# Enabling GPU usage on the VPU ## Using the GPU of the VPU The VPU provides substantial GPU (Graphical Processing Unit) power to the user. The best way to experience this is using CUDA and the samples from NVIDIA. To do so, we are building a container with the sample files from NVIDIA, push it to the VPU and execute it. This, however is not enough. Docker is not using the GPU power if not specified. We need to activate this too via the right runtime. ### Dockerfile sample The following Dockerfile builds the container with the samples from NVIDIA (see ). Dockerfile: ```Docker # Base linux for tegra (l4t) amr64/aarch64 image FROM nvcr.io/nvidia/l4t-base:r32.4.3 AS buildstage # Install necessary updates + git (for cloning the nvidia samples). Tag v10.2 specifies the right commit. VPU runs CUDA 10.2 RUN apt-get update && apt-get install -y --no-install-recommends make g++ git && apt-get install ca-certificates -y RUN git clone --depth 1 --branch v10.2 https://github.com/NVIDIA/cuda-samples.git /tmp/ # Change into the right directory and install/make the samples WORKDIR /tmp/Samples/deviceQuery RUN make clean && make # Multistage build to reduce the image size on the platform FROM nvcr.io/nvidia/l4t-base:r32.4.3 # Copy the samples from the buildstage into the final image RUN mkdir -p /usr/local/bin COPY --from=buildstage /tmp/Samples/deviceQuery/deviceQuery /usr/local/bin # Execute the deviceQuery and check for CUDA support. Don't forget the runtime with the docker run command CMD ["/usr/local/bin/deviceQuery"] ``` Building the container: ```console $ docker image build . -t cuda-samples Sending build context to Docker daemon 875.5MB Step 1/9 : FROM nvcr.io/nvidia/l4t-base:r32.4.3 AS buildstage ---> c93fc89026d9 ... Successfully tagged cuda-samples:latest ``` After building the container, you can follow the steps from the documentation to test the container on the VPU: - [Save the container](./docker.md#save-a-container): ```$ docker save cuda-samples > cuda-samples.tar``` - [Transfer the container](./deployVPU.md#using-scp): ```$ scp cuda-samples.tar oem@192.168.0.69:/home/oem``` - [Load the container](./docker.md#load-and-start-a-container): ```$ docker load < cuda-samples.tar``` ### Start the container with the NVIDIA runtime To use CUDA and the GPU, you have to specify the NVIDIA runtime, either with the `docker run` command, or within the `docker-compose.yml` (see [autostart](./autostart.md#autostart-a-container-on-the-vpu)). #### Using `docker run` Use the `--runtime nvidia` argument when running your container. The output of the running container should look similar to this: ```console o3r-vpu-c0:~$ docker run -it --runtime nvidia cuda-samples /usr/local/bin/deviceQuery Starting... CUDA Device Query (Runtime API) version (CUDART static linking) Detected 1 CUDA Capable device(s) Device 0: "NVIDIA Tegra X2" CUDA Driver Version / Runtime Version 10.2 / 10.2 CUDA Capability Major/Minor version number: 6.2 Total amount of global memory: 3829 MBytes (4014751744 bytes) ( 2) Multiprocessors, (128) CUDA Cores/MP: 256 CUDA Cores GPU Max Clock rate: 1300 MHz (1.30 GHz) Memory Clock rate: 1300 Mhz Memory Bus Width: 128-bit L2 Cache Size: 524288 bytes Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384) Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 2048 layers Total amount of constant memory: 65536 bytes Total amount of shared memory per block: 49152 bytes Total number of registers available per block: 32768 Warp size: 32 Maximum number of threads per multiprocessor: 2048 Maximum number of threads per block: 1024 Max dimension size of a thread block (x,y,z): (1024, 1024, 64) Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535) Maximum memory pitch: 