AI-Hypercomputer/tpu-recipes
Dec 2025 – May 2026
3 Months active
Languages Used
bashmarkdownBashMarkdownPythonShellYAML
Technical Skills
ContainerizationDevOpsMachine LearningPythonCloud ComputingDocker
Cory Kleinheksel
PROFILE
Cory Kleinheksel
Worked on the AI-Hypercomputer/tpu-recipes repository to deliver and refine large-scale machine learning training workflows, focusing on deployment reproducibility and production readiness. Developed and updated training recipes for DeepSeek v3 and Gemma4 models on Ironwood TPU clusters, leveraging Python, Kubernetes, and Bash scripting to standardize configurations and streamline orchestration. Enhanced experimentation speed and reliability by enabling synthetic data workflows and upgrading Python runtimes across recipes. Improved documentation and dependency management to support developer onboarding and CI integration. Addressed configuration bugs and maintained artifact compatibility, resulting in more predictable performance metrics and faster iteration cycles for TPU-based model training environments.
Overall Statistics
Feature vs Bugs
86%Features
Repository Contributions
17Total
Bugs
1
Commits
17
Features
6
Lines of code
3,889
Activity Months3
Your Network
5066 peopleSame Organization
@google.com5015
Benedict OdaiMember
Craig IngramMember
KayyuriMember
Scott SuarezMember
Agent2Agent (A2A) BotMember
Andreas AbelMember
Aadi KapurMember
Aadish GoelMember
Aahil MehtaMember
Work History
May 2026
3 Commits • 1 Features
May 1, 2026May 2026 monthly summary for AI-Hypercomputer/tpu-recipes: Delivered new training recipes for gemma4-26b and gemma4-31b on Ironwood TPU; stabilized TPU training configuration and ensured artifact compatibility by reverting Kubernetes manifest changes to align with original ubench artifacts. Results improve experiment reproducibility, deployment reliability, and training readiness, directly supporting faster AI iteration cycles and more predictable performance metrics.
3 Commits • 1 Features
May 1, 2026May 2026 monthly summary for AI-Hypercomputer/tpu-recipes: Delivered new training recipes for gemma4-26b and gemma4-31b on Ironwood TPU; stabilized TPU training configuration and ensured artifact compatibility by reverting Kubernetes manifest changes to align with original ubench artifacts. Results improve experiment reproducibility, deployment reliability, and training readiness, directly supporting faster AI iteration cycles and more predictable performance metrics.
May 2026
April 2026
13 Commits • 4 Features
Apr 1, 2026In April 2026, the tpu-recipes repository (AI-Hypercomputer/tpu-recipes) advanced production-readiness and deployment capabilities for large-scale training workflows while tightening reliability and consistency across environments. Key features target improved experimentation speed, deployment reliability, and reproducibility with synthetic data support, refreshed runtimes, and expanded model pretraining on Ironwood infrastructure. Key features delivered and enhancements: - DeepSeek v3 training and deployment recipes with docs: 671B 4K BF16/FSDP pretraining on TPU7x 4x8x8, deployment recipes for Ironwood TPU clusters, updated Kubernetes manifests, workload configuration refinements, and documentation corrections (e.g., precision labels and workload naming). Representative commits include 43ecfd65..., 306c2db8..., 4e28b459..., 6c7d0d88..., 6d971019..., 2a1965a5..., 9f5c7d1e... - Synthetic data training workflow: removes dataset_path dependencies to enable synthetic datasets and environment-driven configuration, boosting flexibility and usability across datasets. Commits include d7b8ea2f..., a693387a..., f09befa8... - Gemma4 model pretraining on Ironwood GKE (31B and 26B): expanded pretraining recipes for Gemma4 models on Ironwood GKE using Kubernetes JobSet and XPK for deployment/orchestration. Commits include ebf5691d..., f735e804... - Python version upgrade to 3.12 across recipes: standardization to Python 3.12 to improve compatibility and consistency across the repo (commit 6d45a055...). Major bugs fixed: - Corrected workload details in the k8s README for the deepseek3-671b FP8 workflow. - Fixed workload details and default naming in the xpk and k8s READMEs for deepseek3-671b FP8. - Resolved warnings and updated precision labels to bf16 in READMEs for consistency. - Removed hard-coded dataset_path references to enable synthetic data workflows and avoid environment-specific failures. Overall impact and accomplishments: - Increased deployment flexibility (TPU7x, Ironwood clusters) and reproducibility across environments with refined manifests, workloads, and docs. - Accelerated experimentation by enabling synthetic data workflows and environment-driven configuration, reducing setup time and iteration cycles. - Expanded model pretraining coverage (Gemma4) on robust Ironwood infrastructure, improving readiness for large-scale language model training. - Improved maintainability and consistency by standardizing Python runtime to 3.12 across recipes and aligned documentation. Technologies and skills demonstrated: - TPU7x, Ironwood TPU clusters, Kubernetes, Kubernetes JobSet, and XPK-based orchestration. - Environment-driven data workflows and synthetic data pipelines. - Cross-repo Python runtime standardization and multi-recipe modernization. - Comprehensive documentation discipline, workload naming conventions, and README consistency for release readiness.
