client_scatter.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "38651502-711c-4181-90d4-3ff3ffa73036",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import gcsfs\n",
    "\n",
    "with open('pangeo-181919-e7bc5bdaf4d5.json') as f:\n",
    "    token = json.load(f)\n",
    "gcs = gcsfs.GCSFileSystem(token=token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "72b0ac8d-f5f2-45e3-9903-0f3717ce251b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[ClusterReport<name=prod.9407f93be0e14da99549a834e1083014, status=RUNNING>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dask_gateway import GatewayCluster, Gateway\n",
    "from distributed import Client\n",
    "g = Gateway()\n",
    "g.list_clusters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0fef069d-40db-45b0-981e-914b1ebde9c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster = g.connect(g.list_clusters()[0].name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "705b6ece-a063-4ca9-ae00-bcd95ad36c36",
   "metadata": {},
   "outputs": [],
   "source": [
    "options = g.cluster_options()\n",
    "options.worker_cores = 2; options.worker_memory = 4\n",
    "# Create a cluster with those options\n",
    "cluster = g.new_cluster(options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "14f6bbc3-261c-4aad-ac03-5a97b9545ba9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[ClusterReport<name=prod.9407f93be0e14da99549a834e1083014, status=RUNNING>,\n",
       " ClusterReport<name=prod.25c54850eb694cc39d10c1893d4844bf, status=RUNNING>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.list_clusters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1b2b64c1-bb86-4a52-9f98-0a9e994fec33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-f98088c4-35de-11ee-832f-dee86894ea65</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "\n",
       "        <tr>\n",
       "        \n",
       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> dask_gateway.GatewayCluster</td>\n",
       "        \n",
       "        </tr>\n",
       "\n",
       "        \n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard: </strong> <a href=\"/services/dask-gateway/clusters/prod.25c54850eb694cc39d10c1893d4844bf/status\" target=\"_blank\">/services/dask-gateway/clusters/prod.25c54850eb694cc39d10c1893d4844bf/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\"></td>\n",
       "            </tr>\n",
       "        \n",
       "\n",
       "        </table>\n",
       "\n",
       "        \n",
       "            <button style=\"margin-bottom: 12px;\" data-commandlinker-command=\"dask:populate-and-launch-layout\" data-commandlinker-args='{\"url\": \"/services/dask-gateway/clusters/prod.25c54850eb694cc39d10c1893d4844bf/status\" }'>\n",
       "                Launch dashboard in JupyterLab\n",
       "            </button>\n",
       "        \n",
       "\n",
       "        \n",
       "            <details>\n",
       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
       "            <div style='background-color: #f2f2f2; display: inline-block; padding: 10px; border: 1px solid #999999;'>\n",
       "  <h3>GatewayCluster</h3>\n",
       "  <ul>\n",
       "    <li><b>Name: </b>prod.25c54850eb694cc39d10c1893d4844bf\n",
       "    <li><b>Dashboard: </b><a href='/services/dask-gateway/clusters/prod.25c54850eb694cc39d10c1893d4844bf/status' target='_blank'>/services/dask-gateway/clusters/prod.25c54850eb694cc39d10c1893d4844bf/status</a>\n",
       "  </ul>\n",
       "</div>\n",
       "\n",
       "            </details>\n",
       "        \n",
       "\n",
       "    </div>\n",
       "</div>"
      ],
      "text/plain": [
       "<Client: 'tls://10.9.61.4:8786' processes=0 threads=0, memory=0 B>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client = Client(cluster)\n",
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "00d6bf9b-fba9-46be-a946-24c494c60807",
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster.scale(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b42b108f-e752-4c35-bf43-199985dff7e8",
   "metadata": {},
   "source": [
    "### Define functions and start timing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d3542096-c05e-4710-ae85-503bbc249633",
   "metadata": {},
   "outputs": [],
   "source": [
    "import zarr, numpy as np, dask\n",
    "\n",
    "def load():\n",
    "    mapper = gcs.get_mapper(gcs.ls('pangeo-argo-eke/global/mean_and_anomalies/with_pressure_coordinate/readable_ws_and_NHarm/scattered_data_in_betaparamcomputation')[5])\n",
    "    z = zarr.open_consolidated(mapper)\n",
    "    data, lat, lon, time = z.Anomalies[:][:,0],z.latitude[:],z.longitude[:],z.time[:]    \n",
    "    return data, lat, lon, time\n",
    "\n",
    "@dask.delayed\n",
    "def function_load():\n",
    "    data, lat, lon, time = load()\n",
    "    ### Do computation with lat, lon, data, time    \n",
    "    return None\n",
    "\n",
    "@dask.delayed\n",
    "def function_scattered(data_scattered, lat_scattered, lon_scattered, time_scattered):\n",
    "    data, lat, lon, time = data_scattered, lat_scattered, lon_scattered, time_scattered\n",
    "    ### Do computation with lat, lon, data, time\n",
    "    return None\n",
    "\n",
    "data, lat, lon, time = load()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52a7d799-f702-41e7-9760-9ef537852012",
   "metadata": {},
   "source": [
    "### The loaded data are numpy arrays of equal size stored as zarr (by first creating xarray dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "566a660c-79af-4527-a77a-444378cb1dc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[dtype('float64'), dtype('float64'), dtype('float64'), dtype('float64')]\n"
     ]
    }
   ],
   "source": [
