mirror of
https://github.com/azaion/gps-denied-onboard.git
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Oleksandr Bezdieniezhnykh
9c13ab3bd0
C7 inference (PytorchFp16Runtime / TensorRTRuntime / OnnxTrtEpRuntime)
is CUDA-only by design — `model.half().cuda()` is hard-wired with no
CPU fallback. The Colima/Tier-1 smoke harness can never exercise C3
matcher or C7 inference. Once AZ-614 fixes the tlog time-base mismatch
and the pipeline reaches those stages, Colima runs would hard-fail at
`.cuda()` instead of cleanly skipping.
This commit lays down the Jetson companion harness and wires the
existing `tier2` auto-skip:
* tests/e2e/Dockerfile.jetson — l4t-pytorch:r36.4.0-pth2.3-py3 base,
same /opt layout as the Colima image so AC-4 AST scan + bind mounts
work identically. Built ON the Jetson via run-tests-jetson.sh.
* docker-compose.test.jetson.yml — mirrors docker-compose.test.yml
but with `runtime: nvidia`, GPU device exposure, and
GPS_DENIED_TIER=2 (turns OFF the tier2 auto-skip).
* scripts/run-tests-jetson.sh — rsync → ssh build → ssh up,
exit-code-from e2e-runner so the local exit code reflects the
remote test verdict. No credentials in the repo; uses
`ssh jetson-e2e` alias resolved via ~/.ssh/config.
* _docs/03_implementation/jetson_harness_setup.md — one-time SSH
key + alias + sshd hardening + GPU verification steps. Documents
the smoke vs. Reality Gate split + the GPS_DENIED_TIER switch.
AZ-617 (mark heavy ACs with tier2): adds @pytest.mark.tier2 to AC-1,
AC-2, AC-3, AC-5, AC-6 in tests/e2e/replay/test_derkachi_1min.py.
Reuses the existing tier2 marker + auto-skip in tests/conftest.py
(scope revision documented as a comment on AZ-617). AC-4a/4b/AC-7/AC-9
stay unmarked — they don't touch CUDA.
Defers to follow-up Jira:
* AZ-614 — Derkachi tlog synth time-base mismatch (unblocks tier2 ACs
actually reaching the GPU stage on the Jetson)
* AZ-616 — replace mock-sat with real ../satellite-provider service
Not run yet: the harness needs operator-side SSH setup to come online
before scripts/run-tests-jetson.sh can be executed end-to-end. Setup
steps documented in jetson_harness_setup.md.
Co-authored-by: Cursor <cursoragent@cursor.com>
84 lines
3.8 KiB
Docker
84 lines
3.8 KiB
Docker
# Tier-2 e2e-runner image — Jetson Orin Nano (JetPack 6.x, L4T R36.x).
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#
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# AZ-615: companion image to `tests/e2e/Dockerfile` (Colima/Tier-1 smoke
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# harness) that runs the full Reality Gate — including C3 matcher + C7
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# inference — against a CUDA-capable GPU.
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#
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# Hardware contract (operator-confirmed, 2026-05-17):
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# * Jetson Orin Nano, JetPack 6.2.2+b24, L4T R36.5.0
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# * nvidia-container-toolkit ≥ 1.16
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# * `docker run --runtime=nvidia ... nvidia-smi` returns the GPU
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#
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# Image layout mirrors the Colima Dockerfile (so AC-4 AST scan + bind
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# mounts work the same way):
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# /opt/pyproject.toml
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# /opt/src/gps_denied_onboard/... (SUT package, editable install)
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# /opt/tests/... (bind-mounted from host)
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# /opt/_docs/00_problem/input_data/ (bind-mounted from host)
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#
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# Build context is the repo root (see `docker-compose.test.jetson.yml`
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# → `services.e2e-runner.build.context`).
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#
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# BUILD HOST: this image MUST be built ON the Jetson — cross-building
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# from x86 macOS produces images that miss Tegra-specific shared libs
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# the nvidia-container-runtime later mounts at run time.
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# ---------------------------------------------------------------------------
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# Base — l4t-pytorch ships JetPack runtime + PyTorch wheel ready for `.cuda()`
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#
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# Tag selection: NGC publishes l4t-pytorch on a slight lag from L4T BSP
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# releases. With BSP R36.5 on the device, the closest stable NGC tag at
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# author time is `r36.4.0-pth2.3-py3`. NVIDIA containers are
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# forward-compatible across one minor BSP (the container's userspace
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# can be slightly older than the host's L4T kernel). If a `r36.5.0-*`
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# tag is published, prefer it.
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#
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# Image lookup at run time: `docker manifest inspect nvcr.io/nvidia/l4t-pytorch:r36.4.0-pth2.3-py3`
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FROM nvcr.io/nvidia/l4t-pytorch:r36.4.0-pth2.3-py3 AS runtime
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ARG DEBIAN_FRONTEND=noninteractive
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# System deps mirror tests/e2e/Dockerfile + the Jetson runtime stack:
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# * build-essential / libpq-dev / libspatialindex-dev — same as Colima
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# * python3-pip / python3-venv — l4t-pytorch ships python but not always venv
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# * libgl1 + libglib2.0-0 — OpenCV runtime libs (same reason as Colima)
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# * libpq5 + libspatialindex-c6 — runtime side of psycopg + rtree
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# Note: CUDA / cuDNN / TensorRT come pre-baked in the base image — do NOT
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# attempt to apt-install them (would conflict with the Tegra-specific libs
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# the runtime mounts).
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RUN apt-get update && apt-get install -y --no-install-recommends \
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ca-certificates \
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build-essential \
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libpq-dev \
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libspatialindex-dev \
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libpq5 \
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libspatialindex-c6 \
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libgl1 \
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libglib2.0-0 \
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python3-pip \
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python3-venv \
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&& rm -rf /var/lib/apt/lists/*
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WORKDIR /opt
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# Editable SUT install. Skipping the `[inference]` extra because PyTorch +
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# torchvision are already provided by the l4t-pytorch base image with
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# Tegra-specific CUDA builds; reinstalling them from PyPI would clobber
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# the Tegra wheels with x86-compatible ones that lack the cuDNN / cuBLAS
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# linkage required by Orin.
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COPY pyproject.toml README.md ./
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COPY src ./src
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# `--break-system-packages` is needed because the l4t-pytorch base image
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# uses an externally-managed Python environment (PEP 668). The alternative
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# would be to layer a venv on top of the pre-installed torch, but that
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# would shadow the Tegra-tuned torch wheel and break `.cuda()`. The image
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# IS the environment; embracing system-pip is the path of least drift.
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RUN pip3 install --no-cache-dir --break-system-packages -e ".[dev]"
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# ENTRYPOINT mirrors the Colima Dockerfile — pytest discovers both
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# `tests/e2e/replay/` (heavy tier2 ACs run with GPS_DENIED_TIER=2) and
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# any future `tests/e2e/scenarios/` additions. Rootdir resolves to /opt
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# via the COPY'd pyproject.toml so `from tests.e2e.replay._helpers import ...`
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# works inside the test files.
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ENTRYPOINT ["pytest", "-q", "/opt/tests/e2e/"]
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