">\n",
+       "<tr>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3>Client</h3>\n",
+       "<ul>\n",
+       "  <li><b>Scheduler: </b>tcp://10.80.32.36:46416\n",
+       "  <li><b>Dashboard: </b><a href='http://10.80.32.36:8787/status' target='_blank'>http://10.80.32.36:8787/status</a>\n",
+       "</ul>\n",
+       "</td>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3>Cluster</h3>\n",
+       "<ul>\n",
+       "  <li><b>Workers: </b>4</li>\n",
+       "  <li><b>Cores: </b>48</li>\n",
+       "  <li><b>Memory: </b>200.00 GB</li>\n",
+       "</ul>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Client: scheduler='tcp://10.80.32.36:46416' processes=8 cores=96>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Repeat the calculation from above with larger work loads\n",
+    "\n",
+    "(And watch the dash board!)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0 GB\n",
+      "\tMC pi:  3.14177523200\tErr:  1.826e-04\n",
+      "\tWorkers: 4\t\tTime:   4.355s\n",
+      "10.0 GB\n",
+      "\tMC pi:  3.14153713280\tErr:  5.552e-05\n",
+      "\tWorkers: 4\t\tTime:   2.116s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "JobQueueCluster.scale_up was called with a number of workers lower that what is already running or pending\n",
+      "JobQueueCluster.scale_up was called with a number of workers lower that what is already running or pending\n",
+      "JobQueueCluster.scale_up was called with a number of workers lower that what is already running or pending\n",
+      "JobQueueCluster.scale_up was called with a number of workers lower that what is already running or pending\n",
+      "JobQueueCluster.scale_up was called with a number of workers lower that what is already running or pending\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100.0 GB\n",
+      "\tMC pi:  3.14160726144\tErr:  1.461e-05\n",
+      "\tWorkers: 4\t\tTime:   9.491s\n",
+      "1000.0 GB\n",
+      "\tMC pi:  3.14158722586\tErr:  5.428e-06\n",
+      "\tWorkers: 100\t\tTime:  20.810s\n"
+     ]
+    }
+   ],
+   "source": [
+    "for size in (n * 1e9 for n in (1, 10, 100, 1000)):\n",
+    "    \n",
+    "    \n",
+    "    start = time()\n",
+    "    pi = calc_pi_mc(size, min(size / 1000, 500e6)).compute()\n",
+    "    elaps = time() - start\n",
+    "\n",
+    "    print_pi_stats(size, pi, time_delta=elaps,\n",
+    "                   num_workers=len(cluster.scheduler.workers))\n",
+    "    \n",
+    "    sleep(20)  # allow for scale-down time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "- setup of a `SLURMCluster`\n",
+    "- manual scaling of a cluster\n",
+    "- basic adaptivity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Complete listing of software used here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/usr/bin/sh: module: line 1: syntax error: unexpected end of file\n",
+      "/usr/bin/sh: error importing function definition for `BASH_FUNC_module'\n",
+      "/usr/bin/sh: jutil: line 1: syntax error: unexpected end of file\n",
+      "/usr/bin/sh: error importing function definition for `BASH_FUNC_jutil'\n",
+      "/usr/bin/sh: ml: line 1: syntax error: unexpected end of file\n",
+      "/usr/bin/sh: error importing function definition for `BASH_FUNC_ml'\n",
+      "Package            Version          \n",
+      "------------------ -----------------\n",
+      "asciitree          0.3.3            \n",
+      "aspy.yaml          1.2.0            \n",
+      "backcall           0.1.0            \n",
+      "bokeh              1.1.0            \n",
+      "certifi            2019.3.9         \n",
+      "cfgv               1.6.0            \n",
+      "cftime             1.0.3.4          \n",
+      "Click              7.0              \n",
+      "cloudpickle        1.0.0            \n",
+      "cycler             0.10.0           \n",
+      "cytoolz            0.9.0.1          \n",
+      "dask               1.2.0            \n",
+      "dask-jobqueue      0.4.1+32.g9c3371d\n",
+      "decorator          4.4.0            \n",
+      "distributed        1.27.1           \n",
+      "docrep             0.2.5            \n",
+      "fasteners          0.14.1           \n",
+      "heapdict           1.0.0            \n",
+      "identify           1.4.3            \n",
+      "importlib-metadata 0.13             \n",
+      "ipykernel          5.1.1            \n",
+      "ipython            7.5.0            \n",
+      "ipython-genutils   0.2.0            \n",
+      "jedi               0.13.3           \n",
+      "Jinja2             2.10.1           \n",
+      "jupyter-client     5.2.4            \n",
+      "jupyter-core       4.4.0            \n",
+      "kiwisolver         1.1.0            \n",
+      "llvmlite           0.28.0           \n",
+      "locket             0.2.0            \n",
+      "MarkupSafe         1.1.1            \n",
+      "matplotlib         3.1.0            \n",
+      "monotonic          1.5              \n",
+      "msgpack            0.6.1            \n",
+      "netCDF4            1.5.1.2          \n",
+      "nodeenv            1.3.3            \n",
+      "numba              0.43.1           \n",
+      "numcodecs          0.6.3            \n",
+      "numpy              1.16.3           \n",
+      "olefile            0.46             \n",
+      "packaging          19.0             \n",
+      "pandas             0.24.2           \n",
+      "parso              0.4.0            \n",
+      "partd              0.3.9            \n",
+      "patsy              0.5.1            \n",
+      "pexpect            4.7.0            \n",
+      "pickleshare        0.7.5            \n",
+      "Pillow             6.0.0            \n",
+      "pip                19.1             \n",
+      "pre-commit         1.16.1           \n",
+      "prompt-toolkit     2.0.9            \n",
+      "psutil             5.6.2            \n",
+      "ptyprocess         0.6.0            \n",
+      "Pygments           2.4.0            \n",
+      "pyparsing          2.4.0            \n",
+      "python-dateutil    2.8.0            \n",
+      "pytz               2019.1           \n",
+      "PyYAML             5.1              \n",
+      "pyzmq              18.0.1           \n",
+      "scipy              1.3.0            \n",
+      "seaborn            0.9.0            \n",
+      "setuptools         41.0.1           \n",
+      "six                1.12.0           \n",
+      "sortedcontainers   2.1.0            \n",
+      "statsmodels        0.9.0            \n",
+      "tblib              1.4.0            \n",
+      "toml               0.10.0           \n",
+      "toolz              0.9.0            \n",
+      "tornado            6.0.2            \n",
+      "traitlets          4.3.2            \n",
+      "virtualenv         16.6.0           \n",
+      "wcwidth            0.1.7            \n",
+      "wheel              0.33.4           \n",
+      "xarray             0.12.1           \n",
+      "zarr               2.3.1            \n",
+      "zict               0.1.4            \n",
+      "zipp               0.5.1            \n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/usr/bin/sh: module: line 1: syntax error: unexpected end of file\n",
+      "/usr/bin/sh: error importing function definition for `BASH_FUNC_module'\n",
+      "/usr/bin/sh: jutil: line 1: syntax error: unexpected end of file\n",
+      "/usr/bin/sh: error importing function definition for `BASH_FUNC_jutil'\n",
+      "/usr/bin/sh: ml: line 1: syntax error: unexpected end of file\n",
+      "/usr/bin/sh: error importing function definition for `BASH_FUNC_ml'\n",
+      "# This file may be used to create an environment using:\n",
+      "# $ conda create --name <env> --file <this file>\n",
+      "# platform: linux-64\n",
+      "@EXPLICIT\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/git-lfs-2.7.2-0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2019.3.9-hecc5488_0.tar.bz2\n",
+      "https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-8.2.0-hdf63c60_1.tar.bz2\n",
+      "https://repo.anaconda.com/pkgs/main/linux-64/libgfortran-ng-7.3.0-hdf63c60_0.tar.bz2\n",
+      "https://repo.anaconda.com/pkgs/main/linux-64/libstdcxx-ng-8.2.0-hdf63c60_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.6-h14c3975_1002.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/expat-2.2.5-hf484d3e_1002.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/icu-58.2-hf484d3e_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/jpeg-9c-h14c3975_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libffi-3.2.1-he1b5a44_1006.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.15-h516909a_1005.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libsodium-1.0.16-h14c3975_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.32.1-h14c3975_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.1-hf484d3e_1002.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/openblas-0.3.6-h6e990d7_2.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/openssl-1.1.1b-h14c3975_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pcre-8.41-hf484d3e_1003.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/perl-5.26.2-h516909a_1006.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h14c3975_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.9-h14c3975_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.3-h516909a_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/xz-5.2.4-h14c3975_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/yaml-0.1.7-h14c3975_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/zlib-1.2.11-h14c3975_1004.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/gettext-0.19.8.1-hc5be6a0_1002.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/hdf4-4.2.13-h9a582f1_1002.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/hdf5-1.10.4-nompi_h3c11f04_1106.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libblas-3.8.0-10_openblas.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20170329-hf8c457e_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.37-hed695b0_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.8.2-h22169c7_2.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.0.10-h648cc4a_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.13-h14c3975_1002.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.9.9-h13577e0_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/readline-7.0-hf8c457e_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.9-h84994c4_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.1-hf484d3e_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/freetype-2.10.0-he983fc9_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/glib-2.58.3-hf63aee3_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/krb5-1.16.3-h05b26f9_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.8.0-10_openblas.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.8.0-10_openblas.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.26.0-h67949de_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/dbus-1.13.6-he372182_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.13.1-he4413a7_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.14.4-h66beb1c_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libcurl-7.64.1-hda55be3_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/python-3.7.3-h5b0a415_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/asciitree-0.3.3-py_2.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/backcall-0.1.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/certifi-2019.3.9-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/click-7.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/cloudpickle-1.0.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/curl-7.64.1-hf8cf82a_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/dask-core-1.2.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/decorator-4.4.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.14.4-hdf3bae2_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/heapdict-1.0.0-py37_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/ipython_genutils-0.2.0-py_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.1.0-py37hc9558a2_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/llvmlite-0.28.0-py37hdbcaa40_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/locket-0.2.0-py_2.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/markupsafe-1.1.1-py37h14c3975_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/monotonic-1.5-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/msgpack-python-0.6.1-py37h6bb024c_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/numpy-1.16.3-py37he5ce36f_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/olefile-0.46-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/parso-0.4.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pickleshare-0.7.5-py37_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/psutil-5.6.2-py37h516909a_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/ptyprocess-0.6.0-py_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/pyparsing-2.4.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/pytz-2019.1-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pyyaml-5.1-py37h14c3975_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pyzmq-18.0.1-py37hc4ba49a_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/sip-4.19.8-py37hf484d3e_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/six-1.12.0-py37_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/sortedcontainers-2.1.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/tblib-1.4.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/toolz-0.9.0-py_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/tornado-6.0.2-py37h516909a_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.1.7-py_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/cftime-1.0.3.4-py37hd352d35_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/cycler-0.10.0-py_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/cytoolz-0.9.0.1-py37h14c3975_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/fasteners-0.14.1-py_3.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/git-2.21.0-pl526h2882143_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/jedi-0.13.3-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libnetcdf-4.6.2-hbdf4f91_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/numba-0.43.1-py37hf2d7682_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/numcodecs-0.6.3-py37hf484d3e_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/packaging-19.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/partd-0.3.9-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pexpect-4.7.0-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pillow-6.0.0-py37he7afcd5_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.8.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/qt-5.9.7-h52cfd70_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/scipy-1.3.0-py37hab63836_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/setuptools-41.0.1-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/traitlets-4.3.2-py37_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/zict-0.1.4-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/distributed-1.27.1-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/jinja2-2.10.1-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/jupyter_core-4.4.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.1.0-py37h5f35d83_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/netcdf4-1.5.1.2-py37had58050_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pandas-0.24.2-py37hf484d3e_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/patsy-0.5.1-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/pygments-2.4.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pyqt-5.9.2-py37hcca6a23_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/wheel-0.33.4-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/zarr-2.3.1-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/bokeh-1.1.0-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/jupyter_client-5.2.4-py_3.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.1.0-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pip-19.1-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/prompt_toolkit-2.0.9-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.9.0-py37h3010b51_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/xarray-0.12.1-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/dask-1.2.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/ipython-7.5.0-py37h24bf2e0_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/seaborn-0.9.0-py_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/ipykernel-5.1.1-py37h24bf2e0_0.tar.bz2\n",
+      "\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%conda list --explicit"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [conda env:dask_jobqueue_workshop]",
+   "language": "python",
+   "name": "conda-env-dask_jobqueue_workshop-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/_binder_notebooks/03_tuning_adaptive_clusters.ipynb b/_binder_notebooks/03_tuning_adaptive_clusters.ipynb
new file mode 100644
index 0000000..d62d38c
--- /dev/null
+++ b/_binder_notebooks/03_tuning_adaptive_clusters.ipynb
@@ -0,0 +1,391 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Monte-Carlo Estimate of $\\pi$\n",
+    "\n",
+    "We want to estimate the number $\\pi$ using a [Monte-Carlo method](https://en.wikipedia.org/wiki/Pi#Monte_Carlo_methods) exploiting that the area of a quarter circle of unit radius is $\\pi/4$ and that hence the probability of any randomly chosen point in a unit square to lie in a unit circle centerd at a corner of the unit square is $\\pi/4$ as well.  So for N randomly chosen pairs $(x, y)$ with $x\\in[0, 1)$ and $y\\in[0, 1)$, we count the number $N_{circ}$ of pairs that also satisfy $(x^2 + y^2) < 1$ and estimage $\\pi \\approx 4 \\cdot N_{circ} / N$.\n",
+    "\n",
+    "[<img src=\"https://upload.wikimedia.org/wikipedia/commons/8/84/Pi_30K.gif\" \n",
+    "     width=\"50%\" \n",
+    "     align=top\n",
+    "     alt=\"PI monte-carlo estimate\">](https://en.wikipedia.org/wiki/Pi#Monte_Carlo_methods)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Core Lessons\n",
