Integrated Hugging Face dataset for performance analysis

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Integrated Hugging Face dataset for performance analysis

  • Day: 2025-12-10
  • Time: 18:40 to 19:30
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: Completed
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Hugging Face, Dataset Integration, Data Processing, Mlperf, Automation

Description

Session Goal:

The goal of this session was to integrate a Hugging Face dataset into a Space application for performance analysis, manage large data files, and develop strategies for data processing and merging related to MLPerf datasets.

Key Activities:

  • Dataset Integration: Integrated a Hugging Face dataset, detailing paths for CSV files and methods for downloading data while ensuring dataset consistency and cleaning.
  • File Management: Executed shell commands to identify the largest CSV and JSON files, facilitating efficient data management.
  • Data Analysis: Analyzed file sizes and developed a strategy for data extraction, focusing on the significance of file sizes and practical commands for data analysis.
  • Notebook Analysis: Utilized Python to analyze CSV and JSON files, providing detailed statistics and a cross-file column overlap report.
  • Data Processing: Developed strategies for merging MLPerf datasets, including data cleaning and merging techniques in a Jupyter notebook.
  • Data Ingestion: Outlined strategies for data ingestion and normalization of MLPerf results, including processing recommendations.
  • Database Design: Designed a compact relational database schema for experimental data management, detailing entity relationships and ETL processes.
  • Automation: Automated data cleaning and mapping using Python scripts to convert raw data into standardized CSV formats.

Achievements:

  • Successfully integrated and managed datasets for performance analysis.
  • Developed comprehensive data processing and merging strategies.
  • Automated data cleaning and mapping processes, enhancing data quality and consistency.

Pending Tasks:

  • Further refine the database schema for improved data management.
  • Implement additional automation scripts for data normalization and ingestion.

Evidence

  • source_file=2025-12-10.sessions.jsonl, line_number=3, event_count=0, session_id=f684176a5227452b84812d2b7b3ac8cfe79c7396243fce74a9b1ac954a6e0bb5
  • event_ids: []

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