dask-schedulers.ipynb

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    "<img src=\"assets/dask_horizontal.svg\"\n",
    "     width=\"45%\"\n",
    "     alt=\"Dask logo\\\">\n",
    "     \n",
    "# Parallel Computing in Python with Dask\n",
    "\n",
    "This notebook provides a high-level overview of Dask. We discuss why you might want to use Dask, the high-level and low-level APIs for generating computational graphs, and the schedulers which allow for the parallel execution of these graphs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed2eaf87-82e4-41e1-9d27-c141634a77b8",
   "metadata": {},
   "source": [
    "# Overview\n",
    "\n",
    "### What is Dask?\n",
    "\n",
    "- Dask is a flexible, open source library for parallel computing in Python\n",
    "\n",
    "    - GitHub: [https://github.com/dask/dask](https://github.com/dask/dask)\n",
    "    \n",
    "    - Documentation: [https://docs.dask.org](https://docs.dask.org)\n",
    "\n",
    "- Scales the existing Python ecosystem\n",
    "\n",
    "### Why Dask?\n",
    "\n",
    "- Enables parallel and larger-than-memory computations\n",
    "- Uses familiar APIs you're used to from projects like NumPy, pandas, and scikit-learn\n",
    "- Allows you to scale existing workflows with minimal code changes\n",
    "- Dask works on your laptop, but also scales out to large clusters\n",
    "- Offers great built-in diagnosic tools\n",
    "\n",
    "### Dask vs. Spark\n",
    "\n",
    "#### Reasons you might choose Spark\n",
    "\n",
    "- You prefer Scala or the SQL language\n",
    "- You have mostly JVM infrastructure and legacy systems\n",
    "- You want an established and trusted solution for business\n",
    "- You are mostly doing business analytics with some lightweight machine learning\n",
    "- You want an all-in-one solution\n",
    "\n",
    "#### Reasons you might choose Dask\n",
    "\n",
    "- You prefer Python or native code, or have large legacy code bases that you do not want to entirely rewrite\n",
    "- Your use case is complex or does not cleanly fit the Spark computing model\n",
    "- You want a lighter-weight transition from local computing to cluster computing\n",
    "- You want to interoperate with other technologies and don’t mind installing multiple packages\n",
    "\n",
    "### Components of Dask\n",
    "\n",
    "<img src=\"assets/dask-overview.png\"\n",
    "     width=\"85%\"\n",
    "     alt=\"Dask components\\\">\n",
    "     "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d406f659-ffb8-4dab-b89f-7f68b033ed45",
   "metadata": {},
   "source": [
    "# Schedulers\n",
    "\n",
    "High-level collections like Dask arrays and Dask DataFrames, as well as the low-level `dask.delayed` interface build up task graphs for a computation. After these graphs are generated, they need to be executed (potentially in parallel). This is the job of a task scheduler. Different task schedulers exist within Dask. Each will consume a task graph and compute the same result, but with different performance characteristics. \n",
    "\n",
    "![grid-search](assets/animation.gif \"grid-search\")\n",
    "\n",
    "Dask has two different classes of schedulers: single-machine schedulers and a distributed scheduler."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f0df062-db5d-4023-891b-f652f325ec07",
   "metadata": {},
   "source": [
    "### Single Machine Schedulers\n",
    "\n",
    "Single machine schedulers provide basic features on a local process or thread pool and require no setup (only use the Python standard library). The different single machine schedulers Dask provides are:\n",
    "\n",
    "- `'threads'`: The threaded scheduler executes computations with a local `concurrent.futures.ThreadPoolExecutor`. The threaded scheduler is the default choice for Dask arrays, Dask DataFrames, and Dask delayed. \n",
    "\n",
