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/
archai
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AML Notebooks: filtering dataframes and displaying them.

This commit is contained in:
Luis França 2023-03-31 09:42:07 -03:00
Родитель 324397f586
Коммит ddbb796ca9
3 изменённых файлов: 296 добавлений и 659 удалений
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21
archai/common/notebook_helper.py
Просмотреть файл

@ -28,27 +28,6 @@ def get_search_csv(output_path: Union[str, Path], iteration_num: Optional[int] =
return df
def get_csv_as_stylized_html(df: pd.DataFrame) -> str:
"""Returns a stylized html table from a pandas dataframe with a scrollbar
Args:
df (pd.DataFrame): Pandas dataframe to convert to html table
Returns:
str: Stylized html table
"""
styled_table = df.style.set_properties(**{'background-color': 'lightblue',
'color': 'black',
'border-color': 'white',
'font-size': '12pt'
})
html_table = styled_table.to_html()
html_with_scrollbar = f'<div style="height: 300px; overflow-y: scroll;">{html_table}</div>'
return html_with_scrollbar
def get_arch_abs_path(archid: str, downloaded_folder: Union[str, Path], iteration_num: Optional[int] = -1) -> Path:
"""Returns the absolute path to the architecture file

691
docs/advanced_guide/cloud/azure/notebooks/quickstart/quickstart.ipynb поставляемый
Просмотреть файл

Различия файлов скрыты, потому что одна или несколько строк слишком длинны

243
docs/advanced_guide/cloud/azure/notebooks/text_generation/text_generation.ipynb поставляемый
Просмотреть файл

@ -338,202 +338,127 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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"\n",
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"</style>\n",
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" <tr>\n",
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" <td>0.376884</td>\n",
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" <td id=\"T_8a23d_row9_col3\" class=\"data row9 col3\" >148.552288</td>\n",
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" <td id=\"T_8a23d_row11_col4\" class=\"data row11 col4\" >False</td>\n",
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" <td id=\"T_8a23d_row12_col0\" class=\"data row12 col0\" >gpt2_4b37e60e4c0017177c7722fe3b4b08b4a6969ccb</td>\n",
" <td id=\"T_8a23d_row12_col1\" class=\"data row12 col1\" >29808768.000000</td>\n",
" <td id=\"T_8a23d_row12_col2\" class=\"data row12 col2\" >1.627816</td>\n",
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" <td id=\"T_8a23d_row12_col4\" class=\"data row12 col4\" >True</td>\n",
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" <tr>\n",
" <th id=\"T_8a23d_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
" <td id=\"T_8a23d_row13_col0\" class=\"data row13 col0\" >gpt2_d042734419c3f3b4e04e2db9ca184f5db02ee2ba</td>\n",
" <td id=\"T_8a23d_row13_col1\" class=\"data row13 col1\" >40381312.000000</td>\n",
" <td id=\"T_8a23d_row13_col2\" class=\"data row13 col2\" >1.889455</td>\n",
" <td id=\"T_8a23d_row13_col3\" class=\"data row13 col3\" >342.717808</td>\n",
" <td id=\"T_8a23d_row13_col4\" class=\"data row13 col4\" >True</td>\n",
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" <tr>\n",
" <th id=\"T_8a23d_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
" <td id=\"T_8a23d_row14_col0\" class=\"data row14 col0\" >gpt2_b0111ea37ec42adb5040daa60db0e0b11d164017</td>\n",
" <td id=\"T_8a23d_row14_col1\" class=\"data row14 col1\" >17080384.000000</td>\n",
" <td id=\"T_8a23d_row14_col2\" class=\"data row14 col2\" >0.905016</td>\n",
" <td id=\"T_8a23d_row14_col3\" class=\"data row14 col3\" >228.869057</td>\n",
" <td id=\"T_8a23d_row14_col4\" class=\"data row14 col4\" >True</td>\n",
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" <tr>\n",
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" <td id=\"T_8a23d_row15_col0\" class=\"data row15 col0\" >gpt2_c710d31b0c06ad032125e28d4303a05676902937</td>\n",
" <td id=\"T_8a23d_row15_col1\" class=\"data row15 col1\" >11966848.000000</td>\n",
" <td id=\"T_8a23d_row15_col2\" class=\"data row15 col2\" >0.714716</td>\n",
" <td id=\"T_8a23d_row15_col3\" class=\"data row15 col3\" >134.300035</td>\n",
" <td id=\"T_8a23d_row15_col4\" class=\"data row15 col4\" >True</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_8a23d_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
" <td id=\"T_8a23d_row16_col0\" class=\"data row16 col0\" >gpt2_3c57938472ca145510df49098166e3259e1b6509</td>\n",
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" <td id=\"T_8a23d_row16_col3\" class=\"data row16 col3\" >241.403567</td>\n",
" <td id=\"T_8a23d_row16_col4\" class=\"data row16 col4\" >True</td>\n",
" </tr>\n",
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" <td id=\"T_8a23d_row17_col0\" class=\"data row17 col0\" >gpt2_0ffcee3a558c89448aea22ff34c8dba77208072a</td>\n",
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" <td id=\"T_8a23d_row17_col4\" class=\"data row17 col4\" >False</td>\n",
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" <tr>\n",
" <th id=\"T_8a23d_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
" <td id=\"T_8a23d_row18_col0\" class=\"data row18 col0\" >gpt2_1d573928a72694068c1af9608548862d8519c533</td>\n",
" <td id=\"T_8a23d_row18_col1\" class=\"data row18 col1\" >14199296.000000</td>\n",
" <td id=\"T_8a23d_row18_col2\" class=\"data row18 col2\" >0.851394</td>\n",
" <td id=\"T_8a23d_row18_col3\" class=\"data row18 col3\" >155.320788</td>\n",
" <td id=\"T_8a23d_row18_col4\" class=\"data row18 col4\" >True</td>\n",
" <th>18</th>\n",
" <td>gpt2_1d573928a72694068c1af9608548862d8519c533</td>\n",
" <td>14199296.0</td>\n",
" <td>0.851394</td>\n",
" <td>155.320788</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
" archid non_embedding_params \\\n",
"0 gpt2_cea35b3f3fd242d2af609b8d2a3936cc814a7f41 15144192.0 \n",
"1 gpt2_df106863e1a0c9c140036b05661aa88e92f07701 7769472.0 \n",
"2 gpt2_0f371e7b893319c0d20944d60143fad14e2695d0 14724096.0 \n",
"9 gpt2_85a39fce7fd60bf8df99d17bb91298a450e2b0b1 12424512.0 \n",
"10 gpt2_3748aa9c59880395ceabfba51d411bfa3ca198e8 5917568.0 \n",
"15 gpt2_c710d31b0c06ad032125e28d4303a05676902937 11966848.0 \n",
"18 gpt2_1d573928a72694068c1af9608548862d8519c533 14199296.0 \n",
"\n",
" onnx_latency onnx_memory is_pareto \n",
"0 0.761083 246.531515 True \n",
"1 0.575391 143.315580 True \n",
"2 0.850879 157.321033 True \n",
"9 0.676473 148.552288 True \n",
"10 0.376884 148.768559 True \n",
"15 0.714716 134.300035 True \n",
"18 0.851394 155.320788 True "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "display_data"
"output_type": "execute_result"
}
],
"source": [
"df = nb_helper.get_search_csv(downloaded_folder)\n",
"df = df[['archid', 'non_embedding_params', 'onnx_latency', 'onnx_memory', 'is_pareto']]\n",
"\n",
"csv_as_html = nb_helper.get_csv_as_stylized_html(df)\n",
"display(HTML(csv_as_html))"
"df[(df['onnx_latency'] < 0.9) & (df['is_pareto'] == True)]"
]
},
{