    "print([arr.dtype for arr in (data, lat,lon, time)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "06edcdff-1203-44b1-8bf2-4175fcd545fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x50 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x50 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4XdDc8VW6iaeqw6sLgxu22iNVCnmr1CiVbAkN0UC0jcMBunW0wf1C+UMZE9IWSTfHVg2DZYdT8MvFbFU2EamWGoxBW/Ob7ZCaIkaVOQSJfFoNlK5cuYIRI0aI3i9cuBAAMGPGDERHR+Pjjz/G06dPMXfuXJSUlGDQoEGIiYmBra2taJvNmzfD1NQUU6dOxdOnTzFq1ChER0fDxKTl4RLbiuwnNIu3Ljzn7Sjqw6Jp15c1jdzY243POj9DW1T2tB5n7xVJBDP/ZD6RSVejxIg+Qg+0+L+gSnOY3stPaPz4NJod0TYuhwNnG3Pcb58V26Qd2Hc5BzNCPQE09aEprKjFGD/JrhoNjQKYmijfoGr0pkSl02YVVcFTarCG7fHp+OT5nqL3353JVHp/rfFtQoZOjtPWabXp3fDhw8EwjMwrOjoaQFOp7ooVK5Cfn4+amhokJCTA399fYh8WFhb4+uuvsWrVKri4uCA2NhYTJ07EmTPa65BnaIZ+YbhDXhuq68vC8VaYl0rb/DBzgFbysvnVQNhZNc2bYarhUe8UjZ6jbzN+uIR5v16VGO6UbdJk8cBRV0332Q6j7+YwFCYRbeMA6GBroe9sEKI1yw/fQqOAQU19I1765hze2n1FZtCBbkuPy4y2q6mCquEb4pHysExr/ZCI7ul91Dtl7Nu3DwsWLMDSpUuRnJyMsLAwjBs3DtnZKlSDknZhz5xByFobATsrMyyN8FN6u+4uNmqPRBPobo+zn4yQu35SUJfmNy1EAnOGqBbcGbJrRjYijr77Vyt7n9ZkPoVzRJF2ggN8FtFL37lo9+gRWrt8Pj2GiK+aC9Nf+uacTBrfz45jxeFbePeXJGQWVSk1V5GyXvj6LLZLNYM7c+8xDeZkpIyiZ+emTZswe/ZszJkzBwCwZcsWnDhxAtu3b0dUVJSec0cMibrPkBxw1H4A5XIAcxWq8RXp1al5hMYOtjw8rmj7F9aYVPlzomkNy29d/lTz/Y5Uo/vHJ+EcUaR94ABwtuHpOxuEaF36Y8WTLTNM82imx1M0fw+Sbvb2xq5LAIDD7w3W+LGIdhl8jVJdXR2SkpIQHh4usTw8PBznz7NPwlpbW4vy8nKJF2kndFAr8MEoX9lDKnnclpJxOEBG5HhcXDIKEQGdRMu5HODc4pEAgP5dJSdg3jQ1ULmDGyjxG4o+K3XuKZgt3ZDou+arNagfln7RsNSE6Ne6v2/rOwtERQYfKBUVFaGxsVFm3iQXFxeZOZiEoqKiYGdnJ3q5u7vrIqvEAGhrIjdxk4I6S7zncjgaewDhcAAulwNXOwuJh8qMqAh0trfEvTXj8Otbz0ls83K/LtK70avIY2mYtE22qYMyHrRi1vTWMtFw/zFllVXXY2diOpKzS5XexlgDjld3XtR3Fto1ipMI0a9z92UHNCKGzeADJSHp4XwZhpE7QeSSJUtQVlYmeuXk5Ogii8SIqfIAIZ2Ww1G+JkR8224dbfB8b1fJ9WJ7YnsUNjPhQvx5XvinIfU72JmYodJDv7hD1/I0mxk5MliaZRRV1rGk1L5Z0ZcQeey2zGS68pRU1eP1Xf9oOVfacSlTO6NKEuVQjZJheFReo+8sEEKUZPCBkrOzM0xMTGRqjwoLC2VqmYR4PB74fL7EiyjHlW/cIyK15jlA2W2la604kKxR6u5io9R+Yj8cih1vBMvNg7xKA7YCgjlh3jj6/hCljkvkE87ArktXVQwqK2sbqFSSECP2wd5r+s4CIURJBh8omZubIzg4GLGxsRLLY2NjERoaqqdctV1hvs76zkKr6KLElLVGSWzZ1un9lNyP4rzKG15U3lZG2hrLoKgyXwYhxqalaw4hhBBJBh8oAU0T1X7//ff44YcfkJaWhg8//BDZ2dl455139J01vfnf2yFa2e+swcY9PHWrapTU7N/E5XAkHkAECiIWVY4gv0ZJLI0K+yOEtG8UJhFCiGq0GiitWbMGoaGhsLKygr29PWua7OxsTJgwAdbW1nB2dsb777+PujrJvgJ+fn7o2rUrZs+eDT8/P+zbtw9Hjx5F165dtZl9vXoluLmD/tX/jsHicT2x/91QfDUtCNeWjcFAL0e19vvx8z0Urvdz4yucE6i1bC20OyK9Lh4E2GqUuEo0mVNu3+IBl2rbtrZGKcTbqXU7MBK6HghhwWjflhMRogNcoygabdsKqX9Su/BZRC8M8HRoOSExeFq9bNbV1WHKlCl49913Wdc3NjYiIiICVVVVOHv2LPbu3Yv9+/fjo48+EqUpLy/HmDFj0L9/f9y8eRP79+9HZWUlrly5os2s6534s5wJh4N3hvkguKsDXgx0g72VOQAgM2q8xDZZayNa3K9fJ/n9tfjPgpguDlb46z3N93dJWTkWSZ+NwUt93VjXd+vY3LcnclKAWsdQt0aJw1F+HiXp5itNTe+UrFFq4SBSQ5aotQ91+bm1j758XkuO6fR49VIzwBOiL7oYFZQoNjDylL6z0C7MH9kNI3t2hK/Yc4W6BczqmBnqiV/mDNLZ8Yj2aLV4f+XKlQCA6Oho1vUxMTFITU1FTk4O3NyaHp43btyImTNnYs2aNeDz+dizZw9qamoQHR0NHo8Hf39/3L17F5s2bcLChQvbRZtrjpxwtrWfPT1yPOobBej5378BNA1LLRTQxU4UeHkuPtrivr6eFoT5vyXLLD80bzBOpj3CR+HNNVlfvhaEDVMC8cWJO9iZ2DyHjnhJ//RBHvj04E3VP5QOHgSkj9A0PHjz+9bVKKm2H/G8yOvTpCzq46Qd3yVm6jsLhACg4cFJ+5AeOb7F6R7En2sWj+uJd4b5AABe3HoWN3LLlD7WiQVDcTW7BEsOND2vrHyxN/zc+DA14YKRKiT7dc4gTP+efcRSD0crZMuZHiNrbQQ2x97Fl6fuyax7tb871k5uKljWdSGgsm6sCEefFTH6zoba9