Skip to content

Latest commit

 

History

History
64 lines (47 loc) · 2.85 KB

README.md

File metadata and controls

64 lines (47 loc) · 2.85 KB
Raw

CV-CUDA Samples

Description

Sample applications to show how use some of CV-CUDA's functionalities.

Pre-requisites

  • Recommended linux distros:
    • Ubuntu >= 20.04 (tested with 20.04 and 22.04)
    • WSL2 with Ubuntu >= 20.04 (tested with 20.04)
  • CUDA driver >= 11.7
  • TensorRT == 8.5.2.2
  • torch == 1.13.0
  • torchvision == 0.14.0
  • torchnvjpeg (https://github.com/itsliupeng/torchnvjpeg)

Setting up the following is only required if you want to setup and run the samples in a docker container:

Setup to compile the sample from source.

  1. Get your CUDA and TensorRT installations ready. If you wish to install CUDA and TensorRT on your existing system you may do so by downloading those packages from NVIDIA's website. Or if you wish to work with in a docker container, you can use the TensorRT docker from NVIDIA NGC's catalog. It comes with CUDA and TensorRT pre-installed. Make sure you have setup NGC account properly and that your local docker installation has been logged into nvcr.io domain to be able to pull from that registry. Run the following command to start the container and continue rest of the installation steps in that container. Fill in the local_mount_path and docker_mount_path to reflect any paths on your system which you want to mount inside the container as well. This container uses Ubuntu 20.04 with Python 3.8.10

    docker run -it --gpus=all -v <local_mount_path>:<docker_mount_path> nvcr.io/nvidia/tensorrt:22.09-py3

  2. Install the CV-CUDA packages.

    dpkg -i nvcv-lib-0.2.0_alpha-cuda11-x86_64-linux.deb
dpkg -i nvcv-dev-0.2.0_alpha-cuda11-x86_64-linux.deb
dpkg -i cvcuda-samples-0.2.0_alpha-cuda11-x86_64-linux.deb
dpkg -i nvcv-python3.8-0.2.0_alpha-cuda11-x86_64-linux.deb

  3. Copy the samples folder to the target directory.

    cp -rf /opt/nvidia/cvcuda*/samples ~/
cd ~/samples

  4. Install the dependencies required to run the samples.

    chmod a+x scripts/*.sh
chmod a+x scripts/*.py
./scripts/install_dependencies.sh

  5. Build the samples (whichever samples requires a build)

    ./scripts/build_samples.sh

  6. Run all the samples on by one. The run_samples.sh script runs all the samples. Some samples may use the TensorRT backend to run the inference and it may require a serialization step to convert a PyTorch model into a TensorRT model. This step should take some time depending on the GPUs used but usually it is only done once during the first run of the sample.

    ./scripts/run_samples.sh