'embeded'에 해당되는 글 1456건

  1. 2025.09.05 eiq 데이터 구조
  2. 2025.09.04 ubuntu 22.04 + cuda + cudnn 설치
  3. 2025.09.04 import tensorflow illegal instruction
  4. 2025.09.03 eiq on windows with nvidia
  5. 2025.09.03 vainfo
  6. 2025.09.03 ubuntu nvidia 드라이버 설치
  7. 2025.08.28 SVE(Scalable Vector Extension)
  8. 2025.08.26 eiq 에러들
  9. 2025.08.26 eiq 학습 시도
  10. 2025.08.22 nvidia tao toolkit
embeded/i.mx 8m plus2025. 9. 5. 10:39

eiq 데이터 구조

크게 세 가지가 가능한데

VOC dataset은 detection을 위한 것이고

structured folder는 classification

tfds는 classification과 detection 모두를 위한 것인데...

 

3.3.1 VOC dataset
VOC datasets are introduced in Pascal Visual Object Classes Challenge. This format is also used by the ImageNet
datasets. Images and labels in this dataset can be used to train detection models.

 

3.3.2 Structured folders dataset
Using the Import dataset screen, you can also import datasets that have a specific folder structure. These datasets do
not have to be compressed into TAR files as with VOC datasets. The only requirement is that the images are saved in
specific folders. The datasets created this way can be used to train models for classification tasks.

 

3.3.3 TFDS datasets
TFDS datasets allow you to import some of the datasets provided by tensorflow in their catalog here. The tool allows
you to download datasets for image classification and object detection.

 

그러면 segmentation은 누구냐..

문서 상에서는 cli를 통해서 직접해야 하는건가..

7.1 Image segmentation
The following models target the image segmentation use case:
• deeplab_v3
To run the “deeplab” model, the workspace\models\deeplab_v3\runner_demo.py script runs the inference using the
converted model on the target device and decodes the output. You may also inspect the Python code. The script sends
a simple HTTP request to the device and the rest of the script decodes the output. There are also a few images available
for testing in the imgs folder.
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Posted by 구차니
embeded/i.mx 8m plus2025. 9. 4. 12:36

ubuntu 22.04 + cuda + cudnn 설치

 

압도적 감사!

그런데 비교해보니 환경변수가 필수였나.. 싶네

export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH

[링크 : https://starlane.tistory.com/1]

[링크 : https://bo-10000.tistory.com/209]

 

 

+

하드웨어

i7-10700F / 3070 8GB

 

명령어. 나중에 nouveau 쪽 수행하지 말고 바로 nvidia-driver 설치해봐야 겠다. 그러면 제법 시간이 단축될 듯

sudo apt install build-essential gcc ubuntu-drivers-common dkms vim nvidia-modprobe
sudo apt-get update; sudo apt-get upgrade -y
sudo bash -c "echo blacklist nouveau > /etc/modprobe.d/blacklist-nvidia-nouveau.conf"
sudo bash -c "echo options nouveau modeset=0 >> /etc/modprobe.d/blacklist-nvidia-nouveau.conf"
cat /etc/modprobe.d/blacklist-nvidia-nouveau.conf 
sudo update-initramfs -u

# after reboot
lsmod | grep nouveau
ubuntu-drivers devices
sudo apt install nvidia-driver-575

# add environment variables
echo export PATH=/usr/local/cuda/bin:$PATH > ~/.bashrc
echo export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH > ~/.bashrc

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin
sudo mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/13.0.0/local_installers/cuda-repo-ubuntu2204-13-0-local_13.0.0-580.65.06-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu2204-13-0-local_13.0.0-580.65.06-1_amd64.deb
sudo cp /var/cuda-repo-ubuntu2204-13-0-local/cuda-BE1CC667-keyring.gpg /usr/share/keyrings/

sudo apt-get update
sudo apt-get -y install cuda-toolkit-13-0
sudo apt-get install -y cuda-drivers

# for test
nvcc -V


wget wget https://developer.download.nvidia.com/compute/cudnn/9.12.0/local_installers/cudnn-local-repo-ubuntu2204-9.12.0_1.0-1_amd64.deb
sudo dpkg -i cudnn-local-repo-ubuntu2204-9.12.0_1.0-1_amd64.deb
sudo cp /var/cudnn-local-repo-ubuntu2204-9.12.0/cudnn-*-keyring.gpg /usr/share/keyrings/

 

eIQ 설치를 위한 준비

sudo apt-get install libffi7

 

저작자표시 (새창열림)

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Posted by 구차니
embeded/i.mx 8m plus2025. 9. 4. 10:32

import tensorflow illegal instruction

신형을 쓰면 모를수도 있지만..

