jetsom compile

README.md

@@ -77,7 +77,10 @@ controls plus a pixel-identical live plot through an embedded web UI.
 - For the `sar3d` processor (3D SAR): an NVIDIA GPU with the driver installed
   (`libcuda`) and the external `gpr_cpp` library from the VNA_AlGo project. It
   must be built **before** `make` (see below); its location is configurable via
-  the `GPR_CPP_DIR` make variable (default `/home/awe/Documents/VNA_AlGo/gpr_cpp`).
+  the `GPR_CPP_DIR` make variable (default `$HOME/Documents/VNA_AlGo/gpr_cpp`).
+  CUDA headers/libnvrtc are taken from the pip `nvidia-*` packages in the
+  VNA_AlGo venv when present, otherwise from the system toolkit at
+  `/usr/local/cuda` (Jetson/JetPack, apt `cuda-toolkit`).
 
 ## Build and run
 
@@ -87,8 +90,9 @@ From the repository root:
 python3 -m venv .venv
 .venv/bin/python -m pip install --upgrade pip
 .venv/bin/python -m pip install -r requirements.txt
-# 3D SAR library (external, CMake + NVRTC; CUDA headers come from pip nvidia-* packages):
-bash /home/awe/Documents/VNA_AlGo/gpr_cpp/build_cmake.sh
+# 3D SAR library (external, CMake + NVRTC; CUDA headers come from the pip
+# nvidia-* packages in the VNA_AlGo venv, or /usr/local/cuda as a fallback):
+bash ~/Documents/VNA_AlGo/gpr_cpp/build_cmake.sh
 make                     # build the C++ binaries into build/bin/
 ```
 
@@ -114,6 +118,34 @@ Run the desktop GUI:
 `./start.sh --help`. The acquisition device (LibreVNA, Kamil ADC, …) is detected
 automatically from the active config's `radar.model` — there is no per-device flag.
 
+## Jetson (aarch64) bring-up
+
+Ubuntu 22.04 on Jetson has glibc 2.35, and PyQt6 6.8+ ships only
+`manylinux_2_39` aarch64 wheels — `requirements.txt` therefore pins PyQt6 to
+`<6.8` on aarch64 (6.7.1 is the last release with a compatible wheel). With
+that pin the stock `./start.sh` provisioning works; the extra steps are only
+for the CUDA-backed `sar3d` processor:
+
+```bash
+# 1. gpr_cpp against the JetPack system CUDA (no pip nvidia-* packages needed):
+git clone <VNA_AlGo repo> ~/Documents/VNA_AlGo
+GPR_BUILD_PYTHON=OFF bash ~/Documents/VNA_AlGo/gpr_cpp/build_cmake.sh
+# build_cmake.sh falls back to /usr/local/cuda automatically; override with
+# CUDA_INC=... CUDA_LIB=... if the toolkit lives elsewhere.
+
+# 2. copy the replay dataset (for dataset-replay profiles) and point
+#    radar.replay.dataset_dir in the profile at the local copy — the path in
+#    run_config_examples/*.json is machine-specific.
+
+# 3. normal launch; make picks up /usr/local/cuda when the VNA_AlGo venv has
+#    no pip nvidia-* packages:
+./start.sh --profile run_config_examples/run_config_replay_vna.example.json
+```
+
+If linking fails with `cannot find -lcuda`, the driver stub lives at
+`/usr/local/cuda/lib64/stubs/libcuda.so`; on JetPack the real driver is
+`/usr/lib/aarch64-linux-gnu/nvidia/libcuda.so.1` (present after boot).
+
 ## Running without hardware (emulation)
 
 Two ways to run the full pipeline with no radar attached: