October 2025: Delivered a comprehensive End-to-End LLM model validation and testing framework for SHARK-Platform, spanning export, compile, benchmark, and online serving stages. Established a CI workflow for E2E tests, expanded hardware coverage, and tuned benchmark timing to improve CI stability. Implemented CI improvements to reduce flakiness (remove continue-on-error in E2E tests; refresh gold benchmark timings; update GPU model coverage).

September 2025 (2025-09) monthly summary for nod-ai SHARK projects. This period delivered concrete business-valued features, reliability improvements in CI pipelines, and tooling enhancements that improve performance, predictability, and deployment readiness across SHARK-TestSuite and SHARK-Platform. Key features delivered: - SHARK-TestSuite: ONNX Runtime Optimization expanded to Funnel and FunnelBase models across multiple Opset versions, broadening the set of models eligible for optimization. Commit: d0d9ad5b0ef6e66656aaf4de4e83be03707b3379. - SHARK-Platform: CI Workflow Improvements and Reliability — overhauled CI orchestration and reporting, updated benchmark reporting for 2048 Input Sequence Length, switched SDXL Flux Serving CI to daily cadence, ensured all image model tests run with logging, added resilience so later steps execute even if earlier steps fail, and removed unnecessary CI jobs to streamline pipelines. Commits: 2ce1a61863cfcbcd3a294e30d5cf906186681b60; b62dd98aa8784d7740bc38adcbdb52e2e66eb078; 4e1818573e88879b5e42170198557055f0e85b13; bdbf19143901d2008a5e2c57a0589a9eaaf292f7. - SHARK-Platform: Performance and Export Tooling Flags Enhancements — introduced performance-oriented flags: enable HIP tensor ukernels via a new compilation flag (conditional on TENSOR_PARALLELISM_SIZE) and added explicit KV-cache dtype flags in the export script to ensure consistent typing for exported models. Commits: ed942a53b81e9fa79fae281ea53ab0b67ec6c804; c5d12337fe21c11d1b96f36e70acf2c10e3a0833. Major bugs fixed: - CI pipeline robustness and reliability improvements reduced flaky builds and outages by ensuring downstream steps execute even when earlier steps fail, enhanced logging and reporting, and removal of obsolete jobs that caused confusion. Overall impact and accomplishments: - Broadened optimization coverage for ONNX-based models, delivering faster and more cost-effective inference across a wider set of workloads. - Significantly improved CI reliability and feedback loops, enabling faster, more deterministic validation and deployment pipelines. - Strengthened export readiness and reproducibility through explicit typing guarantees and performance flags, reducing post-export fixes and deployment risk. Technologies/skills demonstrated: - ONNX Runtime optimization, Opset version compatibility, Funnel/FunnelBase model support - CI/CD orchestration, Mistral integration, IREE benchmarking, VMFB handling - HIP tensor ukernel integration, tensor parallelism, KV-cache dtype management - Robust test logging, benchmark reporting, pipeline resilience Business value: - Faster feature delivery to production with more reliable validation, broader model optimization coverage, and deterministic export artifacts, translating to shorter time-to-market and lower operational risk.

In August 2025, SHARK-Platform delivered a major CI pipeline enhancement focusing on logs, reliability, and validations. The updates consolidated CI activities across export, compilation, benchmarks, online serving, and log handling, increasing visibility and reducing post-run surprises. Key improvements include comprehensive log generation for export/compilation and iree_benchmark tests, early-failure checks to prevent cascading failures, always-push of log files even on failures, and online-serving response validation at higher cadence. Standardized log/report directories and robust copy behavior were implemented to prevent workflow disruptions when logs are missing, and logs were deployed to GitHub Pages for easier access and auditability.