+    "\n",
+    "- Adaptive clusters\n",
+    "- Tuning the adaptivity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set up a Slurm cluster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from dask.distributed import Client\n",
+    "from dask_jobqueue import SLURMCluster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cluster = SLURMCluster(\n",
+    "    cores=24,\n",
+    "    processes=2,\n",
+    "    memory=\"100GB\",\n",
+    "    shebang='#!/usr/bin/env bash',\n",
+    "    queue=\"batch\",\n",
+    "    walltime=\"00:30:00\",\n",
+    "    local_directory='/tmp',\n",
+    "    death_timeout=\"15s\",\n",
+    "    interface=\"ib0\",\n",
+    "    log_directory=f'{os.environ[\"SCRATCH_cecam\"]}/{os.environ[\"USER\"]}/dask_jobqueue_logs/',\n",
+    "    project=\"ecam\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client = Client(cluster)\n",
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The job scripts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(cluster.job_script())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Scale the cluster to two nodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cluster.scale(4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The Monte Carlo Method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import dask.array as da\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calc_pi_mc(size_in_bytes, chunksize_in_bytes=200e6):\n",
+    "    \"\"\"Calculate PI using a Monte Carlo estimate.\"\"\"\n",
+    "    \n",
+    "    size = int(size_in_bytes / 8)\n",
+    "    chunksize = int(chunksize_in_bytes / 8)\n",
+    "    \n",
+    "    xy = da.random.uniform(0, 1,\n",
+    "                           size=(size / 2, 2),\n",
+    "                           chunks=(chunksize / 2, 2))\n",
+    "    \n",
+    "    in_circle = ((xy ** 2).sum(axis=-1) < 1)\n",
+    "    pi = 4 * in_circle.mean()\n",
+    "\n",
+    "    return pi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def print_pi_stats(size, pi, time_delta, num_workers):\n",
+    "    \"\"\"Print pi, calculate offset from true value, and print some stats.\"\"\"\n",
+    "    print(f\"{size / 1e9} GB\\n\"\n",
+    "          f\"\\tMC pi: {pi : 13.11f}\"\n",
+    "          f\"\\tErr: {abs(pi - np.pi) : 10.3e}\\n\"\n",
+    "          f\"\\tWorkers: {num_workers}\"\n",
+    "          f\"\\t\\tTime: {time_delta : 7.3f}s\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The actual calculations\n",
+    "\n",
+    "We loop over different volumes of double-precision random numbers and estimate $\\pi$ as described above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from time import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for size in (1e9 * n for n in (1, 10, 100)):\n",
+    "    \n",
+    "    start = time()\n",
+    "    pi = calc_pi_mc(size).compute()\n",
+    "    elaps = time() - start\n",
+    "\n",
+    "    print_pi_stats(size, pi, time_delta=elaps,\n",
+    "                   num_workers=len(cluster.scheduler.workers))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Scaling the Cluster to twice its size\n",
+    "\n",
+    "We increase the number of workers by 2 and the re-run the experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from time import sleep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_num_workers = 2 * len(cluster.scheduler.workers)\n",
+    "\n",
+    "print(f\"Scaling from {len(cluster.scheduler.workers)} to {new_num_workers} workers.\")\n",
+    "\n",
+    "cluster.scale(new_num_workers)\n",
+    "\n",
+    "sleep(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Re-run same experiments with doubled cluster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for size in (1e9 * n for n in (1, 10, 100)):\n",
+    "    \n",
+    "        \n",
+    "    start = time()\n",
+    "    pi = calc_pi_mc(size).compute()\n",
+    "    elaps = time() - start\n",
+    "\n",
+    "    print_pi_stats(size, pi,\n",
+    "                   time_delta=elaps,\n",
+    "                   num_workers=len(cluster.scheduler.workers))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Automatically scale the cluster towards a target duration\n",
+    "\n",
+    "We'll target a wall time of 30 seconds.\n",
+    "\n",
+    "_**Watch** how the cluster will scale down to the minimum a few seconds after being made adaptive._"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ca = cluster.adapt(\n",
+    "    minimum=2, maximum=30,\n",
+    "    target_duration=\"360s\",  # measured in CPU time per worker\n",
+    "                             # -> 30 seconds at 12 cores / worker\n",
+    "    scale_factor=1.0  # prevent from scaling up because of CPU or MEM need\n",
+    ");\n",
+    "\n",
+    "sleep(4)  # Allow for scale-down"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Repeat the calculation from above with larger work loads\n",
+    "\n",
+    "(And watch the dash board!)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for size in (n * 1e9 for n in (200, 400, 800)):\n",
+    "    \n",
+    "    \n",
+    "    start = time()\n",
+    "    pi = calc_pi_mc(size, min(size / 1000, 500e6)).compute()\n",
+    "    elaps = time() - start\n",
+    "\n",
+    "    print_pi_stats(size, pi, time_delta=elaps,\n",
+    "                   num_workers=len(cluster.scheduler.workers))\n",
+    "    \n",
+    "    sleep(20)  # allow for scale-down time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "- adaptivity with a target duration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Complete listing of software used here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%conda list --explicit"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [conda env:dask_jobqueue_workshop]",
+   "language": "python",
+   "name": "conda-env-dask_jobqueue_workshop-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/_binder_notebooks/04_resilient_clusters.ipynb b/_binder_notebooks/04_resilient_clusters.ipynb
new file mode 100644
index 0000000..3196d8b
--- /dev/null
+++ b/_binder_notebooks/04_resilient_clusters.ipynb
@@ -0,0 +1,893 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Monte-Carlo Estimate of $\\pi$\n",
+    "\n",
+    "We want to estimate the number $\\pi$ using a [Monte-Carlo method](https://en.wikipedia.org/wiki/Pi#Monte_Carlo_methods) exploiting that the area of a quarter circle of unit radius is $\\pi/4$ and that hence the probability of any randomly chosen point in a unit square to lie in a unit circle centerd at a corner of the unit square is $\\pi/4$ as well.  So for N randomly chosen pairs $(x, y)$ with $x\\in[0, 1)$ and $y\\in[0, 1)$, we count the number $N_{circ}$ of pairs that also satisfy $(x^2 + y^2) < 1$ and estimage $\\pi \\approx 4 \\cdot N_{circ} / N$.\n",
+    "\n",
+    "[<img src=\"https://upload.wikimedia.org/wikipedia/commons/8/84/Pi_30K.gif\" \n",
+    "     width=\"50%\" \n",
+    "     align=top\n",
+    "     alt=\"PI monte-carlo estimate\">](https://en.wikipedia.org/wiki/Pi#Monte_Carlo_methods)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Core Lessons\n",
+    "\n",
+    "- Resilience against dying workers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set up a Slurm cluster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/p/project/cecam/rath1/miniconda3_20190521/envs/dask_jobqueue_workshop/lib/python3.7/site-packages/docrep/__init__.py:341: MatplotlibDeprecationWarning: \n",
+      "The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.\n",
+      "  s = dedents('\\n' + '\\n'.join(lines[first:]))\n"
+     ]
+    }
+   ],
+   "source": [
+    "from dask.distributed import Client\n",
+    "from dask_jobqueue import SLURMCluster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cluster = SLURMCluster(\n",
+    "    cores=24,\n",
+    "    processes=2,\n",
+    "    memory=\"100GB\",\n",
+    "    shebang='#!/usr/bin/env bash',\n",
+    "    queue=\"batch\",\n",
+    "    walltime=\"00:30:00\",\n",
+    "    local_directory='/tmp',\n",
+    "    death_timeout=\"15s\",\n",
+    "    interface=\"ib0\",\n",
+    "    log_directory=f'{os.environ[\"SCRATCH_cecam\"]}/{os.environ[\"USER\"]}/dask_jobqueue_logs/',\n",
+    "    project=\"ecam\",\n",
+    "    name=\"resilient_clusters\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table style=\"border: 2px solid white;\">\n",
+       "<tr>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3>Client</h3>\n",
+       "<ul>\n",
+       "  <li><b>Scheduler: </b>tcp://10.80.32.41:33451\n",
+       "  <li><b>Dashboard: </b><a href='http://10.80.32.41:8787/status' target='_blank'>http://10.80.32.41:8787/status</a>\n",
+       "</ul>\n",
+       "</td>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3>Cluster</h3>\n",
+       "<ul>\n",
+       "  <li><b>Workers: </b>0</li>\n",
+       "  <li><b>Cores: </b>0</li>\n",
+       "  <li><b>Memory: </b>0 B</li>\n",
+       "</ul>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Client: scheduler='tcp://10.80.32.41:33451' processes=0 cores=0>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "client = Client(cluster)\n",
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The job scripts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#!/usr/bin/env bash\n",
+      "\n",
+      "#SBATCH -J resilient_clusters\n",
+      "#SBATCH -e /p/scratch/cecam/rath1/dask_jobqueue_logs//resilient_clusters-%J.err\n",
+      "#SBATCH -o /p/scratch/cecam/rath1/dask_jobqueue_logs//resilient_clusters-%J.out\n",
+      "#SBATCH -p batch\n",
+      "#SBATCH -A ecam\n",
+      "#SBATCH -n 1\n",
+      "#SBATCH --cpus-per-task=24\n",
+      "#SBATCH --mem=94G\n",
+      "#SBATCH -t 00:30:00\n",
+      "JOB_ID=${SLURM_JOB_ID%;*}\n",
+      "\n",
+      "\n",
+      "\n",
+      "/p/project/cecam/rath1/miniconda3_20190521/envs/dask_jobqueue_workshop/bin/python -m distributed.cli.dask_worker tcp://10.80.32.41:33451 --nthreads 12 --nprocs 2 --memory-limit 50.00GB --name resilient_clusters--${JOB_ID}-- --death-timeout 15s --local-directory /tmp --interface ib0\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(cluster.job_script())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Scale the cluster to two nodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<distributed.deploy.adaptive.Adaptive at 0x2b45b2ef8978>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cluster.adapt(minimum=12, maximum=12)  # will lead to six jobs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The Monte Carlo Method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import dask.array as da\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calc_pi_mc(size_in_bytes, chunksize_in_bytes=200e6):\n",
+    "    \"\"\"Calculate PI using a Monte Carlo estimate.\"\"\"\n",
+    "    \n",
+    "    size = int(size_in_bytes / 8)\n",
+    "    chunksize = int(chunksize_in_bytes / 8)\n",
+    "    \n",
+    "    xy = da.random.uniform(0, 1,\n",
+    "                           size=(size / 2, 2),\n",
+    "                           chunks=(chunksize / 2, 2))\n",
+    "    \n",
+    "    in_circle = ((xy ** 2).sum(axis=-1) < 1)\n",
+    "    pi = 4 * in_circle.mean()\n",
+    "\n",
+    "    return pi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def print_pi_stats(size, pi, time_delta, num_workers):\n",
+    "    \"\"\"Print pi, calculate offset from true value, and print some stats.\"\"\"\n",
+    "    print(f\"{size / 1e9} GB\\n\"\n",
+    "          f\"\\tMC pi: {pi : 13.11f}\"\n",
+    "          f\"\\tErr: {abs(pi - np.pi) : 10.3e}\\n\"\n",
+    "          f\"\\tWorkers: {num_workers}\"\n",
+    "          f\"\\t\\tTime: {time_delta : 7.3f}s\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The actual calculations\n",
+    "\n",
+    "We loop over different volumes of double-precision random numbers and estimate $\\pi$ as described above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from time import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0 GB\n",
+      "\tMC pi:  3.14145440000\tErr:  1.383e-04\n",
+      "\tWorkers: 6\t\tTime:  14.832s\n",
+      "10.0 GB\n",
+      "\tMC pi:  3.14162355200\tErr:  3.090e-05\n",
+      "\tWorkers: 6\t\tTime:   1.158s\n",
+      "100.0 GB\n",
+      "\tMC pi:  3.14158572480\tErr:  6.929e-06\n",
+      "\tWorkers: 12\t\tTime:   4.409s\n"
+     ]
+    }
+   ],
+   "source": [
+    "for size in (1e9 * n for n in (1, 10, 100)):\n",
+    "    \n",
+    "    start = time()\n",
+    "    pi = calc_pi_mc(size).compute()\n",
+    "    elaps = time() - start\n",
+    "\n",
+    "    print_pi_stats(size, pi, time_delta=elaps,\n",
+    "                   num_workers=len(cluster.scheduler.workers))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What happens if a worker dies?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll find out all \"our\" job ids, mark a few of them non-preemptible, filter for the preemptible jobs, and define a function to kill one randomly selected preemptible job."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_current_jobs():\n",
+    "    current_jobs = !squeue | grep R | grep $USER | grep resil | awk '{print $1}'\n",
+    "    return current_jobs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['7191395', '7191396']"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "non_preemptible_jobs = get_current_jobs()[:2]\n",
+    "non_preemptible_jobs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_preemptible_jobs():\n",
+    "    return list(filter(lambda j: j not in non_preemptible_jobs, get_current_jobs()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "\n",
+    "def kill_random_preemptible_job():\n",
+    "    preemptible_jobs = get_preemptible_jobs()\n",
+    "    if preemptible_jobs:\n",
+    "        worker_to_kill = random.choice(preemptible_jobs)\n",
+    "        print(f\"will cancel job {worker_to_kill}\")\n",
+    "        !scancel {worker_to_kill}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Let's start a computation with disappearing workers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pi = calc_pi_mc(1e12, 500e6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<b>Future: finalize</b> <font color=\"gray\">status: </font><font color=\"black\">pending</font>, <font color=\"gray\">key: </font>finalize-2a7825eef9b2726c91452b1374ee6eca"
+      ],
+      "text/plain": [
+       "<Future: status: pending, key: finalize-2a7825eef9b2726c91452b1374ee6eca>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pi = client.compute(pi)\n",
+    "pi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from time import sleep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "will cancel job 7191399\n",
+      "will cancel job 7191400\n",
+      "will cancel job 7191397\n"
+     ]
+    }
+   ],
+   "source": [
+    "sleep(5)\n",
+    "\n",
+    "while not pi.done():\n",
+    "    kill_random_preemptible_job()\n",
+    "    sleep(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<b>Future: finalize</b> <font color=\"gray\">status: </font><font color=\"red\">error</font>, <font color=\"gray\">key: </font>finalize-2a7825eef9b2726c91452b1374ee6eca"
+      ],
+      "text/plain": [
+       "<Future: status: error, key: finalize-2a7825eef9b2726c91452b1374ee6eca>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KilledWorker",
+     "evalue": "(\"('sum-sum-aggregate-uniform-mean_chunk-d15a005003e49b0f8054e3bbec8f3ce1', 1688)\", 'tcp://10.80.35.39:40738')",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKilledWorker\u001b[0m                              Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-21-4850f3d325fa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/p/project/cecam/rath1/miniconda3_20190521/envs/dask_jobqueue_workshop/lib/python3.7/site-packages/distributed/client.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    225\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msync\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_result\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback_timeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraiseit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"error\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 227\u001b[0;31m             \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    228\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"cancelled\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/p/project/cecam/rath1/miniconda3_20190521/envs/dask_jobqueue_workshop/lib/python3.7/site-packages/six.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(tp, value, tb)\u001b[0m\n\u001b[1;32m    691\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    692\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 693\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    694\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    695\u001b[0m             \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKilledWorker\u001b[0m: (\"('sum-sum-aggregate-uniform-mean_chunk-d15a005003e49b0f8054e3bbec8f3ce1', 1688)\", 'tcp://10.80.35.39:40738')"
+     ]
+    }
+   ],
+   "source": [
+    "print(pi.result())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What happened?\n",
+    "\n",
+    "The Dask scheduler keeps a suspiciousness counter for each task it manages.  Whenever a worker dies, all tasks that belong to the worker at the time of its death will have their suspiciousness increased by one. In doing so, the scheduler has no way of telling which exact task was responsible for the death of the worker and just flag all of them as bad.\n",
+    "\n",
+    "All tasks with suspiciousness `>= 3` (default) are considered bad and won't be rescheduled."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Make dask more resilient\n",
+    "\n",
+    "We can increase the number of allowed failures.  Let's practically disable the threshold and re-do the calculation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cluster.scheduler.allowed_failures = 1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pi = calc_pi_mc(1e12, 500e6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<b>Future: finalize</b> <font color=\"gray\">status: </font><font color=\"black\">pending</font>, <font color=\"gray\">key: </font>finalize-054a7564edc5c9907858eac3e79c0d6d"
+      ],
+      "text/plain": [
+       "<Future: status: pending, key: finalize-054a7564edc5c9907858eac3e79c0d6d>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pi = client.compute(pi)\n",
+    "pi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "will cancel job 7191405\n",
+      "will cancel job 7191398\n",
+      "will cancel job 7191406\n",
+      "will cancel job 7191402\n",
+      "will cancel job 7191411\n"
+     ]
+    }
+   ],
+   "source": [
+    "sleep(5)\n",
+    "\n",
+    "while not pi.done():\n",
+    "    kill_random_preemptible_job()\n",
+    "    sleep(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<b>Future: finalize</b> <font color=\"gray\">status: </font><font color=\"black\">finished</font>, <font color=\"gray\">type: </font>float64, <font color=\"gray\">key: </font>finalize-054a7564edc5c9907858eac3e79c0d6d"
+      ],
+      "text/plain": [
+       "<Future: status: finished, type: float64, key: finalize-054a7564edc5c9907858eac3e79c0d6d>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.141595365824\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(pi.result())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "Dask very consciously handles failing tasks and can be made very resilient against suddenly disappearing ressources."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Complete listing of software used here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Package            Version          \n",
+      "------------------ -----------------\n",
+      "asciitree          0.3.3            \n",
+      "aspy.yaml          1.2.0            \n",
+      "backcall           0.1.0            \n",
+      "bokeh              1.1.0            \n",
+      "certifi            2019.3.9         \n",
+      "cfgv               1.6.0            \n",
+      "cftime             1.0.3.4          \n",
+      "Click              7.0              \n",
+      "cloudpickle        1.0.0            \n",
+      "cycler             0.10.0           \n",
+      "cytoolz            0.9.0.1          \n",
+      "dask               1.2.0            \n",
+      "dask-jobqueue      0.4.1+32.g9c3371d\n",
+      "decorator          4.4.0            \n",
+      "distributed        1.27.1           \n",
+      "docrep             0.2.5            \n",
+      "fasteners          0.14.1           \n",
+      "heapdict           1.0.0            \n",
+      "identify           1.4.3            \n",
+      "importlib-metadata 0.13             \n",
+      "ipykernel          5.1.1            \n",
+      "ipython            7.5.0            \n",
+      "ipython-genutils   0.2.0            \n",
+      "jedi               0.13.3           \n",
+      "Jinja2             2.10.1           \n",
+      "jupyter-client     5.2.4            \n",
+      "jupyter-core       4.4.0            \n",
+      "kiwisolver         1.1.0            \n",
+      "llvmlite           0.28.0           \n",
+      "locket             0.2.0            \n",
+      "MarkupSafe         1.1.1            \n",
+      "matplotlib         3.1.0            \n",
+      "monotonic          1.5              \n",
+      "msgpack            0.6.1            \n",
+      "netCDF4            1.5.1.2          \n",
+      "nodeenv            1.3.3            \n",
+      "numba              0.43.1           \n",
+      "numcodecs          0.6.3            \n",
+      "numpy              1.16.3           \n",
+      "olefile            0.46             \n",
+      "packaging          19.0             \n",
+      "pandas             0.24.2           \n",
+      "parso              0.4.0            \n",
+      "partd              0.3.9            \n",
+      "patsy              0.5.1            \n",
+      "pexpect            4.7.0            \n",
+      "pickleshare        0.7.5            \n",
+      "Pillow             6.0.0            \n",
+      "pip                19.1             \n",
+      "pre-commit         1.16.1           \n",
+      "prompt-toolkit     2.0.9            \n",
+      "psutil             5.6.2            \n",
+      "ptyprocess         0.6.0            \n",
+      "Pygments           2.4.0            \n",
+      "pyparsing          2.4.0            \n",
+      "python-dateutil    2.8.0            \n",
+      "pytz               2019.1           \n",
+      "PyYAML             5.1              \n",
+      "pyzmq              18.0.1           \n",
+      "scipy              1.3.0            \n",
+      "seaborn            0.9.0            \n",
+      "setuptools         41.0.1           \n",
+      "six                1.12.0           \n",
+      "sortedcontainers   2.1.0            \n",
+      "statsmodels        0.9.0            \n",
+      "tblib              1.4.0            \n",
+      "toml               0.10.0           \n",
+      "toolz              0.9.0            \n",
+      "tornado            6.0.2            \n",
+      "traitlets          4.3.2            \n",
+      "virtualenv         16.6.0           \n",
+      "wcwidth            0.1.7            \n",
+      "wheel              0.33.4           \n",
+      "xarray             0.12.1           \n",
+      "zarr               2.3.1            \n",
+      "zict               0.1.4            \n",
+      "zipp               0.5.1            \n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# This file may be used to create an environment using:\n",
+      "# $ conda create --name <env> --file <this file>\n",
+      "# platform: linux-64\n",
+      "@EXPLICIT\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/git-lfs-2.7.2-0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2019.3.9-hecc5488_0.tar.bz2\n",
+      "https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-8.2.0-hdf63c60_1.tar.bz2\n",
+      "https://repo.anaconda.com/pkgs/main/linux-64/libgfortran-ng-7.3.0-hdf63c60_0.tar.bz2\n",
+      "https://repo.anaconda.com/pkgs/main/linux-64/libstdcxx-ng-8.2.0-hdf63c60_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.6-h14c3975_1002.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/expat-2.2.5-hf484d3e_1002.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/icu-58.2-hf484d3e_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/jpeg-9c-h14c3975_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libffi-3.2.1-he1b5a44_1006.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.15-h516909a_1005.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libsodium-1.0.16-h14c3975_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.32.1-h14c3975_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.1-hf484d3e_1002.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/openblas-0.3.6-h6e990d7_2.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/openssl-1.1.1b-h14c3975_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pcre-8.41-hf484d3e_1003.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/perl-5.26.2-h516909a_1006.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h14c3975_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.9-h14c3975_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.3-h516909a_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/xz-5.2.4-h14c3975_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/yaml-0.1.7-h14c3975_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/zlib-1.2.11-h14c3975_1004.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/gettext-0.19.8.1-hc5be6a0_1002.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/hdf4-4.2.13-h9a582f1_1002.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/hdf5-1.10.4-nompi_h3c11f04_1106.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libblas-3.8.0-10_openblas.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20170329-hf8c457e_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.37-hed695b0_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.8.2-h22169c7_2.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.0.10-h648cc4a_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.13-h14c3975_1002.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.9.9-h13577e0_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/readline-7.0-hf8c457e_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.9-h84994c4_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.1-hf484d3e_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/freetype-2.10.0-he983fc9_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/glib-2.58.3-hf63aee3_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/krb5-1.16.3-h05b26f9_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.8.0-10_openblas.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.8.0-10_openblas.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.26.0-h67949de_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/dbus-1.13.6-he372182_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.13.1-he4413a7_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.14.4-h66beb1c_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libcurl-7.64.1-hda55be3_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/python-3.7.3-h5b0a415_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/asciitree-0.3.3-py_2.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/backcall-0.1.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/certifi-2019.3.9-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/click-7.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/cloudpickle-1.0.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/curl-7.64.1-hf8cf82a_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/dask-core-1.2.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/decorator-4.4.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.14.4-hdf3bae2_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/heapdict-1.0.0-py37_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/ipython_genutils-0.2.0-py_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.1.0-py37hc9558a2_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/llvmlite-0.28.0-py37hdbcaa40_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/locket-0.2.0-py_2.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/markupsafe-1.1.1-py37h14c3975_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/monotonic-1.5-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/msgpack-python-0.6.1-py37h6bb024c_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/numpy-1.16.3-py37he5ce36f_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/olefile-0.46-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/parso-0.4.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pickleshare-0.7.5-py37_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/psutil-5.6.2-py37h516909a_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/ptyprocess-0.6.0-py_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/pyparsing-2.4.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/pytz-2019.1-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pyyaml-5.1-py37h14c3975_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pyzmq-18.0.1-py37hc4ba49a_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/sip-4.19.8-py37hf484d3e_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/six-1.12.0-py37_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/sortedcontainers-2.1.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/tblib-1.4.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/toolz-0.9.0-py_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/tornado-6.0.2-py37h516909a_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.1.7-py_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/cftime-1.0.3.4-py37hd352d35_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/cycler-0.10.0-py_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/cytoolz-0.9.0.1-py37h14c3975_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/fasteners-0.14.1-py_3.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/git-2.21.0-pl526h2882143_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/jedi-0.13.3-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libnetcdf-4.6.2-hbdf4f91_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/numba-0.43.1-py37hf2d7682_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/numcodecs-0.6.3-py37hf484d3e_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/packaging-19.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/partd-0.3.9-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pexpect-4.7.0-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pillow-6.0.0-py37he7afcd5_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.8.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/qt-5.9.7-h52cfd70_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/scipy-1.3.0-py37hab63836_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/setuptools-41.0.1-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/traitlets-4.3.2-py37_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/zict-0.1.4-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/distributed-1.27.1-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/jinja2-2.10.1-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/jupyter_core-4.4.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.1.0-py37h5f35d83_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/netcdf4-1.5.1.2-py37had58050_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pandas-0.24.2-py37hf484d3e_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/patsy-0.5.1-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/pygments-2.4.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pyqt-5.9.2-py37hcca6a23_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/wheel-0.33.4-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/zarr-2.3.1-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/bokeh-1.1.0-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/jupyter_client-5.2.4-py_3.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.1.0-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pip-19.1-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/prompt_toolkit-2.0.9-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.9.0-py37h3010b51_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/xarray-0.12.1-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/dask-1.2.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/ipython-7.5.0-py37h24bf2e0_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/seaborn-0.9.0-py_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/ipykernel-5.1.1-py37h24bf2e0_0.tar.bz2\n",
+      "\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%conda list --explicit"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [conda env:dask_jobqueue_workshop]",
+   "language": "python",
+   "name": "conda-env-dask_jobqueue_workshop-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/_binder_notebooks/05_cluster_size_and_distributed_IO.ipynb b/_binder_notebooks/05_cluster_size_and_distributed_IO.ipynb
new file mode 100644
index 0000000..224ab86
--- /dev/null
+++ b/_binder_notebooks/05_cluster_size_and_distributed_IO.ipynb
@@ -0,0 +1,793 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Distributed IO and cluster sizes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Core Lesson\n",
+    "\n",
+    "In a distributed file system, spreading IO to as many nodes as possible might speed up your computations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set up a Slurm cluster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/p/project/cecam/rath1/miniconda3_20190521/envs/dask_jobqueue_workshop/lib/python3.7/site-packages/docrep/__init__.py:341: MatplotlibDeprecationWarning: \n",
+      "The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.\n",
+      "  s = dedents('\\n' + '\\n'.join(lines[first:]))\n"
+     ]
+    }
+   ],
+   "source": [
+    "from dask.distributed import Client\n",
+    "from dask_jobqueue import SLURMCluster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cluster = SLURMCluster(\n",
+    "    cores=24,\n",
+    "    processes=2,\n",
+    "    memory=\"100GB\",\n",
+    "    shebang='#!/usr/bin/env bash',\n",
+    "    queue=\"batch\",\n",
+    "    walltime=\"00:30:00\",\n",
+    "    local_directory='/tmp',\n",
+    "    death_timeout=\"15s\",\n",
+    "    interface=\"ib0\",\n",
+    "    log_directory=f'{os.environ[\"SCRATCH_cecam\"]}/{os.environ[\"USER\"]}/dask_jobqueue_logs/',\n",
+    "    project=\"ecam\",\n",
+    "    name=\"resilient_clusters\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table style=\"border: 2px solid white;\">\n",
+       "<tr>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3>Client</h3>\n",
+       "<ul>\n",
+       "  <li><b>Scheduler: </b>tcp://10.80.32.41:39894\n",
+       "  <li><b>Dashboard: </b><a href='http://10.80.32.41:8787/status' target='_blank'>http://10.80.32.41:8787/status</a>\n",
+       "</ul>\n",
+       "</td>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3>Cluster</h3>\n",
+       "<ul>\n",
+       "  <li><b>Workers: </b>0</li>\n",
+       "  <li><b>Cores: </b>0</li>\n",
+       "  <li><b>Memory: </b>0 B</li>\n",
+       "</ul>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Client: scheduler='tcp://10.80.32.41:39894' processes=0 cores=0>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "client = Client(cluster)\n",
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The job scripts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#!/usr/bin/env bash\n",
+      "\n",
+      "#SBATCH -J resilient_clusters\n",
+      "#SBATCH -e /p/scratch/cecam/rath1/dask_jobqueue_logs//resilient_clusters-%J.err\n",
+      "#SBATCH -o /p/scratch/cecam/rath1/dask_jobqueue_logs//resilient_clusters-%J.out\n",
+      "#SBATCH -p batch\n",
+      "#SBATCH -A ecam\n",
+      "#SBATCH -n 1\n",
+      "#SBATCH --cpus-per-task=24\n",
+      "#SBATCH --mem=94G\n",
+      "#SBATCH -t 00:30:00\n",
+      "JOB_ID=${SLURM_JOB_ID%;*}\n",
+      "\n",
+      "\n",
+      "\n",