    "- `'processes'`: The multiprocessing scheduler executes computations with a local `concurrent.futures.ProcessPoolExecutor`.\n",
    "\n",
    "- `'single-threaded'`: The single-threaded synchronous scheduler executes all computations in the local thread, with no parallelism at all. This is particularly valuable for debugging and profiling, which are more difficult when using threads or processes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3de43608-dc04-4718-8642-f2abbbeebf8d",
   "metadata": {},
   "outputs": [
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dask import delayed\n",
    "import time\n",
    "\n",
    "@delayed\n",
    "def inc(x):\n",
    "    time.sleep(1)\n",
    "    return x + 1\n",
    "\n",
    "@delayed\n",
    "def double(x):\n",
    "    time.sleep(1)\n",
    "    return 2 * x\n",
    "\n",
    "@delayed\n",
    "def add(x, y):\n",
    "    time.sleep(1)\n",
    "    return x + y\n",
    "\n",
    "output = []\n",
    "for x in range(10):\n",
    "    a = inc(x)\n",
    "    b = double(x)\n",
    "    c = add(a, b)\n",
    "    output.append(c)\n",
    "\n",
    "result = delayed(sum)(output)\n",
    "result.visualize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f5dc6f27-71f5-4db3-af1e-dea789063c18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Single-threaded scheduler: 0:00:30.076741\n",
      "Multi-processing scheduler: 0:00:10.181815\n",
      "Multi-threading scheduler: 0:00:03.016028\n",
      "Multi-threading scheduler (with 4 workers): 0:00:08.017001\n"
     ]
    }
   ],
   "source": [
    "import dask\n",
    "from datetime import datetime\n",
    "\n",
    "\"\"\"\n",
    "You can configure which scheduler is used in a few different ways. \n",
    "You can set the scheduler globally by using the \n",
    "`dask.config.set(scheduler=)` command\n",
    "\"\"\"\n",
    "tick = datetime.now()\n",
    "dask.config.set(scheduler='single-threaded')\n",
    "result.compute(); # Will use the single-threaded scheduler\n",
    "tock = datetime.now()\n",
    "print(f\"Single-threaded scheduler: {tock - tick}\")\n",
    "\n",
    "\"\"\"\n",
    "or use it as a context manager to set the scheduler for a block of code\n",
    "\"\"\"\n",
    "tick = datetime.now()\n",
    "with dask.config.set(scheduler='processes'):\n",
    "    result.compute()  # Will use the multi-processing scheduler\n",
    "tock = datetime.now()\n",
    "print(f\"Multi-processing scheduler: {tock - tick}\")\n",
    "\n",
    "\"\"\"\n",
    "or even within a single compute call\n",
    "\"\"\"\n",
    "tick = datetime.now()\n",
    "result.compute(scheduler='threads');  # Will use the multi-threading scheduler\n",
    "tock = datetime.now()\n",
    "print(f\"Multi-threading scheduler: {tock - tick}\")\n",
    "\n",
    "\"\"\"\n",
    "The `num_workers` argument is used to specify the number of threads or processes to use\n",
    "\"\"\"\n",
    "tick = datetime.now()\n",
    "result.compute(scheduler='threads', num_workers=4);\n",
    "tock = datetime.now()\n",
    "print(f\"Multi-threading scheduler (with 4 workers): {tock - tick}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0e7b1f5-25d9-4be0-b6e4-eed5b612b289",
   "metadata": {},
   "source": [
    "### Distributed Scheduler\n",
    "\n",
    "Despite having \"distributed\" in it's name, the distributed scheduler works well on both single and multiple machines. Think of it as the \"advanced scheduler\".\n",
    "\n",
    "A Dask distributed cluster is composed of a single centralized scheduler and one or more worker processes. A `Client` object is used as the user-facing entry point to interact with the cluster.\n",
    "\n",
    "<img src=\"assets/dask-cluster.png\"\n",
    "     width=\"85%\"\n",
    "     alt=\"Dask components\\\">\n",
    "     \n",
    "You can manually create a cluster by setting up schedulers and workers using the `dask-scheduler` and `dask-worker` commands on the CLI.\n",
    "\n",
    "```shell\n",
