tenstorrent/tt-forge-fe
Dec 2024 – Feb 2026
12 Months active
Languages Used
PythonYAMLJSONBash
Technical Skills
Computer VisionImage SegmentationMachine LearningModel CompilationModel IntegrationModel Testing
Priyankha Devi A.S
PROFILE
Priyankha Devi A.s
Pradeep Devi spent twelve months engineering robust test automation and CI/CD workflows for the tenstorrent/tt-forge-fe repository, focusing on nightly test suite stability and model validation. He expanded CPU-based model testing for computer vision and language models, integrating PyTorch and Hugging Face Transformers to ensure comprehensive coverage. Through Python and Pytest, Pradeep refined xfail marker management, tuned test durations, and automated log analysis, reducing flakiness and accelerating regression detection. He also delivered reusable scripts for cross-repository regression tracking and streamlined Excel-based reporting. His work improved CI reliability, reduced debugging time, and enabled faster, data-driven release decisions for the development team.
Overall Statistics
Feature vs Bugs
40%Features
Repository Contributions
65Total
Bugs
9
Commits
65
Features
6
Lines of code
2,254
Activity Months12
Your Network
384 peopleWork History
February 2026
1 Commits
Feb 1, 2026February 2026 monthly summary for tenstorrent/tt-forge-fe focused on delivering value through test stability improvements and reliable CI feedback. This period centered on stabilizing the nightly test suite and strengthening the signal from automated tests to accelerate release readiness.
1 Commits
Feb 1, 2026February 2026 monthly summary for tenstorrent/tt-forge-fe focused on delivering value through test stability improvements and reliable CI feedback. This period centered on stabilizing the nightly test suite and strengthening the signal from automated tests to accelerate release readiness.
February 2026
January 2026
4 Commits
Jan 1, 2026January 2026 focused on stabilizing the frontend test suite for tt-forge-fe by tightening regression tracking and xfail/xpass handling across nightly pipelines. Implemented targeted test instrumentation and reporting improvements to reduce flakiness and provide accurate velocity signals for release planning.
January 2026
4 Commits
Jan 1, 2026January 2026 focused on stabilizing the frontend test suite for tt-forge-fe by tightening regression tracking and xfail/xpass handling across nightly pipelines. Implemented targeted test instrumentation and reporting improvements to reduce flakiness and provide accurate velocity signals for release planning.
December 2025
4 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for tenstorrent/tt-forge-fe focusing on Nightly Test Suite Reliability Enhancements. Key features delivered: - Nightly Test Suite Reliability Enhancements implemented to stabilize the nightly CI by refining xfail markers and adjusting test durations to match current expectations and address crash scenarios. Major bugs fixed: - Flaky/false-negative/false-positive behaviors in nightly tests addressed by updating XPASS/xfail handling and validating cases across multiple commits. Overall impact and accomplishments: - Stabilized nightly CI, reducing pipeline noise and crashes, enabling faster feedback and more reliable nightly releases. - Improved developer productivity by lowering triage time for nightly-related failures and increasing confidence in test results. Technologies/skills demonstrated: - Pytest markers (xfail/XPASS), local validation, and test duration tuning. - CI/CD practices in GitHub Actions nightly pipelines. - Traceability and collaboration with cross-team reviews (cc: @nvukobratTT) and issue linkage. Note: Commits involved in this work include 93af5afed4dbe0d03d44b5cfcac71c3f5aa64cc8; 99eb83b479c4e3e9a2c3443eb21917a1f3c484dd; 16a7259723fa903a666a091fab95239c55ad1fc4; 504b3831de128bea7d66ff7c9df4c26a023a1ecf.
