A Step-by-Step Guide for Python Developers

Learn how to install PyTorch, a popular deep learning framework, on your NVIDIA Jetson Nano developer kit. …

Updated July 26, 2023

Learn how to install PyTorch, a popular deep learning framework, on your NVIDIA Jetson Nano developer kit.

As a Python developer interested in deep learning and computer vision, you’ve likely heard of PyTorch, a powerful open-source framework developed by Facebook’s AI Research Lab (FAIR). In this article, we’ll explore how to install PyTorch on your NVIDIA Jetson Nano developer kit. But before we dive into the installation process, let’s briefly discuss what PyTorch is and its importance.

What is PyTorch?

PyTorch is an open-source machine learning library for Python that provides a dynamic computation graph and automatic differentiation. It allows you to easily build, train, and deploy deep neural networks on various platforms, including the Jetson Nano.

PyTorch has gained popularity in recent years due to its simplicity, flexibility, and ease of use. It’s an ideal choice for researchers, students, and developers looking to explore computer vision, natural language processing, and other machine learning applications.

Importance and Use Cases

The Jetson Nano is a popular developer kit that provides a powerful platform for AI and deep learning development. With PyTorch installed on the Jetson Nano, you can:

Build and deploy computer vision models for tasks like object detection, segmentation, and classification
Develop natural language processing (NLP) models for text classification, sentiment analysis, and other NLP applications
Explore other machine learning areas like reinforcement learning, generative models, and more

Step-by-Step Installation Guide

Now that we’ve covered the basics of PyTorch and its importance on the Jetson Nano, let’s move on to the installation process.

Prerequisites:

NVIDIA Jetson Nano Developer Kit: Make sure you have a Jetson Nano board with an updated operating system.
Python 3.x: Ensure that Python 3.x is installed on your Jetson Nano.
pip: The pip package manager should be up-to-date.

Step 1: Install CUDA and cuDNN

The Jetson Nano requires CUDA and cuDNN for PyTorch to function correctly. Run the following commands to install them:

sudo apt-get update
sudo apt-get install -y nvidia-cuda-toolkit
sudo apt-get install -y libcudnn7

Step 2: Install PyTorch

With CUDA and cuDNN installed, you’re ready to install PyTorch. Run the following command:

pip3 install torch torchvision torchaudio

This will download and install the necessary packages.

Step 3: Verify the Installation

To verify that PyTorch has been successfully installed, run the following code snippet:

import torch

# Print the version of PyTorch
print(torch.__version__)

If everything went smoothly, you should see a message indicating the version of PyTorch installed on your Jetson Nano.

Tips and Tricks

Here are some tips to keep in mind when working with PyTorch on the Jetson Nano:

Use a GPU: Make sure to use the GPU for computations whenever possible. This will significantly speed up your training times.
Monitor Your Resources: Keep an eye on your system’s resources (CPU, memory, and disk space) to ensure you’re not running out of capacity.
Optimize Your Models: Optimize your models for inference by using techniques like quantization, pruning, or knowledge distillation.

Conclusion