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Created April 5, 2019 02:49
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import gcsfs\n",
"import pandas as pd\n",
"gcs = gcsfs.GCSFileSystem()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"</style>\n",
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" <th></th>\n",
" <th>collection</th>\n",
" <th>name</th>\n",
" <th>n</th>\n",
" <th>unit</th>\n",
" <th>duration</th>\n",
" <th>rate</th>\n",
" </tr>\n",
" </thead>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>arrays</td>\n",
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" <td>0.930781</td>\n",
" <td>429.738441</td>\n",
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" <td>1.053046</td>\n",
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" <th>4</th>\n",
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" <td>5747.768011</td>\n",
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" <td>0.967302</td>\n",
" <td>13232.684357</td>\n",
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" <th>6</th>\n",
" <td>arrays</td>\n",
" <td>blockwise 100ms tasks</td>\n",
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" <td>1.289514</td>\n",
" <td>19852.058765</td>\n",
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" <th>7</th>\n",
" <td>arrays</td>\n",
" <td>blockwise 100ms tasks</td>\n",
" <td>256</td>\n",
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" <td>1.730550</td>\n",
" <td>29585.964835</td>\n",
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" <td>512</td>\n",
" <td>MB/s</td>\n",
" <td>6.030538</td>\n",
" <td>16980.218490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>arrays</td>\n",
" <td>create random</td>\n",
" <td>2</td>\n",
" <td>MB/s</td>\n",
" <td>0.584174</td>\n",
" <td>684.713900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>arrays</td>\n",
" <td>create random</td>\n",
" <td>4</td>\n",
" <td>MB/s</td>\n",
" <td>0.655029</td>\n",
" <td>1221.319419</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>arrays</td>\n",
" <td>create random</td>\n",
" <td>8</td>\n",
" <td>MB/s</td>\n",
" <td>0.721304</td>\n",
" <td>2218.161714</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>arrays</td>\n",
" <td>create random</td>\n",
" <td>16</td>\n",
" <td>MB/s</td>\n",
" <td>0.664351</td>\n",
" <td>4816.727556</td>\n",
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" <tr>\n",
" <th>13</th>\n",
" <td>arrays</td>\n",
" <td>create random</td>\n",
" <td>32</td>\n",
" <td>MB/s</td>\n",
" <td>0.718007</td>\n",
" <td>8913.387113</td>\n",
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" <th>14</th>\n",
" <td>arrays</td>\n",
" <td>create random</td>\n",
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" <td>create random</td>\n",
" <td>128</td>\n",
" <td>MB/s</td>\n",
" <td>1.200436</td>\n",
" <td>21325.182436</td>\n",
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" <tr>\n",
" <th>16</th>\n",
" <td>arrays</td>\n",
" <td>create random</td>\n",
" <td>256</td>\n",
" <td>MB/s</td>\n",
" <td>2.564872</td>\n",
" <td>19962.007739</td>\n",
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" <tr>\n",
" <th>17</th>\n",
" <td>arrays</td>\n",
" <td>create random</td>\n",
" <td>512</td>\n",
" <td>MB/s</td>\n",
" <td>1.880598</td>\n",
" <td>54450.695152</td>\n",
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" <td>arrays</td>\n",
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" <td>2</td>\n",
" <td>MB/s</td>\n",
" <td>2.708794</td>\n",
" <td>147.664339</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>arrays</td>\n",
" <td>elementwise computation</td>\n",
" <td>4</td>\n",
" <td>MB/s</td>\n",
" <td>2.908163</td>\n",
" <td>275.087715</td>\n",
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" <tr>\n",
" <th>20</th>\n",
" <td>arrays</td>\n",
" <td>elementwise computation</td>\n",
" <td>8</td>\n",
" <td>MB/s</td>\n",
" <td>3.580060</td>\n",
" <td>446.911371</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>arrays</td>\n",
" <td>elementwise computation</td>\n",
" <td>16</td>\n",
" <td>MB/s</td>\n",
" <td>3.964876</td>\n",
" <td>807.087044</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>arrays</td>\n",
" <td>elementwise computation</td>\n",
" <td>32</td>\n",
" <td>MB/s</td>\n",
" <td>5.441955</td>\n",
" <td>1176.025319</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>arrays</td>\n",
" <td>elementwise computation</td>\n",
" <td>64</td>\n",
" <td>MB/s</td>\n",
" <td>7.383476</td>\n",
" <td>1733.600921</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>arrays</td>\n",
" <td>elementwise computation</td>\n",
" <td>128</td>\n",
" <td>MB/s</td>\n",
" <td>13.116995</td>\n",
" <td>1951.629145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>arrays</td>\n",
" <td>elementwise computation</td>\n",
" <td>256</td>\n",
" <td>MB/s</td>\n",
" <td>21.965428</td>\n",
" <td>2330.935700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>arrays</td>\n",
" <td>elementwise computation</td>\n",
" <td>512</td>\n",
" <td>MB/s</td>\n",
" <td>42.592467</td>\n",
" <td>2404.177413</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>arrays</td>\n",
" <td>nearest neighbor 100ms tasks</td>\n",
" <td>2</td>\n",
" <td>MB/s</td>\n",
" <td>1.236899</td>\n",
" <td>323.383141</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>arrays</td>\n",
" <td>nearest neighbor 100ms tasks</td>\n",
" <td>4</td>\n",
" <td>MB/s</td>\n",
" <td>1.160823</td>\n",
" <td>689.166299</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>arrays</td>\n",
" <td>nearest neighbor 100ms tasks</td>\n",
" <td>8</td>\n",
" <td>MB/s</td>\n",
" <td>1.509198</td>\n",
" <td>1060.145264</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>249</th>\n",
" <td>tasks</td>\n",
" <td>task map 1s tasks</td>\n",
" <td>128</td>\n",
" <td>tasks/s</td>\n",
" <td>5.590187</td>\n",
" <td>91.589068</td>\n",
" </tr>\n",
" <tr>\n",
" <th>250</th>\n",
" <td>tasks</td>\n",
" <td>task map 1s tasks</td>\n",
" <td>256</td>\n",
" <td>tasks/s</td>\n",
" <td>5.644582</td>\n",
" <td>181.412908</td>\n",
" </tr>\n",
" <tr>\n",
" <th>251</th>\n",
" <td>tasks</td>\n",
" <td>task map 1s tasks</td>\n",
" <td>512</td>\n",
" <td>tasks/s</td>\n",
" <td>7.549867</td>\n",
" <td>271.263068</td>\n",
" </tr>\n",
" <tr>\n",
" <th>252</th>\n",
" <td>tasks</td>\n",
" <td>task map fast tasks</td>\n",
" <td>2</td>\n",
" <td>tasks/s</td>\n",
" <td>0.221133</td>\n",
" <td>1808.870110</td>\n",
" </tr>\n",
" <tr>\n",
" <th>253</th>\n",
" <td>tasks</td>\n",
" <td>task map fast tasks</td>\n",
" <td>4</td>\n",
" <td>tasks/s</td>\n",
" <td>0.241904</td>\n",
" <td>3307.103271</td>\n",
" </tr>\n",
" <tr>\n",
" <th>254</th>\n",
" <td>tasks</td>\n",
" <td>task map fast tasks</td>\n",
" <td>8</td>\n",
" <td>tasks/s</td>\n",
" <td>0.486054</td>\n",
" <td>3291.817309</td>\n",
" </tr>\n",
" <tr>\n",
" <th>255</th>\n",
" <td>tasks</td>\n",
" <td>task map fast tasks</td>\n",
" <td>16</td>\n",
" <td>tasks/s</td>\n",
" <td>0.800579</td>\n",
" <td>3997.107929</td>\n",
" </tr>\n",
" <tr>\n",
" <th>256</th>\n",
" <td>tasks</td>\n",
" <td>task map fast tasks</td>\n",
" <td>32</td>\n",
" <td>tasks/s</td>\n",
" <td>1.796089</td>\n",
" <td>3563.297941</td>\n",
" </tr>\n",
" <tr>\n",
" <th>257</th>\n",
" <td>tasks</td>\n",
" <td>task map fast tasks</td>\n",
" <td>64</td>\n",
" <td>tasks/s</td>\n",
" <td>3.346050</td>\n",
" <td>3825.405776</td>\n",
" </tr>\n",
" <tr>\n",
" <th>258</th>\n",
" <td>tasks</td>\n",
" <td>task map fast tasks</td>\n",
" <td>128</td>\n",
" <td>tasks/s</td>\n",
" <td>6.896783</td>\n",
" <td>3711.875589</td>\n",
" </tr>\n",
" <tr>\n",
" <th>259</th>\n",
" <td>tasks</td>\n",
" <td>task map fast tasks</td>\n",
" <td>256</td>\n",
" <td>tasks/s</td>\n",
" <td>14.315529</td>\n",
" <td>3576.535636</td>\n",
" </tr>\n",
" <tr>\n",
" <th>260</th>\n",
" <td>tasks</td>\n",
" <td>task map fast tasks</td>\n",
" <td>512</td>\n",
" <td>tasks/s</td>\n",
" <td>29.077214</td>\n",
" <td>3521.657944</td>\n",
" </tr>\n",
" <tr>\n",
" <th>261</th>\n",
" <td>tasks</td>\n",
" <td>tree reduction 100ms tasks</td>\n",
" <td>2</td>\n",
" <td>tasks/s</td>\n",
" <td>6.856049</td>\n",
" <td>37.339291</td>\n",
" </tr>\n",
" <tr>\n",
" <th>262</th>\n",
" <td>tasks</td>\n",
" <td>tree reduction 100ms tasks</td>\n",
" <td>4</td>\n",
" <td>tasks/s</td>\n",
" <td>7.081078</td>\n",
" <td>72.305377</td>\n",
" </tr>\n",
" <tr>\n",
" <th>263</th>\n",
" <td>tasks</td>\n",
" <td>tree reduction 100ms tasks</td>\n",
" <td>8</td>\n",
" <td>tasks/s</td>\n",
" <td>7.219373</td>\n",
" <td>141.840563</td>\n",
" </tr>\n",
" <tr>\n",
" <th>264</th>\n",
" <td>tasks</td>\n",
" <td>tree reduction 100ms tasks</td>\n",
" <td>16</td>\n",
" <td>tasks/s</td>\n",
" <td>7.429270</td>\n",
" <td>275.666383</td>\n",
" </tr>\n",
" <tr>\n",
" <th>265</th>\n",
" <td>tasks</td>\n",
" <td>tree reduction 100ms tasks</td>\n",
" <td>32</td>\n",
" <td>tasks/s</td>\n",
" <td>7.793831</td>\n",
" <td>525.543849</td>\n",
" </tr>\n",
" <tr>\n",
" <th>266</th>\n",
" <td>tasks</td>\n",
" <td>tree reduction 100ms tasks</td>\n",
" <td>64</td>\n",
" <td>tasks/s</td>\n",
" <td>8.285013</td>\n",
" <td>988.773376</td>\n",
" </tr>\n",
" <tr>\n",
" <th>267</th>\n",
" <td>tasks</td>\n",
" <td>tree reduction 100ms tasks</td>\n",
" <td>128</td>\n",
" <td>tasks/s</td>\n",
" <td>8.970450</td>\n",
" <td>1826.441178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>268</th>\n",
" <td>tasks</td>\n",
" <td>tree reduction 100ms tasks</td>\n",
" <td>256</td>\n",
" <td>tasks/s</td>\n",
" <td>12.109690</td>\n",
" <td>2705.932256</td>\n",
" </tr>\n",
" <tr>\n",
" <th>269</th>\n",
" <td>tasks</td>\n",
" <td>tree reduction 100ms tasks</td>\n",
" <td>512</td>\n",
" <td>tasks/s</td>\n",
" <td>18.223943</td>\n",
" <td>3596.148117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>270</th>\n",
" <td>tasks</td>\n",
" <td>tree reduction fast tasks</td>\n",
" <td>2</td>\n",
" <td>tasks/s</td>\n",
" <td>0.202223</td>\n",
" <td>1265.927313</td>\n",
" </tr>\n",
" <tr>\n",
" <th>271</th>\n",
" <td>tasks</td>\n",
" <td>tree reduction fast tasks</td>\n",
" <td>4</td>\n",
" <td>tasks/s</td>\n",
" <td>0.294252</td>\n",
" <td>1740.005646</td>\n",
" </tr>\n",
" <tr>\n",
" <th>272</th>\n",
" <td>tasks</td>\n",
" <td>tree reduction fast tasks</td>\n",
" <td>8</td>\n",
" <td>tasks/s</td>\n",
" <td>0.505821</td>\n",
" <td>2024.432563</td>\n",
" </tr>\n",
" <tr>\n",
" <th>273</th>\n",
" <td>tasks</td>\n",
" <td>tree reduction fast tasks</td>\n",
" <td>16</td>\n",
" <td>tasks/s</td>\n",
" <td>0.864661</td>\n",
" <td>2368.557067</td>\n",
" </tr>\n",
" <tr>\n",
" <th>274</th>\n",
" <td>tasks</td>\n",
" <td>tree reduction fast tasks</td>\n",
" <td>32</td>\n",
" <td>tasks/s</td>\n",
" <td>1.395554</td>\n",
" <td>2935.035983</td>\n",
" </tr>\n",
" <tr>\n",
" <th>275</th>\n",
" <td>tasks</td>\n",
" <td>tree reduction fast tasks</td>\n",
" <td>64</td>\n",
" <td>tasks/s</td>\n",
" <td>2.916794</td>\n",
" <td>2808.563041</td>\n",
" </tr>\n",
" <tr>\n",
" <th>276</th>\n",
" <td>tasks</td>\n",
" <td>tree reduction fast tasks</td>\n",
" <td>128</td>\n",
" <td>tasks/s</td>\n",
" <td>5.950359</td>\n",
" <td>2753.447264</td>\n",
" </tr>\n",
" <tr>\n",
" <th>277</th>\n",
" <td>tasks</td>\n",
" <td>tree reduction fast tasks</td>\n",
" <td>256</td>\n",
" <td>tasks/s</td>\n",
" <td>11.853272</td>\n",
" <td>2764.468701</td>\n",
" </tr>\n",
" <tr>\n",
" <th>278</th>\n",
" <td>tasks</td>\n",
" <td>tree reduction fast tasks</td>\n",
" <td>512</td>\n",
" <td>tasks/s</td>\n",
" <td>25.379409</td>\n",
" <td>2582.250800</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>279 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" collection name n unit duration \\\n",
"0 arrays blockwise 100ms tasks 2 MB/s 0.930781 \n",
"1 arrays blockwise 100ms tasks 4 MB/s 0.847067 \n",
"2 arrays blockwise 100ms tasks 8 MB/s 1.053046 \n",
"3 arrays blockwise 100ms tasks 16 MB/s 0.967746 \n",
"4 arrays blockwise 100ms tasks 32 MB/s 1.113454 \n",
"5 arrays blockwise 100ms tasks 64 MB/s 0.967302 \n",
"6 arrays blockwise 100ms tasks 128 MB/s 1.289514 \n",
"7 arrays blockwise 100ms tasks 256 MB/s 1.730550 \n",
"8 arrays blockwise 100ms tasks 512 MB/s 6.030538 \n",
"9 arrays create random 2 MB/s 0.584174 \n",
"10 arrays create random 4 MB/s 0.655029 \n",
"11 arrays create random 8 MB/s 0.721304 \n",
"12 arrays create random 16 MB/s 0.664351 \n",
"13 arrays create random 32 MB/s 0.718007 \n",
"14 arrays create random 64 MB/s 0.738002 \n",
"15 arrays create random 128 MB/s 1.200436 \n",
"16 arrays create random 256 MB/s 2.564872 \n",
"17 arrays create random 512 MB/s 1.880598 \n",
"18 arrays elementwise computation 2 MB/s 2.708794 \n",
"19 arrays elementwise computation 4 MB/s 2.908163 \n",
"20 arrays elementwise computation 8 MB/s 3.580060 \n",
"21 arrays elementwise computation 16 MB/s 3.964876 \n",
"22 arrays elementwise computation 32 MB/s 5.441955 \n",
"23 arrays elementwise computation 64 MB/s 7.383476 \n",
"24 arrays elementwise computation 128 MB/s 13.116995 \n",
"25 arrays elementwise computation 256 MB/s 21.965428 \n",
"26 arrays elementwise computation 512 MB/s 42.592467 \n",
"27 arrays nearest neighbor 100ms tasks 2 MB/s 1.236899 \n",
"28 arrays nearest neighbor 100ms tasks 4 MB/s 1.160823 \n",
"29 arrays nearest neighbor 100ms tasks 8 MB/s 1.509198 \n",
".. ... ... ... ... ... \n",
"249 tasks task map 1s tasks 128 tasks/s 5.590187 \n",
"250 tasks task map 1s tasks 256 tasks/s 5.644582 \n",
"251 tasks task map 1s tasks 512 tasks/s 7.549867 \n",
"252 tasks task map fast tasks 2 tasks/s 0.221133 \n",
"253 tasks task map fast tasks 4 tasks/s 0.241904 \n",
"254 tasks task map fast tasks 8 tasks/s 0.486054 \n",
"255 tasks task map fast tasks 16 tasks/s 0.800579 \n",
"256 tasks task map fast tasks 32 tasks/s 1.796089 \n",
"257 tasks task map fast tasks 64 tasks/s 3.346050 \n",
"258 tasks task map fast tasks 128 tasks/s 6.896783 \n",
"259 tasks task map fast tasks 256 tasks/s 14.315529 \n",
"260 tasks task map fast tasks 512 tasks/s 29.077214 \n",
"261 tasks tree reduction 100ms tasks 2 tasks/s 6.856049 \n",
"262 tasks tree reduction 100ms tasks 4 tasks/s 7.081078 \n",
"263 tasks tree reduction 100ms tasks 8 tasks/s 7.219373 \n",
"264 tasks tree reduction 100ms tasks 16 tasks/s 7.429270 \n",
"265 tasks tree reduction 100ms tasks 32 tasks/s 7.793831 \n",
"266 tasks tree reduction 100ms tasks 64 tasks/s 8.285013 \n",
"267 tasks tree reduction 100ms tasks 128 tasks/s 8.970450 \n",
"268 tasks tree reduction 100ms tasks 256 tasks/s 12.109690 \n",
"269 tasks tree reduction 100ms tasks 512 tasks/s 18.223943 \n",
"270 tasks tree reduction fast tasks 2 tasks/s 0.202223 \n",
"271 tasks tree reduction fast tasks 4 tasks/s 0.294252 \n",
"272 tasks tree reduction fast tasks 8 tasks/s 0.505821 \n",
"273 tasks tree reduction fast tasks 16 tasks/s 0.864661 \n",
"274 tasks tree reduction fast tasks 32 tasks/s 1.395554 \n",
"275 tasks tree reduction fast tasks 64 tasks/s 2.916794 \n",
"276 tasks tree reduction fast tasks 128 tasks/s 5.950359 \n",
"277 tasks tree reduction fast tasks 256 tasks/s 11.853272 \n",
"278 tasks tree reduction fast tasks 512 tasks/s 25.379409 \n",
"\n",
" rate \n",
"0 429.738441 \n",
"1 944.435197 \n",
"2 1519.373375 \n",
"3 3306.651083 \n",
"4 5747.768011 \n",
"5 13232.684357 \n",
"6 19852.058765 \n",
"7 29585.964835 \n",
"8 16980.218490 \n",
"9 684.713900 \n",
"10 1221.319419 \n",
"11 2218.161714 \n",
"12 4816.727556 \n",
"13 8913.387113 \n",
"14 17344.136192 \n",
"15 21325.182436 \n",
"16 19962.007739 \n",
"17 54450.695152 \n",
"18 147.664339 \n",
"19 275.087715 \n",
"20 446.911371 \n",
"21 807.087044 \n",
"22 1176.025319 \n",
"23 1733.600921 \n",
"24 1951.629145 \n",
"25 2330.935700 \n",
"26 2404.177413 \n",
"27 323.383141 \n",
"28 689.166299 \n",
"29 1060.145264 \n",
".. ... \n",
"249 91.589068 \n",
"250 181.412908 \n",
"251 271.263068 \n",
"252 1808.870110 \n",
"253 3307.103271 \n",
"254 3291.817309 \n",
"255 3997.107929 \n",
"256 3563.297941 \n",
"257 3825.405776 \n",
"258 3711.875589 \n",
"259 3576.535636 \n",
"260 3521.657944 \n",
"261 37.339291 \n",
"262 72.305377 \n",
"263 141.840563 \n",
"264 275.666383 \n",
"265 525.543849 \n",
"266 988.773376 \n",
"267 1826.441178 \n",
"268 2705.932256 \n",
"269 3596.148117 \n",
"270 1265.927313 \n",
"271 1740.005646 \n",
"272 2024.432563 \n",
"273 2368.557067 \n",
"274 2935.035983 \n",
"275 2808.563041 \n",
"276 2753.447264 \n",
"277 2764.468701 \n",
"278 2582.250800 \n",
"\n",
"[279 rows x 6 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with gcs.open('dask-data/scaling-data.csv') as f:\n",
" df = pd.read_csv(f)\n",
" \n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
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" <span id=\"051394a1-e58a-4bb0-9e61-336b796ef193\">Loading BokehJS ...</span>\n",
" </div>"
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"\n",
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" \"<div style='background-color: #fdd'>\\n\"+\n",
" \"<p>\\n\"+\n",
" \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
" \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
" \"</p>\\n\"+\n",
" \"<ul>\\n\"+\n",
" \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
" \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
" \"</ul>\\n\"+\n",
" \"<code>\\n\"+\n",
" \"from bokeh.resources import INLINE\\n\"+\n",
" \"output_notebook(resources=INLINE)\\n\"+\n",
" \"</code>\\n\"+\n",
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"\n",
" function display_loaded() {\n",
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" }\n",
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"\n",
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" \n",
" function(Bokeh) {\n",
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" console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.6.css\");\n",
" Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.6.css\");\n",
" }\n",
" ];\n",
"\n",
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"\n",
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" run_inline_js();\n",
" });\n",
" }\n",
"}(this));"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from bokeh.plotting import figure, show, output_notebook\n",
"from bokeh.layouts import Row, Column, gridplot\n",
"output_notebook()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def scaling_plot(part, axis_type='linear'):\n",
" row = part.iloc[0]\n",
" title = (row['collection'] + ': ' + row['name']).title().replace('0M', '0m').replace('1S', '1s')\n",
"\n",
" fig = figure(title=title, sizing_mode='scale_width', x_axis_type=axis_type, y_axis_type=axis_type)\n",
" fig.line(x=part.n, y=part.rate)\n",
" fig.circle(x=part.n, y=part.rate)\n",
" fig.xaxis.axis_label = 'cores'\n",
" fig.yaxis.axis_label = row['unit']\n",
" fig.x_range.start = 0\n",
" fig.y_range.start = 0\n",
"\n",
" # Add in perfect scaling line\n",
" y_end = fig.y_range.end\n",
" mn = part.n.min()\n",
" mx = part.n.max()\n",
" slope = part[part.n == mn].iloc[0]['rate'] / mn\n",
" fig.line(x=[0, mx], y=[0, slope * mx], color='gray', line_dash='dashed')\n",
" fig.y_range.end = part.rate.max()\n",
" \n",
" fig.xaxis.ticker = part.n\n",
"\n",
" return fig"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"collection name \n",
"arrays blockwise 100ms tasks Figure(id='bcca8fc0-7fe6-458b-9c19-6cb29d44131...\n",
" create random Figure(id='d8bcdc1e-f3d9-42c6-94ce-75464f676e8...\n",
" elementwise computation Figure(id='617352a4-ce31-45bd-ae48-9f273d04978...\n",
" nearest neighbor 100ms tasks Figure(id='c533f293-6da4-4fcd-9981-cd4a228fcec...\n",
" nearest neighbor fast tasks Figure(id='181550ff-8183-4494-899d-9e7b16db240...\n",
" random access Figure(id='e68756de-ce1c-440f-b554-3910b12598f...\n",
" rechunk large Figure(id='98b62a74-238a-4dbb-a00f-aac75621172...\n",
" rechunk small Figure(id='2a98d534-b0e7-4c5a-bd28-32d5aae870f...\n",
" reduction Figure(id='0834997a-d482-4e1c-a0cf-8c980bd5da8...\n",
" reduction along axis Figure(id='60a7509c-35e7-46cc-b547-2072701d7fc...\n",
" transpose addition Figure(id='f4429e42-0003-4c4a-b53f-d32be1fe2a7...\n",
"dataframes arithmetic Figure(id='bb3cdc16-306a-4eca-8bea-d78af7d011d...\n",
" blockwise 100ms tasks Figure(id='6c694c69-c303-49b3-8b45-b3aaa65486e...\n",
" create random Figure(id='7fa63a9f-6a95-4d73-a8dc-a138c0e305d...\n",
" dataframe reduction Figure(id='3b2726ce-eefe-42f8-8771-fd816e2065c...\n",
" groupby apply (full shuffle) Figure(id='fe8d51a6-bedf-421b-abd7-3c1c36d5eeb...\n",
" groupby reduction Figure(id='345dd894-9c0e-4029-a5f3-93a7bcf4646...\n",
" random access Figure(id='52e54ceb-d341-4a3f-a7ec-927da4854b5...\n",
" rolling aggregations Figure(id='d9b90423-449b-4214-8a57-5f4c2834716...\n",
" series reduction Figure(id='6a1b3578-4c22-4fcf-99c9-aaff125fc6e...\n",
" set index (full shuffle) Figure(id='57ed361b-be6c-488d-938b-2c723fb1750...\n",
"tasks dynamic tree reduction 100ms tasks Figure(id='e4df49b6-7857-4bfb-b716-fb3ddaed3f9...\n",
" dynamic tree reduction fast tasks Figure(id='efb02e2b-e8e1-41e7-9146-b183ed9475f...\n",
" nearest neighbor 100ms tasks Figure(id='3dfadbf7-7a01-41e6-a4e3-907cf0a6189...\n",
" nearest neighbor fast tasks Figure(id='8e774f01-9d70-4656-a01f-71f1186dbda...\n",
" sequential Figure(id='c9d51a70-70c8-4b7b-b976-260fd0756c9...\n",
" task map 100ms tasks Figure(id='682118d9-44c2-4cd3-bd82-9716c23ec89...\n",
" task map 1s tasks Figure(id='504aa97b-a65b-470f-bf26-42d88766680...\n",
" task map fast tasks Figure(id='b2a2e084-afb3-4e2d-b29f-9802b4ddd34...\n",
" tree reduction 100ms tasks Figure(id='ee7ad037-8c0a-4b84-baa0-77e64dff90b...\n",
" tree reduction fast tasks Figure(id='5599840f-08e1-436d-afd9-0049ff7905f...\n",
"dtype: object"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2 = df.groupby(['collection', 'name'])['collection', 'name', 'n', 'rate', 'unit'].apply(scaling_plot)\n",
"df2"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
