NVIDIA Jetson Nano 2GB Developer Kit available now

@julyighor, The insight is appreciated. Thanks.

@janmouli

I’ve tried building my OpenCV script on a 2GB nano. My tips for that would be:

  • use a swap file/device
  • start it at night and i’ll be done in the morning

If you have any issues, please report them on GitHub with any logs and i’ll try to get around to looking into the issue. I do have a 2GB Nano to test with. I’ve been busy recently with work, but I try to prioritize popular repos like the OpenCV build script.

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If you install JupyterLab natively onto your device (outside of container), then it should have access to the OpenCV that comes with JetPack. If you are running it inside of a container, then you need to install OpenCV into the container as well.

Hi @julyighor, we plan to upgrade Jetson devices to newer 5.x kernel and 20.04 LTS next year.

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That is great news! But if you are going to release single kernel version patches per 3 years, it makes no sense.
And if you chosen LTS kernel version but released no updates, it is also makes no sense to chose LTS.
LTS made for you to make you able release kernel patches updates as often as the official kernel.org releases with minimal efforts, not just once.

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The kernel version upgrade to 4.9 was released in 2019. There are a large number of patches required to the upstream kernel to support Tegra. We also backport patches from upstream. Even though the kernel version is 4.9, there are updates to it every JetPack release. However we do have plans for the future to help mitigate this and shorten the release cycle.

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That is not something that customers can do by their own, right @kayccc?

Nice, please tell where can I find the list of patches ported from a newer kernels to the 4.9.140?

If you needed full GPU / accelerator support, due to the volume not typically - although there are some software ecosystem partners with the expertise. As mentioned, this is something we are trying to make significantly easier in the future.

I’m not aware of a specific list of just those patches, but the commit logs can be found at http://nv-tegra.nvidia.com/gitweb/?p=linux-4.9.git From what I can tell they are mostly high-priority security/stability patches. There are also many patches each release from NVIDIA. We understand however that some developers wish to move to newer kernel, so we plan to do that and also make future upgrades easier to do.

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Can you explain what the difference is? Is it possible to support both versions with the same image at all?

After weeks trying, I still can’t make GPU/CUDA working along with (at least close to) latest OpenCV (4.4+) and TensorFlow (2+) via Jupyter Lab remotely on my Jetson Nano 2GB with JetPack 4.4.1 (L4T R32.4.4). It seems to me that I was working on a semi-open platform. Please let me know if there is actually a way by which can accomplish the setup.
Meanwhile, I hope this request could be addressed within the plan upgrading Jetson devices to newer 5.x kernel and 20.04 LTS next year. I would be too ignorant to waste more weeks on trying another similar semi-open platform again. Thank you.

Someone please help !

Sorry Sir!  I am newbie.

After I  bought Jetson nano 2GB, I joined two free course.  One is "Getting Started with DeepStream for Video Analytics on Jetson Nano"!

However, I try the course SD image dsnano_v1-0-0_20GB_200131A on Jetson nano 2GB.  It does not work!  Can you help me?  Or create appropriate SD image for Jetson nano 2GB?

Hi @TomEdge, the device tree is different (since the memory is different), hence it uses a different image. I don’t believe it’s possible to boot the same image on different devices.

Hi @gathermind, I don’t believe that particular DLI has been updated for the Nano 2GB. I recommend to try the Getting Started with AI on Jetson Nano course, which does work with Nano 2GB. This course is part of the Jetson AI Fundamentals program which also works on Nano 2GB.

Does Deepstream work on Nano 2GB? I installed Deepstream on it and ran ./deepstream-app -c …/samples/configs/deepstream-app/source8_1080p_dec_infer-resnet_tracker_tiled_display_fp16_nano.txt

The video window doesn’t show up and I get warnings like
There may be a timestamping problem, or this computer is too slow.
WARNING from sink_sub_bin_sink1: A lot of buffers are being dropped.
Debug info: gstbasesink.c(2902): gst_base_sink_is_too_late (): /GstPipeline:pipeline/GstBin:processing_bin_0/GstBin:sink_bin/GstBin:sink_sub_bin1/GstNvOverlaySink-nvoverlaysink:sink_sub_bin_sink1:
There may be a timestamping problem, or this computer is too slow.
WARNING from sink_sub_bin_sink1: A lot of buffers are being dropped.
Debug info: gstbasesink.c(2902): gst_base_sink_is_too_late (): /GstPipeline:pipeline/GstBin:processing_bin_0/GstBin:sink_bin/GstBin:sink_sub_bin1/GstNvOverlaySink-nvoverlaysink:sink_sub_bin_sink1:
There may be a timestamping problem, or this computer is too slow.
**PERF: 26.17 (26.08) 26.57 (26.19) 26.37 (26.08) 26.57 (26.15) 26.57 (26.08) 26.37 (26.06) 26.57 (26.13) 26.37 (26.02)
** INFO: <bus_callback:204>: Received EOS. Exiting …

Quitting
App run successful

DeepStream is supported on Nano 2GB, it is a matter of the number of streams and which config. From your log it does appear that the streams are running. You could try reducing the number of streams and/or editing the config file. I would recommend posting to the DeepStream forum to get their suggestions.

May I ask
I know that Jetson Nano can install such as Tensorflow, PyTorch, Keras, but I;m not sure whether Jetson Nano can run RNN?

hello sir I have 2 gb jetson nano. I need it for optical character recognition using python. I installed vs code as well as tesseract engine but vs code cant recognise tesseract. plz help me i installed tesseract using sudo command.is it installation problem or any other plz help

Hi @starsiddhir, if you run a python or python3 interpreter, are you able to import the module that way?

For additional follow-up, please start a new topic or in this other topic you posted to. Thanks.