notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import flor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>ctx_id</th>\n",
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       "      <td>8.0</td>\n",
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       "      <td>1500</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>epoch</td>\n",
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       "      <th>13</th>\n",
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       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>16.0</td>\n",
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       "      <td>5</td>\n",
       "      <td>500</td>\n",
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       "      <th>18</th>\n",
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       "      <td>16.0</td>\n",
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       "      <td>1000</td>\n",
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       "      <td>19</td>\n",
       "      <td>16.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>7</td>\n",
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       "      <th>25</th>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>24.0</td>\n",
       "      <td>step</td>\n",
       "      <td>7</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>24.0</td>\n",
       "      <td>step</td>\n",
       "      <td>7</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>epoch</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>28.0</td>\n",
       "      <td>step</td>\n",
       "      <td>8</td>\n",
       "      <td>500</td>\n",
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       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
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       "      <th>31</th>\n",
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       "      <td>8</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>epoch</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33</td>\n",
       "      <td>32.0</td>\n",
       "      <td>step</td>\n",
       "      <td>9</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>32.0</td>\n",
       "      <td>step</td>\n",
       "      <td>9</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35</td>\n",
       "      <td>32.0</td>\n",
       "      <td>step</td>\n",
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       "      <th>36</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>epoch</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>37</td>\n",
       "      <td>36.0</td>\n",
       "      <td>step</td>\n",
       "      <td>10</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>38</td>\n",
       "      <td>36.0</td>\n",
       "      <td>step</td>\n",
       "      <td>10</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>39</td>\n",
       "      <td>36.0</td>\n",
       "      <td>step</td>\n",
       "      <td>10</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ctx_id  parent_ctx_id loop_name  loop_entries  loop_iteration\n",
       "0        0            NaN     epoch             1               1\n",
       "1        1            0.0      step             1             500\n",
       "2        2            0.0      step             1            1000\n",
       "3        3            0.0      step             1            1500\n",
       "4        4            NaN     epoch             1               2\n",
       "5        5            4.0      step             2             500\n",
       "6        6            4.0      step             2            1000\n",
       "7        7            4.0      step             2            1500\n",
       "8        8            NaN     epoch             1               3\n",
       "9        9            8.0      step             3             500\n",
       "10      10            8.0      step             3            1000\n",
       "11      11            8.0      step             3            1500\n",
       "12      12            NaN     epoch             1               4\n",
       "13      13           12.0      step             4             500\n",
       "14      14           12.0      step             4            1000\n",
       "15      15           12.0      step             4            1500\n",
       "16      16            NaN     epoch             1               5\n",
       "17      17           16.0      step             5             500\n",
       "18      18           16.0      step             5            1000\n",
       "19      19           16.0      step             5            1500\n",
       "20      20            NaN     epoch             1               6\n",
       "21      21           20.0      step             6             500\n",
       "22      22           20.0      step             6            1000\n",
       "23      23           20.0      step             6            1500\n",
       "24      24            NaN     epoch             1               7\n",
       "25      25           24.0      step             7             500\n",
       "26      26           24.0      step             7            1000\n",
       "27      27           24.0      step             7            1500\n",
       "28      28            NaN     epoch             1               8\n",
       "29      29           28.0      step             8             500\n",
       "30      30           28.0      step             8            1000\n",
       "31      31           28.0      step             8            1500\n",
       "32      32            NaN     epoch             1               9\n",
       "33      33           32.0      step             9             500\n",
       "34      34           32.0      step             9            1000\n",
       "35      35           32.0      step             9            1500\n",
       "36      36            NaN     epoch             1              10\n",
       "37      37           36.0      step            10             500\n",
       "38      38           36.0      step            10            1000\n",
       "39      39           36.0      step            10            1500"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flor.query(\"SELECT * FROM loops\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>projid</th>\n",
       "      <th>tstamp</th>\n",
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       "      <td>1500</td>\n",
       "      <td>0.00920849107205868</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:08:13</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.94</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>500</td>\n",
       "      <td>0.0110081704333425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ml_tutorial</td>\n",
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       "      <td>train.py</td>\n",
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       "      <td>1000</td>\n",
       "      <td>0.00759784132242203</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ml_tutorial</td>\n",
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       "      <td>train.py</td>\n",
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       "      <th>9</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:08:13</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.94</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>500</td>\n",
       "      <td>0.00754651427268982</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:08:13</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.94</td>\n",
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       "      <td>4</td>\n",
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       "      <td>1000</td>\n",
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       "      <th>11</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:08:13</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.94</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1500</td>\n",
       "      <td>0.0020167117472738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:08:13</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.94</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>500</td>\n",