    "$ dask-scheduler\n",
    "distributed.scheduler - INFO - -----------------------------------------------\n",
    "distributed.scheduler - INFO - -----------------------------------------------\n",
    "distributed.scheduler - INFO - Clear task state\n",
    "distributed.scheduler - INFO -   Scheduler at: tcp://192.168.62.218:8786\n",
    "distributed.scheduler - INFO -   dashboard at:                     :8787\n",
    "```\n",
    "\n",
    "Then, you can launch a worker (or workers), providing the address of the `dask-scheduler` node.\n",
    "\n",
    "```shell\n",
    "$ dask-worker tcp://192.168.62.218:8786\n",
    "distributed.nanny - INFO -         Start Nanny at: 'tcp://192.168.62.218:53109'\n",
    "distributed.worker - INFO -       Start worker at: tcp://192.168.62.218:53110\n",
    "distributed.worker - INFO -          Listening to: tcp://192.168.62.218:53110\n",
    "distributed.worker - INFO -          dashboard at:       192.168.62.218:53111\n",
    "distributed.worker - INFO - Waiting to connect to:  tcp://192.168.62.218:8786\n",
    "distributed.worker - INFO - -------------------------------------------------\n",
    "distributed.worker - INFO -               Threads:                         16\n",
    "distributed.worker - INFO -                Memory:                  16.00 GiB\n",
    "distributed.worker - INFO -       Local Directory: /Users/drewfustin/dask-worker-space/worker-3oc_5d0j\n",
    "distributed.worker - INFO - -------------------------------------------------\n",
    "distributed.worker - INFO -         Registered to:  tcp://192.168.62.218:8786\n",
    "distributed.worker - INFO - -------------------------------------------------\n",
    "distributed.core - INFO - Starting established connection\n",
    "```\n",
    "\n",
    "You can then create a `Client` object pointing to the scheduler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "166decef-0ef5-41bd-b30b-f1a03c7f9832",
   "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 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://192.168.62.218:8786</li>\n",
       "  <li><b>Dashboard: </b><a href='http://192.168.62.218:8787/status' target='_blank'>http://192.168.62.218:8787/status</a></li>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>1</li>\n",
       "  <li><b>Cores: </b>16</li>\n",
       "  <li><b>Memory: </b>16.00 GiB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: 'tcp://192.168.62.218:8786' processes=1 threads=16, memory=16.00 GiB>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dask.distributed import Client\n",
    "\n",
    "client = Client('tcp://192.168.62.218:8786')\n",
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f13a96b0-66e5-478a-8d30-a912d7e76ce5",
   "metadata": {},
   "outputs": [],
   "source": [
    "client.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a329709-17f9-4736-a467-3238f3780c98",
   "metadata": {},
   "source": [
    "There are, of course, helpers to handle this from python. \n",
    "\n",
    "You can either create a cluster object, which automatically provisions the scheduler and workers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d0aaf01d-0479-4063-a3e4-aad6b3c2f87f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cd4765eaedd84429b04cf3d883a5203c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<h2>LocalCluster</h2>'), HBox(children=(HTML(value='\\n<div>\\n  <style scoped>\\n    …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from dask.distributed import LocalCluster\n",
    "\n",
    "# Launch a scheduler and 4 workers on my local machine\n",
    "cluster = LocalCluster(n_workers=4, threads_per_worker=2)\n",
    "cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ca92b135-7223-4f3e-9db3-07bdbe07588c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale up to 10 workers\n",
    "cluster.scale(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4269b9e6-6954-4bb1-ba06-5ae43a0da7c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale down to 2 workers\n",
    "cluster.scale(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c0f7f4b9-40a9-419f-9e03-f03ebc4a0778",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<details>\n",
       "<summary style='display:list-item'>Cluster</summary>\n",
       "<pre><code>\n",
       "\n",
       "</code></pre>\n",
       "</details>\n",
       "<details>\n",
       "<summary style='display:list-item'>Scheduler</summary>\n",
       "<pre><code>\n",
       "distributed.scheduler - INFO - Clear task state\n",
       "distributed.scheduler - INFO -   Scheduler at:     tcp://127.0.0.1:60987\n",
       "distributed.scheduler - INFO -   dashboard at:            127.0.0.1:8787\n",
       "distributed.scheduler - INFO - Register worker &lt;Worker &#x27;tcp://127.0.0.1:60993&#x27;, name: 1, memory: 0, processing: 0&gt;\n",
       "distributed.scheduler - INFO - Starting worker compute stream, tcp://127.0.0.1:60993\n",
       "distributed.scheduler - INFO - Register worker &lt;Worker &#x27;tcp://127.0.0.1:60995&#x27;, name: 2, memory: 0, processing: 0&gt;\n",
       "distributed.scheduler - INFO - Starting worker compute stream, tcp://127.0.0.1:60995\n",
       "distributed.scheduler - INFO - Register worker &lt;Worker &#x27;tcp://127.0.0.1:60994&#x27;, name: 3, memory: 0, processing: 0&gt;\n",
       "distributed.scheduler - INFO - Starting worker compute stream, tcp://127.0.0.1:60994\n",
       "distributed.scheduler - INFO - Register worker &lt;Worker &#x27;tcp://127.0.0.1:60996&#x27;, name: 0, memory: 0, processing: 0&gt;\n",
       "distributed.scheduler - INFO - Starting worker compute stream, tcp://127.0.0.1:60996\n",
       "distributed.scheduler - INFO - Register worker &lt;Worker &#x27;tcp://127.0.0.1:61016&#x27;, name: 5, memory: 0, processing: 0&gt;\n",
       "distributed.scheduler - INFO - Starting worker compute stream, tcp://127.0.0.1:61016\n",
       "distributed.scheduler - INFO - Register worker &lt;Worker &#x27;tcp://127.0.0.1:61015&#x27;, name: 4, memory: 0, processing: 0&gt;\n",
       "distributed.scheduler - INFO - Starting worker compute stream, tcp://127.0.0.1:61015\n",
       "distributed.scheduler - INFO - Register worker &lt;Worker &#x27;tcp://127.0.0.1:61017&#x27;, name: 7, memory: 0, processing: 0&gt;\n",
       "distributed.scheduler - INFO - Starting worker compute stream, tcp://127.0.0.1:61017\n",
       "distributed.scheduler - INFO - Register worker &lt;Worker &#x27;tcp://127.0.0.1:61025&#x27;, name: 8, memory: 0, processing: 0&gt;\n",
       "distributed.scheduler - INFO - Starting worker compute stream, tcp://127.0.0.1:61025\n",
       "distributed.scheduler - INFO - Register worker &lt;Worker &#x27;tcp://127.0.0.1:61024&#x27;, name: 9, memory: 0, processing: 0&gt;\n",
       "distributed.scheduler - INFO - Starting worker compute stream, tcp://127.0.0.1:61024\n",
       "distributed.scheduler - INFO - Register worker &lt;Worker &#x27;tcp://127.0.0.1:61030&#x27;, name: 6, memory: 0, processing: 0&gt;\n",
       "distributed.scheduler - INFO - Starting worker compute stream, tcp://127.0.0.1:61030\n",
       "distributed.scheduler - INFO - Remove worker &lt;Worker &#x27;tcp://127.0.0.1:60995&#x27;, name: 2, memory: 0, processing: 0&gt;\n",
       "distributed.scheduler - INFO - Remove worker &lt;Worker &#x27;tcp://127.0.0.1:60994&#x27;, name: 3, memory: 0, processing: 0&gt;\n",
       "distributed.scheduler - INFO - Remove worker &lt;Worker &#x27;tcp://127.0.0.1:61015&#x27;, name: 4, memory: 0, processing: 0&gt;\n",
       "distributed.scheduler - INFO - Remove worker &lt;Worker &#x27;tcp://127.0.0.1:61016&#x27;, name: 5, memory: 0, processing: 0&gt;\n",
       "distributed.scheduler - INFO - Remove worker &lt;Worker &#x27;tcp://127.0.0.1:61030&#x27;, name: 6, memory: 0, processing: 0&gt;\n",
       "distributed.scheduler - INFO - Remove worker &lt;Worker &#x27;tcp://127.0.0.1:61017&#x27;, name: 7, memory: 0, processing: 0&gt;\n",