FoRf+HCBfj7+4uCJAAYO3YsamtrkZSUJEozbNgw8Hg8iTR5eXnIysrSdZZ1RvyhV1sDFEhfR9Q5zvVl4chaG4EJgW44v3gkYj4cKrE+0N1eIkgSMjPh4tPxzUNaf/FKH5WPzaZ1fZTUOwaHw5Ec1ltBwNHihLNK7keURvzvVgY6rQ202qK7q8fhyHz22tVBXo649OmoFvdRRzVKxEBQnETaOm9na6XmxBMf4fetMG/R32xbHpk/BNbmJqz74XCA1wa4Y9kLfjj+QRhmhHpigGdTzZWZSfMj9vgAV4R2c0bW2ggcmCs7EFlLWZbXZLx3Z/6zFjEcZEaNx/014/Cb1FyLAPByv84sW2vev1n6uRt7IaxeA6WCggKZIb4dHBxgbm4uGg6cLY3wvbwJZ2tra1FeXi7xMjpiJ5Ymb27i56t0jZQ6823aWZmJ/nazt0R3F1uVRs47t3gkDs0bjCn93TXymK6PBwEOpAdYUP+TtGY/rf3+jP1ipg3mplz4d7bD2N6yUxH09bBHRyWG0/90fE9tZI0QlbWHFhikfZOekF2eWYM9RX+LB1aVtQ0yaf072+HqsjGs+2m6/3Pw7yFe6KWga4MweAKAfh4O8HK2Fr1fMNoX5qbqPY6L/09zOByYmnAR4uOEi0skC/FeEZuYfr2GCqbZWPNkA8oGIy8sVPmXWbFihSh6lfdSpf8Q24VbejJZtslm5W0LAFFRUbCzsxO93N3dlc6PoRB/ZtVojZLUw7D4rjV1E1XlgbuzvSUC3e2fbdj6Y6v7XQkvdsqlZeuj1Pxe0SAMqmRPmcEcJJreUaSjUTNDPUV/szXFeFrXCABwsjZXuJ//G+qj0XwRoi6Kk0hbp+w5PmuwF6YP8sB3b/aXWJ7OMhk5APBMTZAZNV6mL3hLzw3/GuSBDrY8ifsJAMQtGo6stRG4t2YcFozuji2vBsGFz8P6yexBjLy7u7wCblc7C/w4c4DovfjzxNT+7kiPHM+yVeuxZcfYn0xUDpTee+89pKWlKXz5+/srtS9XV1eZWqGSkhLU19eLao3Y0hQWFgKA3AlnlyxZgrKyMtErJydH1Y+pd+IPX5q8uTVKPX2LP/SrU6PERtFgBopopEZJBw8C0sdo6qPUvFBR5/2WSo0kAx91ckc05Y2Q5lE1pW+mAJBw9zEA5ToI9/Ow11i+CFEXxUmkrVP2GcDclIvISQEY48f+HMm+b9mdt3S8NZMCcHnpaLkBlbB5np8bHxeXjMLUAewF+/KeBxQVDitqoaJM80S1sOTH2YbHktB4qBwoOTs7o2fPngpfFhYtN0cBgJCQEKSkpCA/P1+0LCYmBjweD8HBwaI0iYmJEkOGx8TEwM3NDZ6enqz75fF44PP5Ei9jsGRccxMdN3tL0d+afPhX9M9hoqEDqR0oaSAyaM2oTkr3UZJ6z+Uo/92tnhiArk5WiHqZfVQ//Ta9015k9mZIV6VGZdSWwd1UH/rcRaxZnX9nO/wyW3IEI2E7cGXO91UTlSs8IkSbqOkdaet0PbKjJo+m6P9T1Qnopfen6nQj6tJW/KVPWu2jlJ2djWvXriE7OxuNjY24du0arl27hsrKSgBAeHg4/Pz88MYbbyA5ORmnTp3CokWL8NZbb4mCm+nTp4PH42HmzJlISUnBwYMHERkZ2SZHvHN3tBL9rbWmd1K00fRO3X9IY6lRkr0yccDlcjB7iBemDXSXaIsszcvZGgn/GYFpAz1a3rmO51HS1HX0xALJAT3MTDhYNFbx/F2tdeZj+XN/mZlwsGdOy+3Ws9ZG4Ne3moMhG57koKD+nZsLXC4uGSUqzJA+39n6JPV2s2vx+MZq+iB55zIxNG3xIcaY7f73QH1noc3R9Tmu7+dQRc+H4t+FugXYqtLm86q+aDVQWrZsGYKCgrB8+XJUVlYiKCgIQUFBoj5MJiYmOHr0KCwsLDB48GBMnToVEydOxIYNG0T7sLOzQ2xsLHJzc9G/f3/MnTsXCxcuxMKFC7WZdb0QP4/FS/c1eeIpqqXQVFWsQM1ISd9NzZSeR0lOGc5/X/BD1Mut6yTJUT9OUmsLia019P07WJnhzMcjsO1f/Z61wR4PvkXToB87Xg9WeX+7/z0Ql5eOFr0/t3ikxPoQbyeJQoaBXo5I/E9z4LRhSqDSxwr1ccZX04Jw7P0wmXXigZOjWNNY6UmU21ufpHAVmq4Q/aJ5lAyLNc8EO17vp+9stCmtDVxsxa7zw3t0kOlbJE1XgZnc+7OC45uLjbqnqZLQET06iP5+zlu2ULgtFsZodR6l6OhouXMoCXl4eODIkSMK0wQEBCAxMVGDOTM8Xs7WciN+jY56Jz2Yg9jf+u6jJO2raUH4/UoOztwr0sj+NEX682nywiC+K12VAGmacCQ48eBF6Hl/V1ETvJEb45Ehp+OsOHNTLjrY8pC1NkJmoBcACOveNMri5lcDkVdag3kjurW4z7urx+FGbilKquvx1u4r6GDb3Ib6xUD2CZFNTbi4vjwcYCT7mi0e1xMHrj5k3UaVESD1obO9JR6WPm3VPoSDWijDvzMfKQ+NcBTStqINPsQYs6ZrGf0omtTa+7GluQkqno18Fz2r5Ro/XRU+yCt/VlSQ3t/TEcO6d4B3B/bny88iemH10TSV8iF+PLbPru8aNm3Q6/DgpNkf74TInRNHk+ed9L6UaeJnZ2nGulwe9ZveSW74YqAbfp6t2szW6s5ZY8LlKP0PXlMv+WCorapmVeMkY5tHaecbsgMksBGvoWT7jXq5NjWJmxTUhTVIYtvG3JSL/p6OGOPngtMfDUP8ouFK5cXO0kxiSHwA6GirXJ9MTYmcFIAX+nTSyL6ka+fUkVHUcrArdGge+5xURDc4oNExDUltvXEPm2yIWhu4zHo2D9DInh2VO56O4gITOU/rpor6nXM5+OnfA7F8Qm/W54M5Yd7IWhuBl/qyFw6yEf+8ohGL2zgKlAzA6895wMmGh+CuDqJlg7s1l0QreoD//KXeKh2ru4utxHvxQQhmyKliZpscTZG3hzZN3sY294wiU4KbRnsJasUIYZ3FBsFgew8AFmayp71wVm5luDtYYYbYiGjTNNhHQ/z3Gf2sSZO9lfxAVXzSXg+WGhx5xOdwEBof0PTwLf6diU9SZ8rl4F9yPuu6yeyDUyjSraMNUlaORWZU8zClXs7WMoG5J0teU1aOxY7X+2HjlEAMF2sKwKa3W1Mg9e7wpt94UpDkxHveHWxgzdNs5XqId9PgEa8/13yeLBzTXW76Ux8Nw6apyjURDPVxwtbpks11FN0s5Rndq+n8Cujcuv5Tw7pLfv/m8u7oaLpxC78bonuvDXRvk30I2IT5OrNOvGlIujhYItDduPov9hd7TlGGeN9OXWCbx0cVbw/1xsG5odj2L/lNIsWfFxxamB6itZI+a2p2PjNUdiJXABLPjYr0VvA7KBsUvvFcV0SIFdKNYLn3St8P2gIO0w6Kl8rKymBvb4+cnBy9j4CXXliBA8kPEeLjjLLqOthamiHU2wmmzx4ucourwbcyA9/CDH9cyUFHvgWGKjjxGIbBweSH6OthD29nG5TX1GPVX6lYOKY7yp7WI+72Y1iac3Ew+SEWjO6OET074kZuKWwtTOHlbAMAuPqgGDcfluHNEE+5QVlsagFyiquxKfYe3hnqjR2JGfhr/mDRPqQ9LKmGq52lSv2eGhoFuJxVgoAudhL9QR5X1GDytvMorq7H5lf74pu4e3h7qA9c7XjgW5rj+I187EjMwBBfJ+x4XbKWorS6DrfyymFhxsWDomrYW5sjyN0eN/PKMPeXqwCAgZ6O2DWzPzgcDlLzyhB/5zG8O1ijsqYBHfg88C1MkZpXjtBuzrCzMIPjs6EuX/v2AjrZW2Dzq0Eyn6W4qg7zf72KdZP7oEsLAUxOcTVWH03FjFBPhPo0B8gMw+BSRjF8XW0l+sQAwJl7j3G/sAKzBntLLN8Ycwcn0x7hrTBvLDt0C91dbBDazRlvD/XG4WsPUVBRC3cHK0zo44ak7BJ4OFriVNojvNDHDR1sLXC/sAKd7CxFgUOjgMH2+HQ42ZhhTC9X2Fqa4kL6E2QWVSK4qyOcbXiwNjeFnZUZUh6WwtmGB1c72eC0JXmlT5FX8hT9vRxR9rQe13JK0MXBClW1DejTxV7l/QFN5+CTqjrR9o0CBikPS9Grk53ak/vJ87i8Bt8mZmDBmO6w4ZmivlGAvNKn6OrUHOQJBAxu5ZWhi4MVRm5MAM+Ug5MfDZc41w9ezYUNzxQ8MxN4O1vjm7j7CO3mhCUHUuBkbY4NUwIx4Nlw5MVVdRi6Pg7TBrrjk+d74nJWCd7aLTuHnaOVGeaP8sVLfTtj3d9p6OXKRw9XW/i52cGEywHDMPi/n5NwIf0JAGDli34Y5OWEnYkZSC0ox9ZpQTDlcvHThSy8PcwHSQ+KUdcgwIf7rmPr9CAM79ERYzYlIL+sBl+80gcjenbE0PWnUV0nwJmPR+BWfjne+TkJiR+PgKO1OapqG3Ajtwx7/nmAmvpG+HS0wSdje2L3hSxwOEDG4yoM69ERfp1s8fbPSeByONj3dghS88vwpLIOn+y/gU/H9cL4Pp0w95eruJRVDAAI7eaE8/efSHz2CYGdcOxmgWhahMn9OuPozXx8FuGHk2kFiL9ThJUv+qG2QYCOfAss2HtNtO3X04Lwy8UHqKptQEpeOSb2dUNBeQ0uZhTjwNwQvLztgsx3PXe4D7bFp8ssH9bdGfNH+uKVHU3bmJlwUd8owKwhnigqr8XIXh0Re+sRjqUUYHgPZ0wd4IFz94qw559sAMDW6UGob2TwxYnbyCutgb2lKcYFdMJvl3Kw683+mC31u9vwTHDs/TDceVQJPzc+LmUWI7OoUtR/Lu52Ieb/loxJQW7w72yHVUfS4GhlhneG+yDy2G0AwK43++NOYQVseab476Fbon1PG+iO3y41TblxYclIhESdFn3XDY0Mjqc0TecR4uOEC+lPYGHGRY1YzUn0rAH463oeknNKkfG4CiN7dsDk4C5YcegWHlfWibYDAJ4ZF5P6dsbRm3moqGkU7dfRygyZRdXY9/ZzWH/iDrgc4FDyQ5Q+bWoy5eFoiYPzBoNnaoKsJ1WYuPUcGgQMOBzJmvf+XR1w5UEJAGBod2cUVtTidn6FaP03/wrCvD3J+Hn2QHSxt8QH+67h85d6gwPg+7OZ+Ot6PgZ5OSK7uBr5ZTX47s3+Mv+DEQGuOHqz6Ts5+v4QmHI5eFrfiIqaBgR5ND3kLj+Ugv3Pmu++P6obdp/PQunTBgzycsTjylqZJsorJvhhxV+povd/vBOC35NysO9yLgBgy2t9sS3uPkb3coGVuQkOJj9knR/oxUA3HL6eJ7NcXJivEwrKajHE1xkFZTX4Ykogvjx5F9+dycS6yQH4ZP9NDPF1QkFZDXq68sEz5aKjrQXuFpbDhmeGVS/5Izb1EU6lPYKJCQdggKCuDvj8Wf5/nDkA83+7Che+BeobBcgubmoKHNDZDlP6d8GyZ+de3KJhuPOoEuZcLqIvZCLxbhGCPRzgwufh2LNz7ocZAzCQpd+MpjEMg5Opj9DF0RK9Omk+0L2eU4J/fX8J61/pIyrABIDs4ipYmJog8e5jrPgrFZtf7avS8OaZRZXgW5jBSWq4boZhcD79CXq42OJAci6uZpdgUXgPTPzmPADgi1f6wM7KDP27OsLMhINjN/NhbmqCMX4uuPeoApO2NaWbNdgTH4X3wMm0R1h5+BYGeTni/4Z5o7sLHyVVdZi75yoeldfg7wVDNX4PVkd5eTnc3d1RWloKOzv5v2O7CJRyc3ONctJZQgghhBBCiHbk5OSgS5cucte3i0BJIBAgLy8Ptra2eu9oJoxgDaF2ixBpdH4SQ0XnJjFUdG4SQ0bnJzuGYVBRUQE3NzdwufJruLQ66p2h4HK5CqNFfTCmiXBJ+0PnJzFUdG4SQ0XnJjFkdH7KUtTkTkj/jQQJIYQQQgghxMBQoEQIIYQQQgghUihQ0jEej4fly5eDx+O1nJgQHaPzkxgqOjeJoaJzkxgyOj9bp10M5kAIIYQQQgghqqAaJUIIIYQQQgiRQoESIYQQQgghhEihQIkQQgghhBBCpFCgRAghhBBCCCFSKFDSgm3btsHLywsWFhYIDg7GmTNnFKZPSEhAcHAwLCws4O3tjR07dugop6S9UeXcjI+PB4fDkXndvn1bhzkm7UFiYiImTJgANzc3cDgc/Pnnny1uQ9dNoiuqnp907SS6EBUVhQEDBsDW1hYdO3bExIkTcefOnRa3o2unaihQ0rB9+/ZhwYIFWLp0KZKTkxEWFoZx48YhOzubNX1mZibGjx+PsLAwJCcn49NPP8X777+P/fv36zjnpK1T9dwUunPnDvLz80UvX19fHeWYtBdVVVUIDAzE1q1blUpP102iS6qen0J07STalJCQgHnz5uHixYuIjY1FQ0MDwsPDUVVVJXcbunaqjoYH17BBgwahX79+2L59u2hZr169MHHiRERFRcmk/+STT3D48GGkpaWJlr3zzju4fv06Lly4oJM8k/ZB1XMzPj4eI0aMQElJCezt7XWYU9KecTgcHDx4EBMnTpSbhq6bRF+UOT/p2kn04fHjx+jYsSMSEhIwdOhQ1jR07VQd1ShpUF1dHZKSkhAeHi6xPDw8HOfPn2fd5sKFCzLpx44diytXrqC+vl5reSXtizrnplBQUBA6deqEUaNGIS4uTpvZJEQpdN0kxoCunUSXysrKAACOjo5y09C1U3UUKGlQUVERGhsb4eLiIrHcxcUFBQUFrNsUFBSwpm9oaEBRUZHW8kraF3XOzU6dOmHnzp3Yv38/Dhw4gB49emDUqFFITEzURZYJkYuum8SQ0bWT6BrDMFi4cCGGDBkCf39/ueno2qk6U31noC3icDgS7xmGkVnWUnq25YS0lirnZo8ePdCjRw/R+5CQEOTk5GDDhg1yq/UJ0RW6bhJDRddOomvvvfcebty4gbNnz7aYlq6dqqEaJQ1ydnaGiYmJTAl9YWGhTAQv5Orqypre1NQUTk5OWssraV/UOTfZPPfcc7h3756ms0eISui6SYwNXTuJtsyfPx+HDx9GXFwcunTpojAtXTtVR4GSBpmbmyM4OBixsbESy2NjYxEaGsq6TUhIiEz6mJgY9O/fH2ZmZlrLK2lf1Dk32SQnJ6NTp06azh4hKqHrJjE2dO0kmsYwDN577z0cOHAAp0+fhpeXV4vb0LVTDQzRqL179zJmZmbMrl27mNTUVGbBggWMtbU1k5WVxTAMwyxevJh54403ROkzMjIYKysr5sMPP2RSU1OZXbt2MWZmZswff/yhr49A2ihVz83NmzczBw8eZO7evcukpKQwixcvZgAw+/fv19dHIG1URUUFk5yczCQnJzMAmE2bNjHJycnMgwcPGIah6ybRL1XPT7p2El149913GTs7OyY+Pp7Jz88Xvaqrq0Vp6NrZehQoacE333zDdO3alTE3N2f69evHJCQkiNbNmDGDGTZsmET6+Ph4JigoiDE3N2c8PT2Z7du36zjHpL1Q5dxct24d4+Pjw1hYWDAODg7MkCFDmKNHj+oh16Sti4uLYwDIvGbMmMEwDF03iX6pen7StZPoAts5CYD58ccfRWno2tl6NI8SIYQQQgghhEihPkqEEEIIIYQQIoUCJUIIIYQQQgiRQoESIYQQQgghhEihQIkQQgghhBBCpFCgRAghhBBCCCFSKFAihBBCCCGEECkUKBFCCCGEEEKIFAqUCCGEEEIIIQYjMTEREyZMgJubGzgcDv7880+V98EwDDZs2IDu3buDx+PB3d0dkZGRKu3DVOWjEkIIIYQQQoiWVFVVITAwELNmzcLkyZPV2scHH3yAmJgYbNiwAQEBASgrK0NRUZFK++AwDMOodXRCCCGEEEII0SIOh4ODBw9i4sSJomV1dXX47LPPsGfPHpSWlsLf3x/r1q3D8OHDAQBpaWno06cPUlJS0KNHD7WPTU3vCCGEEEIIIUZj1qxZOHfuHPbu3YsbN25gypQpeP7553Hv3j0AwF9//QVvb28cOXIEXl5e8PT0xJw5c1BcXKzScShQIoQQQgghhBiF9PR0/Pbbb/j9998RFhYGHx8fLFq0CEOGDMGPP/4IAMjIyMCDBw/w+++/Y/fu3YiOjkZSUhJeeeUVlY5FfZQIIYQQQgghRuHq1atgGAbdu3eXWF5bWwsnJycAgEAgQG1tLXbv3i1Kt2vXLgQHB+POnTtKN8ejQIkQQgghhBBiFAQCAUxMTJCUlAQTExOJdTY2NgCATp06wdTUVCKY6tWrFwAgOzubAiVCCCGEEEJI2xIUFITGxkYUFhYiLCyMNc3gwYPR0NCA9PR0+Pj4AADu3r0LAOjatavSx6JR7wghhBBCCCEGo7KyEvfv3wfQFBht2rQJI0aMgKOjIzw8PPD666/j3Llz2LhxI4KCglBUVITTp08jICAA48ePh0AgwIABA2BjY4MtW7ZAIBBg3rx54PP5iImJUTofFCgRQgghhBBCDEZ8fDxGjBghs3zGjBmIjo5GfX09Vq9ejd27d+Phw4dwcnJCSEgIVq5ciYCAAABAXl4e5s+fj5iYGFhbW2PcuHHYuHEjHB0dlc4HBUqEEEIIIYQQIoWGByeEEEIIIYQQKRQoEUIIIYQQQogUCpQIIYQQQgghRAoFSoQQQgghhBAihQIlQgghhBBCCJFCgRIhhBBCCCGESKFAiRBCCCGEEEKkUKBECCGEEEIIIVIoUCKEEEIIIYQQKRQoEUIIIYQQQogUCpQIIYQQQgghRAoFSoQQQgghhBAi5f8BlTfVmbRMAnYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x50 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x50 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10,0.5));plt.plot(data);plt.show();\n",
    "plt.figure(figsize=(10,0.5));plt.plot(lat);plt.show()\n",
    "plt.figure(figsize=(10,0.5));plt.plot(lon);plt.show()\n",
    "plt.figure(figsize=(10,0.5));plt.plot(time);plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7de3de2-0394-4d5b-9b2e-7e37a75d3368",
   "metadata": {},
   "source": [
    "Load data in each task:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a44f4bcb-92d4-44b3-8c41-7567f12987ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 16s, sys: 2.56 s, total: 1min 18s\n",
      "Wall time: 8min 56s\n"
     ]
    }
   ],
   "source": [
    "%time result = dask.compute((function_load() for i in range(35_000)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "03daa9f1-8e13-488e-87dc-3f78ae4eab2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 10s, sys: 2.03 s, total: 1min 12s\n",
      "Wall time: 6min 50s\n"
     ]
    }
   ],
   "source": [
    "%time result = dask.compute((function_load() for i in range(35_000)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d4e38543-d9b9-4be3-810d-24decfb18305",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-08-08 12:07:45,267 - distributed.client - WARNING - Couldn't gather 5 keys, rescheduling {'function_load-a73cf055-a3d1-4dd2-8ce6-96bf03110887': (), 'function_load-2eba9d75-d3e3-4651-809e-b07d0cbb4dc7': (), 'function_load-179c42b2-ea95-4d7d-98aa-a907c77d20c7': (), 'function_load-77e4446c-43b7-4e57-904a-3f429b9b9260': (), 'function_load-0ff05458-88d8-4d9d-8426-07998120c97c': ()}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 13s, sys: 2.53 s, total: 1min 15s\n",
      "Wall time: 12min 37s\n"
     ]
    }
   ],
   "source": [
    "%time result = dask.compute((function_load() for i in range(35_000)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "253d1515-3625-4e1b-92cb-25e65134def4",
   "metadata": {},
   "source": [
    "Scatter data beforehand:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "36425077-10f8-448d-8e35-c8d0c424452b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 1s, sys: 625 ms, total: 1min 2s\n",
      "Wall time: 1min 23s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "data_scat,lat_scat,lon_scat,time_scat = client.scatter(data),client.scatter(lat),client.scatter(lon),client.scatter(time)\n",
    "result = dask.compute((function_scattered(data_scat,lat_scat,lon_scat,time_scat) for i in range(35_000)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f93af562-82f0-4ad8-9f24-8659a097038f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min, sys: 652 ms, total: 1min 1s\n",
      "Wall time: 1min 25s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "data_scat,lat_scat,lon_scat,time_scat = client.scatter(data),client.scatter(lat),client.scatter(lon),client.scatter(time)\n",
    "result = dask.compute((function_scattered(data_scat,lat_scat,lon_scat,time_scat) for i in range(35_000)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9cb052f2-627a-4981-b378-0be6277e565f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 59.1 s, sys: 657 ms, total: 59.8 s\n",
