ibm-granite-community/granite-code-cookbook
Oct 2024 – Dec 2024
2 Months active
Languages Used
YAMLJupyter NotebookPython
Technical Skills
CI/CDShell ScriptingCode GenerationJupyter NotebooksLangchainLarge Language Models (LLMs)
Dean Wampler
PROFILE
Dean Wampler
Worked on the ibm-granite-community/granite-code-cookbook repository, focusing on automation and reliability in Python-based workflows. Delivered an end-to-end code generation pipeline that uses Hypothesis property-based tests and large language models to automatically implement and validate a Rational class, all orchestrated within Jupyter Notebooks. Enhanced workflow hygiene by updating YAML-driven CI/CD scripts to ignore commented notebook entries, reducing runtime errors and manual intervention. Leveraged skills in Python, shell scripting, and test-driven development to streamline notebook processing and code generation. The work established a foundation for automated, test-driven workflows and improved the maintainability of the project’s automation infrastructure.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
568
Activity Months2
Your Network
7 peopleShared Repositories
7
Work History
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024: Delivered an automated Python code-generation workflow from unit tests for the Granite code cookbook. Introduced a new recipe and Jupyter notebook that defines Hypothesis tests for a Rational class and uses an LLM to generate the implementation, along with setup instructions, code-generation steps, and guidance to execute the generated code against the tests. This work establishes an end-to-end, test-driven automation workflow and lays the foundation for broader automation across projects.
1 Commits • 1 Features
Dec 1, 2024December 2024: Delivered an automated Python code-generation workflow from unit tests for the Granite code cookbook. Introduced a new recipe and Jupyter notebook that defines Hypothesis tests for a Rational class and uses an LLM to generate the implementation, along with setup instructions, code-generation steps, and guidance to execute the generated code against the tests. This work establishes an end-to-end, test-driven automation workflow and lays the foundation for broader automation across projects.
December 2024
October 2024
1 Commits
Oct 1, 20242024-10 monthly summary focusing on reliability and automation improvements in granite-code-cookbook workflows. The primary effort this month was a targeted bug fix to ensure notebook processing only uses active entries, thereby reducing runtime errors and manual cleanups in automated pipelines.
October 2024
1 Commits
Oct 1, 20242024-10 monthly summary focusing on reliability and automation improvements in granite-code-cookbook workflows. The primary effort this month was a targeted bug fix to ensure notebook processing only uses active entries, thereby reducing runtime errors and manual cleanups in automated pipelines.
Activity
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Quality Metrics
Correctness85.0%
Maintainability80.0%
Architecture80.0%
Performance75.0%
AI Usage50.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPythonYAML
Technical Skills
CI/CDCode GenerationJupyter NotebooksLangchainLarge Language Models (LLMs)Property-Based TestingPythonShell ScriptingTest-Driven Development (TDD)
Repositories Contributed To
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