아무생각 없이 구형에 펜티엄 cpu를 쓰다보니 이 사단이 나버리는 구먼.. -_-

조사를 해보니 숫자가 비슷하긴 한데.. 세대가 많이 차이가 났구먼..

 

G3460 / 하스웰 펜티엄 / SSE4.2

[링크 : https://www.intel.co.kr/content/www/kr/ko/products/sku/83428/intel-pentium-processor-g3460-3m-cache-3-50-ghz/specifications.html]

 

G4400T / 스카이레이크 펜티엄 / SSE4.2

[링크 : https://www.intel.co.kr/content/www/kr/ko/products/sku/90614/intel-pentium-processor-g4400t-3m-cache-2-90-ghz/specifications.html]

 

치사(?)하게 셀러론과 펜티엄은 뺐고, 타이거레이크(11세대) 이후 부터 펜티엄과 셀러론에도 넣어주었다고

CPUs with AVX
Intel
Sandy Bridge processors (Q1 2011) and newer, except models branded as Celeron and Pentium.[9]
Pentium and Celeron branded processors starting with Tiger Lake (Q3 2020) and newer.[10]
AMD:
Bulldozer processors (Q4 2011) and newer.[11]
Issues regarding compatibility between future Intel and AMD processors are discussed under XOP instruction set.

VIA:
Nano QuadCore
Eden X4
Zhaoxin:
WuDaoKou-based processors (KX-5000 and KH-20000)

[링크 : https://en.wikipedia.org/wiki/Advanced_Vector_Extensions]

 

텐서플로우 릴리즈 노트 찾아보니 1.5.0 rc 부터 AVX 넣어놔서 안될수도 있다고 한다.

TensorFlow 1.5.1
Release 1.5.1
Bug Fixes and Other Changes
Fixes a potential security vulnerability where on-the-fly changes to the dtype of a tensor reference may lead to undefined behavior.
Linux binaries on pypi are built with AVX. Please use GCS binaries for non-AVX machines.

TensorFlow 1.5.0
Release 1.5.0
Breaking Changes
Prebuilt binaries are now built against CUDA 9 and cuDNN 7.
Starting from 1.6 release, our prebuilt binaries will use AVX instructions.
This may break TF on older CPUs.

[링크 : https://github.com/tensorflow/tensorflow/releases?q=avx&expanded=true]

[링크 : https://github.com/tensorflow/tensorflow/issues/17411]

[링크 : https://m.blog.naver.com/kimground/221552744535]

[링크 : https://hiseon.me/data-analytics/tensorflow/tensorflow-errors/]

저작자표시 (새창열림)

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Posted by 구차니
embeded/i.mx 8m plus2025. 9. 3. 17:41

eiq on windows with nvidia

G3460 / win 10 클린설치하고

nvidia 580.97 드라이버

cuda 13.0.0.0

cudnn 9.12.0 설치하고

eiq 1.16.0.106_b250703 을 설치하고 실행하는데 아래와 같이 뜨길래

해보니 결론은.. 

msvc++ redist 2015-2011 14.44.35211 설치하고 해결 -_-

 

그런데 왜 학습할 때 gpu 0% 뜨냐 -_-

 

C:\nxp\eIQ_Toolkit_v1.16.0>
]eIQ Portal version 2.16.0
------------------------------------------------
C:\nxp\eIQ_Toolkit_v1.16.0\resources\app.asar
------------------------------------------------
Launch -> C:\nxp\eIQ_Toolkit_v1.16.0
16:08:22.435 > Launching Application
Display size is 1920x1080
[2025-09-03T07:08:22.572Z] ExtensionHostController running as pid 7112

