{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Coffea-Casa Benchmark Example 1" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import coffea.processor as processor\n", "import awkward as ak\n", "import hist\n", "from coffea.nanoevents import schemas\n", "from coffea.nanoevents.schemas import BaseSchema, NanoAODSchema" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "class Q1Processor(processor.ProcessorABC):\n", " def process(self, events):\n", " return (\n", " hist.Hist.new.Reg(100, 0, 200, name=\"met\", label=\"$E_{T}^{miss}$ [GeV]\")\n", " .Double()\n", " .fill(events.met_pt)\n", " )\n", "\n", " def postprocess(self, accumulator):\n", " return accumulator\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "
\n", "
\n", "

Client

\n", "

Client-687fed24-c250-11ec-9352-7ed8580c18e7

\n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "
Connection method: Direct
\n", " Dashboard: /user/oksana.shadura@cern.ch/proxy/8787/status\n", "
\n", "\n", " \n", "
\n", "

Scheduler Info

\n", "
\n", "
\n", "
\n", "
\n", "

Scheduler

\n", "

Scheduler-29f1bd3d-9f2b-4282-af2d-ce17c9cd4c36

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", " Comm: tcp://127.0.0.1:41775\n", " \n", " Workers: 1\n", "
\n", " Dashboard: /user/oksana.shadura@cern.ch/proxy/8787/status\n", " \n", " Total threads: 4\n", "
\n", " Started: 5 hours ago\n", " \n", " Total memory: 15.70 GiB\n", "
\n", "
\n", "
\n", "\n", "
\n", " \n", "

Workers

\n", "
\n", "\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 8

\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:33089\n", " \n", " Total threads: 4\n", "
\n", " Dashboard: /user/oksana.shadura@cern.ch/proxy/37031/status\n", " \n", " Memory: 15.70 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:33895\n", "
\n", " Local directory: /home/cms-jovyan/dask-worker-space/worker-098e4b0c\n", "
\n", " Tasks executing: 0\n", " \n", " Tasks in memory: 0\n", "
\n", " Tasks ready: 0\n", " \n", " Tasks in flight: 0\n", "
\n", " CPU usage: 2.0%\n", " \n", " Last seen: Just now\n", "
\n", " Memory usage: 465.52 MiB\n", " \n", " Spilled bytes: 0 B\n", "
\n", " Read bytes: 1.87 kiB\n", " \n", " Write bytes: 1.87 kiB\n", "
\n", "
\n", "
\n", "
\n", " \n", "\n", "
\n", "
\n", "
\n", " \n", "\n", "
\n", "
" ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from dask.distributed import Client\n", "\n", "client = Client(\"tcp://127.0.0.1:41775\")\n", "client" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[########################################] | 100% Completed | 0.1s\r" ] } ], "source": [ "run = processor.Runner(\n", " executor=processor.DaskExecutor(client=client),\n", " use_skyhook=True,\n", " format=\"parquet\",\n", " schema=BaseSchema,\n", " )\n", "\n", "out = run(\n", " {'SingleMu' : \"/mnt/cephfs/AGC/modified_agc.parquet\"},\n", " \"events\",\n", " processor_instance=Q1Processor()\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[StairsArtists(stairs=, errorbar=, legend_artist=)]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "out.plot1d()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.8.13" } }, "nbformat": 4, "nbformat_minor": 4 }