Month 2026-03 — SHARK-Platform: CI/CD Reliability Enhancements for RDNA4 nightly builds. Delivered targeted CI/CD quality improvements to stabilize RDNA4 nightly builds by correcting the tarball retrieval path and removing brittle numerics checks for all_proxy during performance checks. These changes reduce flaky runs and speed up feedback for nightly validation.

February 2026 Monthly Summary (nod-ai/SHARK-Platform, nod-ai/SHARK-TestSuite) Key features delivered: - SHARK-Platform: CI Workflow Enhancements for Boo Framework. Consolidated CI improvements: added new input parameters for production convolution, all proxy shapes, GEMM shapes, and batch normalization; added backend option 'inductor' for batch normalization; expanded argument handling in production conversion script for more accurate workflow dispatch; updated installation script to include test dependencies and test dataset path; implemented RXDNA4/RDNA4 CI workflow configurations and logging; updated SHARK-Platform CI config to fix Torch ROCm version and environment. - SHARK-TestSuite: ROCm Test Suite Artifact Organization Improvement (reorganizing ROCm test suite cache directory for faster test analysis and easier sharing); Regression Testing Recheck Mechanism (scripts to re-run tests based on previous results and a regex fix to improve status parsing). Major bugs fixed: - CI stability fixes for Llama 405b/405 CI; corrected GPU access group permissions; alignment of Torch ROCm version and RDNA4 runner configurations across multiple CI jobs. - Bug fixes in CI for production-conv argument handling to ensure consistent workflow dispatch. Overall impact and accomplishments: - Significantly improved CI reliability and test feedback loop across platforms (AMD ROCm and RDNA4), enabling faster issue diagnosis and reduced flaky builds. - Improved test artifact management and reduced false regressions through the new recheck mechanism; - Broader, more flexible automation for production workflows and test data handling, improving reproducibility and developer productivity. Technologies/skills demonstrated: - CI/CD orchestration and workflow optimization, GPU/ROCm (Radeon) environments, RDNA4/Inductor backend integration, Python scripting for test management and workflow scripts, regex-based parsing for regression testing, and artifact organization.

January 2026 focused on strengthening testing fidelity, CI reliability, and cross-repo consistency for SHARK platforms. Key work delivered enhancements to model benchmarking and testing configuration, CI infrastructure, and regression testing reporting across two repositories, leading to faster, more reliable evaluations and clearer traceability for deployment decisions. Highlights: - SHARK-Platform: Model benchmarking and testing configuration improvements enabling robust Llama 8B FP8 benchmarking and IREE compatibility, along with data/model config refinements and environment script improvements (commits include f0c98262180b5eb4cd38922be12c967103d45edf, ae952410c3ea8bf0621b24d877c640797938441b, 33d05779449a40aa2532927c438148b5ebf45238, 5a604ac8b6a0da6e6bc4e4bcc038034363fc69d9, afbbc92be30b129e4e110ded35912afd2c94a39a). - SHARK-Platform: CI infrastructure, logging, and presubmit dependency updates to improve testing reliability and traceability (commits include cab3ead793611f98785ad976792259b42b298ca3, ca633f812ea77d19842e61a5959282877b1b5410, d58c2fc4b3087a0630753dfc0c967cfb041e98fa, 53bc5ec2c178fe95da4c0212220449139acda3b4). - SHARK-TestSuite: Enhanced regression testing and reporting for model status validations, including rechecks and ONNX model support (commits include 55613985e3592acf184b3c56b4ad51adce626754, be5de6637119e4037ce9bfabb48bb664b701ddbd, 0f34cad4f763b1bdb40f1c73950bce78c4dcebe6, 4196cf3744471fa456450c7d721ec9d9b23466c9). Impact: - Reduced test cycle times and improved confidence in benchmarking results. - Greater reliability of CI tests and logging enabling faster triage and debugging. - Clearer model status validations and ONNX support improving end-to-end workflow from development to deployment. Technologies/skills demonstrated: - CI/CD orchestration, Python tooling, ML benchmarking tooling, IREE integration, environment management, and regression testing automation.

Month: 2025-12 Overview: December delivered substantial CI/CD and benchmarking improvements across SHARK-Platform and SHARK-TestSuite, with a focus on reliability, reproducibility, and broader model validation. The work enabled more robust end-to-end testing, stabilized nightly builds, and ensured accurate artifact retrieval, all driving faster, more trustworthy feedback for model evaluation and deployment pipelines. Key features delivered: - Llama and related model CI resource path alignment: updated CI resource paths, IRPA references, and input data paths to ensure correct model loading in tests and benchmarks. This reduced path-related flakiness and improved benchmark reproducibility. - E2E testing framework enhancements and expanded model coverage: extended end-to-end CI to include GPT-oss and Llama 405b benchmarks, added repetitions and performance flags to improve reliability, and wired in IREE benchmarks for these models. - CI deployment configuration updates for SDXL/FLUX: refreshed artifact locations and configuration references to ensure pipelines pull the correct deployment artifacts. - Nightly build dependency and environment updates: hardened nightly setup by fixing migrations for setenv --nightly, updating URLs to fetch the latest wheels, and installing dataclasses-json to improve data handling during tests. - SHARK-TestSuite: CI test artifacts URL fixes: corrected storage URLs and resolved Azure storage access issues to ensure CI can fetch latest test results and regression reports reliably. Major bugs fixed: - CI Irpa and inputs path issues: aligned IRPA file paths and input references across multiple commits to fix loading failures in CI tests and benchmarks. - Test artifacts retrieval fixes: corrected CI storage URLs and Azure storage handling to ensure consistent access to test results. Overall impact and accomplishments: - Significantly improved CI reliability and benchmark reproducibility, enabling faster feedback and more trustworthy performance validations. - Broadened model coverage in E2E tests, including GPT-oss and Llama 405b, with better benchmarking stability via IREE. - Hardened nightly build pipelines and artifact dissemination, reducing maintenance overhead and deployment risk. Technologies and skills demonstrated: - CI/CD engineering, IRPA/resource path management, and end-to-end testing orchestration - Model benchmarking with IREE, and integration of GPT-oss and Llama 405b in CI - Environment provisioning and dependency management (setenv, nightly wheels, dataclasses-json) - Artifact management and cloud storage reliability (Azure storage, test artifacts URLs)

Month: 2025-11. This month delivered targeted CI improvements across ROCm/TheRock and nod-ai/SHARK-Platform, focusing on faster feedback, reliability, and alignment with CI infrastructure migrations. Key outcomes include the creation of a dedicated Navi3 Linux Nightly CI runner on gfx1101 for ROCm/TheRock, and a migration-driven fix for CI deployment config on nod-ai/SHARK-Platform to amdshark with corrected tokens and repository names. These enhancements reduce flaky tests, improve deployment accuracy, and strengthen cross-repo CI hygiene. Technologies demonstrated include CI/CD automation, Linux-based test infra, GPU driver-related testing, secret management, and robust commit-driven collaboration.

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.