argonne-lcf/user-guides
Dec 2024 – Dec 2024
1 Month active
Languages Used
Markdown
Technical Skills
DocumentationPython Environment Management
AmakaOkafor
PROFILE
Amakaokafor
Nkechi Okafor developed a GPU-accelerated DBSCAN clustering notebook for the argonne-lcf/ALCF_Hands_on_HPC_Workshop repository, enabling scalable data analysis on Polaris using Dask and RAPIDS cuML. She implemented performance comparisons between GPU and CPU, capturing silhouette scores to demonstrate the impact of hardware acceleration for high-performance computing analytics. Her work included detailed setup instructions for Dask-RAPIDS clusters and guidance on managing project environments in Jupyter Notebook and Python. Additionally, for argonne-lcf/user-guides, she delivered comprehensive documentation on ipykernel installation and Jupyter kernel creation, improving onboarding and reproducibility. Her contributions reflect depth in data science, GPU computing, and technical documentation.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
9Total
Bugs
0
Commits
9
Features
2
Lines of code
3,119
Activity Months2
Your Network
131 people
Work History
December 2024
8 Commits • 1 Features
Dec 1, 2024December 2024 – Argonne-LCF User Guides: Delivered comprehensive documentation for installing ipykernel and creating a Jupyter kernel from Python virtual environments. The updates improve formatting, placeholder alignment, and readability for JupyterHub workflows, strengthening reproducibility and reducing onboarding time. Eight commits to python.md reflect iterative quality improvements and strong traceability. No major bugs reported; documentation-focused work aligns with our docs modernization and user support goals.
8 Commits • 1 Features
Dec 1, 2024December 2024 – Argonne-LCF User Guides: Delivered comprehensive documentation for installing ipykernel and creating a Jupyter kernel from Python virtual environments. The updates improve formatting, placeholder alignment, and readability for JupyterHub workflows, strengthening reproducibility and reducing onboarding time. Eight commits to python.md reflect iterative quality improvements and strong traceability. No major bugs reported; documentation-focused work aligns with our docs modernization and user support goals.
December 2024
October 2024
1 Commits • 1 Features
Oct 1, 2024Concise monthly summary for Oct 2024 focusing on features delivered, major fixes, impact, and skills demonstrated. Repository: argonne-lcf/ALCF_Hands_on_HPC_Workshop. The month centered on delivering a GPU-accelerated data clustering notebook and enabling scalable analysis via Dask-RAPIDS on Polaris.
October 2024
1 Commits • 1 Features
Oct 1, 2024Concise monthly summary for Oct 2024 focusing on features delivered, major fixes, impact, and skills demonstrated. Repository: argonne-lcf/ALCF_Hands_on_HPC_Workshop. The month centered on delivering a GPU-accelerated data clustering notebook and enabling scalable analysis via Dask-RAPIDS on Polaris.
Activity
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Quality Metrics
Correctness97.8%
Maintainability97.8%
Architecture97.8%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookMarkdownPython
Technical Skills
ClusteringDaskData ScienceDocumentationGPU ComputingMachine LearningPerformance OptimizationPython Environment ManagementRAPIDSScikit-learncuML
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline
argonne-lcf/ALCF_Hands_on_HPC_Workshop
Oct 2024 – Oct 2024
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
ClusteringDaskData ScienceGPU ComputingMachine LearningPerformance Optimization