       "      <td>0.00216090632602572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:08:13</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.94</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.0716521963477135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:08:13</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.94</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1500</td>\n",
       "      <td>0.0267916955053806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>500</td>\n",
       "      <td>0.23156401515007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.124277129769325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1500</td>\n",
       "      <td>0.048449270427227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "      <td>0.0174014121294022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.0396086052060127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1500</td>\n",
       "      <td>0.0574071854352951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>500</td>\n",
       "      <td>0.00561360269784927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.0153215192258358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1500</td>\n",
       "      <td>0.046303104609251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>500</td>\n",
       "      <td>0.00374085083603859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.0191184896975756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1500</td>\n",
       "      <td>0.0621689409017563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>500</td>\n",
       "      <td>0.000821844441816211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.19994068145752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1500</td>\n",
       "      <td>0.00394831039011478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>500</td>\n",
       "      <td>0.078400269150734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.00824527535587549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1500</td>\n",
       "      <td>0.00135427142959088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>500</td>\n",
       "      <td>0.0255366861820221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.0148378144949675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1500</td>\n",
       "      <td>0.0310402438044548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>500</td>\n",
       "      <td>0.151195377111435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.000315459707053378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1500</td>\n",
       "      <td>0.000217557651922107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>500</td>\n",
       "      <td>0.00433748820796609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.0034062578342855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1500</td>\n",
       "      <td>0.00115611404180527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>500</td>\n",
       "      <td>0.00735217891633511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.00323224393650889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:22:51</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.65</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>1500</td>\n",
       "      <td>2.1943849787931e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:24:34</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.72</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>500</td>\n",
       "      <td>0.340162873268127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:24:34</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.72</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.0543907471001148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:24:34</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.72</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1500</td>\n",
       "      <td>0.0874033570289612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:24:34</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.72</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "      <td>0.0656906738877296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:24:34</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.72</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.253334999084473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:24:34</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.72</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1500</td>\n",
       "      <td>0.102150581777096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:24:34</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.72</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>500</td>\n",
       "      <td>0.0450273714959621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:24:34</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.72</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.196081161499023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:24:34</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.72</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1500</td>\n",
       "      <td>0.00821158196777105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:24:34</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.72</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>500</td>\n",
       "      <td>0.00277980044484138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:24:34</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.72</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.00559155829250813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:24:34</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.72</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1500</td>\n",
       "      <td>0.0122450031340122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:24:34</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.72</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>500</td>\n",
       "      <td>0.003230374539271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:24:34</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.72</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.0773211792111397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>ml_tutorial</td>\n",
       "      <td>2023-08-17T14:24:34</td>\n",
       "      <td>train.py</td>\n",
       "      <td>97.72</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1500</td>\n",
       "      <td>0.0118529098108411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         projid               tstamp  filename accuracy  epoch_entries  \\\n",
       "0   ml_tutorial  2023-08-17T14:08:13  train.py    97.94              1   \n",
       "1   ml_tutorial  2023-08-17T14:08:13  train.py    97.94              1   \n",
       "2   ml_tutorial  2023-08-17T14:08:13  train.py    97.94              1   \n",
       "3   ml_tutorial  2023-08-17T14:08:13  train.py    97.94              1   \n",
       "4   ml_tutorial  2023-08-17T14:08:13  train.py    97.94              1   \n",
       "5   ml_tutorial  2023-08-17T14:08:13  train.py    97.94              1   \n",
       "6   ml_tutorial  2023-08-17T14:08:13  train.py    97.94              1   \n",
       "7   ml_tutorial  2023-08-17T14:08:13  train.py    97.94              1   \n",
       "8   ml_tutorial  2023-08-17T14:08:13  train.py    97.94              1   \n",
       "9   ml_tutorial  2023-08-17T14:08:13  train.py    97.94              1   \n",
       "10  ml_tutorial  2023-08-17T14:08:13  train.py    97.94              1   \n",
       "11  ml_tutorial  2023-08-17T14:08:13  train.py    97.94              1   \n",
       "12  ml_tutorial  2023-08-17T14:08:13  train.py    97.94              1   \n",
       "13  ml_tutorial  2023-08-17T14:08:13  train.py    97.94              1   \n",
       "14  ml_tutorial  2023-08-17T14:08:13  train.py    97.94              1   \n",
