Objective
Evaluate and integrate target determinator into ExecuTorch's OSS CI so that only tests affected by a PR's changes are executed, reducing CI cost and latency.
Background
Target determinator is used by PyTorch core to intelligently select which tests to run based on the files changed in a PR. Instead of running the full test suite on every PR, it maps code changes to relevant test targets — significantly reducing CI time and compute cost without sacrificing signal quality.
Action Items
-
Understand target determinator's current capabilities and integration requirements (talk to PyTorch CI infra team)
-
Evaluate ExecuTorch's test-to-source mapping complexity — how well can TD work given our repo structure (buck + CMake + GH Actions)?
-
Run a pilot: enable TD on a subset of workflows and measure:
- Reduction in tests run per PR
- CI time savings
- Any missed regressions (false negatives)
-
Define rollout plan: which workflows first, timeline, fallback strategy
-
Full integration with monitoring for false-negative rate
Success Criteria
- Target determinator integrated on at least the top 3 most expensive workflows
- Measurable reduction in average CI time per PR (target: 30%+)
- False-negative rate < 1% (regressions not caught by selective testing)
- Clear rollout timeline documented
References
PyTorch Target Determinator: https://github.com/pytorch/pytorch/wiki/Testing-infrastructure-overview
ExecuTorch Cost Dashboard: https://hud.pytorch.org/cost_analysis?dateRange=7&granularity=day&groupby=workflow_name&chartType=stacked_bar&yAxis=cost&repos=pytorch%2Fexecutorch
Objective
Evaluate and integrate target determinator into ExecuTorch's OSS CI so that only tests affected by a PR's changes are executed, reducing CI cost and latency.
Background
Target determinator is used by PyTorch core to intelligently select which tests to run based on the files changed in a PR. Instead of running the full test suite on every PR, it maps code changes to relevant test targets — significantly reducing CI time and compute cost without sacrificing signal quality.
Action Items
Understand target determinator's current capabilities and integration requirements (talk to PyTorch CI infra team)
Evaluate ExecuTorch's test-to-source mapping complexity — how well can TD work given our repo structure (buck + CMake + GH Actions)?
Run a pilot: enable TD on a subset of workflows and measure:
Define rollout plan: which workflows first, timeline, fallback strategy
Full integration with monitoring for false-negative rate
Success Criteria
References
PyTorch Target Determinator: https://github.com/pytorch/pytorch/wiki/Testing-infrastructure-overview
ExecuTorch Cost Dashboard: https://hud.pytorch.org/cost_analysis?dateRange=7&granularity=day&groupby=workflow_name&chartType=stacked_bar&yAxis=cost&repos=pytorch%2Fexecutorch