AdvancedResearchComputing/examples
Apr 2025 – Jul 2025
2 Months active
Languages Used
ShellBashCPython
Technical Skills
Shell ScriptingBash ScriptingC ProgrammingData ScienceHPCMachine Learning
Eslam Ali Hassan Hussein
PROFILE
Eslam Ali Hassan Hussein
During their two-month contribution to the AdvancedResearchComputing/examples repository, Ehab Hussein developed three features focused on reproducible machine learning and data validation workflows for high-performance computing environments. Ehab created a cluster job submission script template using shell scripting and SBATCH directives, enabling scalable scikit-learn training runs with managed resource allocation. They also delivered a Python-based demo for the Iris dataset, showcasing data preprocessing and logistic regression, accompanied by an automated environment setup script. Additionally, Ehab implemented cross-cluster netCDF validation using C and Bash, providing scripts and tests that ensured consistent netCDF functionality across multiple HPC clusters, supporting robust onboarding and experimentation.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
3
Lines of code
2,413
Activity Months2
Your Network
21 peopleSame Organization
@tinkercliffs1.cm.cluster2
Work History
July 2025
2 Commits • 2 Features
Jul 1, 2025Monthly summary for 2025-07 (AdvancedResearchComputing/examples): Key features delivered include a Python-based ML demo illustrating scikit-learn usage on the Iris dataset (load, train/test split, feature scaling, Logistic Regression, accuracy evaluation) with an environment-setup shell script, and a NetCDF Utilities Demo Across Hardware consisting of cross-cluster example scripts and a C program to validate netCDF read/write functionality on Falcon, OWL, and Tinkercliffs. No major bugs fixed this month. Overall impact: delivers ready-to-run, reproducible experimentation assets that accelerate onboarding, cross-cluster validation, and research throughput. Technologies/skills demonstrated: Python, scikit-learn, shell scripting, C, HPC cross-cluster workflows, environment automation, data preprocessing, model training, and basic netCDF validation.
2 Commits • 2 Features
Jul 1, 2025Monthly summary for 2025-07 (AdvancedResearchComputing/examples): Key features delivered include a Python-based ML demo illustrating scikit-learn usage on the Iris dataset (load, train/test split, feature scaling, Logistic Regression, accuracy evaluation) with an environment-setup shell script, and a NetCDF Utilities Demo Across Hardware consisting of cross-cluster example scripts and a C program to validate netCDF read/write functionality on Falcon, OWL, and Tinkercliffs. No major bugs fixed this month. Overall impact: delivers ready-to-run, reproducible experimentation assets that accelerate onboarding, cross-cluster validation, and research throughput. Technologies/skills demonstrated: Python, scikit-learn, shell scripting, C, HPC cross-cluster workflows, environment automation, data preprocessing, model training, and basic netCDF validation.
July 2025
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for AdvancedResearchComputing/examples: Delivered a Cluster Job Submission Script Template for Scikit-Learn Training to streamline HPC workflows. The new shell script template includes an SBATCH directive to allocate resources and enable scalable experiments for machine learning training on the cluster. This work is anchored by commit 9136efdcd2e687448e20e501653e9df43a325238 ("adding scikit-learn").
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for AdvancedResearchComputing/examples: Delivered a Cluster Job Submission Script Template for Scikit-Learn Training to streamline HPC workflows. The new shell script template includes an SBATCH directive to allocate resources and enable scalable experiments for machine learning training on the cluster. This work is anchored by commit 9136efdcd2e687448e20e501653e9df43a325238 ("adding scikit-learn").
Activity
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Quality Metrics
Correctness86.6%
Maintainability86.6%
Architecture80.0%
Performance73.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
BashCPythonShell
Technical Skills
Bash ScriptingC ProgrammingData ScienceHPCMachine LearningPythonShell ScriptingnetCDF
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