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jung-geun
813433000a
Migrate the package and examples to the tensor-native PyTorch implementation, add benchmark evidence, and add the guarded post-training convergence protocol with TensorBoard progress monitoring and hash-verified recovery. Constraint: Preserve one-shot official-test sealing and auditable research artifacts Rejected: Commit local .omc runs and downloaded datasets | multi-gigabyte runtime state is machine-local Confidence: high Scope-risk: broad Not-tested: Production CUDA run on pieroot-server
1334 lines
37 KiB
JSON
1334 lines
37 KiB
JSON
{
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"epoch_convergence_protocol_version": "1.0.0",
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"tuning_protocol_version": "1.0.0",
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"pso_version": "4.0.0",
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"torch_version": "2.13.0",
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"hardware": {
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"platform": "macOS-26.5.2-arm64-arm-64bit",
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"system": "Darwin",
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"machine": "arm64",
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"processor": "arm",
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"torch_version": "2.13.0",
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"device_type": "mps",
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"device_str": "mps",
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"mps_available": true
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},
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"device": "mps",
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"timestamp": "2026-09-02 00:01:42",
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"baseline_path": "benchmark_results/pso_v4_tuning.json",
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"data_fingerprint": "dfe645918ece54c0",
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"config": {
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71,
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75
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"passed": true
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