First of all, if you’ve been working with AI for any amount of time, chances are you’re already familiar with PyTorch. It’s a popular open-source machine learning framework that allows you to build and train deep neural networks in Python. And the best part? It’s free!
But here’s where things get interesting the Jetson Nano is a tiny, low-power development board designed specifically for AI applications. It has a quad-core ARM Cortex-A57 processor, 4GB of RAM, and supports both CUDA and OpenCL acceleration. And guess what? PyTorch works great on it!
So why would you want to install PyTorch on the Jetson Nano? Well, for starters, it’s a lot cheaper than buying an expensive GPU-powered machine learning server. Plus, it’s small enough to fit in your pocket (literally), which makes it perfect for field testing and prototyping.
But here’s where things get tricky installing PyTorch on the Jetson Nano is not as straightforward as you might think. In fact, there are several different ways to do it, depending on your specific needs and preferences. So let’s dive in!
First of all, you need to make sure that your Jetson Nano has the latest version of Ubuntu installed (18.04 or later). This is important because PyTorch requires certain libraries and dependencies that are only available on this operating system. If you’re not already familiar with Ubuntu, don’t worry it’s pretty easy to install!
Once you have Ubuntu up and running, the next step is to download the latest version of PyTorch from the official website (https://pytorch.org/). This can be done using a simple command in your terminal:
# This script is used to download the latest version of PyTorch from the official website (https://pytorch.org/).
# It uses the wget command to retrieve the PyTorch wheel file for Ubuntu.
# The wget command is used to download files from the internet.
# The -O flag specifies the output file name, in this case "torch-1.8.1+cu102-cp39-cp39-linux_x64.whl".
# The URL for the PyTorch wheel file is provided after the -O flag.
wget -O torch-1.8.1+cu102-cp39-cp39-linux_x64.whl https://download.pytorch.org/whl/cu102/torch-1.8.1%2Bcu102-cp39-cp39-linux_x64.whl
This will download the latest version of PyTorch for CUDA 10.2 (which is what we’re using on our Jetson Nano). If you don’t have CUDA installed, you can skip this step and use the CPU-only version instead:
# This script downloads the latest version of PyTorch for CUDA 10.2 (which is what we're using on our Jetson Nano).
# If you don't have CUDA installed, you can skip this step and use the CPU-only version instead.
# Import the wget library to enable downloading files from a URL
import wget
# Set the URL for the PyTorch download, specifying the CPU-only version
url = 'https://download.pytorch.org/whl/cpu/torch-1.8.1-cp39-cp39-linux_x64.whl'
# Use the wget library to download the file from the specified URL
wget.download(url)
# The downloaded file will be saved in the current working directory.
Once you have downloaded the PyTorch wheel file, you can install it using pip:
# Install PyTorch using pip
# The following code uses pip to install the PyTorch wheel file
# The wheel file is a pre-compiled package that contains all the necessary files for installation
# The file name includes information about the version of PyTorch, the CUDA version, and the operating system
# The %2B symbol is used to represent the "+" character in the file name
# The cp39-cp39 indicates that the package is built for Python version 3.9
# The linux_x64 indicates that the package is built for a 64-bit Linux system
# The .whl extension stands for "wheel" and is the standard file format for Python packages
# The file name may vary depending on the version of PyTorch and the operating system
pip install torch-1.8.1%2Bcu102-cp39-cp39-linux_x64.whl
This will install PyTorch and all of its dependencies on your Jetson Nano. If you want to use the CPU-only version, just replace “torch-1.8.1%2Bcu102” with “torch-1.8.1”.
And that’s it! You now have PyTorch installed and ready to go on your Jetson Nano. But wait there’s more! If you want to use CUDA, you need to make sure that it’s properly configured as well:
# Set the environment variable LD_LIBRARY_PATH to include the CUDA library path
# This allows the system to locate the necessary CUDA libraries when running programs that require them
export LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64:$LD_LIBRARY_PATH
This will add the CUDA libraries to your system path, which is important for running PyTorch on the GPU. If you’re using a different version of CUDA (or no CUDA at all), just replace “cuda-10.2” with the appropriate value.
And that’s it! You now have PyTorch and CUDA installed and configured on your Jetson Nano, which means you can start building and training deep neural networks right away.