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" var render_items = [{\"docid\":\"00297a39-c3e7-45c2-ab4f-089cd672569b\",\"elementid\":\"c661cb6d-99f2-4a6d-910a-9136181a2838\",\"modelid\":\"28ac675f-8f1c-436a-b52e-4230664f1f76\"}];\n",
" \n",
" Bokeh.embed.embed_items(docs_json, render_items);\n",
" };\n",
" if (document.readyState != \"loading\") fn();\n",
" else document.addEventListener(\"DOMContentLoaded\", fn);\n",
" })();\n",
" },\n",
" function(Bokeh) {\n",
" }\n",
" ];\n",
" \n",
" function run_inline_js() {\n",
" \n",
" if ((window.Bokeh !== undefined) || (force === true)) {\n",
" for (var i = 0; i < inline_js.length; i++) {\n",
" inline_js[i](window.Bokeh);\n",
" }if (force === true) {\n",
" display_loaded();\n",
" }} else if (Date.now() < window._bokeh_timeout) {\n",
" setTimeout(run_inline_js, 100);\n",
" } else if (!window._bokeh_failed_load) {\n",
" console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
" window._bokeh_failed_load = true;\n",
" } else if (force !== true) {\n",
" var cell = $(document.getElementById(\"c661cb6d-99f2-4a6d-910a-9136181a2838\")).parents('.cell').data().cell;\n",
" cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
" }\n",
" \n",
" }\n",
" \n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
" run_inline_js();\n",
" } else {\n",
" load_libs(js_urls, function() {\n",
" console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
" });\n",
" }\n",
" }(this));\n",
"</script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"names = ['task map 1s tasks', 'task map 100ms tasks', 'task map fast tasks',\n",
" 'tree reduction 100ms tasks', 'tree reduction fast tasks', 'sequential',\n",
" 'nearest neighbor 100ms tasks', 'nearest neighbor fast tasks', \n",
" 'dynamic tree reduction 100ms tasks', 'dynamic tree reduction fast tasks']\n",
"from toolz import partition_all\n",
"L = df2.loc['tasks'].loc[names].values.tolist()\n",
"grid = list(partition_all(3, L))\n",
"# show(Column(*[Row(*g, sizing_mode='scale_width') for g in grid], sizing_mode='scale_width'))\n",
"show(gridplot(grid, sizing_mode='scale_width'))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
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" </div>\n",
"<script type=\"text/javascript\">\n",
" \n",
" (function(global) {\n",
" function now() {\n",
" return new Date();\n",
" }\n",
" \n",
" var force = false;\n",
" \n",
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" window._bokeh_is_loading = undefined;\n",
" }\n",
" \n",
" \n",
" \n",
" if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
" window._bokeh_timeout = Date.now() + 0;\n",
" window._bokeh_failed_load = false;\n",
" }\n",
" \n",
" var NB_LOAD_WARNING = {'data': {'text/html':\n",
" \"<div style='background-color: #fdd'>\\n\"+\n",
" \"<p>\\n\"+\n",
" \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
" \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
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" \"<ul>\\n\"+\n",
" \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
" \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
" \"</ul>\\n\"+\n",
" \"<code>\\n\"+\n",
" \"from bokeh.resources import INLINE\\n\"+\n",
" \"output_notebook(resources=INLINE)\\n\"+\n",
" \"</code>\\n\"+\n",
" \"</div>\"}};\n",
" \n",
" function display_loaded() {\n",
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" function run_callbacks() {\n",
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" window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
" }\n",
" finally {\n",
" delete window._bokeh_onload_callbacks\n",
" }\n",
" console.info(\"Bokeh: all callbacks have finished\");\n",
" }\n",
" \n",
" function load_libs(js_urls, callback) {\n",
" window._bokeh_onload_callbacks.push(callback);\n",
" if (window._bokeh_is_loading > 0) {\n",
" console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
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" console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
" run_callbacks()\n",
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" s.onerror = function() {\n",
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" }\n",
" };var element = document.getElementById(\"5d975889-763d-43d5-8939-1fd4ef17fdd4\");\n",
" if (element == null) {\n",
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" var render_items = [{\"docid\":\"87bd99e7-1dfe-4e0c-8de8-d1923c84acc4\",\"elementid\":\"5d975889-763d-43d5-8939-1fd4ef17fdd4\",\"modelid\":\"2858b845-77c3-40cf-8cf3-b27ff5707229\"}];\n",
" \n",
" Bokeh.embed.embed_items(docs_json, render_items);\n",
" };\n",
" if (document.readyState != \"loading\") fn();\n",
" else document.addEventListener(\"DOMContentLoaded\", fn);\n",
" })();\n",
" },\n",
" function(Bokeh) {\n",
" }\n",
" ];\n",
" \n",
" function run_inline_js() {\n",
" \n",
" if ((window.Bokeh !== undefined) || (force === true)) {\n",
" for (var i = 0; i < inline_js.length; i++) {\n",
" inline_js[i](window.Bokeh);\n",
" }if (force === true) {\n",
" display_loaded();\n",
" }} else if (Date.now() < window._bokeh_timeout) {\n",
" setTimeout(run_inline_js, 100);\n",
" } else if (!window._bokeh_failed_load) {\n",
" console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
" window._bokeh_failed_load = true;\n",
" } else if (force !== true) {\n",
" var cell = $(document.getElementById(\"5d975889-763d-43d5-8939-1fd4ef17fdd4\")).parents('.cell').data().cell;\n",
" cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
" }\n",
" \n",
" }\n",
