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A modular, config-driven framework to: - ingest PyTorch or ONNX models, - convert them into Qualcomm QNN/QAIRT artifacts and context binaries, - profile runtime on device targets (mock backend included, QAI Hub backend scaffolded), - iterate optimization decisions with a LangGraph control loop until latency targets are met.
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Merge pull request #1 from bharaj0207/codex/add-profiling-support-for-physical-devices
b863478View on GitHubHarden physical-device SSH profiling and jump-host handling
9fcf116View on GitHubAdd real-mode preflight checks to container smoke script
40b814eView on GitHubAdd official QAI Hub SDK profiling mode and metric parsing
5cbdd6dView on GitHubAdd Dockerized environment, SDK hooks, and container smoke-test flow
dc43153View on GitHubAdd configuration examples, documentation, and strategy tests
e4c5f8fView on GitHubImplement QNN conversion, profiling, and LangGraph optimization loop
57567e7View on GitHubInitialize modular pipeline scaffold and target registry
410d0d5View on GitHub