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@smcveigh-phunware
Created March 23, 2018 23:31
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Tutorial Using Tensorflow with concurrent.Futures
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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import typing\n",
"import logging\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"import concurrent.futures as futures\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"logging.basicConfig(level=logging.DEBUG)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def worker(X: np.ndarray) -> np.ndarray:\n",
" graph = tf.Graph()\n",
" \n",
" with graph.as_default():\n",
" values = tf.convert_to_tensor(X, dtype=tf.float64)\n",
" dist = tf.distributions.Normal(loc=0., scale=1.)\n",
" choice = tf.greater(dist.sample(), tf.constant(0, dtype=dist.dtype))\n",
" a = (1.0 + values[:, 0]) / (1.0 + values[:, 1] ** 2)\n",
" b = values[:, 0] * (tf.square(values[:, 0]) - tf.square(values[:, 1]))\n",
" func = tf.where(choice, a, b)\n",
" \n",
" with tf.Session(graph=graph) as s:\n",
" return s.run(func)\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def cartesian_product(*args: np.ndarray) -> np.ndarray:\n",
" for i, a in enumerate(args):\n",
" assert a.ndim == 1, \"arg {:d} is not rank 1\".format(i)\n",
" return np.concatenate([np.reshape(xi, [-1, 1]) for xi in np.meshgrid(*args)], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"DEBUG:root:submitted job 0\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"DEBUG:root:submitted job 1\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"DEBUG:root:submitted job 2\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"DEBUG:root:submitted job 3\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"DEBUG:root:submitted job 4\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"DEBUG:root:submitted job 5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"DEBUG:root:submitted job 6\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"DEBUG:root:submitted job 7\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"DEBUG:root:submitted job 8\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"DEBUG:root:submitted job 9\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:root:waiting for completion or timeout\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:root:completed all tasks before timeout\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11fbdeda0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11fdabb70>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11fe9ca20>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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hSGnaAIejaY6GvSil8xqQ1rSRGodjdlIPR6VoSFohaYekXcWvy0fss17StyXdL+keSR8o3fYlST+UdHfxtb7Kesy6LKVpAxyOWdr2r29KNh5VJ41LgVsjYi1wa3F9rmeB346IXwXOAf67pJeXbv+jiFhffN1dcT3WM6lNGw7HbKUUDkhz6qgajY3AdcXl64Dz5u4QEQ9HxK7i8j8DTwJpvRIrSO0dVKkdorLZczhmK7VwVI3GCRGxr7j8BHDCfDtLOgNYCjxS2vzp4rDVVZKOnee+WyTtlLTzwAvPVVy25cTTxuw5HLOVUjgWjIakWyTdN+JrY3m/iAhg7O9eSScCXwE+FBEvFJsvA9YBvwasAD427v4RsTUiNkTEhqVH/crC/2W2aJ42Zi/FcKQmxXCkEI8FoxERZ0XEG0Z8fQP4cRGDYRSeHPUYkl4G3AhcHhF3lh57XwzsB74InFHHf5T1T2rTBqQXjtSmDUgvHND9qaPq4antwObi8mbgG3N3kLQU+Drw5YjYNue2YXDE4HzIfRXXY2Yz9KM9r0wuHjueWJdcPLo8dVSNxpXA2ZJ2AWcV15G0QdI1xT7vB94GXDTirbV/Jele4F5gJfCpiuvppNROhkOah6g8bTQntXCAp466aHAqIi3Ljjk+3rLygraXMZUD61a1vYSpPfPase9L6LSfr1bbS5ja/tccaHsJi3LKqp+0vYSpnf2qB9tewqKc/7LvVX6Mda/Z992I2FDlMfyJcBsrxWkjVZ44mpPi4SroztThaFh2UjxMBQ5H01INR9vxcDQakuJ5DUh32nA4muVwNKvNeDgali2Ho1kpvrMK0j1cBe0csnI0bEGpThspSzUckPbUkWI8mp46HA3LWqrTBjgcbUkxHNBcPByNBqV6XgPSnjYcjnakergK0p06YPbxcDSsFxyO9qQaDkh36oDZxcPRsImlPG1A+uFIOR6ph8PxOMTRsKk4HO1KPRyOR3vqCoej0bCUz2vkwuFoV8rhgPTjUZWjYVNLfdrIQQ7hcDzS5GhYL6U+bUD64YD0pw5I+2T5YjgaLcjhEFUO00Yu4Ug9Hp460uJo2KI5HN2RejjA8UiFo2G953B0S+rhgLzj4Wi0JIdDVJDHtAF5hSOHeOQwdUCe8XA0rDKHo3tyCAfkF48cAlIpGpJWSNohaVfx6/Ix+z1f+vfBt5e2r5H0HUm7JX1VUh6vdEvWSx+LbOKRy9QB+cQD0p8+qk4alwK3RsRa4Nbi+ijPRcT64uvc0vbPAFdFxOuAp4GLK64nKbkcooJ8po2hXMIB+UwdkMf5jqFU41E1GhuB64rL1wHnTXpHSQLOBLYt5v7WPQ5Hd3nq6K7U4lE1GidExL7i8hPACWP2O07STkl3ShqG4RXAzyLiYHF9D3DSuG8kaUvxGDsPvPBcxWV3R07TBjgcXZdLOMDxaMvRC+0g6RbgVSNuurx8JSJC0rjfYasjYq+kU4HbJN0LPDPNQiNiK7AVYNkxx+f1O9k67aWPBT9frbaXUZthOPa/5kDLK6nHMBynrPpJyyupRzkcZ7/qwRZXMtqC0YiIs8bdJunHkk6MiH2STgSeHPMYe4tfH5V0B3A68DfAyyUdXUwbq4C9i/hvsI5Z9sh+nnntsW0vo1a5hQMG8cglHJBfPOBQQLoUj6qHp7YDm4vLm4FvzN1B0nJJxxaXVwJvBR6IiABuB86f7/59kNshKsjvMBXkd6gK8jrXMZTbYSvo1qGrqtG4Ejhb0i7grOI6kjZIuqbY5/XATknfZxCJKyPigeK2jwEflbSbwTmOL1Rcj3VIruHINR65yTkebQZEg7/wp2XZMcfHW1Ze0PYyandg3aq2lzATuR2qGsrtcNVQToesynI6bFU2zaGrj7/hxu9GxIYq32/BcxpmVeV4jgPyPM8B+Z0oH8rxnAc0f+LcP0bErIIcD1UN5XjICvI8bDXUxKErTxodsvTBPdkeosp12oBD4fDUkZZyODx9TM7RsMbkHA7I93AV5B0PyPfQFcz9lwVvrPx4PjzVMTm+/bYsx3dUleV8uAryfItuWc6HruriaFjj+hAOxyNtjsd4jkYH5T5tQP7hgPynDsj3ZPnQMB4OyCGOhrWmL+HIPR65Tx1DjseAo9FRfZg2oB/hgP5MHY5H/hwNa53DkZe+xaNvAXE0Oqwv0wb0KxyOR376FA9HwzqjL+GA/kwd0M945BwQR6Pj+jRtQP/C4XjkK9d4OBrWOX0KB/Rr6oD+xiOXgDgaCejbtAH9DIfjkb8c4uGfPWWdlfvPqhol5x9+OE7uP9dqlJR/WKInjUT0cdqA/k0cQ32bOqCfkwekd/jK0bDO63M4+hyPPgekyypFQ9IKSTsk7Sp+XT5in3dKurv09QtJ5xW3fUnSD0u3ra+yntz1ddqA/oYD+jl1DPU9Hl0MSNVJ41Lg1ohYC9xaXD9MRNweEesjYj1wJvAs8H9Ku/zR8PaIuLvieixjfQ+H49G/eED3AlI1GhuB64rL1wHnLbD/+cA3I+LZit+3t/o8bUC/wwGOR5/jAd0ISNVonBAR+4rLTwAnLLD/JuD6Ods+LekeSVdJ6tdbZWxR+h4OcDz6fN5jqK2ALBgNSbdIum/E18byfhERwNhXsaQTgTcCN5c2XwasA34NWAF8bJ77b5G0U9LOAy88t9Cys9b3aQMG4XA8+n2+Y6jv8YBmA7Lg5zQi4qxxt0n6saQTI2JfEYUn53mo9wNfj4hflh57OKXsl/RF4A/nWcdWYCvAsmOO7/3vlKUP7uHAulVtL6N1ffwsx1x9/GzHKH38vMcos/73zqsentoObC4ubwa+Mc++FzLn0FQRGiSJwfmQ+yqux3rIE8dA3w9ZDfnQ1cCspo+q0bgSOFvSLuCs4jqSNki6ZriTpFOAk4G/n3P/v5J0L3AvsBL4VMX19IoPUx3icBzieBzieAzUGY9KP0YkIn4KvGvE9p3A75Su/wg4acR+Z1b5/mZlPlR1uJc+Fr0/ZDXkQ1f18SfCE+dp43A+QX44Tx2H86Gr6hyNDDgcR3I4Dud4HMnxWBxHw7LlcBzJ8TiSp4/pOBqZ8LQxmsMxmuMxmuOxMEcjIw7HaD7PMZ7jMZqnj/EcDesNh2M8x2M8B+RwjkZmPG3Mz+GYn+MxP8fD/9yr9dAwHP5Mx3j+0STzK4ejb5/98KSRIU8bk/HUsTBPHgvr2/ThaGTK4ZiMwzEZx2NhfTn34WhkzOGYjN9dNTnHYzI5B8TRMCs4HJNzPCaXW0Acjcx52piOwzEdx2M6OcTD0egBh2M6Plw1PcdjOilPH46G2RgOx/Qcj+mlFhBHoyc8bSyOp47FcTwWJ4WAOBo94nAsnsOxOMN4OCDT62pA/Ilwswn5k+TV+FPmi9elT6B70ugZTxvVeeqoxpNHNW1PIJWiIekCSfdLekHShnn2O0fSQ5J2S7q0tH2NpO8U278qqVtzWKYcjup8rqM6x6O6NgJSddK4D/hPwLfG7SBpCXA18F7gNOBCSacVN38GuCoiXgc8DVxccT02IYejHg5HdY5HPZoKSKVoRMQPIuKhBXY7A9gdEY9GxAHgBmCjJAFnAtuK/a4DzquyHrM2eOqoh0+a12eWAWniRPhJwOOl63uANwOvAH4WEQdL208a9yCStgBbiqv7//cTf3nfDNZat5XAv7S9iLGeePFSt9c50P013gGksM4Br7Neqazz31d9gAWjIekW4FUjbro8Ir5RdQGTioitwNZiTTsjYuw5lK7wOuuTwhrB66yb11kvSTurPsaC0YiIsyp+j73AyaXrq4ptPwVeLunoYtoYbjczs45q4i23dwFri3dKLQU2AdsjIoDbgfOL/TYDjU0uZmY2vapvuf0NSXuA/wjcKOnmYvurJd0EUEwRlwA3Az8AvhYR9xcP8THgo5J2MzjH8YUJv/XWKutukNdZnxTWCF5n3bzOelVepwZ/4TczM1uYPxFuZmYTczTMzGxinY1GCj+iRNIKSTsk7Sp+XT5in3dKurv09QtJ5xW3fUnSD0u3ra97jZOus9jv+dJatpe2N/LjXiZ8PtdL+nbx2rhH0gdKt830+Rz3Wivdfmzx/Owunq9TSrddVmx/SNJ76lzXItb5UUkPFM/frZJWl24b+RpoYY0XSfpJaS2/U7ptc/Ea2SVp86zWOOE6ryqt8WFJPyvd1shzWXyvayU9KWnk59c08OfFf8c9kt5Uum265zMiOvkFvJ7BB1HuADaM2WcJ8AhwKrAU+D5wWnHb14BNxeXPAR+ewRo/C1xaXL4U+MwC+68AngL+TXH9S8D5DTyXE60T+H9jts/8uZx0ncC/A9YWl18N7ANePuvnc77XWmmf/wJ8rri8Cfhqcfm0Yv9jgTXF4yxpcZ3vLL0GPzxc53yvgRbWeBHwFyPuuwJ4tPh1eXF5eVvrnLP/7wPXNvlclr7X24A3AfeNuf19wDcBAb8OfGexz2dnJ41I40eUbCwee9LvcT7wzYh4dgZrmc+063xRg88lTLDOiHg4InYVl/8ZeBJ45YzWUzbytTZnn/L6twHvKp6/jcANEbE/In4I7C4er5V1RsTtpdfgnQw+I9WkSZ7Lcd4D7IiIpyLiaWAHcE5H1nkhcP2M1jKviPgWg7+QjrMR+HIM3MngM3Insojns7PRmNCoH1FyElP+iJIKToiIfcXlJ4ATFth/E0e+qD5djItXSZrVP9Qw6TqPk7RT0p3DQ2g091xOs04AJJ3B4G+Aj5Q2z+r5HPdaG7lP8Xw9w+D5m+S+Ta6z7GIGfwMdGvUaqNuka/zN4v/lNknDDwh38rksDvGtAW4rbW7iuZzUuP+WqZ/PVv8RJnXkR5TMZ741lq9EREga+/7loupvZPB5laHLGPzhuJTB+6c/BlzR4jpXR8ReSacCt0m6l8EffLWp+fn8CrA5Il4oNtf2fPaBpA8CG4C3lzYf8RqIiEdGP8JM/R1wfUTsl/S7DCa4M1tYx6Q2Adsi4vnStq48l7VqNRqRwI8omW+Nkn4s6cSI2Ff8IfbkPA/1fuDrEfHL0mMP/1a9X9IXgT9czBrrWmdE7C1+fVTSHcDpwN9Q4497qWOdkl4G3MjgLxd3lh67tudzhHGvtVH77JF0NLCMwWtxkvs2uU4kncUg1G+PiBd/RO+Y10Ddf9AtuMaI+Gnp6jUMzncN7/uOOfe9o+b1DU3z/20T8JHyhoaey0mN+2+Z+vlM/fBU2z+iZHvx2JN8jyOOdxZ/MA7PG5zH4N8nmYUF1ylp+fBwjqSVwFuBBxp8Lidd51Lg6wyOz26bc9ssn8+Rr7V51n8+cFvx/G0HNmnw7qo1wFrgH2tc21TrlHQ68Hng3Ih4srR95GugpTWeWLp6LoOfJgGDSf3dxVqXA+/m8Om90XUWa13H4CTyt0vbmnouJ7Ud+O3iXVS/DjxT/CVr+uezqbP7034Bv8Hg+Np+4MfAzcX2VwM3lfZ7H/Awg4JfXtp+KoPfmLuBvwaOncEaXwHcCuwCbgFWFNs3ANeU9juFQdGPmnP/24B7Gfzh9r+Al8zouVxwncBbirV8v/j14iafyynW+UHgl8Ddpa/1TTyfo15rDA5/nVtcPq54fnYXz9eppfteXtzvIeC9M/69s9A6byl+Tw2fv+0LvQZaWON/A+4v1nI7sK503/9cPMe7gQ+1+VwW1/8YuHLO/Rp7Lovvdz2DdxL+ksGfmxcDvwf8XnG7GPxjeI8U69lQuu9Uz6d/jIiZmU0s9cNTZmbWIEfDzMwm5miYmdnEHA0zM5uYo2FmZhNzNMzMbGKOhpmZTez/A2nOIS6URZ9TAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1206679b0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f5166a0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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bkZjVpYrQOAl4pPR6bzFt5DIR8QLwNPC6CdcFQNIWSbsk7Tr40nMVNNvMzKaVzEB4RGyLiA0RsWHuVb/RdnPMXuauKOuTKi7u2wecXHq9spg2apm9ko4EjgWemHBds04aFRblriqHieWoikrjVmCtpDWS5hgMbO+Yt8wOYHPx/DzgxoiIYvqm4uyqNcBa4B8raJNZrcYFQnm6xzosRzNXGhHxgqSLgOuAI4ArIuJuSZcAuyJiB/A14FuS9gBPMggWiuWuAe4BXgA+GREvztoms7pMWz105bbrZlWp5N5TEXEtcO28aZ8vPf818KEx634J+FIV7VjMwXUrm3gby9QsB/5lD885OCwLvmGh2SKqOti76rAcJHP2lFkb6jjAe6yjXQ/uPaHtJiTNlcYSPf3mZW03wWpUdzXgqsNS5UrDbJ4mD+Sz3j3XrGkOjQQ8s0ptN6EX2rzBoIPDUuHuKeu9rnQRucvKUuBKw3qtiwdoVx02zvb/+/a2m+DQsH7q+t+68FhHPnY+uq7tJlTKoWG90+WwmM/BUQ+fdrt0vRnTqPJq8CZPt/UgeHVSCosyj3VYl7jSsF7I4YDrqsO6oDeVhvVTDmFR5qrD2uZKw7KV84HVVcfsmhzXqHIwvO0zqFxpdJjHM5Ym57Aoc9VhbehFpZHqILhNr48HUJ+ea03qRWhY/rp+3UUTHBzWBIdGR7lranJ9D4syVx3T8fUa03NoWLJcXYzn4OieXAbDZwoNScdJ2ilpd/Fz+Yhl1kv6kaS7Jd0h6SOled+Q9HNJtxWP9bO0ZxSPZ+THYTEZVx2TcbUxnVkrja3ADRGxFriheD3fs8DHIuK3gLOBv5D02tL8P4mI9cXjthnbkwV3TY3nsJieg8OqNGtobASuLJ5fCZw7f4GI+FlE7C6e/zPwGOBot6m4upiNq45uyOHmhbOGxokRsb94/ihw4kILSzodmAPuL03+UtFtdZmksf0/krZI2iVp18GXnpux2dNz11R7HBbVcXCMlmIXVVvjGote3CfpeuANI2ZdXH4RESEpFtjOCuBbwOaIeKmY/DkGYTMHbAM+C1wyav2I2FYsw7FHvX7s+5RVOZ7RFHdNvcJhUQ9fFGizWDQ0IuKMcfMk/ULSiojYX4TCY2OWew3wPeDiiLiltO1hlXJA0teBz0zVesuWD2j1W/bwnPdzC3Y+uo4z3/DTtpuxZLN2T+0ANhfPNwPfnb+ApDngO8A3I2L7vHkrip9iMB5y14ztqUVTXVOuMjx20TSPdbzCXVSTmTU0LgXOlLQbOKN4jaQNki4vlvkw8LvABSNOrf0bSXcCdwLHA382Y3telmLXVN85LNrj4LBJKWKi4YFOOfao18c7j//QgstUFRquMuqXW1ikfgDO7f9jWqtXPt7I+1TVRXXea/5p4mXXvWn/TyJiwyzv5yvCrVV9P0B1kbusbCFZhkZqXVN9rDI8dtF9fQ2O1MY2mh7XyDI0quJrM+rhsEiHq476pHqhn0OjZX2qMlxdpKtvweFqY7zsQiO1AfA+cFjkwVVH9VKsNrILjZT0ocpwWOSnL8GRWrXRlKxCI6UqI/fAcHWRt75UHU0ER1XVRlNdVFmFhnWDw6I/+hAcdiiHxjyuMpbO1UU/5V51uNo4VDahkdq1GblxWFjOwWGvyCY0quAqY3quLqws16ojpWqjblmERipVRo6BYTaKg6M9dXdRZREaVfB1GZNzdWGTyLXqqFMK1UbyoeEqo1kOC5tWTuHhaiOD0KhC3VVGDoHh6sJm5eCYTNerjaRDI5UqI3UOC6tKTlVH19VVbSQdGlVwlTGeqwurS+rB0edqI9nQSKHKSDUwHBbWhNSrjhTGN+qoNmYKDUnHSdopaXfxc/mY5V4s/X3wHaXpayT9WNIeSd+W1OgnqM4qI+XAMGtSysFRp65WG7NWGluBGyJiLXBD8XqU5yJiffE4pzT9y8BlEfEW4CngwkneNI6e/UPmU2wP5erC2pRq1ZFCN1XV1casobERuLJ4fiVw7qQrShLwXmD7UtbvstSqDIeFdYWDo/tmDY0TI2J/8fxR4MQxyx0taZekWyQNg+F1wC8j4oXi9V7gpHFvJGlLsY1dB5//1UyNdrfUgKsL66IUq446g6Nr1caioSHpekl3jXhsLC8XEQHEmM2siogNwH8E/kLSm6dtaERsi4gNEbFh7qhXT7v6yxwYAw4L6zoHxyu6FBxHLrZARJwxbp6kX0haERH7Ja0AHhuzjX3Fzwck3QycBvwd8FpJRxbVxkpg3xL+DZ2QSmA4LCwlw+Dw57Y7Zu2e2gFsLp5vBr47fwFJyyUtK54fD7wLuKeoTG4Czlto/Sr1ffDbv3iWqlSqjq5XG1WYNTQuBc6UtBs4o3iNpA2SLi+WeSuwS9LtDELi0oi4p5j3WeDTkvYwGOP42oztGavP3VIeu7AcpDLWkfvA+KLdUwuJiCeA942Yvgv4ePH8h8Dbxqz/AHD6LG1oWwqBYZaTFLqsHtx7AqtXPl75dnc+uo4z3/DTyrc7jWSvCJ9GXVVGlwPD1YXlrutVR10VR9vdVNmHRt8Cw2FhfdL1LqscgyP70KhDlwPDrI+6HBy5yTo06qgyuhgYri7Mult15FZtZBsafQoMM3uFg6NeWYZGH67HcHVhNl4Xq45cTsXNMjTq0KUqw2FhNpk+BEfT1UZ2oZFzt5SrC7Ppda3qSD04sgqN3APDzJbOwVGNbEIj18BwdWFWnS5VHakGRxahkXNgmFn1cg6OumURGlVrOzBcXZjVrytVR9XBUXe1kXxoVF1ldCEwzKw5XQiPlIIj6dDIKTBcXZi1y8ExmWRDI5fAcFiYdUfbVUcKwZFkaLx4dLUH+DYDw8y6x8ExXpKhUaU2AsPVhVn3tVl1dPmsql6HRluBYWbpyCE4dj66rrKKY6bQkHScpJ2Sdhc/l49Y5j2Sbis9fi3p3GLeNyT9vDRv/SztmUbTgeHqwixdbVUdXaw4Zq00tgI3RMRa4Ibi9SEi4qaIWB8R64H3As8C/7u0yJ8M50fEbTO2ZyJtBIaZpc/BMXtobASuLJ5fCZy7yPLnAd+PiGdnfN8leWaVGg0MVxdm+Wmj6uhScMwaGidGxP7i+aPAiYssvwm4at60L0m6Q9Jlkmr7QxiuLsysSn0NjkVDQ9L1ku4a8dhYXi4iAogFtrMCeBtwXWny54B1wL8DjgM+u8D6WyTtkrTrhed+tVizD+Hqwszq0HTV0YXgOHKxBSLijHHzJP1C0oqI2F+EwmMLbOrDwHci4vnStodVygFJXwc+s0A7tgHbAF59wsljw2m+pgPDzPpnGBxNHAMe3HsCq1c+Xvv7jDNr99QOYHPxfDPw3QWWPZ95XVNF0CBJDMZD7pqxPYdoKjBcXZgZNNdl1WbFMWtoXAqcKWk3cEbxGkkbJF0+XEjSauBk4B/mrf83ku4E7gSOB/5sxva8rMnAMDMbaqrL6sG9J7QSHot2Ty0kIp4A3jdi+i7g46XXDwInjVjuvbO8/zhNBIbDwswWsuzhuSy7q7K7IrzuwHBXlJlNqsmqoylZhUYTgWFmNq2cgmOm7qmucFiYWdc1cYZVE11VyVcaDgwzS0ndVUfdFUfSoVFnYHjswszqUvdYR53BkWxo1B0YZmZ1qzs46giPJEPjxZr2s6sLM2taalVHFgPhVcgpLOoaCOvCfW/MclXndR1VDpD3PjRSDoum7z8z6v0cJLYUxzw08e3jJtbGX+KsWp1nWFX1u9rr0EgpMNq8QdlC5rfLIWLz1REQ07xPimHS5A0Qp9XL0Ojif8R8XQ2JxQzb7fDot6aCYhKj2pJKkDR1K5Jp9C40uvYfMJRqSIxT/vc4QPqhS0GxmPlt7XKIdK3q6E1odGWHl+UWFOO4+shXSkGxkPK/o6sB0pWqI/vQ6MJOLutLUIzi8MhHLmExSperkC5UHVmHRlcCo89BMYrDI105h8U4XaxC2qw6sgyNLoSFg2Jxq1c+7uBISB8DY74uBUhbVUd2odF2YDgspuOqo/scFqMN90sXwqPJ4142odFmWDgoZueqo5scGIvrQvXRZNWRRWi0ERgOiuo5OLrDYbE0bQdIE1XHTDcslPQhSXdLeknShgWWO1vSfZL2SNpamr5G0o+L6d+WNNVdu9q4weDqlY87MGrkfds+B0Y1jnkoWtmXdd8Acda73N4F/AfgB+MWkHQE8BXgA8CpwPmSTi1mfxm4LCLeAjwFXDjpGzss8uV93R4HRvWG4dH0vq0rOGYKjYi4NyLuW2Sx04E9EfFARBwErgY2ShLwXmB7sdyVwLkTve9cczvfBzDrCwdG/ZoOjzqqjibGNE4CHim93gv8DvA64JcR8UJp+knjNiJpC7CleHngoQu23lVDWw/z0GyrHw/8SyUNqVcK7UyhjeB2Vs3trNa/mXUDi4aGpOuBN4yYdXFEfHfWBkwqIrYB24o27YqIsWMoXeF2VieFNoLbWTW3s1qSds26jUVDIyLOmPE99gEnl16vLKY9AbxW0pFFtTGcbmZmHdXEn3u9FVhbnCk1B2wCdkREADcB5xXLbQYaq1zMzGx6s55y+/uS9gL/HviepOuK6W+UdC1AUUVcBFwH3AtcExF3F5v4LPBpSXsYjHF8bcK33jZLuxvkdlYnhTaC21k1t7NaM7dTgy/8ZmZmi2uie8rMzDLh0DAzs4l1NjTavkXJhG08TtJOSbuLn8tHLPMeSbeVHr+WdG4x7xuSfl6at77qNk7azmK5F0tt2VGaXvu+nLSdktZL+lHx2bhD0kdK82rdn+M+a6X5y4r9s6fYX6tL8z5XTL9P0vurbNcS2vlpSfcU++8GSatK80Z+Blpo4wWSHi+15eOleZuLz8huSZvrauOE7bys1MafSfplaV4j+7J4ryskPSZp5PVrGvjL4t9xh6S3l+ZNtz8jopMP4K0MLkS5GdgwZpkjgPuBU4A54Hbg1GLeNcCm4vlXgU/U0MY/B7YWz7cCX15k+eOAJ4F/Vbz+BnBeA/tyonYC/2/M9Nr35aTtBP41sLZ4/kZgP/DauvfnQp+10jL/Bfhq8XwT8O3i+anF8suANcV2jmixne8pfQY/MWznQp+BFtp4AfA/Rqx7HPBA8XN58Xx5W+2ct/yngCua3Jel9/pd4O3AXWPmfxD4PiDgHcCPl7o/O1tpREu3KJnSxmLbk77HecD3I+LZGtqykGnb+bIG9yVM0M6I+FlE7C6e/zPwGNDErXFHftbmLVNu/3bgfcX+2whcHREHIuLnwJ5ie620MyJuKn0Gb2FwjVSTJtmX47wf2BkRT0bEU8BO4OyOtPN84Kqa2rKgiPgBgy+k42wEvhkDtzC4Rm4FS9ifnQ2NCY26RclJTHmLkhmcGBH7i+ePAicusvwmDv9QfakoFy+TtKzyFg5M2s6jJe2SdMuwC43m9uU07QRA0ukMvgHeX5pc1/4c91kbuUyxv55msP8mWbfJdpZdyOAb6NCoz0DVJm3jHxT/l9slDS8Q7uS+LLr41gA3liY3sS8nNe7fMvX+bPXvaagjtyhZyEJtLL+IiJA09vzlItXfxuB6laHPMTg4zjE4f/qzwCUttnNVROyTdApwo6Q7GRz4KlPx/vwWsDkiXiomV7Y/+0DSR4ENwLtLkw/7DETE/aO3UKu/B66KiAOS/jODCu69LbRjUpuA7RHxYmlaV/ZlpVoNjUjgFiULtVHSLyStiIj9xUHssQU29WHgOxHxfGnbw2/VByR9HfjMUtpYVTsjYl/x8wFJNwOnAX9Hhbd7qaKdkl4DfI/Bl4tbStuubH+OMO6zNmqZvZKOBI5l8FmcZN0m24mkMxgE9bsj4sBw+pjPQNUHukXbGBFPlF5ezmC8a7ju781b9+aK2zc0zf/bJuCT5QkN7ctJjfu3TL0/U++eavsWJTuKbU/yHof1dxYHxuG4wbkM/j5JHRZtp6Tlw+4cSccD7wLuaXBfTtrOOeA7DPpnt8+bV+f+HPlZW6D95wE3FvtvB7BJg7Or1gBrgX+ssG1TtVPSacBfA+dExGOl6SM/Ay21cUXp5TkM7iYBg0r9rKKty4GzOLR6b7SdRVvXMRhE/lFpWlP7clI7gI8VZ1G9A3i6+JI1/f5sanR/2gfw+wz61w4AvwDMrAMgAAABA0lEQVSuK6a/Ebi2tNwHgZ8xSPCLS9NPYfCLuQf4W2BZDW18HXADsBu4HjiumL4BuLy03GoGif6qeevfCNzJ4OD2v4DfrGlfLtpO4J1FW24vfl7Y5L6cop0fBZ4Hbis91jexP0d91hh0f51TPD+62D97iv11Smndi4v17gM+UPPvzmLtvL74nRruvx2LfQZaaON/A+4u2nITsK607n8q9vEe4A/b3JfF6y8Cl85br7F9WbzfVQzOJHyewXHzQuCPgD8q5ovBH8O7v2jPhtK6U+1P30bEzMwmlnr3lJmZNcihYWZmE3NomJnZxBwaZmY2MYeGmZlNzKFhZmYTc2iYmdnE/j+WGwjuxv8WTAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f9a2390>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x121b53630>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x121b26320>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11ff2b5c0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11fdd4748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"levels = 100\n",
"\n",
"with futures.ProcessPoolExecutor() as executor:\n",
"\n",
" # submit jobs while retaining a mapping of jobs to arguments\n",
" jobs: typing.Dict[futures.Future, np.ndarray] = dict()\n",
" for i in range(10):\n",
" task = cartesian_product(*[np.linspace(-1, 1, num=levels)]*2)\n",
" future = executor.submit(worker, task)\n",
" logging.debug(\"submitted job {}\".format(i))\n",
" jobs[future] = task\n",
"\n",
" logging.info(\"waiting for completion or timeout\")\n",
" done, undone = futures.wait(jobs, 5.0)\n",
"\n",
" # if failed to complete jobs before timeout, send warning\n",
" if undone:\n",
" logging.warning(\"timeout encountered\")\n",
" debug = [\"undone tasks:\"]\n",
" for future in undone:\n",
" debug.append(str(jobs[future]))\n",
" logging.debug(\"\\n\".join(debug))\n",
" else:\n",
" logging.info(\"completed all tasks before timeout\")\n",
"\n",
" for future in done:\n",
" try:\n",
" result: np.ndarray = future.result() # raises stored exception in process (if exists)\n",
" except:\n",
" logging.exception(\"encountered error while doing task '{}'\".format(jobs[future]))\n",
" else:\n",
" fig = plt.figure()\n",
" ax = fig.gca()\n",
" xi = jobs[future][:,0].reshape(levels, levels)\n",
" yi = jobs[future][:,1].reshape(levels, levels)\n",
" zi = result.reshape(levels, levels)\n",
" ax.contourf(xi, yi, zi)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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