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Performance Test. Ryzen 9 3900X Ubuntu 18.04
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# Python Performance Tests\n", | |
"A small collection of operations that are typical for my daily work with real data to compare different setups." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": { | |
"scrolled": true | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Created `%t` as an alias for `%timeit`.\n", | |
"Created `%%t` as an alias for `%%timeit`.\n" | |
] | |
} | |
], | |
"source": [ | |
"%alias_magic t timeit\n", | |
"\n", | |
"import pandas as pd\n", | |
"import dask.dataframe as dd\n", | |
"\n", | |
"df = pd.read_json('test-tweets.jsonl', lines=True)\n", | |
"dask_df = dd.from_pandas(df, npartitions=5)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Let's start with loading data. About half a million Tweet objects as json lines." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"1min 3s ± 160 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" | |
] | |
} | |
], | |
"source": [ | |
"%t df = pd.read_json('test-tweets.jsonl', lines=True)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"For some multi-threaded processing I want the dataframe as a dask dataframe as well." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"31.6 s ± 171 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" | |
] | |
} | |
], | |
"source": [ | |
"%t dask_df = dd.from_pandas(df, npartitions=5)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"def get_full_text(row):\n", | |
" if row['truncated']:\n", | |
" return(row['extended_tweet']['full_text'])\n", | |
" return (row['text'])" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Twitter hides the full text of Tweets with more than 140 characters in a sub-field. I want one column that has always the complete text." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"27.1 s ± 510 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" | |
] | |
} | |
], | |
"source": [ | |
"%t df.apply(get_full_text, axis=1)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Can this be done faster with Dask? " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 6, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"44 s ± 222 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" | |
] | |
} | |
], | |
"source": [ | |
"%t dask_df.apply(get_full_text, axis=1, meta=('string')).compute()" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Maybe with processes instead of threads?" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"5min 57s ± 4.84 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" | |
] | |
} | |
], | |
"source": [ | |
"%t dask_df.apply(get_full_text, axis=1, meta=('string')).compute(scheduler='processes')" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Or by computing it partition wise?" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": { | |
"scrolled": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"45.5 s ± 171 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" | |
] | |
} | |
], | |
"source": [ | |
"%t dask_df.map_partitions(lambda ldf: ldf.apply(get_full_text, axis=1), meta=('string')).compute()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 6, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"6min 3s ± 6.64 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" | |
] | |
} | |
], | |
"source": [ | |
"%t dask_df.map_partitions(lambda ldf: ldf.apply(get_full_text, axis=1), meta=('string')).compute(scheduler='processes')" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"That didn't work. It's the first time I tried Dask as I hoped that it would make better use of the high core count. I will have to find a better approach.\n", | |
"\n", | |
"But grouping is faster with Dask. For example to show which apps were used and for how many Tweets." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 7, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"1.01 s ± 5.25 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" | |
] | |
} | |
], | |
"source": [ | |
"%t len(df.groupby('source').count())" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 8, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"934 ms ± 123 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" | |
] | |
} | |
], | |
"source": [ | |
"%t len(dask_df.groupby('source').count().compute())" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Finally, I want to store some data…" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 9, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"111 ms ± 8.86 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" | |
] | |
} | |
], | |
"source": [ | |
"%t df[['created_at', 'id', 'text']].to_feather('perf_test.feather')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 10, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"166 ms ± 8.99 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" | |
] | |
} | |
], | |
"source": [ | |
"%t df[['created_at', 'id', 'text']].to_parquet('perf_test.parquet')" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"…and read it again." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 11, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"38.7 ms ± 859 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" | |
] | |
} | |
], | |
"source": [ | |
"%t pd.read_feather('perf_test.feather')" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# Dasking around" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 14, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/html": [ | |
"<table style=\"border: 2px solid white;\">\n", | |
"<tr>\n", | |
"<td style=\"vertical-align: top; border: 0px solid white\">\n", | |
"<h3 style=\"text-align: left;\">Client</h3>\n", | |
"<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n", | |
