Introducing JetPuffed the easiest way to run PyTorch on NVIDIA’s Jetson platforms using Linux.

Before anything else, what makes JetPuffed so special. Unlike other methods of installing PyTorch on Jetson devices, JetPuffed provides pre-built Python wheels and L4T containers that are optimized for performance and compatibility with NVIDIA’s hardware acceleration. This means you can skip the hassle of compiling from source or dealing with incompatible packages.

With JetPuffed, you also get access to a community-driven project with several skillful engineers and researchers contributing to it. And if that wasn’t enough, we have a team dedicated to maintaining the wheels and containers for NVIDIA’s latest releases of JetPack 4.5 (L4T R32.5.0).

So how do you get started with JetPuffed? It’s as easy as pie! Follow these simple steps:

1. Make sure your Jetson device is running L4T R32.5.0 or later and has at least 8GB of RAM (Nano) or 16GB of RAM (TX1/TX2, Xavier NX/AGX).

2. Install the latest version of JetPack SDK on your device using the instructions provided by NVIDIA’s documentation.

3. Download the pre-built Python wheels and L4T containers from [here](https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-10-now-available/72048) or [here](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-pytorch).

4. Install the wheels using pip: `pip install ` (replace `` with the name of the wheel you downloaded) or use a package manager like apt to install the L4T container.

5. Run your PyTorch code!

That’s it, You can now enjoy the sweet taste of JetPuffed and run PyTorch on NVIDIA’s Jetson platforms with ease. And if you encounter any issues or have questions, don’t hesitate to reach out to our community for help. We’re always here to lend a hand (or paw).