NVlabs
Kernel died on CoverageMap with 1080Ti GPU ad Docker
Hi, I set up the Sionna environment with docker using the associated makefile. The Notebook Sionna_Ray_Tracing_Coverage_Map.ipynb con load the libraries, and the GPU is correctly configured. The scene is loaded successfully, but the kernel died when I computed:
cm = scene.coverage_map(max_depth=2, # Maximum number of ray scene interactions
num_samples=int(2e6), # If you increase: less noise, but more memory required
cm_cell_size=(5, 5), # Resolution of the coverage map
cm_center=[0, 0, 0], # Center of the coverage map
cm_size=[400, 400], # Total size of the coverage map
cm_orientation=[0, 0, 0]) # Orientation of the coverage map, e.g., could be also vertical
The same configuration works fine and smooth on Server with RTX A5000 and A100 GPUs.
Server configuration
sudo apt-get update
sudo apt-get install -y cuda-drivers-560
sudo apt-get -y install cuda-toolkit-12-6
sudo usermod -aG docker myuser
sudo apt-get install -y nvidia-container-toolkit
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker
Hello @lillidith,
What error message was shown in the terminal when the kernel died?
The most likely cause is running out of memory. You could try reducing num_samples and trying again.
Same issue on a workstation with 1080Ti 11Gb:
scene = load_scene() # Load empty scene
scene.tx_array = PlanarArray(num_rows=1,
num_cols=1,
vertical_spacing=0.5, # relative to wavelength
horizontal_spacing=0.5, # relative to wavelength
pattern="iso",
polarization="V")
scene.rx_array = scene.tx_array
tx0 = Transmitter(name='tx0',
position=[15, -10, 20],
orientation=[np.pi*5/6, 0, 0],
power_dbm=10)
scene.add(tx0)
cm = scene.coverage_map(max_depth=5, # Maximum number of ray scene interactions
num_samples=int(100), # If you increase: less noise, but more memory required
cm_cell_size=(5, 5), # Resolution of the coverage map
cm_center=[0, 0, 0], # Center of the coverage map
cm_size=[50, 50], # Total size of the coverage map
cm_orientation=[0, 0, 0])
.... of corse all seem perfect in workstation with A100 40Gb but it's not a memory pb ( num_samples=int(100) )! Compute capability? Or incompatible GPU ? I dont find a minimun requirement on hardware in the Sionna Doc site.
By the way, as a workaround you can set the environment variable CUDA_VISIBLE_DEVICE="" before launching the Jupyter server to fall back on the CPU backend. Depending on your hardware, it could be a viable alternative.
it's seem that with 2080Ti with 11 Gb it works so the the 1080Ti 11Gb card it's to old for sionna.
We may be able to find out the root cause if you share the error message printed in the terminal when the kernel dies.
