
Manjunath A C contributed to IBM/project-pim by delivering three features over two months, focusing on deployment reliability and storage flexibility. He enhanced the installer by initializing the user profile before dependency installation, ensuring environment variables were correctly loaded for robust setup. Manjunath also expanded storage options by enabling the use and creation of virtual disks, improving resource utilization in the backend. In August, he modernized deployment by containerizing the HMC Agent, introducing Docker-based service management with systemd integration. His work leveraged Python, Shell scripting, and DevOps practices, resulting in reproducible, scalable deployments across development, testing, and production environments.
In 2025-09, delivered a Triton Inference Server-based deployment stack enabling fraud-detection inference with multi-app model serving, containerized for scalable production use. Generalized the Triton deployment to support different AI use-cases, establishing reusable patterns for multi-app serving. Refactored the repository layout to store and manage multiple models, simplifying onboarding of new AI apps and improving scalability. Implemented an automated build pipeline to generate AI application container images from the ai-demos repo, including critical fixes to build/run scripts and container entrypoints to ensure reliable training and deployment. Provided comprehensive end-to-end setup instructions and documentation, including host-path alignment fixes for model directories on the HTTP host server to ensure consistent serving.
In 2025-09, delivered a Triton Inference Server-based deployment stack enabling fraud-detection inference with multi-app model serving, containerized for scalable production use. Generalized the Triton deployment to support different AI use-cases, establishing reusable patterns for multi-app serving. Refactored the repository layout to store and manage multiple models, simplifying onboarding of new AI apps and improving scalability. Implemented an automated build pipeline to generate AI application container images from the ai-demos repo, including critical fixes to build/run scripts and container entrypoints to ensure reliable training and deployment. Provided comprehensive end-to-end setup instructions and documentation, including host-path alignment fixes for model directories on the HTTP host server to ensure consistent serving.
June 2025 performance highlights for IBM/project-pim: Delivered three core capabilities to strengthen HMC integration, visibility, and resource insight; improved reliability and observability through enhanced error handling and logging; and updated dependencies to support new functionality. Key outcomes include a new HMC version retrieval API, a Power Systems listing tool for MCP server visibility, and partition monitoring with granular statistics (memory/CPU) replacing the old status model. These changes enable faster issue diagnosis, better hardware visibility, and data-driven capacity planning, delivering tangible business value in operations efficiency and system reliability. Demonstrated technical excellence in API design, tooling, logging, and dependency management.
June 2025 performance highlights for IBM/project-pim: Delivered three core capabilities to strengthen HMC integration, visibility, and resource insight; improved reliability and observability through enhanced error handling and logging; and updated dependencies to support new functionality. Key outcomes include a new HMC version retrieval API, a Power Systems listing tool for MCP server visibility, and partition monitoring with granular statistics (memory/CPU) replacing the old status model. These changes enable faster issue diagnosis, better hardware visibility, and data-driven capacity planning, delivering tangible business value in operations efficiency and system reliability. Demonstrated technical excellence in API design, tooling, logging, and dependency management.
May 2025 focused on reliability and operational efficiency in IBM/project-pim. Delivered impactful features for ISO upload handling, enhanced partition management robustness, and vOPT lifecycle cleanup, underpinned by improved logging and state checks. These changes reduce duplicate uploads, prevent reuploads during reruns, and improve stability of storage lifecycle operations, delivering measurable business value in deployment reliability and reduced manual intervention.
May 2025 focused on reliability and operational efficiency in IBM/project-pim. Delivered impactful features for ISO upload handling, enhanced partition management robustness, and vOPT lifecycle cleanup, underpinned by improved logging and state checks. These changes reduce duplicate uploads, prevent reuploads during reruns, and improve stability of storage lifecycle operations, delivering measurable business value in deployment reliability and reduced manual intervention.