" <li><b>Scheduler: </b>tcp://127.0.0.1:44711</li>\n", | |
" <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a>\n", | |
"</ul>\n", | |
"</td>\n", | |
"<td style=\"vertical-align: top; border: 0px solid white\">\n", | |
"<h3 style=\"text-align: left;\">Cluster</h3>\n", | |
"<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n", | |
" <li><b>Workers: </b>12</li>\n", | |
" <li><b>Cores: </b>24</li>\n", | |
" <li><b>Memory: </b>67.44 GB</li>\n", | |
"</ul>\n", | |
"</td>\n", | |
"</tr>\n", | |
"</table>" | |
], | |
"text/plain": [ | |
"<Client: 'tcp://127.0.0.1:44711' processes=12 threads=24, memory=67.44 GB>" | |
] | |
}, | |
"execution_count": 14, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"import dask.dataframe as dd\n", | |
"from dask.distributed import Client, LocalCluster\n", | |
"cluster = LocalCluster(n_workers=12, threads_per_worker=2)\n", | |
"client = Client(cluster)\n", | |
"client" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 15, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"dask_df = dd.from_pandas(df, npartitions=24)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 16, | |
"metadata": { | |
"scrolled": true | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"35.7 s ± 949 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" | |
] | |
} | |
], | |
"source": [ | |
"%t dask_df.apply(get_full_text, axis=1, meta=('string')).compute()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 17, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"8min 1s ± 4.56 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" | |
] | |
} | |
], | |
"source": [ | |
"%t dask_df.apply(get_full_text, axis=1, meta=('string')).compute(scheduler='processes')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 18, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"dask_df = dd.from_pandas(df, npartitions=6)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 19, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"distributed.nanny - WARNING - Worker exceeded 95% memory budget. Restarting\n", | |
"distributed.nanny - WARNING - Worker exceeded 95% memory budget. Restarting\n", | |
"distributed.nanny - WARNING - Restarting worker\n", | |
"distributed.nanny - WARNING - Restarting worker\n", | |
"distributed.nanny - WARNING - Worker exceeded 95% memory budget. Restarting\n", | |
"distributed.nanny - WARNING - Worker exceeded 95% memory budget. Restarting\n", | |
"distributed.nanny - WARNING - Restarting worker\n", | |
"distributed.nanny - WARNING - Restarting worker\n", | |
"distributed.nanny - WARNING - Worker exceeded 95% memory budget. Restarting\n", | |
"distributed.nanny - WARNING - Restarting worker\n", | |
"distributed.nanny - WARNING - Worker exceeded 95% memory budget. Restarting\n", | |
"distributed.nanny - WARNING - Restarting worker\n", | |
"distributed.nanny - WARNING - Worker exceeded 95% memory budget. Restarting\n", | |
"distributed.nanny - WARNING - Restarting worker\n", | |
"distributed.nanny - WARNING - Worker exceeded 95% memory budget. Restarting\n", | |
"distributed.nanny - WARNING - Worker exceeded 95% memory budget. Restarting\n", | |
"distributed.nanny - WARNING - Worker exceeded 95% memory budget. Restarting\n", | |
"distributed.nanny - WARNING - Restarting worker\n", | |
"distributed.nanny - WARNING - Restarting worker\n", | |
"distributed.nanny - WARNING - Worker exceeded 95% memory budget. Restarting\n", | |
"distributed.nanny - WARNING - Restarting worker\n", | |
"distributed.nanny - WARNING - Restarting worker\n", | |
"distributed.nanny - WARNING - Worker exceeded 95% memory budget. Restarting\n", | |
"distributed.nanny - WARNING - Restarting worker\n", | |
"distributed.nanny - WARNING - Worker exceeded 95% memory budget. Restarting\n", | |
"distributed.nanny - WARNING - Restarting worker\n", | |
"distributed.nanny - WARNING - Worker exceeded 95% memory budget. Restarting\n", | |
"distributed.nanny - WARNING - Restarting worker\n", | |
"distributed.nanny - WARNING - Worker exceeded 95% memory budget. Restarting\n", | |
"distributed.nanny - WARNING - Worker exceeded 95% memory budget. Restarting\n", | |
"distributed.nanny - WARNING - Restarting worker\n", | |
"distributed.nanny - WARNING - Restarting worker\n", | |
"distributed.nanny - WARNING - Worker exceeded 95% memory budget. Restarting\n", | |
"distributed.nanny - WARNING - Restarting worker\n", | |
"distributed.nanny - WARNING - Worker exceeded 95% memory budget. Restarting\n", | |
"distributed.nanny - WARNING - Restarting worker\n" | |
] | |
}, | |
{ | |
"ename": "KilledWorker", | |
"evalue": "(\"('from_pandas-b2d19880245fa409754d376116c32473', 3)\", <Worker 'tcp://127.0.0.1:36947', name: 5, memory: 0, processing: 1>)", | |
"output_type": "error", | |
"traceback": [ | |
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | |
"\u001b[0;31mKilledWorker\u001b[0m Traceback (most recent call last)", | |
"\u001b[0;32m<ipython-input-19-a968bb31253d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m't'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"dask_df.apply(get_full_text, axis=1, meta=('string')).compute()\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", | |
"\u001b[0;32m~/anaconda3/envs/PerformanceTest/lib/python3.7/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2315\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2316\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2317\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2318\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2319\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", | |
