Releases: SnapDragonfly/pytorch
Releases · SnapDragonfly/pytorch
Release pytorch-v2.5.1+l4t35.6-cp38-cp38-aarch64
Note: Build for Jetpack 5.1.4/6.2 with USE_FLASH_ATTENTION=0.
Note: CUDA: 11.8.89 / 12.6.68
- Jetpack 5
Software part of jetson-stats 4.2.12 - (c) 2024, Raffaello Bonghi
Model: NVIDIA Orin Nano Developer Kit - Jetpack 5.1.4 [L4T 35.6.0]
NV Power Mode[0]: 15W
Serial Number: [XXX Show with: jetson_release -s XXX]
Hardware:
- P-Number: p3767-0005
- Module: NVIDIA Jetson Orin Nano (Developer kit)
Platform:
- Distribution: Ubuntu 20.04 focal
- Release: 5.10.216-tegra
jtop:
- Version: 4.2.12
- Service: Active
Libraries:
- CUDA: 11.8.89
- cuDNN: 8.6.0.166
- TensorRT: 8.5.2.2
- VPI: 2.4.8
- Vulkan: 1.3.204
- OpenCV: 4.9.0 - with CUDA: YES
- Jetpack 6.2
Software part of jetson-stats 4.3.1 - (c) 2024, Raffaello Bonghi
Model: NVIDIA Jetson Orin Nano Developer Kit - Jetpack 6.2 [L4T 36.4.3]
NV Power Mode[0]: 15W
Serial Number: [XXX Show with: jetson_release -s XXX]
Hardware:
- P-Number: p3767-0005
- Module: NVIDIA Jetson Orin Nano (Developer kit)
Platform:
- Distribution: Ubuntu 22.04 Jammy Jellyfish
- Release: 5.15.148-tegra
jtop:
- Version: 4.3.1
- Service: Active
Libraries:
- CUDA: 12.6.68
- cuDNN: 9.3.0.75
- TensorRT: 10.3.0.30
- VPI: 3.2.4
- Vulkan: 1.3.204
- OpenCV: 4.11.0 - with CUDA: YES
Release pytorch-v2.3.1+l4t35.6-cp38-cp38-aarch64
- pytorch v2.3.1 build for nvidia jetson orin nano 8GB #143856
- pytorch v2.3.1 build failed - CUDA kernel function #143935
- PyTorch 2.3.1 Release, bug fix release
Note: Build for Jetpack 5.1.4 with USE_FLASH_ATTENTION=0.
Note: CUDA: 11.4.315
Software part of jetson-stats 4.2.12 - (c) 2024, Raffaello Bonghi
Model: NVIDIA Orin Nano Developer Kit - Jetpack 5.1.4 [L4T 35.6.0]
NV Power Mode[0]: 15W
Serial Number: [XXX Show with: jetson_release -s XXX]
Hardware:
- P-Number: p3767-0005
- Module: NVIDIA Jetson Orin Nano (Developer kit)
Platform:
- Distribution: Ubuntu 20.04 focal
- Release: 5.10.216-tegra
jtop:
- Version: 4.2.12
- Service: Active
Libraries:
- CUDA: 11.4.315
- cuDNN: 8.6.0.166
- TensorRT: 8.5.2.2
- VPI: 2.4.8
- OpenCV: 4.9.0 - with CUDA: YES
DeepStream C/C++ SDK version: 6.3
Python Environment:
Python 3.8.10
GStreamer: YES (1.16.3)
NVIDIA CUDA: YES (ver 11.4, CUFFT CUBLAS FAST_MATH)
OpenCV version: 4.9.0 CUDA True
YOLO version: 8.3.33
Torch version: 2.1.0a0+41361538.nv23.06
Torchvision version: 0.16.1+fdea156
DeepStream SDK version: 1.1.8
Release pytorch-v2.4.1+l4t35.6-cp38-cp38-aarch64
Note: Build for Jetpack 5.1.4 with USE_FLASH_ATTENTION=0.
