File size: 3,662 Bytes
3c098f3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reading parquet files...\n",
"Processing dataframes...\n",
"\n",
"Merging dataframes...\n",
"\n",
"Formatting output...\n",
"\n",
"Total pairs found: 216775\n",
"\n",
"Saving to parquet file...\n",
"\n",
"Sample pairs:\n",
" hip_filename \\\n",
"0 fcd394853732933cc2ddcf59fa29d561f0263cb1.hip \n",
"1 d654bdeca448d1a413a7cc87ccc3b4b7f18a965d.hip \n",
"2 464e3d1584f0013dfda51116d9aaaf21bd91bc13.hip \n",
"3 21a2390523ec5438ddf21ad9d91b04ae044ec944.hip \n",
"4 2b375ca1064061439fdc87fb32d664cc9434d26e.hip \n",
"\n",
" cuda_filename \n",
"0 fcd394853732933cc2ddcf59fa29d561f0263cb1.cu \n",
"1 d654bdeca448d1a413a7cc87ccc3b4b7f18a965d.cu \n",
"2 464e3d1584f0013dfda51116d9aaaf21bd91bc13.cu \n",
"3 21a2390523ec5438ddf21ad9d91b04ae044ec944.cu \n",
"4 2b375ca1064061439fdc87fb32d664cc9434d26e.cu \n"
]
}
],
"source": [
"import pandas as pd\n",
"import multiprocessing as mp\n",
"from tqdm import tqdm\n",
"\n",
"def create_paired_dataset(cuda_df, hip_df):\n",
" print(\"Processing dataframes...\")\n",
" \n",
" # Create base names for both dataframes at once\n",
" cuda_df['base_name'] = cuda_df['filename'].str.replace(r'\\.cu[h]?$', '', regex=True)\n",
" hip_df['base_name'] = hip_df['filename'].str.replace(r'\\.hip$', '', regex=True)\n",
" \n",
" # Merge dataframes on base_name - this is much faster than iterative matching\n",
" print(\"\\nMerging dataframes...\")\n",
" paired_df = pd.merge(\n",
" hip_df,\n",
" cuda_df,\n",
" on='base_name',\n",
" suffixes=('_hip', '_cuda')\n",
" )\n",
" \n",
" # Rename columns to match desired output format\n",
" print(\"\\nFormatting output...\")\n",
" result_df = pd.DataFrame({\n",
" 'hip_filename': paired_df['filename_hip'],\n",
" 'hip_content': paired_df['content_hip'],\n",
" 'cuda_filename': paired_df['filename_cuda'],\n",
" 'cuda_content': paired_df['content_cuda']\n",
" })\n",
" \n",
" print(f\"\\nTotal pairs found: {len(result_df)}\")\n",
" \n",
" print(\"\\nSaving to parquet file...\")\n",
" result_df.to_parquet('cuda_hip_paired.parquet')\n",
" \n",
" print(\"\\nSample pairs:\")\n",
" print(result_df[['hip_filename', 'cuda_filename']].head())\n",
" return result_df\n",
"\n",
"if __name__ == '__main__':\n",
" print(\"Reading parquet files...\")\n",
" cuda_df = pd.read_parquet('cuda_files.parquet')\n",
" hip_df = pd.read_parquet('hip_files.parquet')\n",
" create_paired_dataset(cuda_df, hip_df)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "llava_med_v2",
"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": "3.10.15"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|