" \n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
" run_inline_js();\n",
" } else {\n",
" load_libs(js_urls, function() {\n",
" console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
" });\n",
" }\n",
" }(this));\n",
"</script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"names = ['create random', 'blockwise 100ms tasks', 'elementwise computation', 'reduction', \n",
" 'reduction along axis', 'random access', 'transpose addition', 'rechunk large', \n",
" 'nearest neighbor fast tasks', 'nearest neighbor 100ms tasks']\n",
"from toolz import partition_all\n",
"L = df2.loc['arrays'].loc[names].values.tolist()\n",
"grid = list(partition_all(3, L))\n",
"# show(Column(*[Row(*g, sizing_mode='scale_width') for g in grid], sizing_mode='scale_width'))\n",
"show(gridplot(grid, sizing_mode='scale_width'))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
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"metadata": {},
"output_type": "display_data"
}
],
"source": [
"names = ['task map 1s tasks', 'task map 100ms tasks', 'task map fast tasks',\n",
" 'tree reduction 100ms tasks', 'tree reduction fast tasks', 'sequential',\n",
" 'nearest neighbor 100ms tasks', 'nearest neighbor fast tasks', \n",
" 'dynamic tree reduction 100ms tasks', 'dynamic tree reduction fast tasks']\n",
"L = df2.loc['dataframes'].values.tolist()\n",
"grid = list(partition_all(3, L))\n",
"# show(Column(*[Row(*g, sizing_mode='scale_width') for g in grid], sizing_mode='scale_width'))\n",
"show(gridplot(grid, sizing_mode='scale_width'))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from bokeh.palettes import viridis, Category10\n",
"from bokeh.models.widgets import Panel, Tabs\n",
"\n",
"df3 = df.set_index(['collection', 'name'])[['n', 'rate', 'unit']]\n",
"n = df3.loc['tasks', 'task map 1s tasks'].n.values\n",
"\n",
"colors = Category10[3]\n",
"\n",
"\n",
"def log_linear_plot(collection, names, title, legends=('100ms', '1us')):\n",
" panels = []\n",
" unit = df3.loc[collection, names[0]].iloc[0]['unit']\n",
" \n",
" for axis_type in ['log', 'linear']:\n",
" fig = figure(title=title, # sizing_mode='scale_width', \n",
" x_axis_type=axis_type, y_axis_type=axis_type, height=400, width=400)\n",
" for i, name in enumerate(names):\n",
" x = df3.loc[collection, name].n.values\n",
" y = df3.loc[collection, name].rate.values\n",
" fig.line(x=x, y=y, color=colors[i], legend=legends[i])\n",
" fig.circle(x=x, y=y)\n",
"\n",
" for name in names:\n",
" # Add in perfect scaling line\n",
" x = df3.loc[collection, name].n.values\n",
" y = df3.loc[collection, name].rate.values\n",
" mn = x.min()\n",
" mx = x.max()\n",
" slope = y[0] / mn\n",
" fig.line(x=[0, mx], y=[0, slope * mx], color='gray', line_dash='dashed')\n",
"\n",
" fig.y_range.end = max([df3.loc[collection, name].rate.max() for name in names])\n",
"\n",
" fig.xaxis.axis_label = 'cores'\n",
" fig.yaxis.axis_label = unit\n",
" fig.x_range.start = 0\n",
" fig.y_range.start = 0\n",
"\n",
" fig.xaxis.ticker = x\n",
" fig.legend.location = 'bottom_right'\n",
"\n",
" panel = Panel(child=fig, title=axis_type)\n",
" panels.append(panel)\n",
"\n",
" tabs = Tabs(tabs=panels)\n",
" return tabs"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"figures = {}\n",
"\n",
"names = ['task map 1s tasks', 'task map 100ms tasks', 'task map fast tasks',]\n",
"legends = ['1s', '100ms', '1us']\n",
"fig = log_linear_plot('tasks', names, 'Tasks: Embarrassingly Parallel', legends)\n",
"figures['tasks-embarrasssing'] = fig\n",
"\n",
"fig = log_linear_plot('tasks', ['tree reduction 100ms tasks', 'tree reduction fast tasks'], 'Tasks: Tree Reduction')\n",
"figures['tasks-reduction'] = fig\n",
"\n",
"fig = log_linear_plot('tasks', ['nearest neighbor 100ms tasks', 'nearest neighbor fast tasks'], 'Tasks: Nearest Neighbor')\n",
"figures['tasks-nearest-neighbor'] = fig\n",
"\n",
"fig = log_linear_plot('tasks', ['sequential'], 'Tasks: Sequential', legends=['fast'])\n",
"figures['tasks-sequential'] = fig\n",
"\n",
"fig = log_linear_plot('tasks', ['dynamic tree reduction 100ms tasks', 'dynamic tree reduction fast tasks'], 'Tasks: Dynamic Reduction')\n",
"figures['tasks-dynamic-reduction'] = fig\n",
"\n",
"L = [['tasks-embarrasssing', 'tasks-sequential'], ['tasks-nearest-neighbor', 'tasks-dynamic-reduction']]\n",
"figs = [[figures[k] for k in kk] for kk in L]\n",
"grid = gridplot(figs, sizing_mode='scale_width')\n",
"figures['tasks-grid'] = grid"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Arrays"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
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{
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"text/plain": [
"array(['blockwise 100ms tasks', 'create random', 'elementwise computation',\n",
" 'nearest neighbor 100ms tasks', 'nearest neighbor fast tasks',\n",
" 'random access', 'rechunk large', 'rechunk small', 'reduction',\n",
" 'reduction along axis', 'transpose addition'], dtype=object)"
]
},
"execution_count": 12,
"metadata": {},
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"source": [
"df[df.collection == 'arrays'].name.unique()"
]
},
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"cell_type": "code",
"execution_count": 13,
"metadata": {
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"source": [
"fig = log_linear_plot('arrays', ['create random'], 'Arrays: Create', legends=['random'])\n",
"figures['array-create'] = fig\n",
"\n",
"fig = log_linear_plot('arrays', ['elementwise computation'], 'Arrays: Elementwise', legends=['sin(x)**2 + cos(x)**2'])\n",
"figures['array-elementwise'] = fig\n",
"\n",
"fig = log_linear_plot('arrays', ['reduction', 'reduction along axis'], 'Arrays: Nearest Neighbor', legends=['x.std()', 'x.std(axis=0)'])\n",
"figures['array-reductions'] = fig\n",
"\n",
"fig = log_linear_plot('arrays', ['random access'], 'Arrays: Random Access', legends=['x[12345, 23456]'])\n",
"figures['array-random-access'] = fig\n",
"\n",
"fig = log_linear_plot('arrays', ['transpose addition'], 'Arrays: Bulk Communication', legends=['x + x.T'])\n",
"figures['array-transpose'] = fig\n",
"\n",
"fig = log_linear_plot('arrays', ['rechunk large'], 'Arrays: Rechunking', legends=['x.rechunk(...)'])\n",
"figures['array-rechunk'] = fig\n",
"\n",
"fig = log_linear_plot('arrays', ['nearest neighbor 100ms tasks', 'nearest neighbor fast tasks'], 'Arrays: Map Overlap')\n",
"figures['array-overlap'] = fig\n",
"\n",
"grid = [['array-create', 'array-elementwise'],\n",
" ['array-reductions', 'array-random-access'],\n",
" ['array-transpose', 'array-rechunk']]\n",
"grid = gridplot([[figures[name] for name in L] for L in grid], sizing_mode='scale_width')\n",
"figures['array-grid'] = grid"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
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" window._bokeh_onload_callbacks = [];\n",
" window._bokeh_is_loading = undefined;\n",
" }\n",
" \n",
" \n",
" \n",
" if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
" window._bokeh_timeout = Date.now() + 0;\n",
" window._bokeh_failed_load = false;\n",
" }\n",
" \n",
" var NB_LOAD_WARNING = {'data': {'text/html':\n",
" \"<div style='background-color: #fdd'>\\n\"+\n",
" \"<p>\\n\"+\n",
" \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
" \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
" \"</p>\\n\"+\n",
" \"<ul>\\n\"+\n",
" \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
" \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
" \"</ul>\\n\"+\n",
" \"<code>\\n\"+\n",
" \"from bokeh.resources import INLINE\\n\"+\n",
" \"output_notebook(resources=INLINE)\\n\"+\n",
" \"</code>\\n\"+\n",
" \"</div>\"}};\n",
" \n",
" function display_loaded() {\n",
" if (window.Bokeh !== undefined) {\n",
" var el = document.getElementById(\"1cc2788f-c990-4c9b-accb-8d27ed986cac\");\n",
" el.textContent = \"BokehJS \" + Bokeh.version + \" successfully loaded.\";\n",
" } else if (Date.now() < window._bokeh_timeout) {\n",
" setTimeout(display_loaded, 100)\n",
" }\n",
" }\n",
" \n",
" function run_callbacks() {\n",
" try {\n",
" window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
" }\n",
" finally {\n",
" delete window._bokeh_onload_callbacks\n",
" }\n",
" console.info(\"Bokeh: all callbacks have finished\");\n",
" }\n",
" \n",
" function load_libs(js_urls, callback) {\n",
" window._bokeh_onload_callbacks.push(callback);\n",
" if (window._bokeh_is_loading > 0) {\n",
" console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
" return null;\n",
" }\n",
" if (js_urls == null || js_urls.length === 0) {\n",
" run_callbacks();\n",
" return null;\n",
" }\n",
" console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
" window._bokeh_is_loading = js_urls.length;\n",
" for (var i = 0; i < js_urls.length; i++) {\n",
" var url = js_urls[i];\n",
" var s = document.createElement('script');\n",
" s.src = url;\n",
" s.async = false;\n",
" s.onreadystatechange = s.onload = function() {\n",
" window._bokeh_is_loading--;\n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
" run_callbacks()\n",
" }\n",
" };\n",
" s.onerror = function() {\n",
" console.warn(\"failed to load library \" + url);\n",
" };\n",
" console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" }\n",
" };var element = document.getElementById(\"1cc2788f-c990-4c9b-accb-8d27ed986cac\");\n",
" if (element == null) {\n",
" console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1cc2788f-c990-4c9b-accb-8d27ed986cac' but no matching script tag was found. \")\n",
" return false;\n",
" }\n",
" \n",
" var js_urls = [];\n",
" \n",
" var inline_js = [\n",
" function(Bokeh) {\n",
" (function() {\n",
" var fn = function() {\n",
" var docs_json = 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"metadata": {},
"output_type": "display_data"
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],
"source": [
"show(figures['array-grid'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dataframes"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['arithmetic', 'blockwise 100ms tasks', 'create random',\n",
" 'dataframe reduction', 'groupby apply (full shuffle)',\n",
" 'groupby reduction', 'random access', 'rolling aggregations',\n",
" 'series reduction', 'set index (full shuffle)'], dtype=object)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df.collection == 'dataframes'].name.unique()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig = log_linear_plot('dataframes', ['create random'], 'DataFrames: Create', legends=['random'])\n",
"figures['dataframe-create'] = fig\n",
"\n",
"fig = log_linear_plot('dataframes', ['blockwise 100ms tasks', 'arithmetic'], 'DataFrames: Elementwise', legends=['100ms', 'arithmetic'])\n",
"figures['dataframe-elementwise'] = fig\n",
"\n",
"fig = log_linear_plot('dataframes', ['random access'], 'DataFrames: Random Access', legends=['df.loc[123456]'])\n",
"figures['dataframe-random-access'] = fig\n",
"\n",
"\n",
"fig = log_linear_plot('dataframes', ['dataframe reduction', 'series reduction', 'groupby reduction'], \n",