April 2025 performance summary for IBM/project-pim: Delivered automated IBMi PIM installer and enhanced environment provisioning, added a safety check to prevent unintended shutdown of logical partitions, and refreshed the UI to reflect entity extraction with vLLM. These changes reduce manual setup time, mitigate operational risk, and improve user clarity for PIM capabilities.
April 2025 performance summary for IBM/project-pim: Delivered automated IBMi PIM installer and enhanced environment provisioning, added a safety check to prevent unintended shutdown of logical partitions, and refreshed the UI to reflect entity extraction with vLLM. These changes reduce manual setup time, mitigate operational risk, and improve user clarity for PIM capabilities.
March 2025 performance highlights for IBM/project-pim: Delivered key modernization and reliability improvements across partition management, storage workflows, deployment automation, and code quality. Achievements include a refactor of partition management with new modules, enhanced storage attach/birth sequences enabling automatic boot from bootstrap ISO and post-write reboot, password-based authentication enabling rotation (with planned revert to hardcoded in config), VIOS ISO handling with checksum validation and cleanup, and targeted code quality improvements. Bug fixes addressed XML parsing for IBMi partitions and SCP path handling, reducing deployment failures. These efforts improve maintainability, deployment reliability, and security posture, delivering faster onboarding and consistent branding across the codebase.
March 2025 performance highlights for IBM/project-pim: Delivered key modernization and reliability improvements across partition management, storage workflows, deployment automation, and code quality. Achievements include a refactor of partition management with new modules, enhanced storage attach/birth sequences enabling automatic boot from bootstrap ISO and post-write reboot, password-based authentication enabling rotation (with planned revert to hardcoded in config), VIOS ISO handling with checksum validation and cleanup, and targeted code quality improvements. Bug fixes addressed XML parsing for IBMi partitions and SCP path handling, reducing deployment failures. These efforts improve maintainability, deployment reliability, and security posture, delivering faster onboarding and consistent branding across the codebase.
February 2025 performance summary for IBM/project-pim: Delivered two key features that enhance storage visibility and installation feedback, aligning with storage modernization goals and reducing manual troubleshooting time. Achievements include implementing VIOS Disk Logical Address Discovery to read and expose logical addresses from VIOS responses for improved storage resource management, and launching Real-time ISO Installation Log Streaming to stream installation logs line-by-line to the host console for immediate feedback during OS deployment. These work items, backed by focused commits, improved operational visibility, reduced installation debugging time, and demonstrate proficiency in system-level data extraction, streaming, and integration with host consoles.
February 2025 performance summary for IBM/project-pim: Delivered two key features that enhance storage visibility and installation feedback, aligning with storage modernization goals and reducing manual troubleshooting time. Achievements include implementing VIOS Disk Logical Address Discovery to read and expose logical addresses from VIOS responses for improved storage resource management, and launching Real-time ISO Installation Log Streaming to stream installation logs line-by-line to the host console for immediate feedback during OS deployment. These work items, backed by focused commits, improved operational visibility, reduced installation debugging time, and demonstrate proficiency in system-level data extraction, streaming, and integration with host consoles.
January 2025 monthly summary for IBM/project-pim. Highlights include delivery of end-to-end automation for partition management and ISO deployment, AI chatbot prompt handling and reliability improvements, and security/observability enhancements that reduce manual intervention and improve operational insight.
January 2025 monthly summary for IBM/project-pim. Highlights include delivery of end-to-end automation for partition management and ISO deployment, AI chatbot prompt handling and reliability improvements, and security/observability enhancements that reduce manual intervention and improve operational insight.
December 2024 (IBM/project-pim) delivered a comprehensive VM lifecycle automation capability alongside targeted enhancements across virtualization, storage, networking, and dependency management. The work established repeatable, auditable provisioning workflows for IBM Power Systems, improving time-to-value and reliability for production environments.
December 2024 (IBM/project-pim) delivered a comprehensive VM lifecycle automation capability alongside targeted enhancements across virtualization, storage, networking, and dependency management. The work established repeatable, auditable provisioning workflows for IBM Power Systems, improving time-to-value and reliability for production environments.

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