April 2026
13 Commits • 4 Features
Apr 1, 2026In April 2026, the tpu-recipes repository (AI-Hypercomputer/tpu-recipes) advanced production-readiness and deployment capabilities for large-scale training workflows while tightening reliability and consistency across environments. Key features target improved experimentation speed, deployment reliability, and reproducibility with synthetic data support, refreshed runtimes, and expanded model pretraining on Ironwood infrastructure. Key features delivered and enhancements: - DeepSeek v3 training and deployment recipes with docs: 671B 4K BF16/FSDP pretraining on TPU7x 4x8x8, deployment recipes for Ironwood TPU clusters, updated Kubernetes manifests, workload configuration refinements, and documentation corrections (e.g., precision labels and workload naming). Representative commits include 43ecfd65..., 306c2db8..., 4e28b459..., 6c7d0d88..., 6d971019..., 2a1965a5..., 9f5c7d1e... - Synthetic data training workflow: removes dataset_path dependencies to enable synthetic datasets and environment-driven configuration, boosting flexibility and usability across datasets. Commits include d7b8ea2f..., a693387a..., f09befa8... - Gemma4 model pretraining on Ironwood GKE (31B and 26B): expanded pretraining recipes for Gemma4 models on Ironwood GKE using Kubernetes JobSet and XPK for deployment/orchestration. Commits include ebf5691d..., f735e804... - Python version upgrade to 3.12 across recipes: standardization to Python 3.12 to improve compatibility and consistency across the repo (commit 6d45a055...). Major bugs fixed: - Corrected workload details in the k8s README for the deepseek3-671b FP8 workflow. - Fixed workload details and default naming in the xpk and k8s READMEs for deepseek3-671b FP8. - Resolved warnings and updated precision labels to bf16 in READMEs for consistency. - Removed hard-coded dataset_path references to enable synthetic data workflows and avoid environment-specific failures. Overall impact and accomplishments: - Increased deployment flexibility (TPU7x, Ironwood clusters) and reproducibility across environments with refined manifests, workloads, and docs. - Accelerated experimentation by enabling synthetic data workflows and environment-driven configuration, reducing setup time and iteration cycles. - Expanded model pretraining coverage (Gemma4) on robust Ironwood infrastructure, improving readiness for large-scale language model training. - Improved maintainability and consistency by standardizing Python runtime to 3.12 across recipes and aligned documentation. Technologies and skills demonstrated: - TPU7x, Ironwood TPU clusters, Kubernetes, Kubernetes JobSet, and XPK-based orchestration. - Environment-driven data workflows and synthetic data pipelines. - Cross-repo Python runtime standardization and multi-recipe modernization. - Comprehensive documentation discipline, workload naming conventions, and README consistency for release readiness.
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025 (AI-Hypercomputer/tpu-recipes): Focused on improving deployment reproducibility and developer onboarding through documentation updates and dependency pinning. The primary deliverable was aligning the Docker image build workflow with MaxText's script and updating JAX and LibTPU versions in the docs. No major bugs fixed this month for this repository. These changes reduce build friction, improve consistency across environments, and support smoother CI pipelines.
1 Commits • 1 Features
Dec 1, 2025December 2025 (AI-Hypercomputer/tpu-recipes): Focused on improving deployment reproducibility and developer onboarding through documentation updates and dependency pinning. The primary deliverable was aligning the Docker image build workflow with MaxText's script and updating JAX and LibTPU versions in the docs. No major bugs fixed this month for this repository. These changes reduce build friction, improve consistency across environments, and support smoother CI pipelines.
December 2025
Activity
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Quality Metrics
Correctness96.4%
Maintainability90.6%
Architecture95.4%
Performance90.6%
AI Usage38.8%
Skills & Technologies
Programming Languages
BashMarkdownPythonShellYAMLbashmarkdown
Technical Skills
Bash scriptingCloud ComputingContainerizationDevOpsDockerGCPKubernetesMachine LearningPythonPython DevelopmentScriptingShell ScriptingTPU ManagementTPU configurationTPU deployment
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