+      "/p/project/cecam/rath1/miniconda3_20190521/envs/dask_jobqueue_workshop/bin/python -m distributed.cli.dask_worker tcp://10.80.32.41:39894 --nthreads 12 --nprocs 2 --memory-limit 50.00GB --name resilient_clusters--${JOB_ID}-- --death-timeout 15s --local-directory /tmp --interface ib0\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(cluster.job_script())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Scale the cluster to a single node"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cluster.scale(2)  # will lead to one node"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create some data and dump it to disk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from dask import array as da"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dask.array<uniform, shape=(62500000000, 1), dtype=float64, chunksize=(25000000, 1)>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "random_numbers = da.random.uniform(0, 1, size=(1/8 * 500e9, 1), chunks=(1/8 * 200e6, 1))\n",
+    "random_numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!rm -rf temporary_random_numbers.zarr/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 31.7 s, sys: 2.19 s, total: 33.9 s\n",
+      "Wall time: 3min 26s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "random_numbers.to_zarr(\"temporary_random_numbers.zarr\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "500.0 GB\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(random_numbers.nbytes / 1e9, \"GB\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "406G\ttemporary_random_numbers.zarr\n"
+     ]
+    }
+   ],
+   "source": [
+    "!du -sh temporary_random_numbers.zarr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Re-read and average"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dask.array<zarr, shape=(62500000000, 1), dtype=float64, chunksize=(25000000, 1)>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reread_random_numbers = da.from_zarr(\"temporary_random_numbers.zarr/\")\n",
+    "reread_random_numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "500.0 GB\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(reread_random_numbers.nbytes / 1e9, \"GB\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.49999963422700916\n",
+      "CPU times: user 31.5 s, sys: 2.34 s, total: 33.9 s\n",
+      "Wall time: 1min 46s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "print(reread_random_numbers.mean().compute())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Scale the cluster to four nodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cluster.scale(8)  # will lead to four nodes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Re-read and average again"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dask.array<zarr, shape=(62500000000, 1), dtype=float64, chunksize=(25000000, 1)>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reread_random_numbers = da.from_zarr(\"temporary_random_numbers.zarr/\")\n",
+    "reread_random_numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "500.0 GB\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(reread_random_numbers.nbytes / 1e9, \"GB\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.49999963422700916\n",
+      "CPU times: user 16.6 s, sys: 1.25 s, total: 17.8 s\n",
+      "Wall time: 23.5 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "print(reread_random_numbers.mean().compute())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## How far can we take this?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cluster.scale(40)  # will lead to 20 nodes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Re-read and average again"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dask.array<zarr, shape=(62500000000, 1), dtype=float64, chunksize=(25000000, 1)>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reread_random_numbers = da.from_zarr(\"temporary_random_numbers.zarr/\")\n",
+    "reread_random_numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "500.0 GB\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(reread_random_numbers.nbytes / 1e9, \"GB\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.49999963422700916\n",
+      "CPU times: user 8.24 s, sys: 579 ms, total: 8.82 s\n",
+      "Wall time: 9.11 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "print(reread_random_numbers.mean().compute())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "We have seen that IO bandwidth increases with the number of nodes we use for the computation.  This is obvious on one hand, but it tells us that from an analytics perspective where a lot of data lying around on disk is analysed, spreading out resources to as many nodes as possible may be the way to go."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!rm -rf temporary_random_numbers.zarr/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Complete listing of software used here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Package            Version          \n",
+      "------------------ -----------------\n",
+      "asciitree          0.3.3            \n",
+      "aspy.yaml          1.2.0            \n",
+      "backcall           0.1.0            \n",
+      "bokeh              1.1.0            \n",
+      "certifi            2019.3.9         \n",
+      "cfgv               1.6.0            \n",
+      "cftime             1.0.3.4          \n",
+      "Click              7.0              \n",
+      "cloudpickle        1.0.0            \n",
+      "cycler             0.10.0           \n",
+      "cytoolz            0.9.0.1          \n",
+      "dask               1.2.0            \n",
+      "dask-jobqueue      0.4.1+32.g9c3371d\n",
+      "decorator          4.4.0            \n",
+      "distributed        1.27.1           \n",
+      "docrep             0.2.5            \n",
+      "fasteners          0.14.1           \n",
+      "heapdict           1.0.0            \n",
+      "identify           1.4.3            \n",
+      "importlib-metadata 0.13             \n",
+      "ipykernel          5.1.1            \n",
+      "ipython            7.5.0            \n",
+      "ipython-genutils   0.2.0            \n",
+      "jedi               0.13.3           \n",
+      "Jinja2             2.10.1           \n",
+      "jupyter-client     5.2.4            \n",
+      "jupyter-core       4.4.0            \n",
+      "kiwisolver         1.1.0            \n",
+      "llvmlite           0.28.0           \n",
+      "locket             0.2.0            \n",
+      "MarkupSafe         1.1.1            \n",
+      "matplotlib         3.1.0            \n",
+      "monotonic          1.5              \n",
+      "msgpack            0.6.1            \n",
+      "netCDF4            1.5.1.2          \n",
+      "nodeenv            1.3.3            \n",
+      "numba              0.43.1           \n",
+      "numcodecs          0.6.3            \n",
+      "numpy              1.16.3           \n",
+      "olefile            0.46             \n",
+      "packaging          19.0             \n",
+      "pandas             0.24.2           \n",
+      "parso              0.4.0            \n",
+      "partd              0.3.9            \n",
+      "patsy              0.5.1            \n",
+      "pexpect            4.7.0            \n",
+      "pickleshare        0.7.5            \n",
+      "Pillow             6.0.0            \n",
+      "pip                19.1             \n",
+      "pre-commit         1.16.1           \n",
+      "prompt-toolkit     2.0.9            \n",
+      "psutil             5.6.2            \n",
+      "ptyprocess         0.6.0            \n",
+      "Pygments           2.4.0            \n",
+      "pyparsing          2.4.0            \n",
+      "python-dateutil    2.8.0            \n",
+      "pytz               2019.1           \n",
+      "PyYAML             5.1              \n",
+      "pyzmq              18.0.1           \n",
+      "scipy              1.3.0            \n",
+      "seaborn            0.9.0            \n",
+      "setuptools         41.0.1           \n",
+      "six                1.12.0           \n",
+      "sortedcontainers   2.1.0            \n",
+      "statsmodels        0.9.0            \n",
+      "tblib              1.4.0            \n",
+      "toml               0.10.0           \n",
+      "toolz              0.9.0            \n",
+      "tornado            6.0.2            \n",
+      "traitlets          4.3.2            \n",
+      "virtualenv         16.6.0           \n",
+      "wcwidth            0.1.7            \n",
+      "wheel              0.33.4           \n",
+      "xarray             0.12.1           \n",
+      "zarr               2.3.1            \n",
+      "zict               0.1.4            \n",
+      "zipp               0.5.1            \n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# This file may be used to create an environment using:\n",
+      "# $ conda create --name <env> --file <this file>\n",
+      "# platform: linux-64\n",
+      "@EXPLICIT\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/git-lfs-2.7.2-0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2019.3.9-hecc5488_0.tar.bz2\n",
+      "https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-8.2.0-hdf63c60_1.tar.bz2\n",
+      "https://repo.anaconda.com/pkgs/main/linux-64/libgfortran-ng-7.3.0-hdf63c60_0.tar.bz2\n",
+      "https://repo.anaconda.com/pkgs/main/linux-64/libstdcxx-ng-8.2.0-hdf63c60_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.6-h14c3975_1002.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/expat-2.2.5-hf484d3e_1002.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/icu-58.2-hf484d3e_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/jpeg-9c-h14c3975_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libffi-3.2.1-he1b5a44_1006.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.15-h516909a_1005.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libsodium-1.0.16-h14c3975_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.32.1-h14c3975_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.1-hf484d3e_1002.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/openblas-0.3.6-h6e990d7_2.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/openssl-1.1.1b-h14c3975_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pcre-8.41-hf484d3e_1003.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/perl-5.26.2-h516909a_1006.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h14c3975_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.9-h14c3975_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.3-h516909a_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/xz-5.2.4-h14c3975_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/yaml-0.1.7-h14c3975_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/zlib-1.2.11-h14c3975_1004.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/gettext-0.19.8.1-hc5be6a0_1002.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/hdf4-4.2.13-h9a582f1_1002.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/hdf5-1.10.4-nompi_h3c11f04_1106.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libblas-3.8.0-10_openblas.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20170329-hf8c457e_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.37-hed695b0_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.8.2-h22169c7_2.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.0.10-h648cc4a_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.13-h14c3975_1002.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.9.9-h13577e0_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/readline-7.0-hf8c457e_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.9-h84994c4_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.1-hf484d3e_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/freetype-2.10.0-he983fc9_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/glib-2.58.3-hf63aee3_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/krb5-1.16.3-h05b26f9_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.8.0-10_openblas.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.8.0-10_openblas.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.26.0-h67949de_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/dbus-1.13.6-he372182_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.13.1-he4413a7_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.14.4-h66beb1c_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libcurl-7.64.1-hda55be3_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/python-3.7.3-h5b0a415_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/asciitree-0.3.3-py_2.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/backcall-0.1.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/certifi-2019.3.9-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/click-7.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/cloudpickle-1.0.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/curl-7.64.1-hf8cf82a_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/dask-core-1.2.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/decorator-4.4.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.14.4-hdf3bae2_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/heapdict-1.0.0-py37_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/ipython_genutils-0.2.0-py_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.1.0-py37hc9558a2_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/llvmlite-0.28.0-py37hdbcaa40_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/locket-0.2.0-py_2.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/markupsafe-1.1.1-py37h14c3975_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/monotonic-1.5-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/msgpack-python-0.6.1-py37h6bb024c_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/numpy-1.16.3-py37he5ce36f_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/olefile-0.46-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/parso-0.4.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pickleshare-0.7.5-py37_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/psutil-5.6.2-py37h516909a_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/ptyprocess-0.6.0-py_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/pyparsing-2.4.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/pytz-2019.1-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pyyaml-5.1-py37h14c3975_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pyzmq-18.0.1-py37hc4ba49a_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/sip-4.19.8-py37hf484d3e_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/six-1.12.0-py37_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/sortedcontainers-2.1.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/tblib-1.4.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/toolz-0.9.0-py_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/tornado-6.0.2-py37h516909a_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.1.7-py_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/cftime-1.0.3.4-py37hd352d35_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/cycler-0.10.0-py_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/cytoolz-0.9.0.1-py37h14c3975_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/fasteners-0.14.1-py_3.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/git-2.21.0-pl526h2882143_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/jedi-0.13.3-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libnetcdf-4.6.2-hbdf4f91_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/numba-0.43.1-py37hf2d7682_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/numcodecs-0.6.3-py37hf484d3e_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/packaging-19.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/partd-0.3.9-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pexpect-4.7.0-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pillow-6.0.0-py37he7afcd5_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.8.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/qt-5.9.7-h52cfd70_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/scipy-1.3.0-py37hab63836_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/setuptools-41.0.1-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/traitlets-4.3.2-py37_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/zict-0.1.4-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/distributed-1.27.1-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/jinja2-2.10.1-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/jupyter_core-4.4.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.1.0-py37h5f35d83_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/netcdf4-1.5.1.2-py37had58050_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pandas-0.24.2-py37hf484d3e_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/patsy-0.5.1-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/pygments-2.4.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pyqt-5.9.2-py37hcca6a23_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/wheel-0.33.4-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/zarr-2.3.1-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/bokeh-1.1.0-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/jupyter_client-5.2.4-py_3.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.1.0-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pip-19.1-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/prompt_toolkit-2.0.9-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.9.0-py37h3010b51_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/xarray-0.12.1-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/dask-1.2.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/ipython-7.5.0-py37h24bf2e0_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/seaborn-0.9.0-py_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/ipykernel-5.1.1-py37h24bf2e0_0.tar.bz2\n",
+      "\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%conda list --explicit"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [conda env:dask_jobqueue_workshop]",
+   "language": "python",
+   "name": "conda-env-dask_jobqueue_workshop-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 02d9ec68e25484c34dde416cce61d5e8be1332ea Mon Sep 17 00:00:00 2001
From: Willi Rath <wrath@geomar.de>
Date: Thu, 20 Jun 2019 16:13:17 +0200
Subject: [PATCH 10/20] Skip own entrypoint and explicitly provide slurm config

