same v0.6.0

same v0.6.0

下载 / Downloads

• Windows x64 and Linux x64: standard includes CPU and optional OpenCL iGPU; cuda additionally includes CUDA.
• macOS Apple Silicon: standard includes CPU and optional OpenCL iGPU; CUDA is unavailable.
• CUDA packages require a compatible NVIDIA driver, not a local CUDA compiler. A GPU is not required for ordinary CPU scanning.
• Windows targets Windows 10/11 x64; Linux packages are built on Ubuntu 24.04 (glibc 2.39+); macOS targets macOS 14+ ARM64 and is not notarized.
• Verify SHA256SUMS and build provenance. Archives include licenses and build-info.json identifying the exact commit and backend configuration.

更新 / Changes

• Added same train <dirs> [options]: select a bounded, size-stratified corpus across multiple directories and measure CPU, CUDA and iGPU on each selected file. With no directory, the corpus defaults to .; the former --corpus=DIR option remains supported. Training updates the same online model subsequently used by automatic scans, not a separate training database. Missing or failing accelerators fail training rather than silently recording CPU fallback as GPU data.
• Added end-to-end training telemetry and an opt-in, sectioned train --summary. Successful training is quiet without --summary; warnings and failures remain visible. Help output also gains color-coded hierarchy in an interactive terminal without changing its plain-text meaning.
• Added same merge [dir] [-r] [--move] [--summary] for model contributions, scan cache, telemetry runs and missing ignore rules. The destination configuration remains unchanged. Descendant ignore rules are scoped to their subtree; rules from an unrelated directory apply at the destination root with a warning. Guarded --move removes only source .same state after successful import and an unchanged-state check.
• Model provenance makes remerging a source idempotent: when that source trains again, its newer contribution replaces its older one rather than double-counting history. Existing model databases migrate to the new schema. Scan cache gains absolute-path archival while ordinary duplicate results remain limited to the current scan scope; an imported digest is reused only after a complete file-stamp match, and exact duplicates still require bytewise verification.
• Telemetry runs are imported by run ID with dependent events, metrics and parameters. A repeated merge skips already imported runs. Imported telemetry remains subject to the destination's later retention policy.

验证与边界 / Validation and limits

Before tagging, local Windows CPU Debug CTest passed 27/27. An isolated Windows Release smoke used physical CUDA and iGPU backends to train three files from two corpus directories: each backend returned three verified digest samples, and all nine accepted training traces persisted. The resulting model and telemetry were imported into a fresh workspace using the Release merge command. This is functional evidence, not a controlled scan-speedup benchmark. Hosted GitHub Actions rebuilds and tests the five release packages across Windows, Linux and macOS before publishing; hosted runners without a physical GPU cannot establish physical GPU performance.

train requires all three backends and representative input; it is not useful on a CPU-only installation. Cold accelerator setup can exceed the cost of short tasks, so normal automatic scans retain their cautious startup policy. Cache stamps are not an adversarial filesystem snapshot. merge is independently transactional and retryable per database, not one atomic transaction across all databases; --move is not globally atomic across multiple sources. Review external ignore rules before scanning. Training calibration and model prediction error do not by themselves prove an end-to-end speedup.

Scanning never deletes input files and remains nonrecursive by default. Use same scan -r --summary to opt into recursion and inspect diagnostics.