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ajtulloch/ResNet-101 performance.ipynb Secret

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ResNet-101 performance.ipynb
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ResNet-101 performance.ipynb
{
"cells": [
{
"metadata": {
"toc": true
},
"cell_type": "markdown",
"source": "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#ResNet-101-Performance\" data-toc-modified-id=\"ResNet-101-Performance-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>ResNet-101 Performance</a></span><ul class=\"toc-item\"><li><span><a href=\"#Compute-the-analytical-FLOPs-per-image\" data-toc-modified-id=\"Compute-the-analytical-FLOPs-per-image-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Compute the analytical FLOPs per image</a></span></li><li><span><a href=\"#PyTorch-->-TensorRT\" data-toc-modified-id=\"PyTorch-->-TensorRT-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>PyTorch -&gt; TensorRT</a></span></li><li><span><a href=\"#Benchmark\" data-toc-modified-id=\"Benchmark-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Benchmark</a></span></li><li><span><a href=\"#Visualization\" data-toc-modified-id=\"Visualization-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>Visualization</a></span></li></ul></li></ul></div>"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# ResNet-101 Performance"
},
{
"metadata": {
"trusted": true
},
"id": "738f0901",
"cell_type": "code",
"source": "import torch\nimport torchvision\n\nmodel = torchvision.models.resnet101(pretrained=True, progress=False)\nmodel = model.cuda().half()",
"execution_count": 1,
"outputs": []
},
{
"metadata": {},
"id": "ee2a0fd0",
"cell_type": "markdown",
"source": "## Compute the analytical FLOPs per image"
},
{
"metadata": {
"trusted": true,
"scrolled": true
},
"id": "6cd9e04a",
"cell_type": "code",
"source": "from ptflops import get_model_complexity_info\n\nmacs, params = get_model_complexity_info(model, (3, 224, 224),\n print_per_layer_stat=False,\n verbose=False)\nflops = 2 * float(macs.split(\" \")[0]) * 1.0e9",
"execution_count": 2,
"outputs": []
},
{
"metadata": {},
"id": "4dbc1b97",
"cell_type": "markdown",
"source": "## PyTorch -> TensorRT"
},
{
"metadata": {
"trusted": true
},
"id": "a474b36f",
"cell_type": "code",
"source": "import torch2trt\n\nx = torch.randn(512, 3, 224, 224, device=\"cuda\", dtype=torch.float16)\ntrtmodel = torch2trt.torch2trt(model,\n inputs=[x],\n fp16_mode=True,\n max_batch_size=512,\n max_workspace_size=int(4e9))",
"execution_count": 3,
"outputs": []
},
{
"metadata": {},
"id": "8352ddc9",
"cell_type": "markdown",
"source": "## Benchmark"
},
{
"metadata": {
"trusted": true
},
"id": "b0cb5b10",
"cell_type": "code",
"source": "def benchmark_torch_function(iters, f):\n f()\n torch.cuda.synchronize()\n start_event = torch.cuda.Event(enable_timing=True)\n end_event = torch.cuda.Event(enable_timing=True)\n start_event.record()\n for i in range(iters):\n f()\n end_event.record()\n torch.cuda.synchronize()\n return (start_event.elapsed_time(end_event) * 1.0e-3) / iters\n\n\ndef benchmark(model, b):\n x = torch.randn(b, 3, 224, 224, device=\"cuda\", dtype=torch.float16)\n t = benchmark_torch_function(10, lambda: model(x))\n return t / b",
"execution_count": 4,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"id": "d9bce9a3",
"cell_type": "code",
"source": "bs = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]\nbaseline = [benchmark(model, b) for b in bs]\ntrt = [benchmark(trtmodel, b) for b in bs]",
"execution_count": 5,
"outputs": []
},
{
"metadata": {},
"id": "1dabb83a",
"cell_type": "markdown",
"source": "## Visualization"
},
{
"metadata": {
"trusted": true
},
"id": "95eefcc8",
"cell_type": "code",
"source": "import matplotlib.pyplot as plt\n%matplotlib inline\n%config InlineBackend.figure_format='retina'\nimport seaborn as sns\n\nsns.set(rc={\"figure.dpi\": 300, 'savefig.dpi': 300})\nsns.set_context('notebook')\nsns.set_style(\"ticks\")\nplt.style.use('ggplot')\n\nimport pandas as pd\n\nrecs = [\n dict(backend=\"PyTorch\", fps=1 / t, t=t, tflops=flops / t / 1.0e12, b=b)\n for (b, t) in zip(bs, baseline)\n] + [\n dict(backend=\"TensorRT\", fps=1 / t, t=t, tflops=flops / t / 1.0e12, b=b)\n for (b, t) in zip(bs, trt)\n]\n\ndf = pd.DataFrame.from_records(recs)",
"execution_count": 6,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"id": "5070389f",
"cell_type": "code",
"source": "g = sns.lineplot(data=df, x=\"b\", y=\"tflops\", hue=\"backend\")\ng.set_title(\"ResNet-101 Performance\")\ng.set_xlabel(\"Batch Size\")\n_ = g.set_ylabel(\"TFLOP/s\")",
"execution_count": 7,
"outputs": [
{
"output_type": "display_data",
"data": {
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go5Dfm8hvSM/iDQBQdNn8wFLI7fn3ZHMEaDazAkBnt3bt2liwYEHamvLy8thhhx0KOsdOO+0UZWVlaWumT5/eqm8Vbi+vU3beeee0j6dSKQvCAB1cpr9civjXh4xaQ4ZvToYDkC9r167NWNOrV69W3V+GbyLDAbqm3/zmN7Fs2bJmHysrK4tvfetbBfvm+VzI783JbwA6Avm9OfkN6bWfnz4AgC5j5syZGWsK/QNLRMSOO+6YsWb27NkFnwMA2rtZs2Zt8VsF3zN27Njo1q1bQefo3r17jBkzJm1NTU1NLFq0KOce2WR/e3mdMmvWrILPAUDhZMrWiOy+TT8dGb45GQ5AvqxatSrt46WlpTF8+PCc7y/DNyfDAbqet956Kx5++OEtPv6Zz3wmRowYUcSJMpPfm5PfAHQE8ntz8hvSs3gDABTd3LlzM9aMHTu24HMMHTo0Kisr09YsWLAgmpqaCj4LALRn2WR3pjcC8yWb1wjZzNuc5cuXx/r169PWVFZWxlZbbZXT/Vsim+c5b968gs8BQOFk8035mX5mzUSGbyLDAciX5cuXx7vvvpu2ZuzYsVFRUZFzDxm+iQwH6Hrq6+tjypQpW/wQ7PDhw+OYY44p8lSZye9N5DcAHYX83kR+Q2YWbwCAotqwYUMsX748bU1paWmMHDmy4LMkEokYPXp02pqGhoZYuHBhwWcBgPasPb3hmE2fXN9wzOa60aNHRyKRyOn+LTFq1KgoLS1NW5Pr8wSgfZg/f37GmsGDB7eqhwzfRIYDkC8vvvhixprdd9+9VT1k+CYyHKDr+d3vfrfFJddEIhGnnnpqlJWVFXmqzOT3JvIbgI5Cfm8ivyEzizcAQFFls8QyaNCgor1Zms2mvsUbALq6bLJw6NChRZgkuz65Znd7ep5lZWUxcODAtDUbNmyINWvWFGUeAPJv+vTpGWuGDx/eqh7tKdtk+CYyHKDjqq2tjT/+8Y9pa8rLy+Owww5rVZ/2lG0yfBMZDlB4s2fPjgceeGCLjx922GGxww47FHGi7LWnXJPfm8hvANJpT7kmvzeR37RX7W/9HwDo1JYtW5axplgv5LPtlc3MANBZJZPJWLFiRca69vSGY67Z3d5ep2y11VaxdOnStDXLli2Lvn37FmcgAPJm8eLFWZ14s+OOO+bcQ4a3vH++yHCAzuuuu+6KVatWpa05+OCDo0+fPjn3kOEt758vMhygbTU2NsaUKVMimUw2+/iAAQPixBNPLPJU2ZHfLe+fL/IboP2ora2NN954I2bOnBnz58+PpUuXxurVq6Ouri5SqVR069YtunXrFn379o0BAwbE4MGDY8yYMTF27NgYOXJkUU5beT/53fL++SK/6ags3gAARZXNC/lBgwYVYZJ/GTx4cMYaizcAdGWrVq2KxsbGtDXdunVr1YeKWqJv375RXl4eDQ0NW6xZsWJFpFKpFr8521Ffp2y33XZFmAaAfHrooYcy1lRWVrbqz3gZvjkZDkBr/d///V88+OCDaWsGDRoUJ5xwQqv6yPDNyXCAruMPf/hDvPPOO1t8/Gtf+1pUVlYWcaLsye/NyW+Aruf666+Pt99+O2321NTURE1NTaxdu/ZDX0zVp0+f2HXXXWO//faLXXfdNUpKSgo9svz+APkNmRX+TyYAgPfJ5psCirmtns0PRxZvAOjK2lt2Z9OvoaEhVq9e3eL7Ll++vNW988nrFIDOac2aNfH4449nrNtnn32ivLw85z4yvGW980mGA3Qu1dXVceONN8Zvf/vbtHXl5eVxxhlnRI8ePVrVT4a3rHc+yXCAtjNv3ry4//77t/j4xIkTY8899yziRC0jv1vWO5/kN0D78NZbb6VdGMlk7dq18eSTT8YVV1wRZ555ZjzwwANRX1+fxwk/TH63rHc+yW86Kos3AEBRrV27NmNNMV/IZ9Nr3bp1hR8EANqp9pbd2fbLJb+zucbrFABa684774y6urqMdR/72Mda1UeGt7x3vshwgM5h3bp18ec//znOOeecePTRR9PWlpeXx7nnnhs77rhjq/vK8Jb3zhcZDtA2mpqaYsqUKdHU1NTs41VVVfGVr3ylyFO1jPxuee98kd8Anc/y5cvj9ttvj7PPPjueeuqpgvWR3y3vnS/ym46qrK0HAAC6lg0bNmSs6dWrVxEm+ZfevXtnrMlmZgDorNavX5+xppjZnW2/bOZ+v8bGxqipqclYl81rh3zJpldLnycAbWv69Onx5JNPZqzbddddY9ttt21VLxm+ORkOwJbU1tZGdXV1VFdXx7vvvhtz586N2bNnx+uvv77FDwC/X9++fePMM8+MCRMm5GUeGb45GQ7Q+d1///0xd+7cLT7+pS99qegfem0p+b05+Q1APqxcuTKuu+66eO655+LUU0/Ne5bK783Jb8jM4g0AUFTZLLFUVlYWYZLse1m8AaAra2/ZnW2/luZ3tvVepwCQq+rq6vjFL34RqVQqbV0ikYjjjz++1f1keMt754sMB2g/7rrrrrj33nsLdv+99947vvGNb+T1wykyvOW980WGAxTfwoUL45577tni4zvvvHMcfPDBRZwoN/K75b3zRX4DdH4vvPBCzJ8/P84///wYMWJE3u4rv1veO1/kNx1VSVsPAAB0Le3th5aysrIoK0u/i1xXVxeNjY1FmggA2peNGzdmrGmPbzhmM3dL68vLy6O0tLRF922NQjxPANrOlClTYsWKFRnrDj/88Bg7dmyr+8nwTWQ4APmUSCTiox/9aFxxxRVx7rnn5v0bYWX4JjIcoHNLJpMxZcqUaGhoaPbxbt26xTe/+c0iT5Ub+b2J/AagEJYuXRqTJ0+OefPm5e2e8nsT+Q3ZceINAFBU2SywdO/evQiTbN4v01z19fUZF3QAoDPa0l96vl9bZHcm9fX1LbpnNs+zoqKiRfdsrWyeZzZzA9D27rvvvnj++ecz1g0aNCi++MUv5qWnDN9EhgOQD2PHjo2DDjoo9tprr+jfv3/B+sjwTWQ4QOf25z//OWbNmrXFxz//+c/HVlttVcSJcie/N5HfABTKxo0b4z//8z/j0ksvjeHDh7f6fvJ7E/kN2fHpUQCgqLJZvCnmBn22/ZqamoowCQC0P10lu7N5niUlxT04OJvn6VQ+gPbvlVdeibvuuitjXUlJSZx55pnRo0ePvPSV4ZvIcADyYd68efH0009HdXV17LPPPjFs2LCC9JHhm8hwgM5ryZIlcffdd2/x8TFjxsSRRx5ZxIlaR35vIr8Bupbu3bvHNttsEyNGjIgRI0bE8OHDo2/fvlFZWRmVlZXRvXv3qKmpiQ0bNsTGjRtj+fLlMXPmzJgxY0bMnTu3xX9Gr1+/Pq655pq44oorWr0sIr83kd+QHYs3AEBRddQfWryYB6CryuaNu86Q3V3leQJQXPPmzYtrr702kslkxtrPfe5zscMOO+Std1fJtq7yPAFoe8lkMmbMmBEzZsyIu+++O/bcc884+uijY7vttstrn66SbV3leQLwYalUKqZMmbLFb2svKSmJU089teg50BpdJde6yvMEIL0xY8bEbrvtFrvssktsv/32UVaW/qPoPXv2jJ49e0ZExLhx42KfffaJiH+dYPO3v/0tHn744Vi2bFnW/RctWhQ33XRTnHHGGbk/ieg6udZVnicUg8UbAKCosvmwUXvconfiDQBdVTYZ2Bmyu6O+4eg1CkD7tWLFirjyyiujtrY2Y+0ee+wRxx57bF77y/CW9c0nGQ7Q+aVSqXjxxRfjxRdfjAMOOCC++tWv5u3UOhnesr75JMMBiuPhhx+ON998c4uPH3nkkTF27NgiTtR68rtlffNJfgMUR+/evWP//fePgw8+OEaOHJmXe1ZVVcWnP/3pOOKII+LJJ5+MW265JWpqarK69sknn4yDDz44xo8fn3N/+d2yvvkkv+moivsnAgDQ5WXzA0k2yzn51B6XgQCgvegq2d1Rn2dH+tZHgK5k3bp1cdlll8WqVasy1g4dOjTOOuusSCQSeZ2ho2abDAcgn7p16xY9evTY4n8qKipa3ePJJ5+Mc889N2bMmJGHiTtutslwALKxfPny+M1vfrPFx4cMGRJf+MIXijhRfnTUXJPfAGRjwIABccMNN8SXv/zlvC3dvF9JSUkcdNBB8ZOf/KRFizS33nprq/tm0h5zTX5D23HiDQBQVGVlZRk30ou9sZ5Nv0zHogJAZ5VNBhY7u7M5Vrql2Z1NfbGPsy7E8wSg8DZs2BA//vGPY9GiRRlr+/fvHxdddFHeviH//WR4y/rmkwwHaD+OOeaYOOaYY9LWJJPJqK6ujurq6li8eHHMmTMn5syZE6+//nrW37S7cuXK+PGPfxwXXHBB7LTTTq2aWYa3rG8+yXCAwvvVr36V9mTYb37zm9GtW7ciTpQf8rtlffNJfgMUXrEWIAYOHBgXX3xxXH/99fH0009nrJ8/f368+uqrsdtuu+XUT363rG8+yW86Kv9UAgBF1R6PirR4AwBb1h7fcMzmG3AK8YZje/ymH69RANqX6urquPzyy2PevHkZa/v06RM//OEPY/DgwQWZRYa3rG8+yXCAjqWkpCR69uwZPXv2jMGDB//7Azt1dXXx/PPPx6OPPhpvvfVWxvvU1dXFFVdcERdddFHssMMOOc8jw1vWN59kOEBhPfbYY/Haa69t8fGDDjooJkyYUMSJ8kd+t6xvPslvgM6ltLQ0zjjjjFi3bl28/vrrGev/8pe/dKrFG/m9ifymPWrZeVMAAK1UXl6esaa+vr4Ik2xSV1eXsSabuQGgM+oq2d1Rn6c3HAHaj9ra2rjiiiti9uzZGWt79eoVkydPjmHDhhVsno6abTIcgPaie/fuccABB8Sll14aZ511VvTp0yfjNXV1dXHddddFdXV1zn07arbJcADSWbVqVdxxxx1bfLxPnz7xpS99qYgT5VdHzTX5DUB7VFpaGmeffXb06tUrY+1rr70WGzZsyKlPR801+Q1tx+INAFBUPXr0yFhTU1NThEn+pbGxMRoaGtLWlJeXW7wBoMtqb9mdbb+qqqoW3TOb51lXV1fUb/spxPMEoDDeW7qZMWNGxtqePXvG5MmTY+TIkQWdSYZvIsMBaK1JkybFtddeG+PHj89Yu2LFirjpppty7iXDN5HhAJ3HjTfemHYx9Stf+Ur07NmziBPll/zeRH4DkA+9e/eOE044IWNdU1NTvPLKKzn1kN+byG/IjsUbAKCosvk2gmL+0FJbW5uxJpuZAaCzam/ZnW2/lv4lbbZ5X8znWojnCUD+1dXVxVVXXRVvvvlmxtqqqqqYPHlyjB49uuBzyfCW984XGQ7QOfXs2TMuvPDC2HnnnTPWPvXUUzF37tyc+sjwlvfOFxkOUBhPPvlkvPzyy1t8fI899oh99923iBPln/xuee98kd8AnddBBx0UAwYMyFj31ltv5XR/+d3y3vkiv+moLN4AAEWVzYviXI8AzUU2vbyQB6Ara2/ZnW2/luZ3WVlZdO/ePWPd+vXrW3Tf1vA6BaD9q6+vjyuvvDKmT5+esbaqqiouvvjiGDNmTBEmk+EfJMMByIfy8vL47ne/G/37989Y+6c//SmnHjJ8czIcoGNbu3Zt3HbbbVt8vLKyMr7xjW8UcaLCkN+bk98A5ENpaWnsv//+GetmzZqV0/3l9+bkN2Rm8QYAKKrevXtnrFmzZk3hB/n/Vq9enbEmm5kBoLNqb9mdbb9c8rtPnz556Z0vXqcAtG/19fVx1VVXZbV006NHj7joooti3LhxRZjsX2R4y3vniwwH6Nx69uwZJ510Usa6Z599NtatW9fi+8vwlvfOFxkOkH833XRT2g9xHn/88Vl9k317J79b3jtf5DdA57b77rtnrHn33Xdzurf8bnnvfJHfdFQWbwCAoho0aFDGmmxeXOdLNj80ZDMzAHRW7S27IzLnd2lpaVbfPvxB2TzXYr7hmE2vwYMHF34QAD7kvaWb119/PWNtZWVlXHTRRbHNNtsUYbJNZHjLeueTDAfo/Pbbb78YMmRI2pqmpqZ44403WnxvGd6y3vkkwwHy64UXXojnn39+i49vt9128fGPf7yIExWO/G5Z73yS3wCd2zbbbBOJRCJtTV1dXU5ffCG/W9Y7n+Q3HZXFGwCgqLJ5Ubx8+fIiTJJ9Ly/kAejKBgwYEKWlpWlr6urqinb09Lp166Kuri5tzcCBA6OkpOVveWTzhqPXKQDksnSz7bbbFmGyzcnwzclwAPIpkUjEvvvum7Eul8UbGb45GQ7QcaXLwbKysvjWt76VU360R/J7c/IbgHwpLy+PgQMHZqzLZfFGfm9OfkNmZW09AADQtWTzonjp0qVFmORflixZkrEm0zcXAkBnVlJSEgMHDsyYz0uWLIlevXoVfJ5CZnc2r1Oy6Z8v2fTyhiNAcdXV1cVVV10V06ZNy1j73tLNdtttV4TJPkyGt7x/vshwgK5h/Pjx8X//939pa+bMmdPi+8rwlvfPFxkOUDzJZDIuvvjigty7sbExY81TTz0VL774Ysa6Sy+9NEaOHJmxTn63vH++yG+Azq9Xr14ZFzUyLaw0R363vH++yG86qs7xtQEAQIcxfPjwjDVLly6NpqamIkwT8e6772asyWZmAOjMssnCbDI1HxYvXpyxJtfsHjFiRMaaYj3PpqamWLZsWdqaHj16RL9+/YoyDwD/+ou7K6+8skMs3bxHhm8iwwHIt2HDhmWsyeUbdyNk+PvJcIDOKZlMRnV1dUH+U19fn7F/U1NTVvdKJpNZPyf5vYn8BiCfunXrlrEmm/xvjvzeRH5DZhZvAICi6t27dwwYMCBtTWNjYyxatKgo88ybNy/t42VlZbH11lsXZRYAaK/GjBmTsSZTpuZLNn2ymTfX64r1PN95552Mi8ijR4+ORCJRlHkAurr3lm6mT5+esbaysjIuvPDCNl+6iWhf2SbDN5HhAJ1D7969M9asX78+p3u3p2yT4ZvIcADSaU+5Jr83kd8AHV82SzXl5eU53bs95Zr83kR+015ZvAEAii6bF/Nz5swp+BxLliyJjRs3pq3Zeuuto6ysrOCzAEB71l6yO9s+ub7hOGTIkKiqqkpbs2HDhozfwJMPhXyeALRMLks322+/fREmy0yGbyLDAci30tLSjDU1NTU53VuGbyLDAego5Pcm8huAfMrmSy0qKipyurf83kR+Q2YWbwCAott2220z1rz11lsFn2PGjBkZa8aNG1fwOQCgvcsmu99+++1obGws6BwNDQ0Z34jr3r17Vkdlb8k222yTsSab1xCtlc1roWxmBaB1amtr44orruiQSzcRMvyDZDgA+VRbW5uxpnv37jndW4ZvToYD0BHI783JbwDyoaGhIVasWJGxrlevXjndX35vTn5DehZvAICimzBhQsaa119/veBzvPbaaxlrdtlll4LPAQDtXb9+/WL48OFpa+rq6mLmzJkFneOtt97KeJT4+PHjs/rW4S3ZeeedM9YU43VKph6JRCKrWQHIXW1tbVx55ZXxxhtvZKytrKyMiy66qF0t3UTI8A+S4QDk0/LlyzPW5PqNuzJ8czIcgI5Afm9OfgOQD7Nnz45UKpW2pnv37tGnT5+c7i+/Nye/IT2LNwBA0Y0dOzbj8ZXLly+PRYsWFWyGZDKZcfHGC3kA2CSbZdRXXnmloDNkc//WLs1mc/3UqVMjmUy2qk86CxYsiJUrV6atGT16dM5vIAOQWS5LN9ttt10RJms5Gb6JDAcgn+bNm5exZsCAATnfX4ZvIsMB6Cjk9ybyG4B8yCbXhgwZ0qoe8nsT+Q3plbX1AABA11NSUhK77rprPPPMM2nrnn766fjCF75QkBmmTZsWa9euTVuz3XbbRc+ePQvSHwA6mt133z3+8pe/pK155pln4sQTTyxI/1QqFc8++2zGut12261VfUaPHh39+vWL1atXb7Fm9erV8eabb8b48eNb1WtLnn766Yw1rX2eAGxZZ1q6iZDh7yfDAcinV199NWPNVlttlfP9ZfgmMhygYzrllFPilFNOKXrfv//97/HLX/4ybc2BBx4Yp59+et57y+9N5DcArdXU1BRPPfVUxrptttmmVX3k9ybyG9Jz4g0A0CYmTZqUseYf//hHwbbon3jiiYw1++23X0F6A0BHNGHChIzfLLN8+fKsPqSci2nTpmX89puxY8fGsGHDWtUnkUjEvvvum7Eum9cSuUgmk1m9gex1CkBhdLalmwgZ/kEyHIB82LBhQ7z44osZ68aMGZNzDxm+ORkOQEcgvzcnvwFojSeeeCJWrFiRsW6HHXZoVR/5vTn5DVtm8QYAaBO77bZb9OrVK23N0qVL46WXXsp771WrVmU8bae0tDSrHzYAoKsoLS2NiRMnZqx74IEHCtI/m/vuv//+eemVzX2efvrpWLNmTV76vd/zzz8fy5cvT1szatSoGDlyZN57A3R1dXV1WS/d9OjRIy6++OJ2v3QTIcM/SIYDkA/33ntvNDQ0ZKxrzTfEyvDNyXAAOgL5vTn5DUCu1q9fH7/97W8z1pWUlLT6hBT5vTn5DVtm8QYAaBNlZWVxwAEHZKy79957I5VK5bX3fffdF01NTWlr9txzz+jdu3de+wJAR3fooYdmrPnnP/8Z8+bNy2vft99+O1599dW0NeXl5Xl7w3Hs2LEZv5W4oaEh7r///rz0e08ymYx77703Y102vw8AtEwuSzfbbrttESbLDxm+iQwHoLXeeuutePDBBzPWDRgwIEaPHt2qXjJ8ExkOQEchvzeR3wDkIplMxnXXXRdr167NWDt+/Pjo27dvq3vK703kN2yZxRsAoM0cccQRUVpamrZmzpw58fjjj+et58KFC+ORRx7JWHf00UfnrScAdBajRo2KXXfdNW1NKpWKW265JW+Ls6lUKm699daM9zv44IPzujR71FFHZax56KGHYvHixXnr+be//S3mz5+ftqZXr15x8MEH560nAJuWbqZPn56x9r2lm2222aYIk+WPDN+cDAfouJqammLdunVt1n/RokVxzTXXRDKZzFi7//77R0lJ6/46XoZvToYD0BHI783Jb4COq7Gxseg9k8lk/OIXv4ipU6dmVX/YYYflpa/83pz8huZZvAEA2szAgQNj0qRJGetuv/32WLp0aav7NTQ0xHXXXZfxtJvx48d3uA9RAUCxfOYzn8lY8+abb+btqO37778/ZsyYkbampKQkPv3pT+el33smTpwYQ4YMSVvT1NQU1113XV7edF6yZEnccccdGes++clPRvfu3VvdD4B/qa+vj6uuuiqrpZuqqqoOuXTzHhm+iQwH6Lhqa2vjjDPOiLvvvjvWr19f1N6vvfZaTJ48OavFn/Ly8vj4xz+el74yfBMZDkBHIb83kd8AHdf3vve9uP/++6O6uroo/VauXBk//vGP4x//+EdW9UOHDo299torb/3l9ybyG5pn8QYAaFPHHXdcxhfN1dXVcfXVV8eGDRty7pNMJmPKlCkZj/xMJBJx0kkn5dwHADq78ePHxx577JGx7je/+U28/PLLrer1z3/+M+6+++6MdR//+MczvjnYUiUlJXHiiSdmrJszZ05MmTIlq2873pL169fH1VdfHTU1NWnr+vXrF0ceeWTOfQDY3HtLN9OmTctYW1VVFZMnT+6wSzcRMvyDZDhAx1VbWxv33HNPfOtb34opU6Zk/JBKa61evTp+9atfxWWXXZb1e9SHHXZYDBw4MC/9ZfjmZDgAHYH83pz8BuiY1qxZE3feeWd861vfihtvvDFmz55dkD6pVCqefPLJ+N73vpfV+/XvOfnkk1t90uz7ye/NyW/4MIs3AECbGjhwYHz2s5/NWLdgwYK49NJLY8WKFS3uUV9fHz//+c/jqaeeylh7yCGHxLhx41rcAwC6kq985StRXl6etqapqSl+8pOfxLPPPptTj6effjp++tOfZjyprnfv3nHcccfl1COTffbZJ3bZZZeMdf/4xz/i+uuvj/r6+hb3WL58eVx66aXxzjvvZKw9+eSTo6KiosU9APiw+vr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ePXr0iFNPPTVOPfXU2LJlS8yYMSOeeuqptJUKfv/7339gOWvgwIFx3nnnxWGHHZbUWl26dIkTTjghTjjhhFiwYEE8+OCDUV5e3uJztbW18bvf/S4uueSS1kTvlAYNGhQHHXRQ0oW3lStXxjXXXBNnn312TJ48OfLz81OccN9w/vnnZ2zvtWvXxs0335zUbHFxcZv32bp1a9xzzz1JlQGzsrJi8uTJcc4557xXxmytfv36xaWXXhqTJ0+Oe++9N6nP4QcffDAOPfRQtzYBAAAA0OllZzoAAAAAAADA3qCysjJuu+22WLRoUYuzWVlZcfHFF6csS1ZWVpxwwgnxne98J6ZOnRoTJ07ssNLN38vOzo5PfepTcccdd8SBBx7Y4nxFRUXMmDGjw3N0dj/72c9i06ZNSc0WFRXFtddeG5dffnm7Szfv16tXrzjnnHPihz/8YZx//vlpeTP7B725/qyzzoo77rgj6dLN+x122GHxrW99a483Af29mTNnRnV1dZv26mxae1PWzp074+c//3lcdtll8fDDD8fatWtTlIz2qqioiNtuuy127drV4mzPnj3bVSb78Y9/HDt37mxxrqSkJG688cb4f//v/7W5dPP3hg8fHrfddluMGTOmxdmqqqp45JFH2r0nAAAAAKSa4g0AAAAAAMAe1NfXx/Tp0+Oqq65KqnQTETF58uQ45JBDUpKnd+/e8d3vfjeuvPLKGDJkSEr2eL+BAwfGLbfcEqWlpS3OPvnkk2lI1Hm89dZbSZeNunXrFt/85jfj6KOPTmmm/Pz8OPPMM+O+++5L6T6JZGdnx+WXXx7nnntu5Obmtmutbt26xTe+8Y3o3bt3i7O1tbXx4osvtmu/zmLSpEltKkBUVlbG73//+7j66qvjK1/5SvzsZz+LefPmJVXyIPVqamri9ttvjy1btrQ4W1RUFFOmTImePXu2aa/nnnsu5s+f3+JcSUlJ3HLLLTF+/Pg27fNBiouLY8qUKTFixIgWZ2fOnJnUzVYAAAAAkEmKNwAAAAAAAO+zdevWmDt3btx///3xhS98IR588MGkbg6IiDjyyCPjggsuSFm20tLSGDBgQMrW/yA9e/aMa6+9NnJycvY4V15eHosXL05Tqsx7+OGHk5orKiqKm2++OQYNGpTiRH+TiluQWvKFL3whJk6c2GHrdevWLenbo2bPnt1h+2ZScXFxnHPOOe1ao6ysLJ544om4/fbb46KLLoqvfOUrcd9998WTTz4ZS5Ysibq6ug5KSzKamprinnvuiZUrV7Y4m5OTE1/96ldj8ODBbdqroaEhfvWrX7U4l5ubG9dcc03KXpPy8/PjqquuipKSkj3ONTY2xm9/+9uUZAAAAACAjtK+HzMFAAAAAADQCZSVlcW0adPa9Gxzc3PU1NTE7t27Y9euXVFeXp50yeb9jjnmmPjyl7/c7ps+OquhQ4fG6aefHk888cQe51544YUYNWpUmlJlzty5c2Pp0qVJzV566aVtfiP93mLSpElxyimndPi6Rx55ZBxxxBExb968Pc4tWrQoampqMlI46miTJ0+OuXPnxmuvvdbutZqbm6OsrCzKyspi1qxZEfFOuWPAgAExbNiwGDZsWAwfPjwGDx4c2dl+bmMq/OQnP0nqBpqId8pr7bmBZsaMGbF58+YW5y655JIYM2ZMm/dJRu/evePf/u3f4rvf/e4e51566aW48MILo7i4OKV5AAAAAKCt9s3v+gAAAAAAAPuVysrKmDlzZsb2LykpiQsvvDBOPPHEjGVIl7POOiv+/Oc/R0NDwwfOvPHGG2lMlDl/+tOfkpo76aST4phjjklxmswqLS2N888/P2XrT548ucXiTWNjYyxdujTGjh2bshzpkpWVFVdccUV8/etfj/Xr13f4+o2NjbF69epYvXp1/OUvf4mIiIKCghg+fHiMGzcuxo8fHwcffHBkZWV1+N77m8ceeyyeeeaZpGY/9alPxcknn9zmvZqbm+Pxxx9vcW7EiBHt2qc1jjnmmBg+fHgsW7bsA2dqa2vj+eefj8mTJ6clEwAAAAC0lh9ZBAAAAAAA0EYHHnhgnHfeeXHvvffuF6WbiIju3bvHYYcdtseZsrKy2LZtW3oCZUh5eXksXLiwxbni4uL4/Oc/n4ZEmfWZz3wmpTfNjB8/Prp169bi3J7e3L+36datW9x8883Rv3//tOxXW1sbb775Zvzyl7+MG264IS677LJ46KGHYtWqVWnZf1/0wgsvxC9/+cukZk844YT43Oc+1679Fi5cGBs3bmxx7oILLmjXPq318Y9/vMWZV155JQ1JAAAAAKBt3HgDAAAAAADQCv369YvTTjstPvShD8XgwYP3yxshxo8fH3Pnzt3jzIoVK6Jnz55pSpR+s2bNiubm5hbnTj/99KQKI3uzHj16pLx4lpOTE0ceeWT89a9/3eNcWVlZSnOkW2lpaXzrW9+KH/zgBzF//vy07r1ly5b44x//GH/84x9j1KhR8bGPfSyOPfbY/fI1ry0WLVoU999/f1KvE2PGjIlLL7203Xu29Pcj4p3X75EjR7Z7r9aYMGFClJaWxtatWz9w5q233oq6urrIz89PYzIAAAAASI4bbwAAAAAAAFph/fr18dvf/jaeeOKJePnll6OxsTHTkdJu2LBhLc6sWbMmDUky59VXX21xJicnJz72sY+lIU1mnXjiiZGTk5PyfYYMGdLizL5WvImIKCkpiRtuuCEuvvjiKCoqykiGxYsXxz333BPXXHNNzJs3LyMZ9ibr16+PO++8M+rr61uc7d+/f1xzzTWRm9u+n5nZ2NiYVDnr+OOPb9c+bZGdnR1jxozZ40x9fX0sXrw4TYkAAAAAoHUUbwAAAAAAAFqpsrIyZs6cGXfffXdcccUV8eSTT0ZNTU2mY6VNnz59WpzZFwsQ76qoqIhVq1a1OHf44Yfv87fdREQcffTRadln0KBBLc5UVFSkIUn6ZWVlxemnnx4/+MEPYvLkyZGXl5eRHGvXro3bb7897rzzzti+fXtGMnR2lZWV8e1vfzuqq6tbnO3evXtMmTIlSkpK2r3vkiVLYufOnXucycnJSdvf1/cbPXp0izMrV65MQxIAAAAAaD3FGwAAAAAAgHbYsmVL/PSnP43LL788Xn755UzHSYtkbt3YVwsQERGLFi2K5ubmFueOPfbYNKTJrKKioqRuQOoIAwcObHGmsrIyDUkyp3v37nHJJZfEvffeG5/4xCeie/fuGcnxyiuvxLXXXhtvvPFGRvbvrGpra+OOO+6ITZs2tThbUFAQU6ZMSarImIxk/luMHj06unTp0iH7tdaoUaNanEmm0AgAAAAAmaB4AwAAAAAA0AGqqqriu9/9btx///2xe/fuTMdJqWRu29iXb8NI9laGsWPHpjhJ5g0ZMiSys9PzLcdkbgVpbGzc5//+RUT07Nkz/u///b/xwx/+MK655po4/vjjkyrEdaR3b3aZNWtWWvftrJqamuL73/9+LFu2rMXZ7OzsuOqqq2Lo0KEdtv/y5ctbnEmmvJYqPXr0aHGmvLw8DUkAAAAAoPVyMx0AAAAAAACgvUaPHh1Tp05t1xr19fVRX18fVVVVsW3btli/fn2sXLkyFi1aFOvWrUt6nWeffTZWr14dX//615MqCqRSfX19rFy5MtasWRPl5eVRXl4elZWVUVVVFdXV1e99zI2NjR2+944dOzp8zc4imeJN3759k3qj+d6uf//+adsrJycn8vPzo66ubo9z9fX1aS+hZEpubm5MmDAhJkyYEPX19fH222/H66+/Hm+++WasWrUqGhoaUrp/Y2NjTJs2LSIiJk6cmNK9Oruf/vSnMXfu3KRmL7roojjyyCM7dP9kbosZMGBAh+7ZGsXFxZGTk7PH82br1q1pTAQAAAAAyVO8AQAAAAAAiHduccnLy4vi4uLo27dvjB49+r3fW79+fTzzzDMxY8aMpG7TWLlyZXzrW9+Km2++OYqLi1MZ+5+sXbs2XnnllZg3b16sWLEi5W+8/yAtlSP2Zhs2bGhxZtCgQWlIknk9e/ZM635FRUVJFW/2R3l5eTF27Nj3blqqr6+PFStWxPLly2PFihWxcuXKKCsri6ampg7dt7m5OX74wx9Gr169/uF1c3/y+OOPx/Tp05Oa/dd//dc47bTTOnT/6urqqKioaHEuk8WbiIguXbpEZWXlB/5+ZWVlNDU1pe0WLQAAAABIluINAAAAAABAC/r16xfnn39+fPzjH4+f/vSn8eKLL7b4zPLly+Ouu+6Km2++ObKyslKar7GxMZ577rl46qmnYtmyZSndK1n7avGmubl5r3iDe7p06dIlrfvl5+e3OJOKG5z2Rnl5eTFq1KgYNWrUe/9bbW1trFq1KlauXBlLly6NJUuWxMaNG9u9V2NjY9x7771x1113Zfymr3R7+eWX46GHHkpq9thjj43Pf/7zHZ5hy5YtSc19/etf7/C9O1JTU1PU1dVFYWFhpqMAAAAAwD9QvAEAAAAAAEjSAQccEFdddVUMGTIkHnnkkRbnFy5cGNOnT48zzjgjZZlmz54dDz/8cIe8eb4j7avlh8rKyqRuEUr3TTCZkkwRhs6joKDgvTLO6aefHhHvlDYWLFgQc+bMiTfeeKPNf3e3bt0av/zlL+OSSy7pyMid2pIlS+IHP/hBNDc3tzg7atSouPzyy1NSxNy2bVuHr5kpijcAAAAAdEaKNwAAAAAAAK101llnRWVlZTzxxBMtzj7yyCNx+OGHR9++fTs0Q3V1dfzoRz+Kl19+uUPXZc927tyZ1NwBBxyQ2iCdRHZ2dqYj0E69evWKSZMmxaRJk2L79u3xl7/8Jf70pz9FZWVlq9eaMWNGfOxjH4t+/fqlIGnnUl5eHnfccUfU19e3OHvQQQfFddddl7Ki2vbt21Oybibsq7elAQAAALB385VwAAAAAACANjj//PNj1KhRLc7V1tbGb3/72w7du7y8PG666SalmwxI9k3hbmxgb3TAAQfEpz71qZg2bVqcc845kZeX16rnGxsb48knn0xRus6jqqoqvv3tb8eOHTtanO3atWtMmTIlunbtmrI8NTU1KVs73ZqamjIdAQAAAAD+ieINAAAAAABAG2RlZcU555yT1OwLL7wQVVVVHbLvli1bYurUqbFhw4YOWY/WSbZ409rCAnQm+fn5cfbZZ8cdd9zR6ttrZs2atU/fWlJXVxd33nlnlJeXtzibl5cX119/fYffePZ+ydy6AwAAAAC0XW6mAwAAAAAAAOytDj300Bg1alQsXrx4j3P19fXxzDPPxKc+9al27VdbWxvf/va3Y9u2be1aJysrK7p16xbdunWLLl26RGFhYRQUFEROTk5kZ2dHdnbLP7tt5syZ7cqwt0r2NoZk/gyhsxswYEDceuut8bWvfS2poknEO7evvP7663HUUUelOF36NTc3x3333dfia37EO6+zV1xxRYwcOTLluRRvAAAAACC1FG8AAAAAAADa4YgjjkjqTdjz5s1rd/HmoYceinXr1rXqmYKCgjjkkENi1KhRcfDBB0e/fv2id+/ekZvbvm8T7a/Fm/z8/KTmvBGefUW3bt1iypQpceONN8bOnTuTeua1117bJ4s3Dz30UMyePTup2fPOOy+OPfbYFCd6R3tfzwEAAACAPfMVOAAAAAAAgHYYM2ZMUnMrVqyIurq6pIsb77dmzZp4+umnk54fMWJE/Mu//EscddRRbd6Tf5bsn2VdXV2Kk0D6HHTQQXHWWWfFww8/nNT8smXLUpwo/aZPnx6PP/54UrOnn356nHnmmSlO9DfJvC517do1HnjggTSkAQAAAIB9j+INAAAAAABAOwwbNixycnKisbFxj3MNDQ2xfPny+NCHPtSmfX79619Hc3Nzi3PFxcVxySWXxAknnNCmfZKxP5dKioqKkpqrqqpKcRJIrzPOOCMef/zxqKysbHF27dq1aUiUPnPnzo3//u//Tmr2qKOOigsvvDC1gd4nmeJNdXV1NDY2Rk5OThoSAQAAAMC+JTvTAQAAAAAAAPZmOTk50aVLl6Rmy8rK2rRHZWVlzJ07t8W5kpKSuOWWW1JauomIqK2tTen6ndkBBxwQWVlZLc5VVFSkIQ2kT35+fhx55JFJzdbV1SVV0NkbLFu2LL7//e8nVXwcNmxYfPnLX47s7PR+G/6AAw5ocaa5uTm2bduW+jAAAAAAsA9SvAEAAAAAAGinZIs3O3bsaNP6s2fPbvFGnYiIyy67LAYPHtymPVpj+/btKd+js8rNzY3u3bu3OFdeXp6GNJBe48ePT3p2XyjebNq0Ke64446kyoZ9+vSJ66+/PgoKCtKQ7B/16tUrqbm2lj8BAAAAYH+neAMAAAAAANBOyRZvqqur27T+m2++2eLM+PHj46ijjmrT+q21devWtOzTWfXu3bvFmTVr1qQhCaTXwIEDk57d22/Gqq6ujttuuy2pAlFJSUlMmTIlqZtnUiHZ4o3XJQAAAABoG8UbAAAAAACAdqqpqUlqbufOnW1af+nSpS3OTJo0qU1rt8X+/ubtZG4VWrNmTdTV1aUhDaRPt27dkp5N5pauzqq+vj7uvPPOpG6Iyc3NjWuvvTb69++fhmSJdenSJXr27Nni3Ntvv52GNAAAAACw71G8AQAAAAAAaKdkb7LJzc1t9dp1dXWxbdu2Pc5kZWXFuHHjWr12W61YsSJte3VGQ4cObXGmvr4+lixZkoY0kD4lJSVJz+bn56cwSeo0NzfH/fffH4sWLWpxNisrKy699NIYPXp0GpLt2fDhw1ucefvtt/fqQhQAAAAAZIriDQAAAAAAQDvt2LEjqbnCwsJWr71ly5Zobm7e40zv3r2jqKio1Wu3RXNzcyxcuDAte3VWI0eOTGpu7ty5KU4C6dWaW5za8nrXGfzyl7+MF154IanZz33uc3HCCSekOFFykineVFdXx5tvvpmGNAAAAACwb1G8AQAAAAAAaIcNGzYk/Wb0Aw44oNXr7969u8WZ7t27t3rdtlq8eHFUVVWlbb+IiOzslr+l1dTUlIYk7xg0aFCUlpa2OPfiiy+mNRekWkVFRdKzbXm9y7RnnnkmHnvssaRmTznllPjkJz+Z2kCtcPjhhyc1N2vWrBQnAQAAAIB9j+INAAAAAABAO7Tm9oC+ffu2ev1kSj1ZWVmtXret/vKXv6Rtr3fl5ua2ONPQ0JCGJH+TzJvct2/fHrNnz05DGkiPtWvXJjXXtWvXKC4uTnGajjV//vx44IEHkpo97LDD4pJLLklxotYZPHhwHHjggS3Ovfjii7F9+/bUBwIAAACAfYjiDQAAAAAAQDu8/vrrSc8OHDiw1evn5OS0OJOuG2i2bdsWL7zwQlr2+nsFBQUtziRzM1BH+vCHP5zU3GOPPRbNzc0pTgPp8cYbbyQ1N2DAgBQn6VgrV66Me+65JxobG1ucHTJkSHzlK19J6rU53Y477rgWZxoaGpK+1QcAAAAAeIfiDQAAAAAAQBtt2rQpXnnllaRmS0tLk7qN4P2KiopanKmoqEjqDePt9eijj0Z9fX3K93m/rl27tjhTWVmZhiR/M2bMmDjooINanFu1alVGbgmCjlZfXx8vvfRSUrOHHHJIitN0nC1btsTtt98eNTU1Lc6WlpbGDTfckNTrciaceuqpkZ3d8lsA/vznP8e6devSkAgAAAAA9g2KNwAAAAAAAG306KOPJl14GT9+fJv2KC0tbXGmtrY23n777Tatn6zFixfHjBkzUrrHB+nevXuLM5s2bUpDkn906qmnJjX38MMPx9atW1Ochn3B0qVLo6mpKdMxEnrqqadix44dSc2OGTMmxWk6xq5du+K2226LioqKFmeLiopiypQp0bNnzzQka5vevXvHUUcd1eJcY2Nj3HvvvdHQ0JCGVAAAAACw91O8AQAAAAAAaIN58+bFrFmzkp6fOHFim/YpLi5OqnjywgsvtGn9ZOzYsSN+8IMfRHNzc8r22JM+ffq0OLNy5co0JPlHkydPjh49erQ4V11dHXfffbc3udOi3/zmN3H11VfHnDlzMh3lH2zatCl+/etfJzXbvXv3OPTQQ1OcqP0aGhriu9/9bqxdu7bF2ZycnLj66qtj0KBBaUjWPp/5zGciKyurxbmVK1fGj3/84zQkAgAAAIC9n+INAAAAAABAK61YsSLuueeepG+mGDhwYHzoQx9q834jRoxoceavf/1rlJWVtXmPD1JXVxd33XVXbN68ucPXTlb//v1bnNm8eXNs2LAhDWn+Jj8/P84+++ykZpcuXap8Q1LKysrirrvuiilTpsTs2bMzfgNOdXV13HnnnbFr166k5j/ykY9Ednbn/zb0j370o3jjjTeSmv3Sl74U48aNS3GijjF48OD46Ec/mtTss88+Gz/72c8yVqqMiKiqqopf/OIXXhsBAAAA6NQ6/1c8AQAAAAAAOpFXXnklvvnNb0ZtbW3Sz3z2s59N6gaCDzJ27NgWZxobG2PatGlRU1PT5n3eb9euXXH77bfHokWLOmzNtujRo0eUlpa2OPfnP/85DWn+0aRJk2LYsGFJzc6dOzfuuOOOqK6uTnGqiIqKinjggQdSvg+ps3z58rj77rvjq1/9ajz99NMd+nc7WVu2bIlbb701Vq9endR8Xl5enHnmmSlO1X7/+7//GzNnzkxq9tOf/nScdNJJqQ3Uwf7P//k/Sd2UFhHxxBNPxL333pv2z69t27bFww8/HJdffnn84Q9/yHjBDAAAAAD2RPEGAAAAAAAgCStXrow777wz7rzzzti5c2fSz40bNy6OOeaYdu19zDHHJFXcWbZsWatuptiTlStXxo033hgLFy5s91od4ZBDDmlx5s9//nPMnj07DWn+Jjs7Oy677LLIy8tLav61116LKVOmpOzPtby8PB544IG44oorMlJEouOtX78+/uu//iu+9KUvxX/913/FsmXLUr5nY2NjPPnkk3HttdfGqlWrkn7u9NNPj549e6YuWAd49tln49FHH01qduLEifHZz342xYk6Xvfu3ePf//3fk55//vnn4/rrr4+33norhakimpubY+HChfH9738/Lrvssvj973+fkUIZAAAAALRWbqYDAAAAAAAAdEb19fWxatWqePPNN+Pll1+O5cuXt3qNkpKSVr35+YP07Nkzjj766JgzZ06Ls2+88UZce+218e///u9x6KGHtnqv7du3x+9///uYPn16NDY2Jpw5+OCDY+XKla1euz2OPvroeOGFF/Y409jYGHfffXccd9xxcfLJJ8fo0aOTLsS0x4ABA+KCCy6In/zkJ0nNb9y4MW699dY49thj41Of+lQMGTKkXfvv3r07Xn311Xj22WfjjTfeiObm5natR+e0e/fuePrpp+Ppp5+O3r17x3HHHRdHHnlkjBgxInJzO+bbvrt37445c+bEY489FmVlZa169qCDDur0JZXKysr40Y9+lNTsoYceGv/2b/+W4kSpc8QRR8QnPvGJ+P3vf5/U/IYNG2Lq1KlxxBFHxMc//vEYPXp0h+RoamqKxYsXx9y5c+Oll16KLVu2dMi6AAAAAJBOijcAAAAAAMBer6ysLKZNm9auNRobG6Ouri527NgR27Zti02bNrWrwJCbmxvXXHNNlJaWtivXu84+++x45ZVXksq0efPmuPXWW2PEiBFx6qmnxrhx4/Z4C0V1dXUsWrQoXnrppZgzZ07U1dV94Gx+fn5ceeWV8ZWvfKVNH0dbHXnkkdGlS5eorq5ucfall16Kl156KXJycqJv377Rq1evKCgoiMLCwhZvDuratWucf/75rc43efLkWLduXUyfPj3pZ2bPnh2zZ8+OYcOGxXHHHReHHnpoDB48OHJycvb43K5du2LNmjWxePHiePPNN+PNN9+M+vr6Vmdm77V58+b4wx/+EH/4wx+isLAwPvShD8XIkSPj4IMPjiFDhiR960xtbW2sXbs2Vq9eHfPnz4/58+e36XMpPz8/rrjiiigoKGj1s+lUW1v7gYXC9ysqKkq6pJMq/fv3j7POOqvNz59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},
"metadata": {
"image/png": {
"width": 1647,
"height": 1185
}
}
}
]
},
{
"metadata": {
"trusted": true
},
"id": "c02f8f26",
"cell_type": "code",
"source": "g = sns.lineplot(data=df, x=\"b\", y=\"fps\", hue=\"backend\")\ng.set_title(\"ResNet-101 Performance\")\ng.set_xlabel(\"Batch Size\")\n_ = g.set_ylabel(\"Images/s\")",
"execution_count": 8,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<Figure size 1800x1200 with 1 Axes>",
"image/png": 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}
}
]
},
{
"metadata": {},
"id": "9082b254",
"cell_type": "markdown",
"source": "Getting >150TFLOP/s (roughly 50% of the peak CUBLAS performance on these machines) is pretty good!"
}
],
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