---
 binder/Dockerfile              |  5 +-
 binder/my-docker-entrypoint.sh |  6 ---
 binder/slurm.conf              | 96 ++++++++++++++++++++++++++++++++++
 3 files changed, 98 insertions(+), 9 deletions(-)
 delete mode 100755 binder/my-docker-entrypoint.sh
 create mode 100644 binder/slurm.conf

diff --git a/binder/Dockerfile b/binder/Dockerfile
index 6443897..2b59c55 100644
--- a/binder/Dockerfile
+++ b/binder/Dockerfile
@@ -10,8 +10,7 @@ ENV PATH /opt/anaconda/bin:$PATH
 
 RUN conda install --yes -c conda-forge python=3.6 dask distributed dask-jobqueue docrep jupyterlab ipywidgets
 
-COPY binder/my-docker-entrypoint.sh /usr/local/bin/my-docker-entrypoint.sh
-ENTRYPOINT ["/usr/local/bin/my-docker-entrypoint.sh"]
+COPY binder/slurm.conf /etc/slurm/slurm.conf
 
 # create user with a home directory
 ARG NB_USER
@@ -29,4 +28,4 @@ RUN echo '%wheel ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
 WORKDIR ${HOME}
 USER ${NB_USER}
 
-COPY _binder_notebooks/*.ipynb ${HOME}/ 
+COPY _binder_notebooks/*.ipynb ${HOME}/
diff --git a/binder/my-docker-entrypoint.sh b/binder/my-docker-entrypoint.sh
deleted file mode 100755
index 51ff6e0..0000000
--- a/binder/my-docker-entrypoint.sh
+++ /dev/null
@@ -1,6 +0,0 @@
-#!/bin/bash
-
-sudo sed -i.bak -e "s@ernie@localhost@" /etc/slurm/slurm.conf
-sudo /usr/local/bin/docker-entrypoint.sh
-
-exec "$@"
diff --git a/binder/slurm.conf b/binder/slurm.conf
new file mode 100644
index 0000000..e6e9ff9
--- /dev/null
+++ b/binder/slurm.conf
@@ -0,0 +1,96 @@
+# slurm.conf
+#
+# See the slurm.conf man page for more information.
+#
+ClusterName=linux
+ControlMachine=localhost
+#ControlAddr=
+#BackupController=
+#BackupAddr=
+#
+SlurmUser=slurm
+#SlurmdUser=root
+SlurmctldPort=6817
+SlurmdPort=6818
+AuthType=auth/munge
+#JobCredentialPrivateKey=
+#JobCredentialPublicCertificate=
+StateSaveLocation=/var/lib/slurmd
+SlurmdSpoolDir=/var/spool/slurmd
+SwitchType=switch/none
+MpiDefault=none
+SlurmctldPidFile=/var/run/slurmd/slurmctld.pid
+SlurmdPidFile=/var/run/slurmd/slurmd.pid
+ProctrackType=proctrack/pgid
+#PluginDir=
+CacheGroups=0
+#FirstJobId=
+ReturnToService=0
+#MaxJobCount=
+#PlugStackConfig=
+#PropagatePrioProcess=
+#PropagateResourceLimits=
+#PropagateResourceLimitsExcept=
+#Prolog=
+#Epilog=
+#SrunProlog=
+#SrunEpilog=
+#TaskProlog=
+#TaskEpilog=
+#TaskPlugin=
+#TrackWCKey=no
+#TreeWidth=50
+#TmpFS=
+#UsePAM=
+#
+# TIMERS
+SlurmctldTimeout=300
+SlurmdTimeout=300
+InactiveLimit=0
+MinJobAge=300
+KillWait=30
+Waittime=0
+#
+# SCHEDULING
+SchedulerType=sched/backfill
+#SchedulerAuth=
+#SchedulerPort=
+#SchedulerRootFilter=
+SelectType=select/cons_res
+SelectTypeParameters=CR_CPU_Memory
+FastSchedule=1
+#PriorityType=priority/multifactor
+#PriorityDecayHalfLife=14-0
+#PriorityUsageResetPeriod=14-0
+#PriorityWeightFairshare=100000
+#PriorityWeightAge=1000
+#PriorityWeightPartition=10000
+#PriorityWeightJobSize=1000
+#PriorityMaxAge=1-0
+#
+# LOGGING
+SlurmctldDebug=3
+SlurmctldLogFile=/var/log/slurm/slurmctld.log
+SlurmdDebug=3
+SlurmdLogFile=/var/log/slurm/slurmd.log
+JobCompType=jobcomp/none
+#JobCompLoc=
+#
+# ACCOUNTING
+#JobAcctGatherType=jobacct_gather/linux
+#JobAcctGatherFrequency=30
+#
+AccountingStorageType=accounting_storage/slurmdbd
+#AccountingStorageHost=localhost
+#AccountingStorageLoc=
+#AccountingStoragePass=
+#AccountingStorageUser=
+#
+# COMPUTE NODES
+GresTypes=gpu
+NodeName=c[1-5] NodeHostName=localhost NodeAddr=127.0.0.1 RealMemory=1000
+NodeName=c[6-10] NodeHostName=localhost NodeAddr=127.0.0.1 RealMemory=1000 Gres=gpu:titanxp:1
+#
+# PARTITIONS
+PartitionName=normal Default=yes Nodes=c[1-5] Priority=50 DefMemPerCPU=500 Shared=YES MaxNodes=5 MaxTime=5-00:00:00 DefaultTime=5-00:00:00 State=UP
+PartitionName=debug Nodes=c[6-10] Priority=50 DefMemPerCPU=500 Shared=NO MaxNodes=5 MaxTime=5-00:00:00 DefaultTime=5-00:00:00 State=UP

From 6a1826532821929ba51636e9539fa186030cc607 Mon Sep 17 00:00:00 2001
From: Willi Rath <wrath@geomar.de>
Date: Thu, 20 Jun 2019 16:17:09 +0200
Subject: [PATCH 11/20] Revert "Skip own entrypoint and explicitly provide
 slurm config"

This reverts commit 02d9ec68e25484c34dde416cce61d5e8be1332ea.
---
 binder/Dockerfile              |  5 +-
 binder/my-docker-entrypoint.sh |  6 +++
 binder/slurm.conf              | 96 ----------------------------------
 3 files changed, 9 insertions(+), 98 deletions(-)
 create mode 100755 binder/my-docker-entrypoint.sh
 delete mode 100644 binder/slurm.conf

diff --git a/binder/Dockerfile b/binder/Dockerfile
index 2b59c55..6443897 100644
--- a/binder/Dockerfile
+++ b/binder/Dockerfile
@@ -10,7 +10,8 @@ ENV PATH /opt/anaconda/bin:$PATH
 
 RUN conda install --yes -c conda-forge python=3.6 dask distributed dask-jobqueue docrep jupyterlab ipywidgets
 
-COPY binder/slurm.conf /etc/slurm/slurm.conf
+COPY binder/my-docker-entrypoint.sh /usr/local/bin/my-docker-entrypoint.sh
+ENTRYPOINT ["/usr/local/bin/my-docker-entrypoint.sh"]
 
 # create user with a home directory
 ARG NB_USER
@@ -28,4 +29,4 @@ RUN echo '%wheel ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
 WORKDIR ${HOME}
 USER ${NB_USER}
 
-COPY _binder_notebooks/*.ipynb ${HOME}/
+COPY _binder_notebooks/*.ipynb ${HOME}/ 
diff --git a/binder/my-docker-entrypoint.sh b/binder/my-docker-entrypoint.sh
new file mode 100755
index 0000000..51ff6e0
--- /dev/null
+++ b/binder/my-docker-entrypoint.sh
@@ -0,0 +1,6 @@
+#!/bin/bash
+
+sudo sed -i.bak -e "s@ernie@localhost@" /etc/slurm/slurm.conf
+sudo /usr/local/bin/docker-entrypoint.sh
+
+exec "$@"
diff --git a/binder/slurm.conf b/binder/slurm.conf
deleted file mode 100644
index e6e9ff9..0000000
--- a/binder/slurm.conf
+++ /dev/null
@@ -1,96 +0,0 @@
-# slurm.conf
-#
-# See the slurm.conf man page for more information.
-#
-ClusterName=linux
-ControlMachine=localhost
-#ControlAddr=
-#BackupController=
-#BackupAddr=
-#
-SlurmUser=slurm
-#SlurmdUser=root
-SlurmctldPort=6817
-SlurmdPort=6818
-AuthType=auth/munge
-#JobCredentialPrivateKey=
-#JobCredentialPublicCertificate=
-StateSaveLocation=/var/lib/slurmd
-SlurmdSpoolDir=/var/spool/slurmd
-SwitchType=switch/none
-MpiDefault=none
-SlurmctldPidFile=/var/run/slurmd/slurmctld.pid
-SlurmdPidFile=/var/run/slurmd/slurmd.pid
-ProctrackType=proctrack/pgid
-#PluginDir=
-CacheGroups=0
-#FirstJobId=
-ReturnToService=0
-#MaxJobCount=
-#PlugStackConfig=
-#PropagatePrioProcess=
-#PropagateResourceLimits=
-#PropagateResourceLimitsExcept=
-#Prolog=
-#Epilog=
-#SrunProlog=
-#SrunEpilog=
-#TaskProlog=
-#TaskEpilog=
-#TaskPlugin=
-#TrackWCKey=no
-#TreeWidth=50
-#TmpFS=
-#UsePAM=
-#
-# TIMERS
-SlurmctldTimeout=300
-SlurmdTimeout=300
-InactiveLimit=0
-MinJobAge=300
-KillWait=30
-Waittime=0
-#
-# SCHEDULING
-SchedulerType=sched/backfill
-#SchedulerAuth=
-#SchedulerPort=
-#SchedulerRootFilter=
-SelectType=select/cons_res
-SelectTypeParameters=CR_CPU_Memory
-FastSchedule=1
-#PriorityType=priority/multifactor
-#PriorityDecayHalfLife=14-0
-#PriorityUsageResetPeriod=14-0
-#PriorityWeightFairshare=100000
-#PriorityWeightAge=1000
-#PriorityWeightPartition=10000
-#PriorityWeightJobSize=1000
-#PriorityMaxAge=1-0
-#
-# LOGGING
-SlurmctldDebug=3
-SlurmctldLogFile=/var/log/slurm/slurmctld.log
-SlurmdDebug=3
-SlurmdLogFile=/var/log/slurm/slurmd.log
-JobCompType=jobcomp/none
-#JobCompLoc=
-#
-# ACCOUNTING
-#JobAcctGatherType=jobacct_gather/linux
-#JobAcctGatherFrequency=30
-#
-AccountingStorageType=accounting_storage/slurmdbd
-#AccountingStorageHost=localhost
-#AccountingStorageLoc=
-#AccountingStoragePass=
-#AccountingStorageUser=
-#
-# COMPUTE NODES
-GresTypes=gpu
-NodeName=c[1-5] NodeHostName=localhost NodeAddr=127.0.0.1 RealMemory=1000
-NodeName=c[6-10] NodeHostName=localhost NodeAddr=127.0.0.1 RealMemory=1000 Gres=gpu:titanxp:1
-#
-# PARTITIONS
-PartitionName=normal Default=yes Nodes=c[1-5] Priority=50 DefMemPerCPU=500 Shared=YES MaxNodes=5 MaxTime=5-00:00:00 DefaultTime=5-00:00:00 State=UP
-PartitionName=debug Nodes=c[6-10] Priority=50 DefMemPerCPU=500 Shared=NO MaxNodes=5 MaxTime=5-00:00:00 DefaultTime=5-00:00:00 State=UP

From fd11d4c54a72c5082ac9205f22bc02739c4ee4c2 Mon Sep 17 00:00:00 2001
From: Willi Rath <wrath@geomar.de>
Date: Thu, 20 Jun 2019 16:18:57 +0200
Subject: [PATCH 12/20] Restore own entrypoint

---
 binder/Dockerfile              | 4 +++-
 binder/my-docker-entrypoint.sh | 2 +-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/binder/Dockerfile b/binder/Dockerfile
index 6443897..6984f01 100644
--- a/binder/Dockerfile
+++ b/binder/Dockerfile
@@ -10,6 +10,8 @@ ENV PATH /opt/anaconda/bin:$PATH
 
 RUN conda install --yes -c conda-forge python=3.6 dask distributed dask-jobqueue docrep jupyterlab ipywidgets
 
+COPY binder/slurm.conf /etc/slurm/slurm.conf
+
 COPY binder/my-docker-entrypoint.sh /usr/local/bin/my-docker-entrypoint.sh
 ENTRYPOINT ["/usr/local/bin/my-docker-entrypoint.sh"]
 
@@ -29,4 +31,4 @@ RUN echo '%wheel ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
 WORKDIR ${HOME}
 USER ${NB_USER}
 
-COPY _binder_notebooks/*.ipynb ${HOME}/ 
+COPY _binder_notebooks/*.ipynb ${HOME}/
diff --git a/binder/my-docker-entrypoint.sh b/binder/my-docker-entrypoint.sh
index 51ff6e0..1664444 100755
--- a/binder/my-docker-entrypoint.sh
+++ b/binder/my-docker-entrypoint.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-sudo sed -i.bak -e "s@ernie@localhost@" /etc/slurm/slurm.conf
+# sudo sed -i.bak -e "s@ernie@localhost@" /etc/slurm/slurm.conf
 sudo /usr/local/bin/docker-entrypoint.sh
 
 exec "$@"

