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andersy005/dask-cupy-multi-node-gpu.ipynb

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dask-cupy-multi-node-gpu.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<dask.config.set at 0x2b75ebd99790>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from dask_jobqueue import SLURMCluster\n",
"from distributed import Client, performance_report\n",
"import dask\n",
"import xarray as xr\n",
"import os\n",
"# dask.config.set({'distributed.dashboard.link': 'https://jupyterhub.ucar.edu/dav/user/abanihi/proxy/{port}/status'})\n",
"dask.config.set({'distributed.dashboard.link': '/proxy/{port}/status'})"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
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"VBox(children=(HTML(value='<h2>SLURMCluster</h2>'), HBox(children=(HTML(value='\\n<div>\\n <style scoped>\\n …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cluster = SLURMCluster(cores=1, processes=1, memory='20GB', \n",
" job_extra=['--constraint=gpu'], \n",
" env_extra=['module load cuda/10.1',]) # ensure cuda is loaded\n",
"client = Client(cluster)\n",
"cluster"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"cluster.scale(2)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"import cupy\n",
"import dask.array as da"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
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" <tbody>\n",
" <tr><th> Bytes </th><td> 320.00 GB </td> <td> 800.00 MB </td></tr>\n",
" <tr><th> Shape </th><td> (200000, 200000) </td> <td> (10000, 10000) </td></tr>\n",
" <tr><th> Count </th><td> 400 Tasks </td><td> 400 Chunks </td></tr>\n",
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"text/plain": [
"dask.array<normal, shape=(200000, 200000), dtype=float64, chunksize=(10000, 10000), chunktype=cupy.ndarray>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# generate chunked dask arrays of many cupy random arrays\n",
"rs = da.random.RandomState(RandomState=cupy.random.RandomState) # <-- we specify cupy here\n",
"x = rs.normal(10, 1, size=(200000, 200000), chunks=(10000, 10000))\n",
"x"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'tcp://10.12.205.15:39417': 'Wed Jun 24 14:26:51 2020 \\n+-----------------------------------------------------------------------------+\\n| NVIDIA-SMI 418.40.04 Driver Version: 418.40.04 CUDA Version: 10.1 |\\n|-------------------------------+----------------------+----------------------+\\n| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\\n| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\\n|===============================+======================+======================|\\n| 0 Quadro GP100 On | 00000000:18:00.0 Off | Off |\\n| 27% 44C P0 36W / 235W | 6254MiB / 16278MiB | 1% Default |\\n+-------------------------------+----------------------+----------------------+\\n \\n+-----------------------------------------------------------------------------+\\n| Processes: GPU Memory |\\n| GPU PID Type Process name Usage |\\n|=============================================================================|\\n| 0 4060 G /usr/bin/X 476MiB |\\n| 0 87815 G /usr/bin/gnome-shell 72MiB |\\n| 0 161577 G /usr/bin/gnome-shell 89MiB |\\n| 0 174417 G ...gnu/7.3.0/3.0.1/linux-x86_64/bin/viewer 48MiB |\\n| 0 174660 G ...7.3.0/3.0.1/linux-x86_64/bin/engine_ser 12MiB |\\n| 0 360709 G /usr/bin/gnome-shell 51MiB |\\n| 0 366697 C ...wares/miniconda3/envs/ds-gpu/bin/python 283MiB |\\n| 0 367375 C ...wares/miniconda3/envs/ds-gpu/bin/python 2575MiB |\\n| 0 368316 C ...wares/miniconda3/envs/ds-gpu/bin/python 2575MiB |\\n| 0 455701 G /usr/bin/gnome-shell 56MiB |\\n+-----------------------------------------------------------------------------+',\n",
" 'tcp://10.12.205.15:45697': 'Wed Jun 24 14:26:51 2020 \\n+-----------------------------------------------------------------------------+\\n| NVIDIA-SMI 418.40.04 Driver Version: 418.40.04 CUDA Version: 10.1 |\\n|-------------------------------+----------------------+----------------------+\\n| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\\n| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\\n|===============================+======================+======================|\\n| 0 Quadro GP100 On | 00000000:18:00.0 Off | Off |\\n| 27% 44C P0 36W / 235W | 6254MiB / 16278MiB | 1% Default |\\n+-------------------------------+----------------------+----------------------+\\n \\n+-----------------------------------------------------------------------------+\\n| Processes: GPU Memory |\\n| GPU PID Type Process name Usage |\\n|=============================================================================|\\n| 0 4060 G /usr/bin/X 476MiB |\\n| 0 87815 G /usr/bin/gnome-shell 72MiB |\\n| 0 161577 G /usr/bin/gnome-shell 89MiB |\\n| 0 174417 G ...gnu/7.3.0/3.0.1/linux-x86_64/bin/viewer 48MiB |\\n| 0 174660 G ...7.3.0/3.0.1/linux-x86_64/bin/engine_ser 12MiB |\\n| 0 360709 G /usr/bin/gnome-shell 51MiB |\\n| 0 366697 C ...wares/miniconda3/envs/ds-gpu/bin/python 283MiB |\\n| 0 367375 C ...wares/miniconda3/envs/ds-gpu/bin/python 2575MiB |\\n| 0 368316 C ...wares/miniconda3/envs/ds-gpu/bin/python 2575MiB |\\n| 0 455701 G /usr/bin/gnome-shell 56MiB |\\n+-----------------------------------------------------------------------------+'}"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Confirm that our dask workers have access to GPU\n",
"def nvidia_smi():\n",
" import subprocess\n",
" p = subprocess.check_output('nvidia-smi').strip().decode('utf-8')\n",
" return p\n",
"\n",
"client.run(nvidia_smi)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<tr>\n",
"<td>\n",
"<table>\n",
" <thead>\n",
" <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr><th> Bytes </th><td> 8 B </td> <td> 8 B </td></tr>\n",
" <tr><th> Shape </th><td> () </td> <td> () </td></tr>\n",
" <tr><th> Count </th><td> 1739 Tasks </td><td> 1 Chunks </td></tr>\n",
" <tr><th> Type </th><td> float64 </td><td> cupy.ndarray </td></tr>\n",
" </tbody>\n",
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"\n",
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"text/plain": [
"dask.array<sum-aggregate, shape=(), dtype=float64, chunksize=(), chunktype=cupy.ndarray>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result = (x + 1)[::2, ::2].sum()\n",
"result"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1.13 s, sys: 80.9 ms, total: 1.21 s\n",
"Wall time: 19.3 s\n"
]
}
],
"source": [
"%%time\n",
"with performance_report(filename=\"dask-perf-report.html\"):\n",
" y = result.compute()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(1.10000077e+11)"
]
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"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
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"y"
]
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{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"cupy.core.core.ndarray"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
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"type(y)"
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"execution_count": 30,
"metadata": {},
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{
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\"},\"id\":\"2912\",\"type\":\"HoverTool\"},{\"attributes\":{\"fill_color\":{\"value\":\"blue\"},\"height\":{\"units\":\"data\",\"value\":1},\"line_color\":{\"value\":\"blue\"},\"width\":{\"field\":\"bandwidth\",\"units\":\"data\"},\"x\":{\"field\":\"bandwidth-half\"},\"y\":{\"field\":\"type\"}},\"id\":\"2935\",\"type\":\"Rect\"},{\"attributes\":{\"overlay\":{\"id\":\"2986\"}},\"id\":\"2871\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"base\":1024,\"mantissas\":[1,2,4,8,16,32,64,128,256,512]},\"id\":\"2909\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{},\"id\":\"2793\",\"type\":\"ResetTool\"},{\"attributes\":{\"formatter\":{\"id\":\"2939\"},\"minor_tick_line_alpha\":0,\"ticker\":{\"id\":\"2941\"}},\"id\":\"2926\",\"type\":\"LinearAxis\"},{\"attributes\":{\"url\":\"./profile?key=@name\"},\"id\":\"2869\",\"type\":\"OpenURL\"},{\"attributes\":{\"dimensions\":\"width\"},\"id\":\"2873\",\"type\":\"PanTool\"},{\"attributes\":{\"source\":{\"id\":\"2881\"}},\"id\":\"2905\",\"type\":\"CDSView\"},{\"attributes\":{\"below\":[{\"id\":\"2782\"}],\"center\":[{\"id\":\"2785\"},{\"id\":\"2789\"}],\"left\":[{\"id\":\"2786\"}],\"renderers\":[{\"id\":\"2803\"}],\"sizing_mode\":\"stretch_both\",\"title\":{\"id\":\"2961\"},\"toolbar\":{\"id\":\"2795\"},\"x_range\":{\"id\":\"2774\"},\"x_scale\":{\"id\":\"2778\"},\"y_range\":{\"id\":\"2776\"},\"y_scale\":{\"id\":\"2780\"}},\"id\":\"2773\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{},\"id\":\"2811\",\"type\":\"DataRange1d\"},{\"attributes\":{\"callback\":{\"id\":\"2869\"}},\"id\":\"2870\",\"type\":\"TapTool\"},{\"attributes\":{},\"id\":\"3012\",\"type\":\"Selection\"},{\"attributes\":{\"data_source\":{\"id\":\"2807\"},\"glyph\":{\"id\":\"2836\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null,\"view\":{\"id\":\"2839\"}},\"id\":\"2838\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"2822\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"3011\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"data\":{\"bottom\":[],\"color\":[],\"filename\":[],\"left\":[],\"line\":[],\"line_number\":[],\"name\":[],\"percentage\":[],\"right\":[],\"time\":[],\"top\":[],\"width\":[]},\"selected\":{\"id\":\"3008\"},\"selection_policy\":{\"id\":\"3009\"}},\"id\":\"2772\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"2787\",\"type\":\"BasicTicker\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":0.5,\"fill_color\":\"lightgrey\",\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":1.0,\"line_color\":\"black\",\"line_dash\":[4,4],\"line_width\":2,\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"2829\",\"type\":\"BoxAnnotation\"},{\"attributes\":{},\"id\":\"3006\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"data\":{\"bandwidth\":[],\"bandwidth-half\":[],\"bandwidth_text\":[],\"type\":[]},\"selected\":{\"id\":\"3012\"},\"selection_policy\":{\"id\":\"3013\"}},\"id\":\"2914\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"callback\":null,\"point_policy\":\"follow_mouse\",\"tooltips\":\"@type: @bandwidth_text / s\"},\"id\":\"2944\",\"type\":\"HoverTool\"},{\"attributes\":{\"axis\":{\"id\":\"2786\"},\"dimension\":1,\"ticker\":null,\"visible\":false},\"id\":\"2789\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"3005\",\"type\":\"Selection\"},{\"attributes\":{\"source\":{\"id\":\"2772\"}},\"id\":\"2804\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"2931\",\"type\":\"CategoricalTicker\"},{\"attributes\":{},\"id\":\"3010\",\"type\":\"Selection\"},{\"attributes\":{\"text\":\"\\n &lt;h1&gt; Dask Performance Report &lt;/h1&gt;\\n\\n &lt;i&gt; Select different tabs on the top for additional information &lt;/i&gt;\\n\\n &lt;h2&gt; Duration: 19.10 s &lt;/h2&gt;\\n &lt;h2&gt; Tasks Information &lt;/h2&gt;\\n &lt;ul&gt;\\n &lt;li&gt; number of tasks: 937 &lt;/li&gt;\\n \\n&lt;li&gt; compute time: 25.44 s &lt;/li&gt;\\n&lt;li&gt; transfer time: 21.38 s &lt;/li&gt;\\n &lt;/ul&gt;\\n\\n &lt;h2&gt; Scheduler Information &lt;/h2&gt;\\n &lt;ul&gt;\\n &lt;li&gt; Address: tcp://10.12.205.15:39484 &lt;/li&gt;\\n &lt;li&gt; Workers: 2 &lt;/li&gt;\\n &lt;li&gt; Threads: 2 &lt;/li&gt;\\n &lt;li&gt; Memory: 40.00 GB &lt;/li&gt;\\n &lt;/ul&gt;\\n\\n &lt;h2&gt; Calling Code &lt;/h2&gt;\\n &lt;pre&gt;\\n def _run(self):\\n try:\\n self._context.run(self._callback, *self._args)\\n except Exception as exc:\\n cb = format_helpers._format_callback_source(\\n self._callback, self._args)\\n msg = f'Exception in callback {cb}'\\n context = {\\n 'message': msg,\\n 'exception': exc,\\n 'handle': self,\\n }\\n if self._source_traceback:\\n context['source_traceback'] = self._source_traceback\\n self._loop.call_exception_handler(context)\\n self = None # Needed to break cycles when an exception occurs.\\n\\n &lt;/pre&gt;\\n \"},\"id\":\"2946\",\"type\":\"Div\"},{\"attributes\":{\"formatter\":{\"id\":\"2977\"},\"ticker\":{\"id\":\"2787\"},\"visible\":false},\"id\":\"2786\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"3008\",\"type\":\"Selection\"},{\"attributes\":{\"dimensions\":\"width\"},\"id\":\"2874\",\"type\":\"WheelZoomTool\"},{\"attributes\":{},\"id\":\"2872\",\"type\":\"ResetTool\"},{\"attributes\":{\"source\":{\"id\":\"2807\"}},\"id\":\"2839\",\"type\":\"CDSView\"},{\"attributes\":{\"start\":0},\"id\":\"2918\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"3009\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"source\":{\"id\":\"2842\"}},\"id\":\"2867\",\"type\":\"CDSView\"},{\"attributes\":{\"data_source\":{\"id\":\"2772\"},\"glyph\":{\"id\":\"2801\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null,\"view\":{\"id\":\"2804\"}},\"id\":\"2803\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"2924\",\"type\":\"CategoricalScale\"},{\"attributes\":{},\"id\":\"2752\",\"type\":\"BasicTicker\"},{\"attributes\":{\"axis\":{\"id\":\"2858\"},\"dimension\":1,\"ticker\":null},\"id\":\"2861\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"2983\",\"type\":\"CategoricalTickFormatter\"},{\"attributes\":{\"base\":60,\"mantissas\":[1,2,5,10,15,20,30],\"max_interval\":1800000.0,\"min_interval\":1000.0,\"num_minor_ticks\":0},\"id\":\"2990\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{},\"id\":\"2743\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"2988\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"formatter\":{\"id\":\"2971\"},\"ticker\":{\"id\":\"2748\"},\"visible\":false},\"id\":\"2747\",\"type\":\"LinearAxis\"},{\"attributes\":{\"text\":\"Bandwidth: 100.00 MB\"},\"id\":\"2916\",\"type\":\"Title\"},{\"attributes\":{\"axis\":{\"id\":\"2821\"},\"dimension\":1,\"ticker\":null,\"visible\":false},\"id\":\"2824\",\"type\":\"Grid\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"logo\":null,\"tools\":[{\"id\":\"2944\"}]},\"id\":\"2933\",\"type\":\"Toolbar\"},{\"attributes\":{\"mantissas\":[1,2,5],\"max_interval\":500.0,\"num_minor_ticks\":0},\"id\":\"2989\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{},\"id\":\"2965\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"overlay\":{\"id\":\"2829\"}},\"id\":\"2826\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"days\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]},\"id\":\"2992\",\"type\":\"DaysTicker\"},{\"attributes\":{\"axis\":{\"id\":\"2747\"},\"ticker\":null,\"visible\":false},\"id\":\"2750\",\"type\":\"Grid\"},{\"attributes\":{\"data\":{\"bottom\":[0,1,2,3,4,5,1,2,2,1,2,3,1],\"color\":[\"#64CB5D\",\"#472A79\",\"#433C84\",\"#433C84\",\"#287A8E\",\"#472A79\",\"#3C4D8A\",\"#D7E219\",\"#D7E219\",\"#471669\",\"#471669\",\"#471669\",\"#472A79\"],\"filename\":[\"\",\"/glade/work/abanihi/softwares/miniconda3/envs/ds-gpu/lib/python3.7/site-packages/dask/optimization.py\",\"/glade/work/abanihi/softwares/miniconda3/envs/ds-gpu/lib/python3.7/site-packages/dask/core.py\",\"/glade/work/abanihi/softwares/miniconda3/envs/ds-gpu/lib/python3.7/site-packages/dask/core.py\",\"/glade/work/abanihi/softwares/miniconda3/envs/ds-gpu/lib/python3.7/site-packages/dask/utils.py\",\"&lt;__array_function__ internals&gt;\",\"/glade/work/abanihi/softwares/miniconda3/envs/ds-gpu/lib/python3.7/site-packages/dask/array/random.py\",\"/glade/work/abanihi/softwares/miniconda3/envs/ds-gpu/lib/python3.7/site-packages/cupy/random/generator.py\",\"/glade/work/abanihi/softwares/miniconda3/envs/ds-gpu/lib/python3.7/site-packages/cupy/random/generator.py\",\"/glade/work/abanihi/softwares/miniconda3/envs/ds-gpu/lib/python3.7/site-packages/dask/array/core.py\",\"/glade/work/abanihi/softwares/miniconda3/envs/ds-gpu/lib/python3.7/site-packages/dask/array/core.py\",\"/glade/work/abanihi/softwares/miniconda3/envs/ds-gpu/lib/python3.7/site-packages/dask/array/core.py\",\"&lt;__array_function__ internals&gt;\"],\"left\":[0,0.0,0.0,0.0,0.0,0.0,0.2966292134831461,0.2966292134831461,0.7910112359550563,0.8516853932584271,0.8516853932584271,0.8516853932584271,0.8719101123595506],\"line\":[\"\",\"return core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))\\n\",\"result = _execute_task(task, cache)\\n\",\"return func(*(_execute_task(a, cache) for a in args))\\n\",\"return func(*args, **kwargs)\\n\",\"\",\"return func(*args, size=size, **kwargs)\\n\",\"return self._generate_normal(func, size, dtype, loc, scale)\\n\",\"self._generator = curand.createGenerator(method)\\n\",\"arrays = [_concatenate2(a, axes=axes[1:]) for a in arrays]\\n\",\"arrays = [_concatenate2(a, axes=axes[1:]) for a in arrays]\\n\",\"return concatenate(arrays, axis=axes[0])\\n\",\"\"],\"line_number\":[0,1022,151,121,31,6,468,414,55,297,297,301,6],\"name\":[\"\",\"__call__\",\"get\",\"_execute_task\",\"apply\",\"sum\",\"_apply_random\",\"normal\",\"__init__\",\"_concatenate2\",\"&lt;listcomp&gt;\",\"_concatenate2\",\"sum\"],\"percentage\":[\"100.0%\",\"29.7%\",\"29.7%\",\"22.9%\",\"22.9%\",\"22.9%\",\"55.5%\",\"49.4%\",\"0.4%\",\"2.0%\",\"1.6%\",\"1.6%\",\"0.2%\"],\"right\":[1,0.2966292134831461,0.2966292134831461,0.22921348314606743,0.22921348314606743,0.22921348314606743,0.8516853932584271,0.7910112359550563,0.7955056179775282,0.8719101123595506,0.8674157303370787,0.8674157303370787,0.8741573033707866],\"time\":[\"4.45 s\",\"1.32 s\",\"1.32 s\",\"1.02 s\",\"1.02 s\",\"1.02 s\",\"2.47 s\",\"2.20 s\",\"20.00 ms\",\"90.00 ms\",\"70.00 ms\",\"70.00 ms\",\"10.00 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Profile (compute)\"},\"id\":\"2948\",\"type\":\"Panel\"},{\"attributes\":{\"dimensions\":\"width\"},\"id\":\"2757\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"fill_color\":{\"field\":\"bandwidth\",\"transform\":{\"id\":\"2882\"}},\"height\":{\"units\":\"data\",\"value\":1},\"line_color\":{\"field\":\"bandwidth\",\"transform\":{\"id\":\"2882\"}},\"width\":{\"units\":\"data\",\"value\":1},\"x\":{\"field\":\"source\"},\"y\":{\"field\":\"destination\"}},\"id\":\"2902\",\"type\":\"Rect\"},{\"attributes\":{\"callback\":null,\"point_policy\":\"follow_mouse\",\"tooltips\":\"\\n &lt;div&gt;\\n &lt;span style=\\\"font-size: 14px; font-weight: bold;\\\"&gt;Name:&lt;/span&gt;&amp;nbsp;\\n &lt;span style=\\\"font-size: 10px; font-family: Monaco, monospace;\\\"&gt;@name&lt;/span&gt;\\n &lt;/div&gt;\\n &lt;div&gt;\\n &lt;span style=\\\"font-size: 14px; font-weight: bold;\\\"&gt;Filename:&lt;/span&gt;&amp;nbsp;\\n &lt;span style=\\\"font-size: 10px; font-family: Monaco, monospace;\\\"&gt;@filename&lt;/span&gt;\\n &lt;/div&gt;\\n &lt;div&gt;\\n &lt;span style=\\\"font-size: 14px; font-weight: bold;\\\"&gt;Line number:&lt;/span&gt;&amp;nbsp;\\n &lt;span style=\\\"font-size: 10px; font-family: Monaco, monospace;\\\"&gt;@line_number&lt;/span&gt;\\n &lt;/div&gt;\\n &lt;div&gt;\\n &lt;span style=\\\"font-size: 14px; font-weight: bold;\\\"&gt;Line:&lt;/span&gt;&amp;nbsp;\\n &lt;span style=\\\"font-size: 10px; font-family: Monaco, monospace;\\\"&gt;@line&lt;/span&gt;\\n &lt;/div&gt;\\n &lt;div&gt;\\n &lt;span style=\\\"font-size: 14px; font-weight: bold;\\\"&gt;Time:&lt;/span&gt;&amp;nbsp;\\n &lt;span style=\\\"font-size: 10px; font-family: Monaco, monospace;\\\"&gt;@time&lt;/span&gt;\\n &lt;/div&gt;\\n &lt;div&gt;\\n &lt;span style=\\\"font-size: 14px; font-weight: bold;\\\"&gt;Percentage:&lt;/span&gt;&amp;nbsp;\\n &lt;span style=\\\"font-size: 10px; font-family: Monaco, monospace;\\\"&gt;@percentage&lt;/span&gt;\\n &lt;/div&gt;\\n \"},\"id\":\"2770\",\"type\":\"HoverTool\"},{\"attributes\":{\"callback\":null,\"point_policy\":\"follow_mouse\",\"tooltips\":\"\\n &lt;div&gt;\\n &lt;span style=\\\"font-size: 14px; font-weight: bold;\\\"&gt;Name:&lt;/span&gt;&amp;nbsp;\\n &lt;span style=\\\"font-size: 10px; font-family: Monaco, monospace;\\\"&gt;@name&lt;/span&gt;\\n &lt;/div&gt;\\n &lt;div&gt;\\n &lt;span style=\\\"font-size: 14px; font-weight: bold;\\\"&gt;Filename:&lt;/span&gt;&amp;nbsp;\\n &lt;span style=\\\"font-size: 10px; font-family: Monaco, monospace;\\\"&gt;@filename&lt;/span&gt;\\n &lt;/div&gt;\\n &lt;div&gt;\\n &lt;span style=\\\"font-size: 14px; font-weight: bold;\\\"&gt;Line number:&lt;/span&gt;&amp;nbsp;\\n &lt;span style=\\\"font-size: 10px; font-family: Monaco, monospace;\\\"&gt;@line_number&lt;/span&gt;\\n &lt;/div&gt;\\n &lt;div&gt;\\n &lt;span style=\\\"font-size: 14px; font-weight: bold;\\\"&gt;Line:&lt;/span&gt;&amp;nbsp;\\n &lt;span style=\\\"font-size: 10px; font-family: Monaco, 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