       "15  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "16  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "17  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "18  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "19  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "20  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "21  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "22  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "23  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "24  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "25  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "26  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "27  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "28  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "29  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "30  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "31  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "32  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "33  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "34  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "35  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "36  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "37  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "38  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "39  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "40  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "41  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "42  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "43  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "44  ml_tutorial  2023-08-17T14:22:51  train.py    97.65              1   \n",
       "45  ml_tutorial  2023-08-17T14:24:34  train.py    97.72              1   \n",
       "46  ml_tutorial  2023-08-17T14:24:34  train.py    97.72              1   \n",
       "47  ml_tutorial  2023-08-17T14:24:34  train.py    97.72              1   \n",
       "48  ml_tutorial  2023-08-17T14:24:34  train.py    97.72              1   \n",
       "49  ml_tutorial  2023-08-17T14:24:34  train.py    97.72              1   \n",
       "50  ml_tutorial  2023-08-17T14:24:34  train.py    97.72              1   \n",
       "51  ml_tutorial  2023-08-17T14:24:34  train.py    97.72              1   \n",
       "52  ml_tutorial  2023-08-17T14:24:34  train.py    97.72              1   \n",
       "53  ml_tutorial  2023-08-17T14:24:34  train.py    97.72              1   \n",
       "54  ml_tutorial  2023-08-17T14:24:34  train.py    97.72              1   \n",
       "55  ml_tutorial  2023-08-17T14:24:34  train.py    97.72              1   \n",
       "56  ml_tutorial  2023-08-17T14:24:34  train.py    97.72              1   \n",
       "57  ml_tutorial  2023-08-17T14:24:34  train.py    97.72              1   \n",
       "58  ml_tutorial  2023-08-17T14:24:34  train.py    97.72              1   \n",
       "59  ml_tutorial  2023-08-17T14:24:34  train.py    97.72              1   \n",
       "\n",
       "    epoch_iteration  step_entries  step_iteration                  loss  \n",
       "0                 1             1             500     0.469383418560028  \n",
       "1                 1             1            1000      0.23906421661377  \n",
       "2                 1             1            1500    0.0495855584740639  \n",
       "3                 2             2             500    0.0858967006206512  \n",
       "4                 2             2            1000     0.239088863134384  \n",
       "5                 2             2            1500   0.00920849107205868  \n",
       "6                 3             3             500    0.0110081704333425  \n",
       "7                 3             3            1000   0.00759784132242203  \n",
       "8                 3             3            1500     0.193404868245125  \n",
       "9                 4             4             500   0.00754651427268982  \n",
       "10                4             4            1000     0.269229918718338  \n",
       "11                4             4            1500    0.0020167117472738  \n",
       "12                5             5             500   0.00216090632602572  \n",
       "13                5             5            1000    0.0716521963477135  \n",
       "14                5             5            1500    0.0267916955053806  \n",
       "15                1             1             500      0.23156401515007  \n",
       "16                1             1            1000     0.124277129769325  \n",
       "17                1             1            1500     0.048449270427227  \n",
       "18                2             2             500    0.0174014121294022  \n",
       "19                2             2            1000    0.0396086052060127  \n",
       "20                2             2            1500    0.0574071854352951  \n",
       "21                3             3             500   0.00561360269784927  \n",
       "22                3             3            1000    0.0153215192258358  \n",
       "23                3             3            1500     0.046303104609251  \n",
       "24                4             4             500   0.00374085083603859  \n",
       "25                4             4            1000    0.0191184896975756  \n",
       "26                4             4            1500    0.0621689409017563  \n",
       "27                5             5             500  0.000821844441816211  \n",
       "28                5             5            1000      0.19994068145752  \n",
       "29                5             5            1500   0.00394831039011478  \n",
       "30                6             6             500     0.078400269150734  \n",
       "31                6             6            1000   0.00824527535587549  \n",
       "32                6             6            1500   0.00135427142959088  \n",
       "33                7             7             500    0.0255366861820221  \n",
       "34                7             7            1000    0.0148378144949675  \n",
       "35                7             7            1500    0.0310402438044548  \n",
       "36                8             8             500     0.151195377111435  \n",
       "37                8             8            1000  0.000315459707053378  \n",
       "38                8             8            1500  0.000217557651922107  \n",
       "39                9             9             500   0.00433748820796609  \n",
       "40                9             9            1000    0.0034062578342855  \n",
       "41                9             9            1500   0.00115611404180527  \n",
       "42               10            10             500   0.00735217891633511  \n",
       "43               10            10            1000   0.00323224393650889  \n",
       "44               10            10            1500   2.1943849787931e-05  \n",
       "45                1             1             500     0.340162873268127  \n",
       "46                1             1            1000    0.0543907471001148  \n",
       "47                1             1            1500    0.0874033570289612  \n",
       "48                2             2             500    0.0656906738877296  \n",
       "49                2             2            1000     0.253334999084473  \n",
       "50                2             2            1500     0.102150581777096  \n",
       "51                3             3             500    0.0450273714959621  \n",
       "52                3             3            1000     0.196081161499023  \n",
       "53                3             3            1500   0.00821158196777105  \n",
       "54                4             4             500   0.00277980044484138  \n",
       "55                4             4            1000   0.00559155829250813  \n",
       "56                4             4            1500    0.0122450031340122  \n",
       "57                5             5             500     0.003230374539271  \n",
       "58                5             5            1000    0.0773211792111397  \n",
       "59                5             5            1500    0.0118529098108411  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flor.pivot('accuracy', 'loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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