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
| PyTorch Version | NVIDIA Framework Container | NVIDIA Framework Wheel | JetPack Version |
|---|---|---|---|
| 2.8.0a0+5228986c39 | 25.05 | - | 6.2 |
| 2.7.0a0+79aa17489c | 25.04 | - | 6.2 |
| 2.7.0a0+7c8ec84dab | 25.03 | - | 6.2 |
| 2.7.0a0+6c54963f75 | 25.02 | - | 6.2 |
| 2.6.0a0+ecf3bae40a | 25.01 | - | 6.1 |
| 2.6.0a0+df5bbc09d1 | 24.12 | - | 6.1 |
| 2.6.0a0+df5bbc0 | 24.11 | - | 6.1 |
| 2.5.0a0+e000cf0ad9 | 24.10 | - | 6.1 |
| 2.5.0a0+b465a5843b | 24.09 | 24.09 | 6.1 |
| 2.5.0a0+872d972e41 | 24.08 | - | 6.0 |
| 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.