v0.1.0 — Local Skillspace training (alpha)

DVIDIA Training v0.1.0 is a standalone, MIT-licensed local CPU pipeline for Skillspace footage. It checks real media, preserves source groups, fits a visual model, and exports inspectable weights and measurements. The optional arm extra fits simulation movement candidates from compatible aligned action labels.

Download the source archive and follow README.md, or install the wheel in a Python 3.12+ virtual environment:

python -m pip install dvidia_training-0.1.0-py3-none-any.whl
dvidia-training-doctor
dvidia-training-studio --directory runs/studio

FFmpeg and ffprobe must be installed separately. The wheel needs NumPy 2.5.3. No GPU, account, API key or hosted inference is required. For disconnected installation, prepare a matching wheelhouse and tools first; see docs/installation.md. This package is not published to PyPI.

Provisional small-example sizing is 1 modern CPU core, 2 GiB RAM and 1 GiB scratch space, plus recordings and installed tools. Recommended headroom is 2–4 cores, 4–8 GiB RAM and at least 2 GiB scratch space scaled to data. These are estimates, not verified minimums.

All eight software checks passed on runtime commit d1f0cbc: visual fitting/studio tests on Linux, macOS and Windows with Python 3.12/3.13, the Linux native arm pilot, and actual Docker training and inspection with networking disabled. Release commit 1718c3b only updates documentation after that tested commit.

The example Skillspaces are explicitly authored synthetic data. The visual result is prediction, not an acquired robot skill. The native example uses simulator action labels and an authored gripper/task supervisor; its candidate passed one exact simulated scene. Neither example learns shoe organization from human video or qualifies physical hardware.

SHA256SUMS covers the attached source, wheel, examples and result archives. Original footage is excluded from result archives; example Skillspace archives supply it separately. See the repository hardware, installation and data-format guides for measurement scope and limits.