tenstorrent/tt-inference-server
May 2026 – Jun 2026
2 Months active
Languages Used
Python
Technical Skills
Deep LearningMachine LearningModel DeploymentPythonAPI integrationModel Optimization
Teja Priya Vardhineni
PROFILE
Teja Priya Vardhineni
Contributed to the tenstorrent/tt-inference-server repository by expanding the Wan2.2 I2V pipeline with experimental model runners for anime fine-tuning, distillation, and style adaptation. Leveraged Python and deep learning techniques to deliver features such as environment-driven configuration for LoRA runners and performance optimizations in the distill workflow. Ensured API compatibility with evolving tt-metal interfaces, refactored backend components for maintainability, and improved code quality through formatting and collaborative development. The work enabled faster, more reliable inference and streamlined deployment by reducing manual configuration, supporting diverse video processing workflows, and preparing the system for future model variants and large-scale optimization efforts.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
6Total
Bugs
0
Commits
6
Features
4
Lines of code
405
Activity Months2
Your Network
427 peopleWork History
June 2026
5 Commits • 3 Features
Jun 1, 2026June 2026 monthly summary for tenstorrent/tt-inference-server focusing on business value and technical performance. Key features delivered: - Wan2.2 platform API compatibility and distill performance improvements: Consolidated API compatibility updates for Wan2.2, addressing WanLoraPipelineI2V rename and prompts/trace changes, with performance-oriented optimizations in the distill workflow. Commits show import/name alignment and API signature fixes. - LoRA model runner environment-based configuration: Added environment variable handling to configure LoRA HW footprints and file paths without code changes (LORA_HIGH_PATH/LORA_LOW_PATH passed to LoRA pipeline). - LightX2V distill optimization and baked-in configuration: Hardcoded 4 inference steps for LightX2V distill and removed negative_prompt pass-through to align with the distill training setup and performance goals; trace/encode parameters baked into the pipeline. Major bugs fixed: - Fixed Wan2.2 pipeline __call__ arguments to match latest tt-metal API (prompts/list and removed height/width/frames from __call__), ensuring runtime compatibility. - Corrected LoRA I2V runner import to match tt-metal class rename (WanLoraPipelineI2V -> WanPipelineI2VLora) across the codebase. Overall impact and accomplishments: - Substantial performance and configurability gains across Wan2.2 and distill paths, enabling faster, more reliable inference with fewer deployment-time changes. The combined changes reduce guesswork for operators and prepare the system for TTMetal distill optimizations. - Improved maintainability and alignment with the updated tt-metal API, reducing risk of regressions in production pipelines. Technologies/skills demonstrated: - Python-based pipeline refactoring and API compatibility work, environment-driven configuration, and integration with tt-metal API changes. - Distill workflow optimization, fast-image-encode path activation, and trace-region tuning for large models. - Collaborative coding practices evidenced by multi-author commits and clear changelogs.
5 Commits • 3 Features
Jun 1, 2026June 2026 monthly summary for tenstorrent/tt-inference-server focusing on business value and technical performance. Key features delivered: - Wan2.2 platform API compatibility and distill performance improvements: Consolidated API compatibility updates for Wan2.2, addressing WanLoraPipelineI2V rename and prompts/trace changes, with performance-oriented optimizations in the distill workflow. Commits show import/name alignment and API signature fixes. - LoRA model runner environment-based configuration: Added environment variable handling to configure LoRA HW footprints and file paths without code changes (LORA_HIGH_PATH/LORA_LOW_PATH passed to LoRA pipeline). - LightX2V distill optimization and baked-in configuration: Hardcoded 4 inference steps for LightX2V distill and removed negative_prompt pass-through to align with the distill training setup and performance goals; trace/encode parameters baked into the pipeline. Major bugs fixed: - Fixed Wan2.2 pipeline __call__ arguments to match latest tt-metal API (prompts/list and removed height/width/frames from __call__), ensuring runtime compatibility. - Corrected LoRA I2V runner import to match tt-metal class rename (WanLoraPipelineI2V -> WanPipelineI2VLora) across the codebase. Overall impact and accomplishments: - Substantial performance and configurability gains across Wan2.2 and distill paths, enabling faster, more reliable inference with fewer deployment-time changes. The combined changes reduce guesswork for operators and prepare the system for TTMetal distill optimizations. - Improved maintainability and alignment with the updated tt-metal API, reducing risk of regressions in production pipelines. Technologies/skills demonstrated: - Python-based pipeline refactoring and API compatibility work, environment-driven configuration, and integration with tt-metal API changes. - Distill workflow optimization, fast-image-encode path activation, and trace-region tuning for large models. - Collaborative coding practices evidenced by multi-author commits and clear changelogs.
June 2026
May 2026
1 Commits • 1 Features
May 1, 2026May 2026 focused on expanding Wan2.2 I2V pipeline capability in tenstorrent/tt-inference-server by delivering experimental model runners and reinforcing the codebase for future model variants. Key feature delivery includes three experimental runners (AniSora, Distill, LoRA) for anime fine-tuning, distillation and style adaptation, built on existing mesh, fabric, and resolution helpers with additive changes. The work involved registering new runners across config, runner registration, and video map components, with no breaking changes to existing functionality. Code quality improvements were made via Ruff formatting fixes. This work positions the project to accelerate model customization, improve output quality, and support diverse I2V workflows, delivering business value through faster experimentation and broader use-cases.
May 2026
1 Commits • 1 Features
May 1, 2026May 2026 focused on expanding Wan2.2 I2V pipeline capability in tenstorrent/tt-inference-server by delivering experimental model runners and reinforcing the codebase for future model variants. Key feature delivery includes three experimental runners (AniSora, Distill, LoRA) for anime fine-tuning, distillation and style adaptation, built on existing mesh, fabric, and resolution helpers with additive changes. The work involved registering new runners across config, runner registration, and video map components, with no breaking changes to existing functionality. Code quality improvements were made via Ruff formatting fixes. This work positions the project to accelerate model customization, improve output quality, and support diverse I2V workflows, delivering business value through faster experimentation and broader use-cases.
Activity
Loading activity data...
Quality Metrics
Correctness100.0%
Maintainability86.6%
Architecture93.4%
Performance93.4%
AI Usage40.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
API integrationDeep LearningMachine LearningModel DeploymentModel OptimizationPythonPython Developmentbackend developmentdata processingenvironment variable managementperformance optimizationvideo processing
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline