
Zachary Koopmans engineered robust GPU driver compatibility and testing infrastructure for the SagerNet/gvisor repository, focusing on cross-architecture support and CI reliability. He implemented automated CUDA and NVIDIA driver validation, refactored test suites for ARM64 and x86_64, and streamlined driver version management to reduce maintenance risk. Using Go, Docker, and Bazel, Zachary enhanced test automation, introduced architecture-aware workflows, and improved error handling in network and probe diagnostics. His work included containerd integration, DRAM encryption status IOCTLs, and dynamic CUDA image selection, resulting in more reliable GPU workload validation and simplified configuration paths. The solutions demonstrated technical depth and maintainability.
Month: 2026-01 | Highlights in google/gvisor: Delivered TCP Probe Creation Error Handling Enhancement, aimed at improving reliability of TCP probe creation and error management during runtime diagnostic probing. Refactored probe file handling to streamline creation, reduce setup failures, and improve maintainability. Commit reference: dcebd869cd0a1079718a843716f277a0d3b3abfc; PiperOrigin-RevId: 858830664.
Month: 2026-01 | Highlights in google/gvisor: Delivered TCP Probe Creation Error Handling Enhancement, aimed at improving reliability of TCP probe creation and error management during runtime diagnostic probing. Refactored probe file handling to streamline creation, reduce setup failures, and improve maintainability. Commit reference: dcebd869cd0a1079718a843716f277a0d3b3abfc; PiperOrigin-RevId: 858830664.
December 2025 – google/gvisor: Stabilized containerd configuration handling through a targeted refactor, removing the ToContainerdConfigTOML function and related structures to streamline the config path. This reduces maintenance burden, minimizes future configuration-related risks, and improves onboarding for new contributors. Change is captured in a single commit (6317c35bb75fc854bb89f71b5cfb5eca6f765153) with internal RevId 843811705.
December 2025 – google/gvisor: Stabilized containerd configuration handling through a targeted refactor, removing the ToContainerdConfigTOML function and related structures to streamline the config path. This reduces maintenance burden, minimizes future configuration-related risks, and improves onboarding for new contributors. Change is captured in a single commit (6317c35bb75fc854bb89f71b5cfb5eca6f765153) with internal RevId 843811705.
November 2025 — google/gvisor: Focused on test suite readiness and CI reliability for CUDA-related tests. Key outcomes include enabling execution of previously skipped tests, removing outdated skip indicators, and validating a patch that updates CUDA test behavior. Key commit: 4e49b809052f8fa09d3937f74d892786264c2177 (Remove old bug message in cuda tests). Impact: faster feedback cycles, reduced release risk, and improved test coverage visibility in CI. Technologies demonstrated: test automation, CI integration, CUDA test infrastructure, and Git patch management.
November 2025 — google/gvisor: Focused on test suite readiness and CI reliability for CUDA-related tests. Key outcomes include enabling execution of previously skipped tests, removing outdated skip indicators, and validating a patch that updates CUDA test behavior. Key commit: 4e49b809052f8fa09d3937f74d892786264c2177 (Remove old bug message in cuda tests). Impact: faster feedback cycles, reduced release risk, and improved test coverage visibility in CI. Technologies demonstrated: test automation, CI integration, CUDA test infrastructure, and Git patch management.
Monthly work summary for 2025-08 focused on SagerNet/gvisor. Delivered internal maintenance improvements to build visibility and driver version management, and introduced a new DRAM encryption status IOCTL with ABI integration. No externally visible user-facing changes this month; improvements primarily enhance testability, configuration simplicity, and driver security observability.
Monthly work summary for 2025-08 focused on SagerNet/gvisor. Delivered internal maintenance improvements to build visibility and driver version management, and introduced a new DRAM encryption status IOCTL with ABI integration. No externally visible user-facing changes this month; improvements primarily enhance testability, configuration simplicity, and driver security observability.
July 2025 contributions focused on expanding cross-architecture support for SagerNet/gvisor by delivering ARM64-compatible NVIDIA driver installation and GPU test suite improvements. Implemented architecture-aware download flows, added checksums and tests for ARM64 validation, and updated test infrastructure to run GPU-related tests on ARM, including skip logic for problematic tests and architecture-agnostic CUDA image tagging. These changes extend deployment options for ARM-based environments and improve reliability of GPU workloads on ARM platforms, aligning with our strategy to broaden platform coverage and reduce maintenance risk.
July 2025 contributions focused on expanding cross-architecture support for SagerNet/gvisor by delivering ARM64-compatible NVIDIA driver installation and GPU test suite improvements. Implemented architecture-aware download flows, added checksums and tests for ARM64 validation, and updated test infrastructure to run GPU-related tests on ARM, including skip logic for problematic tests and architecture-agnostic CUDA image tagging. These changes extend deployment options for ARM-based environments and improve reliability of GPU workloads on ARM platforms, aligning with our strategy to broaden platform coverage and reduce maintenance risk.
June 2025: Delivered CUDA testing support for the GKE tester in SagerNet/gvisor, enabling execution of CUDA workloads on GKE nodes. Refactored CUDA version parsing for reliability, expanded test infrastructure with new files and libraries, and ensured proper configuration and execution within the Kubernetes testing environment. These changes broaden GPU validation coverage and improve end-to-end test reproducibility.
June 2025: Delivered CUDA testing support for the GKE tester in SagerNet/gvisor, enabling execution of CUDA workloads on GKE nodes. Refactored CUDA version parsing for reliability, expanded test infrastructure with new files and libraries, and ensured proper configuration and execution within the Kubernetes testing environment. These changes broaden GPU validation coverage and improve end-to-end test reproducibility.