Note: CUDA: 11.4.315
Software part of jetson-stats 4.2.12 - (c) 2024, Raffaello Bonghi
Model: NVIDIA Orin Nano Developer Kit - Jetpack 5.1.4 [L4T 35.6.0]
NV Power Mode[0]: 15W
Serial Number: [XXX Show with: jetson_release -s XXX]
Hardware:
- P-Number: p3767-0005
- Module: NVIDIA Jetson Orin Nano (Developer kit)
Platform:
- Distribution: Ubuntu 20.04 focal
- Release: 5.10.216-tegra
jtop:
- Version: 4.2.12
- Service: Active
Libraries:
- CUDA: 11.4.315
- cuDNN: 8.6.0.166
- TensorRT: 8.5.2.2
- VPI: 2.4.8
- OpenCV: 4.9.0 - with CUDA: YES
DeepStream C/C++ SDK version: 6.3
Python Environment:
Python 3.8.10
GStreamer: YES (1.16.3)
NVIDIA CUDA: YES (ver 11.4, CUFFT CUBLAS FAST_MATH)
OpenCV version: 4.9.0 CUDA True
YOLO version: 8.3.33
Torch version: 2.1.0a0+41361538.nv23.06
Torchvision version: 0.16.1+fdea156
DeepStream SDK version: 1.1.8
Release pytorch_v2.2.2+l4t35.6-cp38-cp38-aarch64
It seems v2.2.2 would be stable for v2.2.x series. Now build it for Jetpack 5.1.4.
Note: CUDA: 11.4.315
Software part of jetson-stats 4.2.12 - (c) 2024, Raffaello Bonghi
Model: NVIDIA Orin Nano Developer Kit - Jetpack 5.1.4 [L4T 35.6.0]
NV Power Mode[0]: 15W
Serial Number: [XXX Show with: jetson_release -s XXX]
Hardware:
- P-Number: p3767-0005
- Module: NVIDIA Jetson Orin Nano (Developer kit)
Platform:
- Distribution: Ubuntu 20.04 focal
- Release: 5.10.216-tegra
jtop:
- Version: 4.2.12
- Service: Active
Libraries:
- CUDA: 11.4.315
- cuDNN: 8.6.0.166
- TensorRT: 8.5.2.2
- VPI: 2.4.8
- OpenCV: 4.9.0 - with CUDA: YES
DeepStream C/C++ SDK version: 6.3
Python Environment:
Python 3.8.10
GStreamer: YES (1.16.3)
NVIDIA CUDA: YES (ver 11.4, CUFFT CUBLAS FAST_MATH)
OpenCV version: 4.9.0 CUDA True
YOLO version: 8.3.33
Torch version: 2.1.0a0+41361538.nv23.06
Torchvision version: 0.16.1+fdea156
DeepStream SDK version: 1.1.8
Release pytorch_v2.1.2+l4t35.6-cp38-cp38-aarch64
Just a test release for NVIDIA Jetson Orin Nano 8GB
As we have met some difficulties of pytorch support on Jetpack 5.1.4 L4T 35.6(ubuntu 20.04). NVDIA is now on Jetpack6 which bases ubuntu 22.04.
- [REQUEST] build script for pytorch or up to date pytorh binary release supporting jetson boards running L4T35.6(ubuntu20.04)
- Has JetPack 5 reached its end of life (EOL), or is there an EOL planned for it?
- PyTorch 2.1.2 Release, bug fix release
Build Environment:
Software part of jetson-stats 4.2.12 - (c) 2024, Raffaello Bonghi
Model: NVIDIA Orin Nano Developer Kit - Jetpack 5.1.4 [L4T 35.6.0]
NV Power Mode[0]: 15W
Serial Number: [XXX Show with: jetson_release -s XXX]
Hardware:
- P-Number: p3767-0005
- Module: NVIDIA Jetson Orin Nano (Developer kit)
Platform:
- Distribution: Ubuntu 20.04 focal
- Release: 5.10.216-tegra
jtop:
- Version: 4.2.12
- Service: Active
Libraries:
- CUDA: 11.4.315
- cuDNN: 8.6.0.166
- TensorRT: 8.5.2.2
- VPI: 2.4.8
- OpenCV: 4.9.0 - with CUDA: YES
DeepStream C/C++ SDK version: 6.3
Python Environment:
Python 3.8.10
GStreamer: YES (1.16.3)
NVIDIA CUDA: YES (ver 11.4, CUFFT CUBLAS FAST_MATH)
OpenCV version: 4.9.0 CUDA True
YOLO version: 8.3.33
Torch version: 2.1.0a0+41361538.nv23.06
Torchvision version: 0.16.1+fdea156
DeepStream SDK version: 1.1.8