analyze-raw.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "dacfe98a-e81b-4089-9506-97a652993b5b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import time\n",
    "import datetime\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "17f7abe9-ed9e-466c-b034-577489aaf98b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.read_json('anyscale-1697499992_raw.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "56da958f-694d-4e3e-a559-a275ae22d5d4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validity results:\n",
      "Exception    419\n",
      "OK           181\n",
      "Name: valid, dtype: int64\n",
      "Clean DF is: 181\n",
      "Mean TTFT: 2209 ms (mean tokens in: 512, out: 138)\n",
      "TTFT > 3 s: 24.86%\n",
      "ITL (out): 77.51 ms/token, mean tokens/s output (out): 12.93 token/s\n"
     ]
    }
   ],
   "source": [
    "# This is the standard analysis we would do in the real script\n",
    "\n",
    "print('Validity results:')\n",
    "print(df['valid'].value_counts())\n",
    "cdf = df[df.valid !='Exception'].copy()\n",
    "print(f'Clean DF is: {len(cdf)}')\n",
    "cdf['inter_tokens_delay'] = cdf.total_time/cdf.tokens_out\n",
    "cdf['total_tokens_per_s'] = (cdf.tokens_out + cdf.tokens_in)/cdf.total_time\n",
    "cdf['out_tokens_per_s'] = cdf.tokens_out/cdf.total_time\n",
    "mean_tokens_in = cdf['tokens_in'].mean() \n",
    "mean_tokens_out = cdf['tokens_out'].mean() \n",
    "mean_ttft = cdf['ttft'].mean()\n",
    "gt_3_ttft = len(cdf[cdf['ttft'] > 3])/len(cdf)\n",
    "print(f'Mean TTFT: {mean_ttft*1000:.0f} ms (mean tokens in: {mean_tokens_in:.0f}, out: {mean_tokens_out:.0f})')\n",
    "print(f'TTFT > 3 s: {gt_3_ttft*100:.2f}%')\n",
    "print(f'ITL (out): {cdf.inter_tokens_delay.mean()*1000:.2f} ms/token, mean tokens/s output (out): {cdf.out_tokens_per_s.mean():.2f} token/s')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "99936985-a21f-4738-9021-f9db8d67769a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "181"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4f5db74b-63ab-4268-b1a7-10b14641efb1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='tokens_in', ylabel='ttft'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cdf.plot.scatter(y='ttft', x='tokens_in')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f08d9085-d994-4754-a545-390cad1f4806",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='tokens_out', ylabel='total_time'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cdf.plot.scatter(y='total_time', x='tokens_out', ylim=[0,10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6f520389-aea3-4f0f-a23a-9094b57251c8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "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>ttft</th>\n",
       "      <th>total_time</th>\n",
       "      <th>tokens_in</th>\n",
       "      <th>tokens_out</th>\n",
       "      <th>inter_tokens_delay</th>\n",
       "      <th>total_tokens_per_s</th>\n",
       "      <th>out_tokens_per_s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>181.000000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>181.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.209350</td>\n",
       "      <td>10.661870</td>\n",
       "      <td>511.988950</td>\n",
       "      <td>137.944751</td>\n",
       "      <td>0.077509</td>\n",
       "      <td>61.123584</td>\n",
       "      <td>12.934065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.910844</td>\n",
       "      <td>0.810370</td>\n",
       "      <td>136.905198</td>\n",
       "      <td>14.281971</td>\n",
       "      <td>0.003850</td>\n",
       "      <td>12.982601</td>\n",
       "      <td>0.658547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.246783</td>\n",
       "      <td>8.863190</td>\n",
       "      <td>258.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>0.064592</td>\n",
       "      <td>33.386163</td>\n",
       "      <td>11.382172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.646696</td>\n",
       "      <td>10.251724</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>131.000000</td>\n",
       "      <td>0.075630</td>\n",
       "      <td>50.126811</td>\n",
       "      <td>12.559732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.675371</td>\n",
       "      <td>10.556036</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>0.077166</td>\n",
       "      <td>63.105083</td>\n",
       "      <td>12.959061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.998175</td>\n",
       "      <td>10.958826</td>\n",
       "      <td>630.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>0.079620</td>\n",
       "      <td>71.557239</td>\n",
       "      <td>13.222181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.198518</td>\n",
       "      <td>16.923179</td>\n",
       "      <td>738.000000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>0.087857</td>\n",
       "      <td>84.330612</td>\n",
       "      <td>15.481725</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ttft  total_time   tokens_in  tokens_out  inter_tokens_delay  \\\n",
       "count  181.000000  181.000000  181.000000  181.000000          181.000000   \n",
       "mean     2.209350   10.661870  511.988950  137.944751            0.077509   \n",
       "std      0.910844    0.810370  136.905198   14.281971            0.003850   \n",
       "min      0.246783    8.863190  258.000000  120.000000            0.064592   \n",
       "25%      1.646696   10.251724  404.000000  131.000000            0.075630   \n",
       "50%      2.675371   10.556036  517.000000  136.000000            0.077166   \n",
       "75%      2.998175   10.958826  630.000000  140.000000            0.079620   \n",
       "max      3.198518   16.923179  738.000000  262.000000            0.087857   \n",
       "\n",
       "       total_tokens_per_s  out_tokens_per_s  \n",
       "count          181.000000        181.000000  \n",
       "mean            61.123584         12.934065  \n",
       "std             12.982601          0.658547  \n",
       "min             33.386163         11.382172  \n",
       "25%             50.126811         12.559732  \n",
       "50%             63.105083         12.959061  \n",
       "75%             71.557239         13.222181  \n",
       "max             84.330612         15.481725  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "81c9cdee-028f-448b-9179-04aa758e4f37",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cdf['ttft'].plot.hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "cd59c882",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['',\n",
       " '',\n",
       " '',\n",
       " '',\n",
       " '',\n",
       " '',\n",
       " '',\n",
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     "execution_count": 56,
     "metadata": {},
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   ],
   "source": [
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   "execution_count": null,
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