TensorStudio 2.1.0

TensorStudio 2.1.0

TensorStudio 2.1.0 extends the v2 CPU-first framework foundation with a
stronger backend boundary, safer interchange utilities, reproducible vision
dataset manifests, and refreshed local benchmark results.

Highlights:

• TensorFlow-style backend metadata for allocator, runtime, logical-device,
  kernel placement, transfer, and execution-plan diagnostics.
• Native storage telemetry for allocation checkouts, active bytes, cumulative
  bytes, and peak active usage.
• Safe descriptor-only custom-kernel manifest loading, validation, discovery,
  and registration.
• ONNX Runtime named-input inference through run_onnx_inference().
• Safe metadata inspection for Keras archives, TensorFlow SavedModel
  directories, HDF5/Keras weight files, and TensorFlow Lite flatbuffers.
• Deterministic image-folder manifests and ImageManifestDataset.
• README, docs, and benchmark reports updated for 2.1.0.

Validated locally:

• python -m ruff check .
• python -m mypy python\tensorstudio
• python -m pytest -q (237 passed)
• python -m mkdocs build --strict
• python benchmark_all.py
• python benchmarks\bench_matmul.py
• python -m build
• python -m twine check dist\tensorstudio-2.1.0*
• clean wheel install smoke test

Limitations remain explicit: published wheels are CPU-execution only; CUDA,
Metal, plugin execution, and full ONNX/TensorFlow/PyTorch runtime parity remain
future work.