NVIDIA Optimized Frameworks
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PyTorch for Jetson Platform

This document describes the key features, software enhancements and improvements, and known issues regarding PyTorch on the Jetson platform. See Installing PyTorch for Jetson Platform for installation information.

Key Features and Enhancements

This release includes the following key features and enhancements.

  • As of the 23.11 release, NVIDIA optimized PyTorch docker containers will also support iGPU architectures, including some Jetson devices. For more information on the containers themselves, please refer to the PyTorch Container Release Notes.
  • Starting from 24.06 release, cusparselt support has been enabled for Jetson Platform.

Compatibility

Table 1. PyTorch compatibility with NVIDIA containers and Jetpack
PyTorch Version NVIDIA Framework Container NVIDIA Framework Wheel JetPack Version
2.4.0a0+3bcc3cddb5 24.07 24.07 6.0
2.4.0a0+f70bd71a48 24.06 24.06 6.0
2.4.0a0+07cecf4168 24.05 24.05 6.0
2.3.0a0+6ddf5cf85e 24.04 24.04 6.0 Developer Preview
2.3.0a0+40ec155e58 24.03 24.03
2.3.0a0+ebedce2 24.02 24.02
2.2.0a0+81ea7a4 23.12, 24.01 23.12, 24.01
2.2.0a0+6a974bec 23.11 23.11
2.1.0a   23.06 5.1.x
2.0.0   23.05
2.0.0a0+fe05266f   23.04
2.0.0a0+8aa34602   23.03
1.14.0a0+44dac51c   23.02, 23.01
1.13.0a0+936e930   22.11 5.0.2
1.13.0a0+d0d6b1f   22.09, 22,10
1.13.0a0+08820cb 22.07 22.07
1.13.0a0+340c412 22.06 22.06 5.0.1
1.12.0a0+8a1a93a9 22.05 22.05 5.0
1.12.0a0+bd13bc66   22.04
1.12.0a0+2c916ef   22.03
1.11.0a0+bfe5ad28   22.01 4.6.1

Using PyTorch with the Jetson Platform

Storage

If you need more storage, we recommend connecting an external SSD via SATA on TX2 or Xavier devices, or USB on Jetson Nano.

Known Issues

  • TensorRT Model Optimizer is a new module but is not yet supported on Jetson due to its dependency on pytorch distributed functionality, which was disabled for Jetson.
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