From 54f34152d1c9a82b4838efe7f27bc0ed031807b6 Mon Sep 17 00:00:00 2001
From: Willi Rath <wrath@geomar.de>
Date: Thu, 20 Jun 2019 16:20:54 +0200
Subject: [PATCH 13/20] Add slurm config file

---
 binder/slurm.conf | 96 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 96 insertions(+)
 create mode 100644 binder/slurm.conf

diff --git a/binder/slurm.conf b/binder/slurm.conf
new file mode 100644
index 0000000..e6e9ff9
--- /dev/null
+++ b/binder/slurm.conf
@@ -0,0 +1,96 @@
+# slurm.conf
+#
+# See the slurm.conf man page for more information.
+#
+ClusterName=linux
+ControlMachine=localhost
+#ControlAddr=
+#BackupController=
+#BackupAddr=
+#
+SlurmUser=slurm
+#SlurmdUser=root
+SlurmctldPort=6817
+SlurmdPort=6818
+AuthType=auth/munge
+#JobCredentialPrivateKey=
+#JobCredentialPublicCertificate=
+StateSaveLocation=/var/lib/slurmd
+SlurmdSpoolDir=/var/spool/slurmd
+SwitchType=switch/none
+MpiDefault=none
+SlurmctldPidFile=/var/run/slurmd/slurmctld.pid
+SlurmdPidFile=/var/run/slurmd/slurmd.pid
+ProctrackType=proctrack/pgid
+#PluginDir=
+CacheGroups=0
+#FirstJobId=
+ReturnToService=0
+#MaxJobCount=
+#PlugStackConfig=
+#PropagatePrioProcess=
+#PropagateResourceLimits=
+#PropagateResourceLimitsExcept=
+#Prolog=
+#Epilog=
+#SrunProlog=
+#SrunEpilog=
+#TaskProlog=
+#TaskEpilog=
+#TaskPlugin=
+#TrackWCKey=no
+#TreeWidth=50
+#TmpFS=
+#UsePAM=
+#
+# TIMERS
+SlurmctldTimeout=300
+SlurmdTimeout=300
+InactiveLimit=0
+MinJobAge=300
+KillWait=30
+Waittime=0
+#
+# SCHEDULING
+SchedulerType=sched/backfill
+#SchedulerAuth=
+#SchedulerPort=
+#SchedulerRootFilter=
+SelectType=select/cons_res
+SelectTypeParameters=CR_CPU_Memory
+FastSchedule=1
+#PriorityType=priority/multifactor
+#PriorityDecayHalfLife=14-0
+#PriorityUsageResetPeriod=14-0
+#PriorityWeightFairshare=100000
+#PriorityWeightAge=1000
+#PriorityWeightPartition=10000
+#PriorityWeightJobSize=1000
+#PriorityMaxAge=1-0
+#
+# LOGGING
+SlurmctldDebug=3
+SlurmctldLogFile=/var/log/slurm/slurmctld.log
+SlurmdDebug=3
+SlurmdLogFile=/var/log/slurm/slurmd.log
+JobCompType=jobcomp/none
+#JobCompLoc=
+#
+# ACCOUNTING
+#JobAcctGatherType=jobacct_gather/linux
+#JobAcctGatherFrequency=30
+#
+AccountingStorageType=accounting_storage/slurmdbd
+#AccountingStorageHost=localhost
+#AccountingStorageLoc=
+#AccountingStoragePass=
+#AccountingStorageUser=
+#
+# COMPUTE NODES
+GresTypes=gpu
+NodeName=c[1-5] NodeHostName=localhost NodeAddr=127.0.0.1 RealMemory=1000
+NodeName=c[6-10] NodeHostName=localhost NodeAddr=127.0.0.1 RealMemory=1000 Gres=gpu:titanxp:1
+#
+# PARTITIONS
+PartitionName=normal Default=yes Nodes=c[1-5] Priority=50 DefMemPerCPU=500 Shared=YES MaxNodes=5 MaxTime=5-00:00:00 DefaultTime=5-00:00:00 State=UP
+PartitionName=debug Nodes=c[6-10] Priority=50 DefMemPerCPU=500 Shared=NO MaxNodes=5 MaxTime=5-00:00:00 DefaultTime=5-00:00:00 State=UP

From dea257bcfcab08bc840b53c9a9f669bbb9feeb5a Mon Sep 17 00:00:00 2001
From: Willi Rath <wrath@geomar.de>
Date: Thu, 20 Jun 2019 16:30:24 +0200
Subject: [PATCH 14/20] binder/Dockerfile

---
 binder/Dockerfile | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/binder/Dockerfile b/binder/Dockerfile
index 6984f01..c0949d1 100644
--- a/binder/Dockerfile
+++ b/binder/Dockerfile
@@ -12,8 +12,8 @@ RUN conda install --yes -c conda-forge python=3.6 dask distributed dask-jobqueue
 
 COPY binder/slurm.conf /etc/slurm/slurm.conf
 
-COPY binder/my-docker-entrypoint.sh /usr/local/bin/my-docker-entrypoint.sh
-ENTRYPOINT ["/usr/local/bin/my-docker-entrypoint.sh"]
+# COPY binder/my-docker-entrypoint.sh /usr/local/bin/my-docker-entrypoint.sh
+ENTRYPOINT ["sudo", "/usr/local/bin/docker-entrypoint.sh"]
 
 # create user with a home directory
 ARG NB_USER

From 8650e0559121aa6a705b1b8935116bef1c35b308 Mon Sep 17 00:00:00 2001
From: Willi Rath <wrath@geomar.de>
Date: Thu, 20 Jun 2019 16:33:03 +0200
Subject: [PATCH 15/20] Still use own entrypoint

---
 binder/Dockerfile              | 4 ++--
 binder/my-docker-entrypoint.sh | 1 -
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/binder/Dockerfile b/binder/Dockerfile
index c0949d1..6984f01 100644
--- a/binder/Dockerfile
+++ b/binder/Dockerfile
@@ -12,8 +12,8 @@ RUN conda install --yes -c conda-forge python=3.6 dask distributed dask-jobqueue
 
 COPY binder/slurm.conf /etc/slurm/slurm.conf
 
-# COPY binder/my-docker-entrypoint.sh /usr/local/bin/my-docker-entrypoint.sh
-ENTRYPOINT ["sudo", "/usr/local/bin/docker-entrypoint.sh"]
+COPY binder/my-docker-entrypoint.sh /usr/local/bin/my-docker-entrypoint.sh
+ENTRYPOINT ["/usr/local/bin/my-docker-entrypoint.sh"]
 
 # create user with a home directory
 ARG NB_USER
diff --git a/binder/my-docker-entrypoint.sh b/binder/my-docker-entrypoint.sh
index 1664444..f92fdbc 100755
--- a/binder/my-docker-entrypoint.sh
+++ b/binder/my-docker-entrypoint.sh
@@ -1,6 +1,5 @@
 #!/bin/bash
 
-# sudo sed -i.bak -e "s@ernie@localhost@" /etc/slurm/slurm.conf
 sudo /usr/local/bin/docker-entrypoint.sh
 
 exec "$@"

From 9b48e0870025917672d955f2efa59258f2add325 Mon Sep 17 00:00:00 2001
From: Willi Rath <wrath@geomar.de>
Date: Thu, 20 Jun 2019 16:43:28 +0200
Subject: [PATCH 16/20] Assure user owns all of home

---
 binder/Dockerfile | 1 +
 1 file changed, 1 insertion(+)

diff --git a/binder/Dockerfile b/binder/Dockerfile
index 6984f01..46a66c9 100644
--- a/binder/Dockerfile
+++ b/binder/Dockerfile
@@ -32,3 +32,4 @@ WORKDIR ${HOME}
 USER ${NB_USER}
 
 COPY _binder_notebooks/*.ipynb ${HOME}/
+RUN chown -R ${NB_USER}:${NB_USER} ${HOME}

From 410c2929140159f81069e5f3aa28b80f47f84abb Mon Sep 17 00:00:00 2001
From: Willi Rath <wrath@geomar.de>
Date: Thu, 20 Jun 2019 16:48:46 +0200
Subject: [PATCH 17/20] Assure user owns all of home

---
 binder/Dockerfile              | 1 -
 binder/my-docker-entrypoint.sh | 1 +
 2 files changed, 1 insertion(+), 1 deletion(-)

diff --git a/binder/Dockerfile b/binder/Dockerfile
index 46a66c9..6984f01 100644
--- a/binder/Dockerfile
+++ b/binder/Dockerfile
@@ -32,4 +32,3 @@ WORKDIR ${HOME}
 USER ${NB_USER}
 
 COPY _binder_notebooks/*.ipynb ${HOME}/
-RUN chown -R ${NB_USER}:${NB_USER} ${HOME}
diff --git a/binder/my-docker-entrypoint.sh b/binder/my-docker-entrypoint.sh
index f92fdbc..5948569 100755
--- a/binder/my-docker-entrypoint.sh
+++ b/binder/my-docker-entrypoint.sh
@@ -1,5 +1,6 @@
 #!/bin/bash
 
 sudo /usr/local/bin/docker-entrypoint.sh
+sudo chown jovian:jovian ${HOME}/*ipynb
 
 exec "$@"

From 76253e29fc400174cbd5fa9c41feb35c1f7debfd Mon Sep 17 00:00:00 2001
From: Willi Rath <wrath@geomar.de>
Date: Thu, 20 Jun 2019 16:53:04 +0200
Subject: [PATCH 18/20] Use user from env var

---
 binder/my-docker-entrypoint.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/binder/my-docker-entrypoint.sh b/binder/my-docker-entrypoint.sh
index 5948569..f70450b 100755
--- a/binder/my-docker-entrypoint.sh
+++ b/binder/my-docker-entrypoint.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
 sudo /usr/local/bin/docker-entrypoint.sh
-sudo chown jovian:jovian ${HOME}/*ipynb
+sudo chown ${USER}:${USER} ${HOME}/*ipynb
 
 exec "$@"

From 65d1c243365a9ffddbf602c9e76fd1099564151e Mon Sep 17 00:00:00 2001
From: Willi Rath <wrath@geomar.de>
Date: Thu, 20 Jun 2019 17:59:41 +0200
Subject: [PATCH 19/20] Allow for more slurm jobs

---
 binder/slurm.conf | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/binder/slurm.conf b/binder/slurm.conf
index e6e9ff9..4a13bbb 100644
--- a/binder/slurm.conf
+++ b/binder/slurm.conf
@@ -88,9 +88,7 @@ AccountingStorageType=accounting_storage/slurmdbd
 #
 # COMPUTE NODES
 GresTypes=gpu
-NodeName=c[1-5] NodeHostName=localhost NodeAddr=127.0.0.1 RealMemory=1000
-NodeName=c[6-10] NodeHostName=localhost NodeAddr=127.0.0.1 RealMemory=1000 Gres=gpu:titanxp:1
+NodeName=c[1-10] NodeHostName=localhost NodeAddr=127.0.0.1 RealMemory=2000
 #
 # PARTITIONS
-PartitionName=normal Default=yes Nodes=c[1-5] Priority=50 DefMemPerCPU=500 Shared=YES MaxNodes=5 MaxTime=5-00:00:00 DefaultTime=5-00:00:00 State=UP
-PartitionName=debug Nodes=c[6-10] Priority=50 DefMemPerCPU=500 Shared=NO MaxNodes=5 MaxTime=5-00:00:00 DefaultTime=5-00:00:00 State=UP
+PartitionName=normal Default=yes Nodes=c[1-5] Priority=50 DefMemPerCPU=200 Shared=YES MaxNodes=2 MaxTime=5-00:00:00 DefaultTime=5-00:00:00 State=UP

From 7748adb8dbea7ad172fe904a492cea16c8c58bf0 Mon Sep 17 00:00:00 2001
From: Willi Rath <willirath@users.noreply.github.com>
Date: Fri, 21 Jun 2019 09:20:52 +0200
Subject: [PATCH 20/20] Adapt localcluster example to pangeo binder

