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   "source": [
    "# T9 - Deployment\n",
    "\n",
    "This tutorial provides several useful recipes for deploying Covasim."
   ]
  },
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   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "    \n",
    "Click [here](https://mybinder.org/v2/gh/institutefordiseasemodeling/covasim/HEAD?urlpath=lab%2Ftree%2Fdocs%2Ftutorials%2Ftut_deployment.ipynb) to open an interactive version of this notebook.\n",
    "    \n",
    "</div>"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dask\n",
    "\n",
    "[Dask](https://dask.org/) is a powerful library for multiprocessing and \"scalable\" analytics. Using Dask (rather than the built-in `multiprocess`) for parallelization is _relatively_ straightforward:"
   ]
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   "source": [
    "```python\n",
    "import dask\n",
    "from dask.distributed import Client\n",
    "import numpy as np\n",
    "import covasim as cv\n",
    "\n",
    "\n",
    "def run_sim(index, beta):\n",
    "    ''' Run a standard simulation '''\n",
    "    sim = cv.Sim(beta=beta, label=f'Sim {index}, beta={beta}')\n",
    "    sim.run()\n",
    "    return sim\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "\n",
    "    # Run settings\n",
    "    n = 8\n",
    "    n_workers = 4\n",
    "    betas = np.sort(np.random.random(n))\n",
    "\n",
    "    # Create and queue the Dask jobs\n",
    "    client = Client(n_workers=n_workers)\n",
    "    queued = []\n",
    "    for i,beta in enumerate(betas):\n",
    "        run = dask.delayed(run_sim)(i, beta)\n",
    "        queued.append(run)\n",
    "\n",
    "    # Run and process the simulations\n",
    "    sims = list(dask.compute(*queued))\n",
    "    msim = cv.MultiSim(sims)\n",
    "    msim.plot(color_by_sim=True)\n",
    "```"
   ]
  },
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   "metadata": {},
   "source": [
    "## Jupyter/IPython\n",
    "\n",
    "Using Jupyter and [Voilà](https://voila.readthedocs.io/), you can build a Covasim-based webapp in minutes. First, install the required dependencies:\n",
    "\n",
    "```bash\n",
    "pip install jupyter jupyterlab jupyterhub ipympl voila \n",
    "```\n",
    "\n",
    "Here is a very simple interactive webapp that runs a multisim (in parallel!) when the button is pressed, and displays the results:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "```python\n",
    "import numpy as np\n",
    "import covasim as cv\n",
    "import ipywidgets as widgets\n",
    "\n",
    "# Create the button and output area\n",
    "button = widgets.Button(description='Run')\n",
    "output = widgets.Output()\n",
    "\n",
    "@output.capture()\n",
    "def run():\n",
    "    ''' Stochastically run a parallelized multisim '''\n",
    "    sim = cv.Sim(verbose=0, pop_size=20e3, n_days=100, rand_seed=np.random.randint(99))\n",
    "    msim = cv.MultiSim(sim)\n",
    "    msim.run(n_runs=4)\n",
    "    return msim.plot()\n",
    "\n",
    "def click(b):\n",
    "    ''' Rerun on click '''\n",
    "    output.clear_output(wait=True)\n",
    "    run()\n",
    "\n",
    "# Create and show the app\n",
    "button.on_click(click)\n",
    "app = widgets.VBox([button, output])\n",
    "display(app)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you save this as e.g. `msim.ipynb`, then you can turn it into a web server simply by typing `voila msim.ipynb`."
   ]
  }
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