" 'DataFrames: Reductions', legends=['df.std()', 'df[0].std()', 'df.groupby(0)[1].mean()'])\n",
"figures['dataframe-reductions'] = fig\n",
"\n",
"fig = log_linear_plot('dataframes', ['groupby apply (full shuffle)', 'set index (full shuffle)'], \n",
" 'DataFrames: Full Shuffle', legends=['df.groupby(...).apply(...)', 'df.set_index(...)'])\n",
"figures['dataframe-shuffle'] = fig\n",
"\n",
"fig = log_linear_plot('dataframes', ['rolling aggregations'], 'DataFrames: Time Series', legends=['df.rolling(...).mean()'])\n",
"figures['dataframe-time-series'] = fig\n",
"\n",
"grid = [['dataframe-create', 'dataframe-elementwise'],\n",
" ['dataframe-random-access', 'dataframe-reductions'],\n",
" ['dataframe-shuffle', 'dataframe-time-series']]\n",
"grid = gridplot([[figures[name] for name in L] for L in grid], sizing_mode='scale_width')\n",
"figures['dataframe-grid'] = grid"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
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" \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
" \"</p>\\n\"+\n",
" \"<ul>\\n\"+\n",
" \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
" \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
" \"</ul>\\n\"+\n",
" \"<code>\\n\"+\n",
" \"from bokeh.resources import INLINE\\n\"+\n",
" \"output_notebook(resources=INLINE)\\n\"+\n",
" \"</code>\\n\"+\n",
" \"</div>\"}};\n",
" \n",
" function display_loaded() {\n",
" if (window.Bokeh !== undefined) {\n",
" var el = document.getElementById(\"0bb89115-6fd1-4dd3-aae7-ac1914161f37\");\n",
" el.textContent = \"BokehJS \" + Bokeh.version + \" successfully loaded.\";\n",
" } else if (Date.now() < window._bokeh_timeout) {\n",
" setTimeout(display_loaded, 100)\n",
" }\n",
" }\n",
" \n",
" function run_callbacks() {\n",
" try {\n",
" window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
" }\n",
" finally {\n",
" delete window._bokeh_onload_callbacks\n",
" }\n",
" console.info(\"Bokeh: all callbacks have finished\");\n",
" }\n",
" \n",
" function load_libs(js_urls, callback) {\n",
" window._bokeh_onload_callbacks.push(callback);\n",
" if (window._bokeh_is_loading > 0) {\n",
" console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
" return null;\n",
" }\n",
" if (js_urls == null || js_urls.length === 0) {\n",
" run_callbacks();\n",
" return null;\n",
" }\n",
" console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
" window._bokeh_is_loading = js_urls.length;\n",
" for (var i = 0; i < js_urls.length; i++) {\n",
" var url = js_urls[i];\n",
" var s = document.createElement('script');\n",
" s.src = url;\n",
" s.async = false;\n",
" s.onreadystatechange = s.onload = function() {\n",
" window._bokeh_is_loading--;\n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
" run_callbacks()\n",
" }\n",
" };\n",
" s.onerror = function() {\n",
" console.warn(\"failed to load library \" + url);\n",
" };\n",
" console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" }\n",
" };var element = document.getElementById(\"0bb89115-6fd1-4dd3-aae7-ac1914161f37\");\n",
" if (element == null) {\n",
" console.log(\"Bokeh: ERROR: autoload.js configured with elementid '0bb89115-6fd1-4dd3-aae7-ac1914161f37' but no matching script tag was found. \")\n",
" return false;\n",
" }\n",
" \n",
" var js_urls = [];\n",
" \n",
" var inline_js = [\n",
" function(Bokeh) {\n",
" (function() {\n",
" var fn = function() {\n",
" var docs_json = 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" var render_items = [{\"docid\":\"5d100740-604e-4f81-8126-ee86e21bf6b5\",\"elementid\":\"0bb89115-6fd1-4dd3-aae7-ac1914161f37\",\"modelid\":\"7ad96955-325f-40d0-bbcc-3935e5dafd4c\"}];\n",
" \n",
" Bokeh.embed.embed_items(docs_json, render_items);\n",
" };\n",
" if (document.readyState != \"loading\") fn();\n",
" else document.addEventListener(\"DOMContentLoaded\", fn);\n",
" })();\n",
" },\n",
" function(Bokeh) {\n",
" }\n",
" ];\n",
" \n",
" function run_inline_js() {\n",
" \n",
" if ((window.Bokeh !== undefined) || (force === true)) {\n",
" for (var i = 0; i < inline_js.length; i++) {\n",
" inline_js[i](window.Bokeh);\n",
" }if (force === true) {\n",
" display_loaded();\n",
" }} else if (Date.now() < window._bokeh_timeout) {\n",
" setTimeout(run_inline_js, 100);\n",
" } else if (!window._bokeh_failed_load) {\n",
" console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
" window._bokeh_failed_load = true;\n",
" } else if (force !== true) {\n",
" var cell = $(document.getElementById(\"0bb89115-6fd1-4dd3-aae7-ac1914161f37\")).parents('.cell').data().cell;\n",
" cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
" }\n",
" \n",
" }\n",
" \n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
" run_inline_js();\n",
" } else {\n",
" load_libs(js_urls, function() {\n",
" console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
" });\n",
" }\n",
" }(this));\n",
"</script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show(grid)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from bokeh.embed import components\n",
"\n",
"script, divs = components(list(figures.values()))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"```python\n",
"with open('/home/mrocklin/workspace/blog/_posts/work/2017-06-27-scaling.md', 'at') as f:\n",
" \n",
" for div in divs:\n",
" f.write(div)\n",
" f.write('\\n\\n')\n",
" f.write(script)\n",
"```"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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