---
 ...l_cluster_monte_carlo_estimate_of_pi.ipynb | 456 +++++++++---------
 1 file changed, 218 insertions(+), 238 deletions(-)

diff --git a/_binder_notebooks/01_local_cluster_monte_carlo_estimate_of_pi.ipynb b/_binder_notebooks/01_local_cluster_monte_carlo_estimate_of_pi.ipynb
index f754249..e9bc9fc 100644
--- a/_binder_notebooks/01_local_cluster_monte_carlo_estimate_of_pi.ipynb
+++ b/_binder_notebooks/01_local_cluster_monte_carlo_estimate_of_pi.ipynb
@@ -54,23 +54,23 @@
        "<td style=\"vertical-align: top; border: 0px solid white\">\n",
        "<h3>Client</h3>\n",
        "<ul>\n",
-       "  <li><b>Scheduler: </b>tcp://127.0.0.1:43846\n",
+       "  <li><b>Scheduler: </b>tcp://127.0.0.1:41403\n",
        "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a>\n",
        "</ul>\n",
        "</td>\n",
        "<td style=\"vertical-align: top; border: 0px solid white\">\n",
        "<h3>Cluster</h3>\n",
        "<ul>\n",
-       "  <li><b>Workers: </b>2</li>\n",
-       "  <li><b>Cores: </b>2</li>\n",
-       "  <li><b>Memory: </b>2.00 GB</li>\n",
+       "  <li><b>Workers: </b>1</li>\n",
+       "  <li><b>Cores: </b>1</li>\n",
+       "  <li><b>Memory: </b>1000.00 MB</li>\n",
        "</ul>\n",
        "</td>\n",
        "</tr>\n",
        "</table>"
       ],
       "text/plain": [
-       "<Client: scheduler='tcp://127.0.0.1:43846' processes=2 cores=2>"
+       "<Client: scheduler='tcp://127.0.0.1:41403' processes=1 cores=1>"
       ]
      },
      "execution_count": 2,
@@ -79,7 +79,7 @@
     }
    ],
    "source": [
-    "cluster = LocalCluster(n_workers=2, threads_per_worker=1, memory_limit=1e9)\n",
+    "cluster = LocalCluster(n_workers=1, threads_per_worker=1, memory_limit=1e9)\n",
     "client = Client(cluster)\n",
     "client"
    ]
@@ -161,23 +161,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "1.0 GB\n",
+      "\tMC pi:  3.14127948800\tErr:  3.132e-04\n",
+      "\tWorkers: 1\t\tTime:   5.114s\n",
       "2.0 GB\n",
-      "\tMC pi:  3.14165280000\tErr:  6.015e-05\n",
-      "\tWorkers: 2\t\tTime:   3.535s\n",
-      "4.0 GB\n",
-      "\tMC pi:  3.14143952000\tErr:  1.531e-04\n",
-      "\tWorkers: 2\t\tTime:   6.481s\n",
-      "8.0 GB\n",
-      "\tMC pi:  3.14139753600\tErr:  1.951e-04\n",
-      "\tWorkers: 2\t\tTime:  12.800s\n",
-      "16.0 GB\n",
-      "\tMC pi:  3.14158570400\tErr:  6.950e-06\n",
-      "\tWorkers: 2\t\tTime:  25.747s\n"
+      "\tMC pi:  3.14166908800\tErr:  7.643e-05\n",
+      "\tWorkers: 1\t\tTime:  10.141s\n",
+      "3.0 GB\n",
+      "\tMC pi:  3.14187464533\tErr:  2.820e-04\n",
+      "\tWorkers: 1\t\tTime:  15.123s\n"
      ]
     }
    ],
    "source": [
-    "for size in (1e9 * n for n in (2, 4, 8, 16)):\n",
+    "for size in (1e9 * n for n in (1, 2, 3)):\n",
     "    \n",
     "    start = time()\n",
     "    pi = calc_pi_mc(size)\n",
@@ -215,7 +212,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Scaling from 2 to 4 workers.\n"
+      "Scaling from 1 to 2 workers.\n"
      ]
     }
    ],
@@ -242,23 +239,23 @@
        "<td style=\"vertical-align: top; border: 0px solid white\">\n",
        "<h3>Client</h3>\n",
        "<ul>\n",
-       "  <li><b>Scheduler: </b>tcp://127.0.0.1:43846\n",
+       "  <li><b>Scheduler: </b>tcp://127.0.0.1:41403\n",
        "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a>\n",
        "</ul>\n",
        "</td>\n",
        "<td style=\"vertical-align: top; border: 0px solid white\">\n",
        "<h3>Cluster</h3>\n",
        "<ul>\n",
-       "  <li><b>Workers: </b>4</li>\n",
-       "  <li><b>Cores: </b>4</li>\n",
-       "  <li><b>Memory: </b>4.00 GB</li>\n",
+       "  <li><b>Workers: </b>2</li>\n",
+       "  <li><b>Cores: </b>2</li>\n",
+       "  <li><b>Memory: </b>2.00 GB</li>\n",
        "</ul>\n",
        "</td>\n",
        "</tr>\n",
        "</table>"
       ],
       "text/plain": [
-       "<Client: scheduler='tcp://127.0.0.1:43846' processes=4 cores=4>"
+       "<Client: scheduler='tcp://127.0.0.1:41403' processes=2 cores=2>"
       ]
      },
      "execution_count": 10,
@@ -279,23 +276,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "1.0 GB\n",
+      "\tMC pi:  3.14160531200\tErr:  1.266e-05\n",
+      "\tWorkers: 2\t\tTime:   3.171s\n",
       "2.0 GB\n",
-      "\tMC pi:  3.14174988800\tErr:  1.572e-04\n",
-      "\tWorkers: 4\t\tTime:   2.168s\n",
-      "4.0 GB\n",
-      "\tMC pi:  3.14155224000\tErr:  4.041e-05\n",
-      "\tWorkers: 4\t\tTime:   3.630s\n",
-      "8.0 GB\n",
-      "\tMC pi:  3.14148824000\tErr:  1.044e-04\n",
-      "\tWorkers: 4\t\tTime:   7.018s\n",
-      "16.0 GB\n",
-      "\tMC pi:  3.14160793200\tErr:  1.528e-05\n",
-      "\tWorkers: 4\t\tTime:  13.904s\n"
+      "\tMC pi:  3.14142736000\tErr:  1.653e-04\n",
+      "\tWorkers: 2\t\tTime:   5.252s\n",
+      "3.0 GB\n",
+      "\tMC pi:  3.14184736000\tErr:  2.547e-04\n",
+      "\tWorkers: 2\t\tTime:   8.289s\n"
      ]
     }
    ],
    "source": [
-    "for size in (1e9 * n for n in (2, 4, 8, 16)):\n",
+    "for size in (1e9 * n for n in (1, 2, 3)):\n",
     "    \n",
     "    start = time()\n",
     "    pi = calc_pi_mc(size)\n",
@@ -317,14 +311,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Check docstring of distributed.Adaptive for keywords\n",
     "ca = cluster.adapt(\n",
-    "    minimum=1, maximum=8,\n",
-    "    target_duration=\"15s\",\n",
+    "    minimum=1, maximum=4,\n",
+    "    target_duration=\"10s\",\n",
     "    scale_factor=1);\n",
     "\n",
     "sleep(4)  # Allow for scale-down"
@@ -332,7 +326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -343,7 +337,7 @@
        "<td style=\"vertical-align: top; border: 0px solid white\">\n",
        "<h3>Client</h3>\n",
        "<ul>\n",
-       "  <li><b>Scheduler: </b>tcp://127.0.0.1:43846\n",
+       "  <li><b>Scheduler: </b>tcp://127.0.0.1:41403\n",
        "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a>\n",
        "</ul>\n",
        "</td>\n",
@@ -359,10 +353,10 @@
        "</table>"
       ],
       "text/plain": [
-       "<Client: scheduler='tcp://127.0.0.1:43846' processes=4 cores=4>"
+       "<Client: scheduler='tcp://127.0.0.1:41403' processes=1 cores=1>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -380,7 +374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -388,25 +382,19 @@
      "output_type": "stream",
      "text": [
       "2.0 GB\n",
-      "\tMC pi:  3.14140288000\tErr:  1.898e-04\n",
-      "\tWorkers: 1\t\tTime:   6.816s\n",
+      "\tMC pi:  3.14154998400\tErr:  4.267e-05\n",
+      "\tWorkers: 2\t\tTime:   6.208s\n",
       "4.0 GB\n",
-      "\tMC pi:  3.14146401600\tErr:  1.286e-04\n",
-      "\tWorkers: 1\t\tTime:  13.075s\n",
+      "\tMC pi:  3.14154673600\tErr:  4.592e-05\n",
+      "\tWorkers: 3\t\tTime:   8.414s\n",
       "8.0 GB\n",
-      "\tMC pi:  3.14165064800\tErr:  5.799e-05\n",
-      "\tWorkers: 2\t\tTime:  15.139s\n",
-      "16.0 GB\n",
-      "\tMC pi:  3.14153852000\tErr:  5.413e-05\n",
-      "\tWorkers: 4\t\tTime:  15.182s\n",
-      "32.0 GB\n",
-      "\tMC pi:  3.14157405800\tErr:  1.860e-05\n",
-      "\tWorkers: 8\t\tTime:  16.313s\n"
+      "\tMC pi:  3.14142765600\tErr:  1.650e-04\n",
+      "\tWorkers: 4\t\tTime:  12.693s\n"
      ]
     }
    ],
    "source": [
-    "for size in (n * 1e9 for n in (2, 4, 8, 16, 32)):\n",
+    "for size in (n * 1e9 for n in (2, 4, 8)):\n",
     "    \n",
     "    start = time()\n",
     "    pi = calc_pi_mc(size)\n",
@@ -434,82 +422,92 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/usr/bin/sh: module: line 1: syntax error: unexpected end of file\n",
-      "/usr/bin/sh: error importing function definition for `BASH_FUNC_module'\n",
-      "/usr/bin/sh: jutil: line 1: syntax error: unexpected end of file\n",
-      "/usr/bin/sh: error importing function definition for `BASH_FUNC_jutil'\n",
-      "/usr/bin/sh: ml: line 1: syntax error: unexpected end of file\n",
-      "/usr/bin/sh: error importing function definition for `BASH_FUNC_ml'\n",
-      "Package          Version \n",
-      "---------------- --------\n",
-      "asciitree        0.3.3   \n",
-      "backcall         0.1.0   \n",
-      "bokeh            1.1.0   \n",
-      "certifi          2019.3.9\n",
-      "cftime           1.0.3.4 \n",
-      "Click            7.0     \n",
-      "cloudpickle      1.0.0   \n",
-      "cycler           0.10.0  \n",
-      "cytoolz          0.9.0.1 \n",
-      "dask             1.2.2   \n",
-      "dask-jobqueue    0.4.1   \n",
-      "decorator        4.4.0   \n",
-      "distributed      1.28.1  \n",
-      "docrep           0.2.5   \n",
-      "fasteners        0.14.1  \n",
-      "heapdict         1.0.0   \n",
-      "ipykernel        5.1.1   \n",
-      "ipython          7.5.0   \n",
-      "ipython-genutils 0.2.0   \n",
-      "jedi             0.13.3  \n",
-      "Jinja2           2.10.1  \n",
-      "jupyter-client   5.2.4   \n",
-      "jupyter-core     4.4.0   \n",
-      "kiwisolver       1.1.0   \n",
-      "llvmlite         0.28.0  \n",
-      "locket           0.2.0   \n",
-      "MarkupSafe       1.1.1   \n",
-      "matplotlib       3.0.3   \n",
-      "monotonic        1.5     \n",
-      "msgpack          0.6.1   \n",
-      "netCDF4          1.5.1.2 \n",
-      "numba            0.43.1  \n",
-      "numcodecs        0.6.3   \n",
-      "numpy            1.16.3  \n",
-      "olefile          0.46    \n",
-      "packaging        19.0    \n",
-      "pandas           0.24.2  \n",
-      "parso            0.4.0   \n",
-      "partd            0.3.9   \n",
-      "patsy            0.5.1   \n",
-      "pexpect          4.7.0   \n",
-      "pickleshare      0.7.5   \n",
-      "Pillow           6.0.0   \n",
-      "pip              19.1    \n",
-      "prompt-toolkit   2.0.9   \n",
-      "psutil           5.6.2   \n",
-      "ptyprocess       0.6.0   \n",
-      "Pygments         2.4.0   \n",
-      "pyparsing        2.4.0   \n",
-      "python-dateutil  2.8.0   \n",
-      "pytz             2019.1  \n",
-      "PyYAML           5.1     \n",
-      "pyzmq            18.0.1  \n",
-      "scipy            1.2.1   \n",
-      "seaborn          0.9.0   \n",
-      "setuptools       41.0.1  \n",
-      "six              1.12.0  \n",
-      "sortedcontainers 2.1.0   \n",
-      "statsmodels      0.9.0   \n",
-      "tblib            1.3.2   \n",
-      "toolz            0.9.0   \n",
-      "tornado          6.0.2   \n",
-      "traitlets        4.3.2   \n",
-      "wcwidth          0.1.7   \n",
-      "wheel            0.33.4  \n",
-      "xarray           0.12.1  \n",
-      "zarr             2.3.1   \n",
-      "zict             0.1.4   \n",
+      "Package            Version \n",
+      "------------------ --------\n",
+      "asn1crypto         0.24.0  \n",
+      "attrs              19.1.0  \n",
+      "backcall           0.1.0   \n",
+      "bleach             3.1.0   \n",
+      "bokeh              1.2.0   \n",
+      "certifi            2019.3.9\n",
+      "cffi               1.12.3  \n",
+      "chardet            3.0.4   \n",
+      "Click              7.0     \n",
+      "cloudpickle        1.2.1   \n",
+      "conda              4.6.14  \n",
+      "cryptography       2.7     \n",
+      "cytoolz            0.9.0.1 \n",
+      "dask               1.2.2   \n",
+      "dask-jobqueue      0.5.0   \n",
+      "decorator          4.4.0   \n",
+      "defusedxml         0.5.0   \n",
+      "distributed        1.28.1  \n",
+      "docrep             0.2.7   \n",
+      "entrypoints        0.3     \n",
+      "heapdict           1.0.0   \n",
+      "idna               2.8     \n",
+      "ipykernel          5.1.1   \n",
+      "ipython            7.5.0   \n",
+      "ipython-genutils   0.2.0   \n",
+      "ipywidgets         7.4.2   \n",
+      "jedi               0.13.3  \n",
+      "Jinja2             2.10.1  \n",
+      "jsonschema         3.0.1   \n",
+      "jupyter-client     5.2.4   \n",
+      "jupyter-core       4.4.0   \n",
+      "jupyterlab         0.35.6  \n",
+      "jupyterlab-server  0.2.0   \n",
+      "locket             0.2.0   \n",
+      "MarkupSafe         1.1.1   \n",
+      "mistune            0.8.4   \n",
+      "msgpack            0.6.1   \n",
+      "nbconvert          5.5.0   \n",
+      "nbformat           4.4.0   \n",
+      "notebook           5.7.8   \n",
+      "numpy              1.16.4  \n",
+      "olefile            0.46    \n",
+      "packaging          19.0    \n",
+      "pandas             0.24.2  \n",
+      "pandocfilters      1.4.2   \n",
+      "parso              0.4.0   \n",
+      "partd              0.3.10  \n",
+      "pexpect            4.7.0   \n",
+      "pickleshare        0.7.5   \n",
+      "Pillow             6.0.0   \n",
+      "pip                19.1.1  \n",
+      "prometheus-client  0.7.0   \n",
+      "prompt-toolkit     2.0.9   \n",
+      "psutil             5.6.3   \n",
+      "ptyprocess         0.6.0   \n",
+      "pycosat            0.6.3   \n",
+      "pycparser          2.19    \n",
+      "Pygments           2.4.2   \n",
+      "pyOpenSSL          19.0.0  \n",
+      "pyparsing          2.4.0   \n",
+      "pyrsistent         0.15.2  \n",
+      "PySocks            1.7.0   \n",
+      "python-dateutil    2.8.0   \n",
+      "pytz               2019.1  \n",
+      "PyYAML             5.1.1   \n",
+      "pyzmq              18.0.1  \n",
+      "requests           2.22.0  \n",
+      "ruamel-yaml        0.15.71 \n",
+      "Send2Trash         1.5.0   \n",
+      "setuptools         41.0.1  \n",
+      "six                1.12.0  \n",
+      "sortedcontainers   2.1.0   \n",
+      "tblib              1.4.0   \n",
+      "terminado          0.8.2   \n",
+      "testpath           0.4.2   \n",
+      "toolz              0.9.0   \n",
+      "tornado            6.0.2   \n",
+      "traitlets          4.3.2   \n",
+      "urllib3            1.24.3  \n",
+      "wcwidth            0.1.7   \n",
+      "webencodings       0.5.1   \n",
+      "wheel              0.33.4  \n",
+      "widgetsnbextension 3.4.2   \n",
+      "zict               0.1.4   \n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
     }
@@ -527,141 +525,123 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/usr/bin/sh: module: line 1: syntax error: unexpected end of file\n",
-      "/usr/bin/sh: error importing function definition for `BASH_FUNC_module'\n",
-      "/usr/bin/sh: jutil: line 1: syntax error: unexpected end of file\n",
-      "/usr/bin/sh: error importing function definition for `BASH_FUNC_jutil'\n",
-      "/usr/bin/sh: ml: line 1: syntax error: unexpected end of file\n",
-      "/usr/bin/sh: error importing function definition for `BASH_FUNC_ml'\n",
       "# This file may be used to create an environment using:\n",
       "# $ conda create --name <env> --file <this file>\n",
       "# platform: linux-64\n",
       "@EXPLICIT\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/git-lfs-2.7.2-0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2019.3.9-hecc5488_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2019.6.16-hecc5488_0.tar.bz2\n",
       "https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-8.2.0-hdf63c60_1.tar.bz2\n",
-      "https://repo.anaconda.com/pkgs/main/linux-64/libgfortran-ng-7.3.0-hdf63c60_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libgfortran-3.0.0-1.tar.bz2\n",
       "https://repo.anaconda.com/pkgs/main/linux-64/libstdcxx-ng-8.2.0-hdf63c60_1.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.6-h14c3975_1002.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/expat-2.2.5-hf484d3e_1002.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/icu-58.2-hf484d3e_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pandoc-2.7.3-0.tar.bz2\n",
