003-distributed-cluster.ipynb

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   "source": [
    "# Working with a distributed cluster\n",
    "\n",
    "In the [previous tutorial](002-intro-to-dask.ipynb) you saw Dask's capability to figure out how to parallelize work. The complementary capability of the library is it's ability to distribute the work across a cluster of workers. \n",
    "\n",
    "In our setup, we'll use a so-called [Kubernetes cluster](https://kubernetes.dask.org/en/latest/).\n",
    "\n",
    "This launches workers on our scalable cluster and passes the work to them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask.distributed import Client, progress\n",
    "from dask_kubernetes import KubeCluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need two objects. The first is a cluster object. Here you can select how many workers you would like to provision (this will determine how many CPUs and how much RAM you have for your task)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster = KubeCluster(n_workers=2)\n",
    "cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use the cluster, create local client object that is connected to this cluster. Following this, dask operations will go to the cluster, instead of executing locally:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = Client(cluster)\n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the dask bar on the left, you can now open windows that will display the task stream the task graph, with the progress in performing the tasks indicated with a color code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [1, 2, 3, 4, 5, 6, 7, 8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask import delayed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "def inc(x):\n",
    "    time.sleep(1)\n",
    "    return x + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = []\n",
    "for x in data:\n",
    "    y = delayed(inc)(x)\n",
    "    results.append(y)\n",
    "    \n",
    "total = sum(results)\n",
    "#total = total.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "total.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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