"\u001b[0;32m~/anaconda3/envs/PerformanceTest/lib/python3.7/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 699\u001b[0m \u001b[0margs_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic_params\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\" \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 700\u001b[0m \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 701\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 702\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 703\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_in_call\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
"\u001b[0;32m</home/luca/anaconda3/envs/PerformanceTest/lib/python3.7/site-packages/decorator.py:decorator-gen-60>\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n", | |
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"\u001b[0;32m~/anaconda3/envs/PerformanceTest/lib/python3.7/site-packages/distributed/client.py\u001b[0m in \u001b[0;36mgather\u001b[0;34m(self, futures, errors, direct, asynchronous)\u001b[0m\n\u001b[1;32m 1891\u001b[0m \u001b[0mdirect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdirect\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1892\u001b[0m \u001b[0mlocal_worker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlocal_worker\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1893\u001b[0;31m \u001b[0masynchronous\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0masynchronous\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1894\u001b[0m )\n\u001b[1;32m 1895\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", | |
"\u001b[0;32m~/anaconda3/envs/PerformanceTest/lib/python3.7/site-packages/distributed/client.py\u001b[0m in \u001b[0;36msync\u001b[0;34m(self, func, asynchronous, callback_timeout, *args, **kwargs)\u001b[0m\n\u001b[1;32m 778\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 779\u001b[0m return sync(\n\u001b[0;32m--> 780\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback_timeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallback_timeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 781\u001b[0m )\n\u001b[1;32m 782\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", | |
"\u001b[0;32m~/anaconda3/envs/PerformanceTest/lib/python3.7/site-packages/distributed/utils.py\u001b[0m in \u001b[0;36msync\u001b[0;34m(loop, func, callback_timeout, *args, **kwargs)\u001b[0m\n\u001b[1;32m 346\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 347\u001b[0m \u001b[0mtyp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 348\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 349\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 350\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
"\u001b[0;32m~/anaconda3/envs/PerformanceTest/lib/python3.7/site-packages/distributed/utils.py\u001b[0m in \u001b[0;36mf\u001b[0;34m()\u001b[0m\n\u001b[1;32m 330\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcallback_timeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 331\u001b[0m \u001b[0mfuture\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0masyncio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait_for\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfuture\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback_timeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 332\u001b[0;31m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 333\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 334\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
"\u001b[0;32m~/anaconda3/envs/PerformanceTest/lib/python3.7/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 733\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 734\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 735\u001b[0;31m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 736\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 737\u001b[0m \u001b[0mexc_info\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
"\u001b[0;32m~/anaconda3/envs/PerformanceTest/lib/python3.7/site-packages/distributed/client.py\u001b[0m in \u001b[0;36m_gather\u001b[0;34m(self, futures, errors, direct, local_worker)\u001b[0m\n\u001b[1;32m 1750\u001b[0m \u001b[0mexc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCancelledError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1751\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1752\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mexception\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraceback\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1753\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1754\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"skip\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
"\u001b[0;31mKilledWorker\u001b[0m: (\"('from_pandas-b2d19880245fa409754d376116c32473', 3)\", <Worker 'tcp://127.0.0.1:36947', name: 5, memory: 0, processing: 1>)" | |
] | |
} | |
], | |
"source": [ | |
"%t dask_df.apply(get_full_text, axis=1, meta=('string')).compute()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"dask_df = dd.from_pandas(df, npartitions=4)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"distributed.nanny - WARNING - Worker exceeded 95% memory budget. Restarting\n", | |
"distributed.nanny - WARNING - Restarting worker\n", | |
"distributed.nanny - WARNING - Worker exceeded 95% memory budget. Restarting\n", | |
"distributed.nanny - WARNING - Worker exceeded 95% memory budget. Restarting\n", | |
"distributed.nanny - WARNING - Restarting worker\n", | |
"distributed.nanny - WARNING - Restarting worker\n" | |
] | |
} | |
], | |
"source": [ | |
"%t dask_df.apply(get_full_text, axis=1, meta=('string')).compute()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"cluster.close()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
} | |
], | |
"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": "3.7.4" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 4 | |
} |
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