       "https://conda.anaconda.org/conda-forge/linux-64/jpeg-9c-h14c3975_1001.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/libffi-3.2.1-he1b5a44_1006.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.15-h516909a_1005.tar.bz2\n",
+      "https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.2.1-hd88cf55_4.tar.bz2\n",
       "https://conda.anaconda.org/conda-forge/linux-64/libsodium-1.0.16-h14c3975_1001.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.32.1-h14c3975_1000.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.1-hf484d3e_1002.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/openblas-0.3.6-h6e990d7_2.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.8.3-he1b5a44_1001.tar.bz2\n",
+      "https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.1-he6710b0_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/openblas-0.3.5-ha44fe06_0.tar.bz2\n",
       "https://conda.anaconda.org/conda-forge/linux-64/openssl-1.1.1b-h14c3975_1.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/pcre-8.41-hf484d3e_1003.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/perl-5.26.2-h516909a_1006.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h14c3975_1001.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.9-h14c3975_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.3-h516909a_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/xz-5.2.4-h14c3975_1001.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/yaml-0.1.7-h14c3975_1001.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/zlib-1.2.11-h14c3975_1004.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/gettext-0.19.8.1-hc5be6a0_1002.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/hdf4-4.2.13-h9a582f1_1002.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/hdf5-1.10.4-nompi_h3c11f04_1106.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/libblas-3.8.0-10_openblas.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20170329-hf8c457e_1001.tar.bz2\n",
+      "https://repo.anaconda.com/pkgs/main/linux-64/xz-5.2.4-h14c3975_4.tar.bz2\n",
+      "https://repo.anaconda.com/pkgs/main/linux-64/yaml-0.1.7-had09818_2.tar.bz2\n",
+      "https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.11-h7b6447c_3.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libblas-3.8.0-7_openblas.tar.bz2\n",
+      "https://repo.anaconda.com/pkgs/main/linux-64/libedit-3.1.20181209-hc058e9b_0.tar.bz2\n",
       "https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.37-hed695b0_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.8.2-h22169c7_2.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.0.10-h648cc4a_1001.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.13-h14c3975_1002.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.9.9-h13577e0_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/readline-7.0-hf8c457e_1001.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.9-h84994c4_1001.tar.bz2\n",
+      "https://repo.anaconda.com/pkgs/main/linux-64/readline-7.0-h7b6447c_5.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.9-hed695b0_1002.tar.bz2\n",
       "https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.1-hf484d3e_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/zstd-1.4.0-h3b9ef0a_0.tar.bz2\n",
       "https://conda.anaconda.org/conda-forge/linux-64/freetype-2.10.0-he983fc9_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/glib-2.58.3-hf63aee3_1001.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/krb5-1.16.3-h05b26f9_1001.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.8.0-10_openblas.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.8.0-10_openblas.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.26.0-h67949de_1001.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/dbus-1.13.6-he372182_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.13.1-he4413a7_1000.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.14.4-h66beb1c_1001.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/libcurl-7.64.1-hda55be3_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/python-3.7.3-h5b0a415_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/noarch/asciitree-0.3.3-py_2.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.8.0-7_openblas.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.8.0-7_openblas.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.0.10-h57b8799_1003.tar.bz2\n",
+      "https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.27.2-h7b6447c_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/python-3.6.7-h381d211_1004.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/asn1crypto-0.24.0-py36_1003.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/attrs-19.1.0-py_0.tar.bz2\n",
       "https://conda.anaconda.org/conda-forge/noarch/backcall-0.1.0-py_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/certifi-2019.3.9-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/certifi-2019.3.9-py36_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/chardet-3.0.4-py36_1003.tar.bz2\n",
       "https://conda.anaconda.org/conda-forge/noarch/click-7.0-py_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/noarch/cloudpickle-1.0.0-py_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/curl-7.64.1-hf8cf82a_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/cloudpickle-1.2.1-py_0.tar.bz2\n",
       "https://conda.anaconda.org/conda-forge/noarch/dask-core-1.2.2-py_0.tar.bz2\n",
       "https://conda.anaconda.org/conda-forge/noarch/decorator-4.4.0-py_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.14.4-hdf3bae2_1001.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/heapdict-1.0.0-py37_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/defusedxml-0.5.0-py_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/entrypoints-0.3-py36_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/heapdict-1.0.0-py36_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/idna-2.8-py36_1000.tar.bz2\n",
       "https://conda.anaconda.org/conda-forge/noarch/ipython_genutils-0.2.0-py_1.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.1.0-py37hc9558a2_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/llvmlite-0.28.0-py37hdbcaa40_0.tar.bz2\n",
       "https://conda.anaconda.org/conda-forge/noarch/locket-0.2.0-py_2.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/markupsafe-1.1.1-py37h14c3975_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/noarch/monotonic-1.5-py_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/msgpack-python-0.6.1-py37h6bb024c_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/numpy-1.16.3-py37he5ce36f_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/markupsafe-1.1.1-py36h14c3975_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/mistune-0.8.4-py36h14c3975_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/msgpack-python-0.6.1-py36h6bb024c_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/numpy-1.16.4-py36h95a1406_0.tar.bz2\n",
       "https://conda.anaconda.org/conda-forge/noarch/olefile-0.46-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/pandocfilters-1.4.2-py_1.tar.bz2\n",
       "https://conda.anaconda.org/conda-forge/noarch/parso-0.4.0-py_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/pickleshare-0.7.5-py37_1000.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/psutil-5.6.2-py37h516909a_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pickleshare-0.7.5-py36_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/prometheus_client-0.7.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/psutil-5.6.3-py36h516909a_0.tar.bz2\n",
       "https://conda.anaconda.org/conda-forge/noarch/ptyprocess-0.6.0-py_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pycosat-0.6.3-py36h14c3975_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pycparser-2.19-py36_1.tar.bz2\n",
       "https://conda.anaconda.org/conda-forge/noarch/pyparsing-2.4.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pysocks-1.7.0-py36_0.tar.bz2\n",
       "https://conda.anaconda.org/conda-forge/noarch/pytz-2019.1-py_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/pyyaml-5.1-py37h14c3975_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/pyzmq-18.0.1-py37hc4ba49a_1.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/sip-4.19.8-py37hf484d3e_1000.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/six-1.12.0-py37_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pyyaml-5.1.1-py36h516909a_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pyzmq-18.0.1-py36hc4ba49a_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/ruamel_yaml-0.15.71-py36h14c3975_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/send2trash-1.5.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/six-1.12.0-py36_1000.tar.bz2\n",
       "https://conda.anaconda.org/conda-forge/noarch/sortedcontainers-2.1.0-py_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/noarch/tblib-1.3.2-py_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/tblib-1.4.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/testpath-0.4.2-py_1001.tar.bz2\n",
       "https://conda.anaconda.org/conda-forge/noarch/toolz-0.9.0-py_1.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/tornado-6.0.2-py37h516909a_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/tornado-6.0.2-py36h516909a_0.tar.bz2\n",
       "https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.1.7-py_1.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/cftime-1.0.3.4-py37hd352d35_1001.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/noarch/cycler-0.10.0-py_1.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/cytoolz-0.9.0.1-py37h14c3975_1001.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/noarch/docrep-0.2.5-py_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/noarch/fasteners-0.14.1-py_3.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/git-2.21.0-pl526h2882143_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/jedi-0.13.3-py37_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/libnetcdf-4.6.2-hbdf4f91_1001.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/numba-0.43.1-py37hf2d7682_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/numcodecs-0.6.3-py37hf484d3e_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/webencodings-0.5.1-py_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/cffi-1.12.3-py36h8022711_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/cytoolz-0.9.0.1-py36h14c3975_1001.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/docrep-0.2.7-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/jedi-0.13.3-py36_0.tar.bz2\n",
       "https://conda.anaconda.org/conda-forge/noarch/packaging-19.0-py_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/noarch/partd-0.3.9-py_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/pexpect-4.7.0-py37_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/pillow-6.0.0-py37he7afcd5_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/partd-0.3.10-py_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pexpect-4.7.0-py36_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pillow-6.0.0-py36he7afcd5_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pyrsistent-0.15.2-py36h516909a_0.tar.bz2\n",
       "https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.8.0-py_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/qt-5.9.7-h52cfd70_1.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/scipy-1.2.1-py37h09a28d5_1.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/setuptools-41.0.1-py37_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/traitlets-4.3.2-py37_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/setuptools-41.0.1-py36_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/terminado-0.8.2-py36_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/traitlets-4.3.2-py36_1000.tar.bz2\n",
       "https://conda.anaconda.org/conda-forge/noarch/zict-0.1.4-py_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/distributed-1.28.1-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/bleach-3.1.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/cryptography-2.7-py36h72c5cf5_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/distributed-1.28.1-py36_0.tar.bz2\n",
       "https://conda.anaconda.org/conda-forge/noarch/jinja2-2.10.1-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/jsonschema-3.0.1-py36_0.tar.bz2\n",
       "https://conda.anaconda.org/conda-forge/noarch/jupyter_core-4.4.0-py_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.0.3-py37h5f35d83_1.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/netcdf4-1.5.1.2-py37had58050_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/pandas-0.24.2-py37hf484d3e_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/noarch/patsy-0.5.1-py_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/noarch/pygments-2.4.0-py_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/pyqt-5.9.2-py37hcca6a23_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/wheel-0.33.4-py37_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/zarr-2.3.1-py37_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/bokeh-1.1.0-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pandas-0.24.2-py36hb3f55d8_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/pygments-2.4.2-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/wheel-0.33.4-py36_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/bokeh-1.2.0-py36_0.tar.bz2\n",
       "https://conda.anaconda.org/conda-forge/noarch/jupyter_client-5.2.4-py_3.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.0.3-py37_1.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/pip-19.1-py37_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/nbformat-4.4.0-py_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pip-19.1.1-py36_0.tar.bz2\n",
       "https://conda.anaconda.org/conda-forge/noarch/prompt_toolkit-2.0.9-py_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.9.0-py37h3010b51_1000.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/noarch/xarray-0.12.1-py_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/noarch/dask-1.2.2-py_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/ipython-7.5.0-py37h24bf2e0_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/noarch/seaborn-0.9.0-py_1.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/noarch/dask-jobqueue-0.4.1-py_0.tar.bz2\n",
-      "https://conda.anaconda.org/conda-forge/linux-64/ipykernel-5.1.1-py37h24bf2e0_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/pyopenssl-19.0.0-py36_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/dask-1.2.2-py_3.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/ipython-7.5.0-py36h24bf2e0_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/nbconvert-5.5.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/urllib3-1.24.3-py36_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/dask-jobqueue-0.5.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/ipykernel-5.1.1-py36h24bf2e0_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/requests-2.22.0-py36_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/conda-4.6.14-py36_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/notebook-5.7.8-py36_1.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/jupyterlab_server-0.2.0-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/widgetsnbextension-3.4.2-py36_1000.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/noarch/ipywidgets-7.4.2-py_0.tar.bz2\n",
+      "https://conda.anaconda.org/conda-forge/linux-64/jupyterlab-0.35.6-py36_0.tar.bz2\n",
       "\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
@@ -675,9 +655,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [conda env:dask_jobqueue_workshop]",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda-env-dask_jobqueue_workshop-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -689,7 +669,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,