tests/viz_fk_perf.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-30T16:15:27.994729591Z",
     "start_time": "2023-10-30T16:15:27.993707047Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "        chain device          dtype  batch_size      time    time/batch\n0         val    cpu  torch.float32           1  0.001706  1.705588e-03\n1         val    cpu  torch.float32          10  0.001854  1.854159e-04\n2         val    cpu  torch.float32         100  0.002467  2.466516e-05\n3         val    cpu  torch.float32        1000  0.006334  6.334178e-06\n4         val    cpu  torch.float32       10000  0.024186  2.418595e-06\n..        ...    ...            ...         ...       ...           ...\n67  kuka_iiwa   cuda  torch.float64          10  0.001017  1.016614e-04\n68  kuka_iiwa   cuda  torch.float64         100  0.001018  1.017745e-05\n69  kuka_iiwa   cuda  torch.float64        1000  0.001307  1.307084e-06\n70  kuka_iiwa   cuda  torch.float64       10000  0.005168  5.168081e-07\n71  kuka_iiwa   cuda  torch.float64      100000  0.048497  4.849671e-07\n\n[72 rows x 6 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>chain</th>\n      <th>device</th>\n      <th>dtype</th>\n      <th>batch_size</th>\n      <th>time</th>\n      <th>time/batch</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>val</td>\n      <td>cpu</td>\n      <td>torch.float32</td>\n      <td>1</td>\n      <td>0.001706</td>\n      <td>1.705588e-03</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>val</td>\n      <td>cpu</td>\n      <td>torch.float32</td>\n      <td>10</td>\n      <td>0.001854</td>\n      <td>1.854159e-04</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>val</td>\n      <td>cpu</td>\n      <td>torch.float32</td>\n      <td>100</td>\n      <td>0.002467</td>\n      <td>2.466516e-05</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>val</td>\n      <td>cpu</td>\n      <td>torch.float32</td>\n      <td>1000</td>\n      <td>0.006334</td>\n      <td>6.334178e-06</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>val</td>\n      <td>cpu</td>\n      <td>torch.float32</td>\n      <td>10000</td>\n      <td>0.024186</td>\n      <td>2.418595e-06</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>67</th>\n      <td>kuka_iiwa</td>\n      <td>cuda</td>\n      <td>torch.float64</td>\n      <td>10</td>\n      <td>0.001017</td>\n      <td>1.016614e-04</td>\n    </tr>\n    <tr>\n      <th>68</th>\n      <td>kuka_iiwa</td>\n      <td>cuda</td>\n      <td>torch.float64</td>\n      <td>100</td>\n      <td>0.001018</td>\n      <td>1.017745e-05</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>kuka_iiwa</td>\n      <td>cuda</td>\n      <td>torch.float64</td>\n      <td>1000</td>\n      <td>0.001307</td>\n      <td>1.307084e-06</td>\n    </tr>\n    <tr>\n      <th>70</th>\n      <td>kuka_iiwa</td>\n      <td>cuda</td>\n      <td>torch.float64</td>\n      <td>10000</td>\n      <td>0.005168</td>\n      <td>5.168081e-07</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>kuka_iiwa</td>\n      <td>cuda</td>\n      <td>torch.float64</td>\n      <td>100000</td>\n      <td>0.048497</td>\n      <td>4.849671e-07</td>\n    </tr>\n  </tbody>\n</table>\n<p>72 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data\n",
    "import torch\n",
    "import pickle\n",
    "\n",
    "with open('fk_perf.pkl', 'rb') as f:\n",
    "    headers, data = pickle.load(f)\n",
    "\n",
    "import pandas as pd\n",
    "df = pd.DataFrame(data, columns=headers)\n",
    "\n",
    "# add a column that is the time divided by the batch size\n",
    "df['time/batch'] = df['time'] / df['batch_size']\n",
    "\n",
    "df"
   ],
   "id": "e9f94ff7deaec3f4"
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-30T16:15:28.606199427Z",
     "start_time": "2023-10-30T16:15:27.993916371Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='batch_size', ylabel='time'>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot batch_size on the X axis and time on the Y axis, with a line for each chain, averaged over device and dtype.\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# make the X axis log scale\n",
    "plt.xscale('log')\n",
    "sns.lineplot(data=df, x='batch_size', y='time', hue='chain', errorbar=None)"
   ],
   "id": "2c1117400bd43e44"
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-30T16:15:28.712570761Z",
     "start_time": "2023-10-30T16:15:28.600077822Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='chain', ylabel='time'>"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar plot of the average time for each chain, averaged over everything else\n",
    "sns.barplot(data=df, x='chain', y='time', errorbar=None)"
   ],
   "id": "f4922335aa20094e"
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='dtype', ylabel='time'>"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare 32 to 64 bit\n",
    "sns.barplot(data=df, x='dtype', y='time', errorbar=None)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T16:15:28.833907900Z",
     "start_time": "2023-10-30T16:15:28.739415909Z"
    }
   },
   "id": "4dc8bbcd3dea2f27"
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T16:15:28.880353691Z",
     "start_time": "2023-10-30T16:15:28.833655934Z"
    }
   },
   "id": "bb738671a34e6c60"
  }
 ],
 "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}