4 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for tenstorrent/tt-forge-fe focusing on Nightly Test Suite Reliability Enhancements. Key features delivered: - Nightly Test Suite Reliability Enhancements implemented to stabilize the nightly CI by refining xfail markers and adjusting test durations to match current expectations and address crash scenarios. Major bugs fixed: - Flaky/false-negative/false-positive behaviors in nightly tests addressed by updating XPASS/xfail handling and validating cases across multiple commits. Overall impact and accomplishments: - Stabilized nightly CI, reducing pipeline noise and crashes, enabling faster feedback and more reliable nightly releases. - Improved developer productivity by lowering triage time for nightly-related failures and increasing confidence in test results. Technologies/skills demonstrated: - Pytest markers (xfail/XPASS), local validation, and test duration tuning. - CI/CD practices in GitHub Actions nightly pipelines. - Traceability and collaboration with cross-team reviews (cc: @nvukobratTT) and issue linkage. Note: Commits involved in this work include 93af5afed4dbe0d03d44b5cfcac71c3f5aa64cc8; 99eb83b479c4e3e9a2c3443eb21917a1f3c484dd; 16a7259723fa903a666a091fab95239c55ad1fc4; 504b3831de128bea7d66ff7c9df4c26a023a1ecf.
December 2025
November 2025
4 Commits
Nov 1, 2025Monthly summary for 2025-11: Focused on stabilizing the nightly test pipeline for tenstorrent/tt-forge-fe, delivering changes that remove fragile nightly markers and adjust test durations to prevent timeouts and crashes in nightly CI. The work reduces flaky nightly runs and accelerates feedback loops for developers, contributing to more reliable releases and smoother CI.
November 2025
4 Commits
Nov 1, 2025Monthly summary for 2025-11: Focused on stabilizing the nightly test pipeline for tenstorrent/tt-forge-fe, delivering changes that remove fragile nightly markers and adjust test durations to prevent timeouts and crashes in nightly CI. The work reduces flaky nightly runs and accelerates feedback loops for developers, contributing to more reliable releases and smoother CI.
October 2025
13 Commits • 1 Features
Oct 1, 2025October 2025 focused on strengthening reliability and scalability of the nightly CI for tt-forge-fe and enabling scalable regression detection across Tenstorrent repositories. Delivered concrete CI stability improvements and a reusable regression-detection tool that reduces debugging time and accelerates root-cause analysis.
13 Commits • 1 Features
Oct 1, 2025October 2025 focused on strengthening reliability and scalability of the nightly CI for tt-forge-fe and enabling scalable regression detection across Tenstorrent repositories. Delivered concrete CI stability improvements and a reusable regression-detection tool that reduces debugging time and accelerates root-cause analysis.
October 2025
September 2025
9 Commits
Sep 1, 2025September 2025 monthly summary for tenstorrent/tt-forge-fe: Focused on stabilizing the nightly CI by improving test marker hygiene and duration tuning. Delivered targeted Pytest marker improvements and xfail/xpass handling across a sequence of commits to reduce flaky tests and crashes in the nightly pipeline. Major work centered on CI reliability, crash/data-mismatch fixes, and test-duration adjustments, resulting in faster, more reliable feedback loops for the development team. Key changes delivered (highlights across commits): c892d2a84e4f9db310f8e9841b8568fabcab3389 - Update Pytest Marker (#2930) 04f82670b80e5dd2193be43615c91deb3d547b6a - Add Xfail Marker (#2933) bc10933214c393fc933998dab5e49963b4cc343e - Modify Pytest Marker (#2936) with ticket references 38814246cfe2cb4e8c18fb70d5d896cc7b1cc209 - Add Xfail Marker (#2942) 67dc4558a25caa94ac46eaa3492e14394127bade - Update Pytest Marker And Time Duration (#2950) 0b4cdb57bda04106a5a5a2735f76b6820d2e9318 - Fix for Crash Cases (#2958) 5395a6cd67bd7b0ce5e0ef5584f5e1b0bc520b80 - Update Time Duration (#2963) d95ebebae5222ce4be77b3ae777878214f4cb7cd - Update Xfail Marker (#2970) 0aa8c475bdef1ada79275d3a766e76cb0672f5b4 - Update Time Duration (#2975)
