📅 2025-12-10 — Session: Integrated Hugging Face dataset for performance analysis
🕒 18:40–19:30
🏷️ Labels: Hugging Face, Dataset Integration, Data Processing, Mlperf, Automation
📂 Project: Dev
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.