{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "[From the md logbook exercise:]\n", "\n", "# logbook\n", "\n", "as proposed by mooc module 1\n", "\n", "## 18.05.2020\n", "- Today I did some markdown Wohaaaa!\n", "- I decided to write everything into this file as its easiest to start and \n", "splitting into multiple files can be done easily later if necessary.\n", "\n", "As I'm already actively using jupyter as experiment log I just copy paste some of my latest experiments here:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Does the shell work? can I install plotnine?" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "exercice_en.ipynb exercice.ipynb\t exercice_R_fr.org\r\n", "exercice_en.Rmd exercice_python_en.org server.timing-information.csv\r\n", "exercice_fr.ipynb exercice_python_fr.org\r\n", "exercice_fr.Rmd exercice_R_en.org\r\n" ] } ], "source": [ "! ls" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting plotnine\n", " Downloading plotnine-0.6.0-py3-none-any.whl (4.1 MB)\n", "\u001b[K |████████████████████████████████| 4.1 MB 6.4 MB/s eta 0:00:01\n", "\u001b[?25hRequirement already satisfied: patsy>=0.4.1 in /opt/conda/lib/python3.6/site-packages (from plotnine) (0.5.1)\n", "Requirement already satisfied: statsmodels>=0.9.0 in /opt/conda/lib/python3.6/site-packages (from plotnine) (0.9.0)\n", "Collecting mizani>=0.6.0\n", " Downloading mizani-0.6.0-py2.py3-none-any.whl (61 kB)\n", "\u001b[K |████████████████████████████████| 61 kB 396 kB/s eta 0:00:01\n", "\u001b[?25hCollecting numpy>=1.16.0\n", " Downloading numpy-1.18.4-cp36-cp36m-manylinux1_x86_64.whl (20.2 MB)\n", "\u001b[K |████████████████████████████████| 20.2 MB 166 kB/s eta 0:00:01 |████████▎ | 5.2 MB 26.5 MB/s eta 0:00:01 |███████████████████████▎ | 14.7 MB 26.5 MB/s eta 0:00:01\n", "\u001b[?25hCollecting descartes>=1.1.0\n", " Downloading descartes-1.1.0-py3-none-any.whl (5.8 kB)\n", "Collecting pandas>=0.25.0\n", " Downloading pandas-1.0.3-cp36-cp36m-manylinux1_x86_64.whl (10.0 MB)\n", "\u001b[K |████████████████████████████████| 10.0 MB 24.9 MB/s eta 0:00:01\n", "\u001b[?25hCollecting scipy>=1.2.0\n", " Downloading scipy-1.4.1-cp36-cp36m-manylinux1_x86_64.whl (26.1 MB)\n", "\u001b[K |████████████████████████████████| 26.1 MB 38 kB/s s eta 0:00:01 |███████████████▉ | 12.9 MB 33.9 MB/s eta 0:00:01\n", "\u001b[?25hCollecting matplotlib>=3.1.1\n", " Downloading matplotlib-3.2.1-cp36-cp36m-manylinux1_x86_64.whl (12.4 MB)\n", "\u001b[K |████████████████████████████████| 12.4 MB 12.7 MB/s eta 0:00:01 |██████████████████████████████▊ | 11.9 MB 12.7 MB/s eta 0:00:01\n", "\u001b[?25hRequirement already satisfied: six in /opt/conda/lib/python3.6/site-packages (from patsy>=0.4.1->plotnine) (1.14.0)\n", "Collecting palettable\n", " Downloading palettable-3.3.0-py2.py3-none-any.whl (111 kB)\n", "\u001b[K |████████████████████████████████| 111 kB 9.5 MB/s eta 0:00:01\n", "\u001b[?25hRequirement already satisfied: python-dateutil>=2.6.1 in /opt/conda/lib/python3.6/site-packages (from pandas>=0.25.0->plotnine) (2.8.1)\n", "Requirement already satisfied: pytz>=2017.2 in /opt/conda/lib/python3.6/site-packages (from pandas>=0.25.0->plotnine) (2019.3)\n", "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /opt/conda/lib/python3.6/site-packages (from matplotlib>=3.1.1->plotnine) (2.4.6)\n", "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.6/site-packages (from matplotlib>=3.1.1->plotnine) (0.10.0)\n", "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.6/site-packages (from matplotlib>=3.1.1->plotnine) (1.1.0)\n", "Requirement already satisfied: setuptools in /opt/conda/lib/python3.6/site-packages (from kiwisolver>=1.0.1->matplotlib>=3.1.1->plotnine) (45.2.0.post20200209)\n", "Installing collected packages: numpy, palettable, pandas, matplotlib, mizani, descartes, scipy, plotnine\n", " Attempting uninstall: numpy\n", " Found existing installation: numpy 1.15.2\n", " Uninstalling numpy-1.15.2:\n", " Successfully uninstalled numpy-1.15.2\n", " Attempting uninstall: pandas\n", " Found existing installation: pandas 0.22.0\n", " Uninstalling pandas-0.22.0:\n", " Successfully uninstalled pandas-0.22.0\n", " Attempting