      "Wall time: 1min 21s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "data_scat,lat_scat,lon_scat,time_scat = client.scatter(data),client.scatter(lat),client.scatter(lon),client.scatter(time)\n",
    "result = dask.compute((function_scattered(data_scat,lat_scat,lon_scat,time_scat) for i in range(35_000)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8204c8a9-2bc0-427b-8b32-6da29b445235",
   "metadata": {},
   "source": [
    "Scatter data with 'broadcast=True' :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cd55a37e-1918-4033-9c00-4dff6af925df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 2s, sys: 805 ms, total: 1min 3s\n",
      "Wall time: 1min 29s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "data_bcast, lat_bcast, lon_bcast, time_bcast = client.scatter(data, broadcast=True), client.scatter(lat, broadcast=True), client.scatter(lon, broadcast=True), client.scatter(time, broadcast=True)\n",
    "result = dask.compute((function_scattered(data_bcast,lat_bcast,lon_bcast,time_bcast) for i in range(35_000)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "01fa19d2-9f08-4af6-8fc6-77e45a7810d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 58.6 s, sys: 562 ms, total: 59.2 s\n",
      "Wall time: 1min 29s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "data_bcast,lat_bcast,lon_bcast,time_bcast = client.scatter(data, broadcast=True), client.scatter(lat, broadcast=True), client.scatter(lon, broadcast=True), client.scatter(time, broadcast=True)\n",
    "result = dask.compute((function_scattered(data_bcast,lat_bcast,lon_bcast,time_bcast) for i in range(35_000)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ad14957c-da8d-4099-8e49-2d4e6ee32757",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 57.1 s, sys: 589 ms, total: 57.7 s\n",
      "Wall time: 1min 36s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "data_bcast,lat_bcast,lon_bcast,time_bcast = client.scatter(data, broadcast=True), client.scatter(lat, broadcast=True), client.scatter(lon, broadcast=True), client.scatter(time, broadcast=True)\n",
    "result = dask.compute((function_scattered(data_bcast,lat_bcast,lon_bcast,time_bcast) for i in range(35_000)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ad45221b-4dfd-482b-a306-03b44a64a450",
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster.scale(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e4752da7-1bf1-499c-a3e3-74229972f5b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "24cd0d87-8a34-446a-908d-4578099644d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster.shutdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "d1ae9a21-4cc6-4c4f-9706-c03815b7a834",
   "metadata": {},
   "outputs": [],
   "source": [
    "client.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3da75c3d-721e-489b-ad32-891f87c5467f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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