September 2025
9 Commits
Sep 1, 2025September 2025 monthly summary for tenstorrent/tt-forge-fe: Focused on stabilizing the nightly CI by improving test marker hygiene and duration tuning. Delivered targeted Pytest marker improvements and xfail/xpass handling across a sequence of commits to reduce flaky tests and crashes in the nightly pipeline. Major work centered on CI reliability, crash/data-mismatch fixes, and test-duration adjustments, resulting in faster, more reliable feedback loops for the development team. Key changes delivered (highlights across commits): c892d2a84e4f9db310f8e9841b8568fabcab3389 - Update Pytest Marker (#2930) 04f82670b80e5dd2193be43615c91deb3d547b6a - Add Xfail Marker (#2933) bc10933214c393fc933998dab5e49963b4cc343e - Modify Pytest Marker (#2936) with ticket references 38814246cfe2cb4e8c18fb70d5d896cc7b1cc209 - Add Xfail Marker (#2942) 67dc4558a25caa94ac46eaa3492e14394127bade - Update Pytest Marker And Time Duration (#2950) 0b4cdb57bda04106a5a5a2735f76b6820d2e9318 - Fix for Crash Cases (#2958) 5395a6cd67bd7b0ce5e0ef5584f5e1b0bc520b80 - Update Time Duration (#2963) d95ebebae5222ce4be77b3ae777878214f4cb7cd - Update Xfail Marker (#2970) 0aa8c475bdef1ada79275d3a766e76cb0672f5b4 - Update Time Duration (#2975)
August 2025
12 Commits • 1 Features
Aug 1, 2025Concise monthly summary for 2025-08 for tenstorrent/tt-forge-fe: Delivered key features and mitigated critical CI/nightly flakiness to improve reliability of ONNX workflows. Key features delivered include the VILT ONNX model generation function; major bugs fixed include CI nightly ONNX caching stabilization and broader nightly test reliability improvements. Impact: more stable nightly builds, faster feedback, and increased confidence for downstream model deployments. Technologies/skills demonstrated include CI/CD configuration (IRD_LF_CACHE), ONNX workflow enhancements, pytest markers and test duration tuning, and robust test gating across models.
12 Commits • 1 Features
Aug 1, 2025Concise monthly summary for 2025-08 for tenstorrent/tt-forge-fe: Delivered key features and mitigated critical CI/nightly flakiness to improve reliability of ONNX workflows. Key features delivered include the VILT ONNX model generation function; major bugs fixed include CI nightly ONNX caching stabilization and broader nightly test reliability improvements. Impact: more stable nightly builds, faster feedback, and increased confidence for downstream model deployments. Technologies/skills demonstrated include CI/CD configuration (IRD_LF_CACHE), ONNX workflow enhancements, pytest markers and test duration tuning, and robust test gating across models.
August 2025
July 2025
7 Commits
Jul 1, 2025July 2025 focused on stabilizing and hardening the tt-forge-fe nightly test suite and CI workflow. Key improvements across test reliability, CI environment configuration, and process traceability addressed flaky nightly runs, enabling faster feedback and safer release decisions for the TTForge feature set.
July 2025
7 Commits
Jul 1, 2025July 2025 focused on stabilizing and hardening the tt-forge-fe nightly test suite and CI workflow. Key improvements across test reliability, CI environment configuration, and process traceability addressed flaky nightly runs, enabling faster feedback and safer release decisions for the TTForge feature set.
April 2025
2 Commits • 1 Features
Apr 1, 2025Month: 2025-04 Summary: Delivered Nightly Test Results Capture and Automated Analysis for tenstorrent/tt-forge-fe, introducing CI/CD enhancements to capture and analyze nightly test logs, reducing debugging time and enabling data-driven release decisions.
2 Commits • 1 Features
Apr 1, 2025Month: 2025-04 Summary: Delivered Nightly Test Results Capture and Automated Analysis for tenstorrent/tt-forge-fe, introducing CI/CD enhancements to capture and analyze nightly test logs, reducing debugging time and enabling data-driven release decisions.
April 2025
March 2025
1 Commits
Mar 1, 2025Month: 2025-03 Key features delivered: - Stabilized nightly test coverage for Densenet121_hf_xray by tuning the PCC verification threshold from 0.99 to 0.97, reducing false negatives due to minor discrepancies in model output. Major bugs fixed: - PCC mismatch in Densenet121_hf_xray model fixed (commit 0fc3fc2ce7a7200c353d61a27be2ca37301da7d2), lowering CI noise and improving nightly test reliability. Overall impact and accomplishments: - Improved CI reliability and predictability for tenstorrent/tt-forge-fe, accelerating feedback loops and stabilizing release readiness by reducing nightly churn. Technologies/skills demonstrated: - Model evaluation under tolerance variations, patching and verification, CI integration, and version-controlled debugging with traceable commits (e.g., #1365).
March 2025
1 Commits
Mar 1, 2025Month: 2025-03 Key features delivered: - Stabilized nightly test coverage for Densenet121_hf_xray by tuning the PCC verification threshold from 0.99 to 0.97, reducing false negatives due to minor discrepancies in model output. Major bugs fixed: - PCC mismatch in Densenet121_hf_xray model fixed (commit 0fc3fc2ce7a7200c353d61a27be2ca37301da7d2), lowering CI noise and improving nightly test reliability. Overall impact and accomplishments: - Improved CI reliability and predictability for tenstorrent/tt-forge-fe, accelerating feedback loops and stabilizing release readiness by reducing nightly churn. Technologies/skills demonstrated: - Model evaluation under tolerance variations, patching and verification, CI integration, and version-controlled debugging with traceable commits (e.g., #1365).
February 2025
3 Commits • 1 Features
Feb 1, 2025February 2025: Expanded Forge-based CPU model testing coverage for Falcon3 and Nano GPT, including causal language modeling checks and CPU execution validation, with enhanced test logging via a pytest verbosity option. This work is supported by commits 2c25e9d56a4157b1c9d5e30f04ef2d759385f442, 8d6a4da47add3e0de6faa33b746d94a5d97d09c1, and 79523f2aa814e41190674ac77b131c9511db0980. No major bug fixes were reported this month.
3 Commits • 1 Features
Feb 1, 2025February 2025: Expanded Forge-based CPU model testing coverage for Falcon3 and Nano GPT, including causal language modeling checks and CPU execution validation, with enhanced test logging via a pytest verbosity option. This work is supported by commits 2c25e9d56a4157b1c9d5e30f04ef2d759385f442, 8d6a4da47add3e0de6faa33b746d94a5d97d09c1, and 79523f2aa814e41190674ac77b131c9511db0980. No major bug fixes were reported this month.
February 2025
December 2024
5 Commits • 1 Features
Dec 1, 2024In December 2024, delivered expanded CPU test coverage for tenstorrent/tt-forge-fe by adding comprehensive CPU tests across multiple Forge models, including Detr, RegNet (y-040), Whisper large_v3_turbo, Llama 3.1/3.2, and Swin Transformer (v1/v2). These tests exercise preprocessing, model compilation, and runtime paths within the Forge framework, increasing reliability and ensuring ongoing compatibility as models evolve. No major bugs fixed are documented in this scope; the focus was on validating model coverage and stability through rigorous CPU testing.
December 2024
5 Commits • 1 Features
Dec 1, 2024In December 2024, delivered expanded CPU test coverage for tenstorrent/tt-forge-fe by adding comprehensive CPU tests across multiple Forge models, including Detr, RegNet (y-040), Whisper large_v3_turbo, Llama 3.1/3.2, and Swin Transformer (v1/v2). These tests exercise preprocessing, model compilation, and runtime paths within the Forge framework, increasing reliability and ensuring ongoing compatibility as models evolve. No major bugs fixed are documented in this scope; the focus was on validating model coverage and stability through rigorous CPU testing.
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Quality Metrics
Correctness82.2%
Maintainability82.4%
Architecture72.0%
Performance69.8%
AI Usage21.2%
Skills & Technologies
Programming Languages
BashJSONPythonYAML
Technical Skills
API IntegrationAutomationCI/CDComputer VisionData ProcessingDebuggingDevOpsEnvironment VariablesExcel ManipulationGitHub ActionsHugging Face TransformersImage SegmentationMachine LearningModel CompilationModel Deployment
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