uninstall: matplotlib\n", " Found existing installation: matplotlib 2.2.3\n", " Uninstalling matplotlib-2.2.3:\n", " Successfully uninstalled matplotlib-2.2.3\n", " Attempting uninstall: scipy\n", " Found existing installation: scipy 1.1.0\n", " Uninstalling scipy-1.1.0:\n", " Successfully uninstalled scipy-1.1.0\n", "Successfully installed descartes-1.1.0 matplotlib-3.2.1 mizani-0.6.0 numpy-1.18.4 palettable-3.3.0 pandas-1.0.3 plotnine-0.6.0 scipy-1.4.1\n" ] } ], "source": [ "!pip install plotnine" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import os\n", "from matplotlib import pyplot as plt\n", "#from plotnine import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ".... ok seems like the ggplot environment is still not working - so lets use matplotlib" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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iterationwalltime (ms)walltime filter update (ms)max job walltime (ms)min_runnersmax_runnersaccumulated runner idle timecorresponding pdaf state per runner runner idle timepdaf slack/melissa-da slack
009226.972631.22423.62100125405.353117140.0576.6770
116296.542155.019.73675224523.692206450.0487.7550
226364.222195.4810.41640224675.252248530.0480.9440
336307.952150.479.76071224451.922201740.0494.5590
446327.452153.3710.22350224459.782205110.0494.4450
..............................
6956953424.462205.9330.37890121227388.902274020.083.0271
6966963452.032199.2326.87720121227451.002263310.082.4491
6976973463.872201.5427.43590121227655.602266300.081.9473
6986983436.692200.0629.08590121227476.802266630.082.4924
6996993273.052042.0126.83200121225500.302102630.082.4549
\n", "

700 rows × 9 columns

\n", "
" ], "text/plain": [ " iteration walltime (ms) walltime filter update (ms) \\\n", "0 0 9226.97 2631.22 \n", "1 1 6296.54 2155.01 \n", "2 2 6364.22 2195.48 \n", "3 3 6307.95 2150.47 \n", "4 4 6327.45 2153.37 \n", ".. ... ... ... \n", "695 695 3424.46 2205.93 \n", "696 696 3452.03 2199.23 \n", "697 697 3463.87 2201.54 \n", "698 698 3436.69 2200.06 \n", "699 699 3273.05 2042.01 \n", "\n", " max job walltime (ms) min_runners max_runners \\\n", "0 423.62100 1 2 \n", "1 9.73675 2 2 \n", "2 10.41640 2 2 \n", "3 9.76071 2 2 \n", "4 10.22350 2 2 \n", ".. ... ... ... \n", "695 30.37890 12 12 \n", "696 26.87720 12 12 \n", "697 27.43590 12 12 \n", "698 29.08590 12 12 \n", "699 26.83200 12 12 \n", "\n", " accumulated runner idle time \\\n", "0 5405.35 \n", "1 4523.69 \n", "2 4675.25 \n", "3 4451.92 \n", "4 4459.78 \n", ".. ... \n", "695 27388.90 \n", "696 27451.00 \n", "697 27655.60 \n", "698 27476.80 \n", "699 25500.30 \n", "\n", " corresponding pdaf state per runner runner idle time \\\n", "0 3117140.0 \n", "1 2206450.0 \n", "2 2248530.0 \n", "3 2201740.0 \n", "4 2205110.0 \n", ".. ... \n", "695 2274020.0 \n", "696 2263310.0 \n", "697 2266300.0 \n", "698 2266630.0 \n", "699 2102630.0 \n", "\n", " pdaf slack/melissa-da slack \n", "0 576.6770 \n", "1 487.7550 \n", "2 480.9440 \n", "3 494.5590 \n", "4 494.4450 \n", ".. ... \n", "695 83.0271 \n", "696 82.4491 \n", "697 81.9473 \n", "698 82.4924 \n", "699 82.4549 \n", "\n", "[700 rows x 9 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('server.timing-information.csv')\n", "df" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df.plot(\"iteration\", \"walltime (ms)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A warmup phase is clearly visible when plotting the walltime per iteration so don't use the first lets say 50 iterations and dont use the last 10:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df['walltime propagation (ms)'] = df['walltime (ms)'] - df['walltime filter update (ms)']\n", "plt.pie([x.mean() for x in [df['walltime propagation (ms)'][50:-10], df['walltime filter update (ms)'][50:-10]]], labels=['walltime propagation (ms)', 'walltime filter update (ms)'])\n", "#plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For our dummy case we see that the filter update takes longer than the propagation. This relation wil linverse when we use real world simulatoins that are harder to calculate than a simple addition" ] }, { "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }