We are pleased to announce JetPack 4.6, a production release supporting Jetson AGX Xavier series, Jetson Xavier NX, Jetson TX2 series, Jetson TX1, and Jetson Nano.
JetPack 4.6 includes support for Triton Inference Server, new versions of CUDA, cuDNN and TensorRT, VPI 1.1 with support for new computer vision algorithms and python bindings, L4T 32.6.1 with Over-The-Air update features, security features, and a new flashing tool to flash internal or external media connected to Jetson.
In addition to l4t-base container image, new CUDA runtime and TensorRT runtime container images are released on NVIDIA NGC, which include CUDA and TensorRT runtime components inside the container itself, as opposed to mounting those components from the host. These containers are built to containerize AI applications for deployment. Note that the L4T-base container continues to support existing containerized applications that expect it to mount CUDA and TensorRT components from the host.
Highlights of JetPack 4.6 are:
Support for new 20W mode on Jetson Xavier NX enabling better video encode and video decode performance and higher memory bandwidth. The included 10W and 15W nvpmodel configurations will perform exactly as did the 10W and 20W modes with previous JetPack releases. Any custom nvpmodel created with a previous release will require regeneration for use with JetPack 4.6. Please read release notes for details.
Image based Over-The-Air update tools for developing end-to-end OTA solution for Jetson products in the field. Supported on Jetson TX2 series, Jetson Xavier NX and Jetson AGX Xavier series. Download the OTA tools from the L4T page under the Tools section.
A/B Root File System redundancy to flash, maintain and update redundant root file systems. Enhances fault tolerance during OTA by falling back to the working root file system slot in case of a failure. Supported on Jetson TX2 series, Jetson Xavier NX and Jetson AGX Xavier series.
A new flashing tool to flash internal or external media connected to Jetson1. Supports Jetson TX2 series, Jetson Xavier NX and Jetson AGX Xavier. The new tool uses initial RAM disk for flashing and is up to1.5x faster when flashing compared to the previous method2.
Secure boot is enhanced3 for Jetson TX2 series to extend encryption support to kernel, kernel-dtb and initrd.
Disk encryption of external media supported to protect data at rest for Jetson AGX Xavier series, Jetson Xavier NX and Jetson TX2.
NVMe driver added to CBoot for Jetson Xavier NX and Jetson AGX Xavier series. Enables loading kernel, kernel-dtb and initrd from the root file system on NVMe.
Enhanced Jetson-IO tools to configure the camera header interface and dynamically add support for a camera using device tree overlays
Support for Scalable Video Coding (SVC) H.264 encoding
Support for YUV444 8, 10 bit encoding and decoding
JetPack 4.6 containers are live now on NGC!
As mentioned in the announcement, we have release 2 new containers this time, a cuda runtime container and a tensorrt runtime container. These containers include CUDA and TensorRT runtime components inside the container itself, as opposed to mounting those components from the host.
Can you give more precise information on the 32.6.1 Multimedia API update? (which is not called Multimedia API anymore)
I can see libArgus now has 2 types of output streams. What is the advantage of using one over the other? Which one is comparable to the one from previous versions?
Can I download the multimedia api somewhere without installing a 4.6 Jetpack Jetson?
Jetson NX Developer Kit SD Card Image 4.62021/08/04
This SD card image works for the Jetson Xavier NX Developer Kit and is built with JetPack 4.6. Download and write it to your microSD card and use it to boot the developer kit.
md5sum: 6326244400c2f01aeece13a09a63d88d
Thank You for clarifying. Just started to test with this version and have had the system crash here and there. Otherwise it feels a bit more snappy at least until the slowdown to freeze. It truly does not like to run at 6 cores 20W.
Would you please help to create a new topic for how to test and how to observe it can’t run at 6 core 20W?
We will support you there to fix the problem if any.
There are 2 types of OutputStreams, BufferStream and EGLStream
BufferStream
Application-managed Buffer Streams
In addition to outputting capture results to an EGLStream, clients now have the ability to output capture results directly to client-allocated image buffers.
Buffer - Created and owned by an OutputStream, these objects wrap application-managed buffer resources and are used to synchronize data access to the buffer resources between Argus and the client application. Buffers are released to their stream to be used by Argus for a capture request, and are acquired back when the capture is complete and the client is ready to consume the output.
EGLStream-linked OutputStream objects maintain a connection to an EGLStream as the producer endpoint. The EGLStream implementation is responsible for buffer allocation, management, and synchronization as frames are presented to the EGLStream from libargus and then consumed using an EGLStream consumer.
EGLStream is the earlier one already supported.
Please look into the documentation for more details.