       "distributed.scheduler - INFO - Remove worker &lt;Worker &#x27;tcp://127.0.0.1:61025&#x27;, name: 8, memory: 0, processing: 0&gt;\n",
       "distributed.scheduler - INFO - Remove worker &lt;Worker &#x27;tcp://127.0.0.1:61024&#x27;, name: 9, memory: 0, processing: 0&gt;\n",
       "</code></pre>\n",
       "</details>\n",
       "<details>\n",
       "<summary style='display:list-item'>tcp://127.0.0.1:60993</summary>\n",
       "<pre><code>\n",
       "distributed.worker - INFO -       Start worker at:      tcp://127.0.0.1:60993\n",
       "distributed.worker - INFO -          Listening to:      tcp://127.0.0.1:60993\n",
       "distributed.worker - INFO -          dashboard at:            127.0.0.1:61002\n",
       "distributed.worker - INFO - Waiting to connect to:      tcp://127.0.0.1:60987\n",
       "distributed.worker - INFO - -------------------------------------------------\n",
       "distributed.worker - INFO -               Threads:                          2\n",
       "distributed.worker - INFO -                Memory:                   4.00 GiB\n",
       "distributed.worker - INFO -       Local Directory: /Users/drewfustin/code/kar-roundtable-dask/dask-worker-space/worker-9acsoev_\n",
       "distributed.worker - INFO - -------------------------------------------------\n",
       "distributed.worker - INFO -         Registered to:      tcp://127.0.0.1:60987\n",
       "distributed.worker - INFO - -------------------------------------------------\n",
       "</code></pre>\n",
       "</details>\n",
       "<details>\n",
       "<summary style='display:list-item'>tcp://127.0.0.1:60996</summary>\n",
       "<pre><code>\n",
       "distributed.worker - INFO -       Start worker at:      tcp://127.0.0.1:60996\n",
       "distributed.worker - INFO -          Listening to:      tcp://127.0.0.1:60996\n",
       "distributed.worker - INFO -          dashboard at:            127.0.0.1:60997\n",
       "distributed.worker - INFO - Waiting to connect to:      tcp://127.0.0.1:60987\n",
       "distributed.worker - INFO - -------------------------------------------------\n",
       "distributed.worker - INFO -               Threads:                          2\n",
       "distributed.worker - INFO -                Memory:                   4.00 GiB\n",
       "distributed.worker - INFO -       Local Directory: /Users/drewfustin/code/kar-roundtable-dask/dask-worker-space/worker-72acoual\n",
       "distributed.worker - INFO - -------------------------------------------------\n",
       "distributed.worker - INFO -         Registered to:      tcp://127.0.0.1:60987\n",
       "distributed.worker - INFO - -------------------------------------------------\n",
       "</code></pre>\n",
       "</details>"
      ],
      "text/plain": [
       "{'Cluster': '',\n",
       " 'Scheduler': \"distributed.scheduler - INFO - Clear task state\\ndistributed.scheduler - INFO -   Scheduler at:     tcp://127.0.0.1:60987\\ndistributed.scheduler - INFO -   dashboard at:            127.0.0.1:8787\\ndistributed.scheduler - INFO - Register worker <Worker 'tcp://127.0.0.1:60993', name: 1, memory: 0, processing: 0>\\ndistributed.scheduler - INFO - Starting worker compute stream, tcp://127.0.0.1:60993\\ndistributed.scheduler - INFO - Register worker <Worker 'tcp://127.0.0.1:60995', name: 2, memory: 0, processing: 0>\\ndistributed.scheduler - INFO - Starting worker compute stream, tcp://127.0.0.1:60995\\ndistributed.scheduler - INFO - Register worker <Worker 'tcp://127.0.0.1:60994', name: 3, memory: 0, processing: 0>\\ndistributed.scheduler - INFO - Starting worker compute stream, tcp://127.0.0.1:60994\\ndistributed.scheduler - INFO - Register worker <Worker 'tcp://127.0.0.1:60996', name: 0, memory: 0, processing: 0>\\ndistributed.scheduler - INFO - Starting worker compute stream, tcp://127.0.0.1:60996\\ndistributed.scheduler - INFO - Register worker <Worker 'tcp://127.0.0.1:61016', name: 5, memory: 0, processing: 0>\\ndistributed.scheduler - INFO - Starting worker compute stream, tcp://127.0.0.1:61016\\ndistributed.scheduler - INFO - Register worker <Worker 'tcp://127.0.0.1:61015', name: 4, memory: 0, processing: 0>\\ndistributed.scheduler - INFO - Starting worker compute stream, tcp://127.0.0.1:61015\\ndistributed.scheduler - INFO - Register worker <Worker 'tcp://127.0.0.1:61017', name: 7, memory: 0, processing: 0>\\ndistributed.scheduler - INFO - Starting worker compute stream, tcp://127.0.0.1:61017\\ndistributed.scheduler - INFO - Register worker <Worker 'tcp://127.0.0.1:61025', name: 8, memory: 0, processing: 0>\\ndistributed.scheduler - INFO - Starting worker compute stream, tcp://127.0.0.1:61025\\ndistributed.scheduler - INFO - Register worker <Worker 'tcp://127.0.0.1:61024', name: 9, memory: 0, processing: 0>\\ndistributed.scheduler - INFO - Starting worker compute stream, tcp://127.0.0.1:61024\\ndistributed.scheduler - INFO - Register worker <Worker 'tcp://127.0.0.1:61030', name: 6, memory: 0, processing: 0>\\ndistributed.scheduler - INFO - Starting worker compute stream, tcp://127.0.0.1:61030\\ndistributed.scheduler - INFO - Remove worker <Worker 'tcp://127.0.0.1:60995', name: 2, memory: 0, processing: 0>\\ndistributed.scheduler - INFO - Remove worker <Worker 'tcp://127.0.0.1:60994', name: 3, memory: 0, processing: 0>\\ndistributed.scheduler - INFO - Remove worker <Worker 'tcp://127.0.0.1:61015', name: 4, memory: 0, processing: 0>\\ndistributed.scheduler - INFO - Remove worker <Worker 'tcp://127.0.0.1:61016', name: 5, memory: 0, processing: 0>\\ndistributed.scheduler - INFO - Remove worker <Worker 'tcp://127.0.0.1:61030', name: 6, memory: 0, processing: 0>\\ndistributed.scheduler - INFO - Remove worker <Worker 'tcp://127.0.0.1:61017', name: 7, memory: 0, processing: 0>\\ndistributed.scheduler - INFO - Remove worker <Worker 'tcp://127.0.0.1:61025', name: 8, memory: 0, processing: 0>\\ndistributed.scheduler - INFO - Remove worker <Worker 'tcp://127.0.0.1:61024', name: 9, memory: 0, processing: 0>\",\n",
       " 'tcp://127.0.0.1:60993': 'distributed.worker - INFO -       Start worker at:      tcp://127.0.0.1:60993\\ndistributed.worker - INFO -          Listening to:      tcp://127.0.0.1:60993\\ndistributed.worker - INFO -          dashboard at:            127.0.0.1:61002\\ndistributed.worker - INFO - Waiting to connect to:      tcp://127.0.0.1:60987\\ndistributed.worker - INFO - -------------------------------------------------\\ndistributed.worker - INFO -               Threads:                          2\\ndistributed.worker - INFO -                Memory:                   4.00 GiB\\ndistributed.worker - INFO -       Local Directory: /Users/drewfustin/code/kar-roundtable-dask/dask-worker-space/worker-9acsoev_\\ndistributed.worker - INFO - -------------------------------------------------\\ndistributed.worker - INFO -         Registered to:      tcp://127.0.0.1:60987\\ndistributed.worker - INFO - -------------------------------------------------',\n",
       " 'tcp://127.0.0.1:60996': 'distributed.worker - INFO -       Start worker at:      tcp://127.0.0.1:60996\\ndistributed.worker - INFO -          Listening to:      tcp://127.0.0.1:60996\\ndistributed.worker - INFO -          dashboard at:            127.0.0.1:60997\\ndistributed.worker - INFO - Waiting to connect to:      tcp://127.0.0.1:60987\\ndistributed.worker - INFO - -------------------------------------------------\\ndistributed.worker - INFO -               Threads:                          2\\ndistributed.worker - INFO -                Memory:                   4.00 GiB\\ndistributed.worker - INFO -       Local Directory: /Users/drewfustin/code/kar-roundtable-dask/dask-worker-space/worker-72acoual\\ndistributed.worker - INFO - -------------------------------------------------\\ndistributed.worker - INFO -         Registered to:      tcp://127.0.0.1:60987\\ndistributed.worker - INFO - -------------------------------------------------'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retreieve cluster logs\n",