2147483647 bytes Texture alignment: 512 bytes Concurrent copy and kernel execution: Yes with 1 copy engine(s) Run time limit on kernels: No Integrated GPU sharing Host Memory: Yes Support host page-locked memory mapping: Yes Alignment requirement for Surfaces: Yes Device has ECC support: Disabled Device supports Unified Addressing (UVA): Yes Device supports Compute Preemption: Yes Supports Cooperative Kernel Launch: Yes Supports MultiDevice Co-op Kernel Launch: Yes Device PCI Domain ID / Bus ID / location ID: 0 / 0 / 0 Compute Mode: < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) > deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 10.2, CUDA Runtime Version = 10.2, NumDevs = 1 Result = PASS ``` Note that starting the container without the runtime leads to a FAIL: ```console o3r-vpu-c0:~$ docker run -it cuda /usr/local/bin/deviceQuery Starting... CUDA Device Query (Runtime API) version (CUDART static linking) cudaGetDeviceCount returned 35 -> CUDA driver version is insufficient for CUDA runtime version Result = FAIL ``` #### Use `docker-compose` to specify the runtime Specifying the runtime in `docker-compose.yml` is possible for versions above `version: "2.3"` to get the runtime argument. ```yaml version: "2.3" services: cuda: image: cuda runtime: nvidia ``` Start the container using `docker-compose up`: ```console o3r-vpu-c0:~$ docker-compose up Creating network "oem_default" with the default driver Creating oem_cuda_1 ... done Attaching to oem_cuda_1 cuda_1 | /usr/local/bin/deviceQuery Starting... cuda_1 | cuda_1 | CUDA Device Query (Runtime API) version (CUDART static linking) cuda_1 | cuda_1 | Detected 1 CUDA Capable device(s) cuda_1 | cuda_1 | Device 0: "NVIDIA Tegra X2" cuda_1 | CUDA Driver Version / Runtime Version 10.2 / 10.2 cuda_1 | CUDA Capability Major/Minor version number: 6.2 cuda_1 | Total amount of global memory: 3829 MBytes (4014751744 bytes) cuda_1 | ( 2) Multiprocessors, (128) CUDA Cores/MP: 256 CUDA Cores cuda_1 | GPU Max Clock rate: 1300 MHz (1.30 GHz) cuda_1 | Memory Clock rate: 1300 Mhz cuda_1 | Memory Bus Width: 128-bit cuda_1 | L2 Cache Size: 524288 bytes cuda_1 | Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384) cuda_1 | Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers cuda_1 | Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 2048 layers cuda_1 | Total amount of constant memory: 65536 bytes cuda_1 | Total amount of shared memory per block: 49152 bytes cuda_1 | Total number of registers available per block: 32768 cuda_1 | Warp size: 32 cuda_1 | Maximum number of threads per multiprocessor: 2048 cuda_1 | Maximum number of threads per block: 1024 cuda_1 | Max dimension size of a thread block (x,y,z): (1024, 1024, 64) cuda_1 | Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535) cuda_1 | Maximum memory pitch: 2147483647 bytes cuda_1 | Texture alignment: 512 bytes cuda_1 | Concurrent copy and kernel execution: Yes with 1 copy engine(s) cuda_1 | Run time limit on kernels: No cuda_1 | Integrated GPU sharing Host Memory: Yes cuda_1 | Support host page-locked memory mapping: Yes cuda_1 | Alignment requirement for Surfaces: Yes cuda_1 | Device has ECC support: Disabled cuda_1 | Device supports Unified Addressing (UVA): Yes cuda_1 | Device supports Compute Preemption: Yes cuda_1 | Supports Cooperative Kernel Launch: Yes cuda_1 | Supports MultiDevice Co-op Kernel Launch: Yes cuda_1 | Device PCI Domain ID / Bus ID / location ID: 0 / 0 / 0 cuda_1 | Compute Mode: cuda_1 | < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) > cuda_1 | cuda_1 | deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 10.2, CUDA Runtime Version = 10.2, NumDevs = 1 cuda_1 | Result = PASS oem_cuda_1 exited with code 0 ```