May 2025 focused on expanding CUDA 12.8 test coverage and incorporating it into the release validation pipeline for the SagerNet/gvisor project. Delivered a modular CUDA testing library, Docker-based test environments, dynamic CUDA image selection per test, and GPU driver compatibility checks with the runsc runtime. This work increases release confidence for GPU-enabled workloads and accelerates validation cycles.
May 2025 focused on expanding CUDA 12.8 test coverage and incorporating it into the release validation pipeline for the SagerNet/gvisor project. Delivered a modular CUDA testing library, Docker-based test environments, dynamic CUDA image selection per test, and GPU driver compatibility checks with the runsc runtime. This work increases release confidence for GPU-enabled workloads and accelerates validation cycles.
April 2025 monthly summary for SagerNet/gvisor focusing on GPU testing coverage, containerd integration shim, BLACKWELL support, CUDA 12.8 readiness, and test stability improvements. Delivered key features with runtime and driver compatibility improvements, demonstrating strong cross-domain skills in kernel/user-space interfaces, container runtimes, and test automation.
April 2025 monthly summary for SagerNet/gvisor focusing on GPU testing coverage, containerd integration shim, BLACKWELL support, CUDA 12.8 readiness, and test stability improvements. Delivered key features with runtime and driver compatibility improvements, demonstrating strong cross-domain skills in kernel/user-space interfaces, container runtimes, and test automation.
In March 2025, focused on stabilizing GPU-related test infrastructure in SagerNet/gvisor to deliver more reliable, cross-architecture validation. Delivered a targeted CUDA test fix that eliminates an unnecessary x86_64 Dockerfile and tightens cross-compilation checks, ensuring tests are correctly skipped or reported when building for ARM. This reduces flaky GPU test results, shortens feedback loops, and strengthens confidence in ARM support.
In March 2025, focused on stabilizing GPU-related test infrastructure in SagerNet/gvisor to deliver more reliable, cross-architecture validation. Delivered a targeted CUDA test fix that eliminates an unnecessary x86_64 Dockerfile and tightens cross-compilation checks, ensuring tests are correctly skipped or reported when building for ARM. This reduces flaky GPU test results, shortens feedback loops, and strengthens confidence in ARM support.
February 2025 (2025-02) — Summary for SagerNet/gvisor: Implemented ARM Testing Support in CI. Delivered an ARM-focused CI enhancement by adding a Dockerfile for ARM workloads in the cuda-tests and introducing ARM compatibility tagging to flag tests not supported on ARM due to cross-compilation constraints. This enables targeted ARM testing, accelerates feedback on ARM-specific limitations, and informs roadmap for ARM support. Commit: 725669a152674825e233e3557fe8fa401fe6f3d2 (Update cuda-tests for ARM workloads). Business impact: improved ARM validation, reduced risk for ARM deployments, clearer visibility into cross-arch constraints. Technologies: Docker, CI pipelines, cross-compilation awareness, ARM architectures.
February 2025 (2025-02) — Summary for SagerNet/gvisor: Implemented ARM Testing Support in CI. Delivered an ARM-focused CI enhancement by adding a Dockerfile for ARM workloads in the cuda-tests and introducing ARM compatibility tagging to flag tests not supported on ARM due to cross-compilation constraints. This enables targeted ARM testing, accelerates feedback on ARM-specific limitations, and informs roadmap for ARM support. Commit: 725669a152674825e233e3557fe8fa401fe6f3d2 (Update cuda-tests for ARM workloads). Business impact: improved ARM validation, reduced risk for ARM deployments, clearer visibility into cross-arch constraints. Technologies: Docker, CI pipelines, cross-compilation awareness, ARM architectures.
January 2025 monthly summary for SagerNet/gvisor focused on stabilizing GPU driver compatibility tests and strengthening test reliability. Implemented a robust fix to the GPU Driver Compatibility Test to gracefully handle unreleased COS versions (404 handling) and added a validation to fail the build if no tests ran, improving CI feedback and reducing flaky failures.
January 2025 monthly summary for SagerNet/gvisor focused on stabilizing GPU driver compatibility tests and strengthening test reliability. Implemented a robust fix to the GPU Driver Compatibility Test to gracefully handle unreleased COS versions (404 handling) and added a validation to fail the build if no tests ran, improving CI feedback and reducing flaky failures.
December 2024: Delivered targeted documentation enhancement for SagerNet/gvisor to support GPU-accelerated PyTorch workloads under gVisor. The update clarifies how to obtain compatible NVIDIA driver versions, provides runsc version-based driver recommendations, and includes commands to list supported drivers, improving user setup reliability and reducing onboarding friction.
December 2024: Delivered targeted documentation enhancement for SagerNet/gvisor to support GPU-accelerated PyTorch workloads under gVisor. The update clarifies how to obtain compatible NVIDIA driver versions, provides runsc version-based driver recommendations, and includes commands to list supported drivers, improving user setup reliability and reducing onboarding friction.
Nov 2024 monthly summary for SagerNet/gvisor focusing on NVIDIA GPU driver alignment and COS image compatibility, with automated testing enhancements and improved observability to reduce production risk.
Nov 2024 monthly summary for SagerNet/gvisor focusing on NVIDIA GPU driver alignment and COS image compatibility, with automated testing enhancements and improved observability to reduce production risk.

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