    "cluster.get_logs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7278679d-0d03-4b78-92f7-e2c19a0e9b92",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shut down cluster\n",
    "cluster.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8616775-9276-49e0-b41c-b101375e903d",
   "metadata": {},
   "source": [
    "There are several projects in the Dask ecosystem for easily deploying clusters on commonly used computing resources:\n",
    "\n",
    "- [Dask-Kubernetes](https://kubernetes.dask.org/en/latest/) for deploying Dask using native Kubernetes APIs\n",
    "- [Dask-Cloudprovider](https://cloudprovider.dask.org/en/latest/) for deploying Dask clusters on various cloud platforms (e.g. AWS, GCP, Azure, etc.)\n",
    "- [Dask-Yarn](https://yarn.dask.org/en/latest/) for deploying Dask on YARN clusters\n",
    "- [Dask-MPI](http://mpi.dask.org/en/latest/) for deploying Dask on existing MPI environments\n",
    "- [Dask-Jobqueue](https://jobqueue.dask.org/en/latest/) for deploying Dask on job queuing systems (e.g. PBS, Slurm, etc.)\n",
    "\n",
    "Launching clusters with any of these projects follows a similar pattern as using Dask's built-in `LocalCluster`:\n",
    "\n",
    "```python\n",
    "# Launch a Dask cluster on a Kubernetes cluster\n",
    "from dask_kubernetes import KubeCluster\n",
    "cluster = KubeCluster(...)\n",
    "\n",
    "# Launch a Dask cluster on AWS Fargate\n",
    "from dask_cloudprovider.aws import FargateCluster\n",
    "cluster = FargateCluster(...)\n",
    "\n",
    "# Launch a Dask cluster on a PBS job queueing system\n",
    "from dask_jobqueue import PBSCluster\n",
    "cluster = PBSCluster(...)\n",
    "```\n",
    "\n",
    "Additionally, there are compaines like [Coiled](https://coiled.io) and [Saturn Cloud](https://www.saturncloud.io) which have Dask deployment-as-a-service offerings.\n",
    "\n",
    "#### Related Documentation\n",
    "\n",
    "- [Cluster setup](https://docs.dask.org/en/latest/setup.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c949d178-b8a2-4642-8195-0b4e90c1ccf7",
   "metadata": {},
   "source": [
    "You can also create the distributed scheduler locally by creating a `Client` object, which lets you interact with the \"cluster\" (local threads or processes on your machine), which is automatically set up. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "09eadde6-b6c2-41d9-8301-747e3ed3a949",
   "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 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://127.0.0.1:61047</li>\n",
       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a></li>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>4</li>\n",
       "  <li><b>Cores: </b>16</li>\n",
       "  <li><b>Memory: </b>16.00 GiB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: 'tcp://127.0.0.1:61047' processes=4 threads=16, memory=16.00 GiB>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dask.distributed import Client\n",
    "\n",
    "client = Client()\n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "148cd473-cb82-4498-8399-3ec66e1dc43f",
   "metadata": {},
   "source": [
    "Notice the dashboard link. You can go there, or could make use of the `dask-labextension` package to view several plots within your Jupyter Lab. \n",
    "\n",
    "Let's open the Task Stream, Graph, Progress, and Scheduler System."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6bd38c16-60f1-4d7d-9c1c-0978d286d8e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "145"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d45a3b90-391c-4150-b0c7-ba2dae439af5",
   "metadata": {},
   "outputs": [],
   "source": [
    "client.shutdown()"
   ]
  }
 ],
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