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        "author": "Alessandro Berti",
        "author_email": "alessandro.berti89@gmail.com",
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        "description": "# pm4pygpu-source\n\nWelcome to Process Mining on GPU!\n\nPM4Py GPU utilities using NVIDIA Rapids framework\n\nDFG calculus performance:\n\nOn a CSV log with 1M cases and 5M events (328M on drive),\nPandas takes 5,26 seconds to load the log and calculate the DFG,\nRapids takes 0,75 seconds to do all!\n\nOn a CSV log with 10M cases and 48M events (3.3GB on drive),\nPandas takes 53,17 seconds to load the log and calculate the DFG,\nRapids takes 3,46 seconds to do all!\n\nLimitations:\n-Strings are still not supported directly. At the moment, RAPIDS can load them as int64, but with the limitation to two characters.\n-Column names with non alphanumeric characters are struggling with RAPIDS\n\n\n\nTo import a CSV and get a DFG out of the CSV, use the following instructions:\n\nfrom pm4pygpu.objects.log.importer.adapters.rapids import rapids_csv_import\nfrom pm4pygpu.algo.dfg.adapters.rapids import df_statistics\n\ndf = rapids_csv_import.import_rapids_dataframe(\"output.csv\")\ndfg = df_statistics.calculate_dfg_frequency(df)\nprint(dfg)\n\n\nResult:\n\n{('A', 'C'): 14962, ('B', 'D'): 7480, ('A', 'B'): 15038, ('E', 'F'): 11217, ('C', 'D'): 7494, ('B', 'E'): 7558, ('E', 'G'): 3809, ('F', 'G'): 22421, ('D', 'G'): 3770, ('C', 'E'): 7468, ('D', 'F'): 11204}\n\n",
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        "license": "GPL 3.0",
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        "name": "pm4pygpu",
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        "summary": "Process Mining for Python - GPU module",
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