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22 "cells" : [
3+ {
4+ "cell_type" : " code" ,
5+ "execution_count" : null ,
6+ "metadata" : {},
7+ "outputs" : [],
8+ "source" : [
9+ " import os\n " ,
10+ " #os.environ[\" XLA_FLAGS\" ] = \" --xla_gpu_cuda_data_dir=/usr/lib/nvidia-cuda-toolkit\" "
11+ ]
12+ },
313 {
414 "cell_type" : " code" ,
515 "execution_count" : null ,
616 "metadata" : {},
717 "outputs" : [],
818 "source" : [
919 " import logging\n " ,
10- " \n " ,
20+ " import os\n " ,
21+ " import shutil\n " ,
22+ " from pathlib import Path\n " ,
23+ " import yaml\n " ,
1124 " # --- Make sure your notebook's working directory is the project root ---\n " ,
1225 " # (e.g., /home/samorah/_data/ml_binary_classification_gridsearch_hyperOpt/)\n " ,
1326 " from pathlib import Path\n " ,
118131 " an `ml_grid_object`. It is intended for debugging the data pipeline in isolation.\n " ,
119132 " \"\"\"\n " ,
120133 " \n " ,
121- " import os\n " ,
122- " import shutil\n " ,
123- " from pathlib import Path\n " ,
124- " \n " ,
125- " import yaml\n " ,
126- " \n " ,
127134 " # --- Essential imports from your ml_grid project ---\n " ,
128135 " # Ensure your project is installed (e.g., `pip install -e .`) or the path is configured\n " ,
129136 " # so these imports can be found.\n " ,
695702 " print(\"\\ n6. Clean up complete.\" )\n "
696703 ]
697704 },
698- {
699- "cell_type" : " code" ,
700- "execution_count" : null ,
701- "metadata" : {},
702- "outputs" : [],
703- "source" : []
704- },
705705 {
706706 "cell_type" : " code" ,
707707 "execution_count" : null ,
15241524 "source" : [
15251525 " df.sort_values(by='run_time', ascending=False)"
15261526 ]
1527+ },
1528+ {
1529+ "cell_type" : " code" ,
1530+ "execution_count" : null ,
1531+ "metadata" : {},
1532+ "outputs" : [],
1533+ "source" : []
15271534 }
15281535 ],
15291536 "metadata" : {
15421549 "name" : " python" ,
15431550 "nbconvert_exporter" : " python" ,
15441551 "pygments_lexer" : " ipython3" ,
1545- "version" : " 3.12.3 "
1552+ "version" : " 3.10.19 "
15461553 }
15471554 },
15481555 "nbformat" : 4 ,
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