Records the first Jetson Tier-2 run results in the step-11 report:
17 pass / 5 fail / 1 skip / 1 xfail (24 total, 10m09s) — identical to
Colima because all 5 failures hit AZ-614 (tlog time-base mismatch)
BEFORE reaching the GPU. So the infrastructure is proven (image
builds, GPU exposed inside container, SUT subprocess runs to the
auto-sync stage) but the heavy ACs haven't yet exercised ALIKED /
DISK LightGlue. Fixing AZ-614 is the gating prerequisite to actually
drive the GPU stages.
Also captures lessons learned that are now in the setup doc:
* Only dustynv/l4t-pytorch:r36.4.0 is a usable Jetson PyTorch base
on Docker Hub for R36 / JetPack 6 (l4t-base deprecated, official
l4t-pytorch has no R36 tags).
* The dustynv image bakes a maintainer-LAN-only pip mirror into
/etc/pip.conf — must be wiped + --index-url pinned to pypi.org.
* pip 24.2 (image default) rejects gtsam-4.3a0 pre-release; pip 26.x
accepts the same wheel for `gtsam<5.0,>=4.2` because there are no
stable aarch64 builds. Upgrade pip in the build, don't relax pin.
* nvidia-container-runtime mounts nvidia-smi from host, so the GPU
smoke test needs only ubuntu:22.04 (80 MB), not l4t-jetpack (5 GB).
Autodev state advances to phase 7 / jetson-harness-online.
Co-authored-by: Cursor <cursoragent@cursor.com>
Oleksandr Bezdieniezhnykh
8e563efd4c