Running TensorRT and VPI Samples in NVIDIA JetPack Containers

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Introducing…Jetpackin’ It Up!

That’s right , with our new guide to running TensorRT and VPI samples in NVIDIA JetPack containers, you can now enjoy the sweet taste of success without any hassle or stress. No more pulling your hair out trying to figure out why your code isn’t working or dealing with ***** dependencies that won’t install properly.

So let’s dive right into it!

Step 1: Get Your Hands on Some JetPack Containers

To kick things off, you need to have some JetPack containers handy. You can easily download them from the NVIDIA NGC Registry using your favorite package manager (e.g., apt or yum). Here’s an example command for Ubuntu:


# Pull the JetPack container from the NVIDIA NGC Registry
docker pull nvidia/tensorrt:23.05-py3 # Pulls the specified container from the registry

# Install the JetPack container using your preferred package manager
apt install nvidia/tensorrt:23.05-py3 # Installs the container using apt package manager for Ubuntu

# Alternatively, you can use yum package manager for other Linux distributions
yum install nvidia/tensorrt:23.05-py3 # Installs the container using yum package manager for other Linux distributions

This will download the TensorRT container with Python 3 support. You can replace “tensorrt” with “nvinfer-samples”, “nvjpeg”, or any other JetPack container you need for your project.

Step 2: Run Your Container and Enjoy!

Once you have your containers, it’s time to run them using Docker Compose. Here’s an example configuration file that runs both the TensorRT and VPI samples in separate containers:

# This is a Docker Compose configuration file that runs two containers for TensorRT and VPI samples.

version: '3' # Specifies the version of Docker Compose being used.

services: # Defines the services/containers to be run.

  tensorrt-sample: # Name of the first service/container.
    image: nvidia/tensorrt:23.05-py3 # Specifies the image to be used for the container.
    working_dir: /opt/nvidia/deeplearning/examples/TensorRT # Sets the working directory for the container.
    command: bash -c "cd TensorRT && python tensorrt_demo.py" # Specifies the command to be executed when the container is run.

  vpi-sample: # Name of the second service/container.
    image: nvidia/nvjpeg:21.08 # Specifies the image to be used for the container.
    working_dir: /opt/nvidia/deeplearning/examples/VPI # Sets the working directory for the container.
    command: bash -c "cd VPI && python vpi_demo.py" # Specifies the command to be executed when the container is run.

This configuration file creates two services, one for the TensorRT sample and another for the VPI sample. The working directory is set to the appropriate location inside each container, and the command runs the corresponding demo script.

Step 3: Customize Your Containers as Needed

If you need to customize your containers (e.g., install additional dependencies or modify environment variables), you can do so by creating a Dockerfile for each container and building it using Docker Compose. Here’s an example Dockerfile that adds some extra packages to the TensorRT container:

# Use the nvidia/tensorrt image with version 23.05 and Python 3
FROM nvidia/tensorrt:23.05-py3

# Update the package list and install python3-pip
RUN apt update && apt install -y python3-pip

# Install specific versions of numpy and scikit-learn using pip3
RUN pip3 install numpy==1.21.4 scikit-learn==0.24.2

This Dockerfile adds the “numpy” and “scikit-learn” packages to the TensorRT container using pip, which can be useful for certain machine learning workloads. You can replace these commands with any other customizations you need.

Step 4: Enjoy Your Success!

That’s it ! With our guide to running TensorRT and VPI samples in NVIDIA JetPack containers, you should now have a smooth and hassle-free experience. No more pulling your hair out or dealing with ***** dependencies that won’t install properly. Just sit back, relax, and enjoy the sweet taste of success!

Cheers!

SICORPS