is-elevated: false
[2025-09-03T07:08:24.417Z] Socket.io logging server connected to socket 9rpMJdiuTUcknHa9AAAB
[2025-09-03T07:08:24.443Z] InstalledExtensionsReady barrier opened
(buffered) [2025-09-03T07:08:23.262Z] ExtensionHost running as pid 8976
(buffered) [2025-09-03T07:08:23.474Z] Connected to sqlite://C:\Users\user\AppData\Roaming\eiqportal-nodejs\Config\state.sqlite
(buffered) [2025-09-03T07:08:24.425Z] Preparing Python environment...
(buffered) [2025-09-03T07:08:24.426Z] Installing Flask@3.0.3
(buffered) [2025-09-03T07:08:24.427Z] Activated 5 extensions
- NXP.eiqtlk-ext-armvela version 1.3.1
   from C:\Users\user\.eiqportal\extensions\eiqtlk-ext-armvela
- nxp.explainability version 0.4.0
   from C:\Users\user\.eiqportal\extensions\eiqtlk-ext-explainability
- NXP.eiqtlk-ext-tao version 0.0.1
   from C:\Users\user\.eiqportal\extensions\eiqtlk-ext-tao
- NXP.eiqtlk-ext-vision-pipeline version 1.4.0-rc2
   from C:\Users\user\.eiqportal\extensions\eiqtlk-ext-vision-pipeline
- NXP.eiqtlk-ext-watermarking version 1.2.3
   from C:\Users\user\.eiqportal\extensions\eiqtlk-ext-watermarking
16:08:27.202 > [CONVERTER] failed to import eiq.modelserver
16:08:27.227 > [CONVERTER] Traceback (most recent call last):
  File "C:\nxp\eIQ_Toolkit_v1.16.0\python\Lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 70, in <module>
    from tensorflow.python._pywrap_tensorflow_internal import *
ImportError: DLL load failed while importing _pywrap_tensorflow_internal: DLL 占십깍옙화 占쏙옙틴占쏙옙 占쏙옙占쏙옙占쏙옙 占쏙옙 占쏙옙占쏙옙占싹댐옙.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:\nxp\eIQ_Toolkit_v1.16.0\python\Lib\site-packages\eiq\modelserver\__init__.py", line 4, in <module>
    import tensorflow as tf
  File "C:\nxp\eIQ_Toolkit_v1.16.0\python\Lib\site-packages\tensorflow\__init__.py", line 40, in <module>
    from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow  # pylint: disable=unused-import
  File "C:\nxp\eIQ_Toolkit_v1.16.0\python\Lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 85, in <module>
    raise ImportError(
ImportError: Traceback (most recent call last):
  File "C:\nxp\eIQ_Toolkit_v1.16.0\python\Lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 70, in <module>
    from tensorflow.python._pywrap_tensorflow_internal import *
ImportError: DLL load failed while importing _pywrap_tensorflow_internal: DLL 占십깍옙화 占쏙옙틴占쏙옙 占쏙옙占쏙옙占쏙옙 占쏙옙 占쏙옙占쏙옙占싹댐옙.


Failed to load the native TensorFlow runtime.
See https://www.tensorflow.org/install/errors for some common causes and solutions.
If you need help, create an issue at https://github.com/tensorflow/tensorflow/issues and include the entire stack trace above this error message.

16:08:27.280 > [TRAINER] failed to import eiq.trainer.server.__main__Traceback (most recent call last):
  File "C:\nxp\eIQ_Toolkit_v1.16.0\python\Lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 70, in <module>
    from tensorflow.python._pywrap_tensorflow_internal import *
ImportError: DLL load failed while importing _pywrap_tensorflow_internal: DLL 占십깍옙화 占쏙옙틴占쏙옙 占쏙옙占쏙옙占쏙옙 占쏙옙 占쏙옙占쏙옙占싹댐옙.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:\nxp\eIQ_Toolkit_v1.16.0\python\Lib\site-packages\eiq\trainer\server\__main__.py", line 3, in <module>

16:08:27.299 > [TRAINER] LOADING PYTHON -> C:\nxp\eIQ_Toolkit_v1.16.0\python\python310.dll
PYTHONPATH: C:\nxp\eIQ_Toolkit_v1.16.0\python;C:\nxp\eIQ_Toolkit_v1.16.0\python\DLLs;C:\nxp\eIQ_Toolkit_v1.16.0\python\Lib;C:\nxp\eIQ_Toolkit_v1.16.0\python\Lib\site-packages

16:08:27.323 > eiq-converter terminated
16:08:27.352 > [TRAINER]     from eiq.trainer.utils.messenger import ColoredPrint
  File "eiq/trainer/utils/messenger.py", line 3, in init eiq.trainer.utils.messenger
  File "C:\nxp\eIQ_Toolkit_v1.16.0\python\Lib\site-packages\tensorflow\__init__.py", line 40, in <module>
    from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow  # pylint: disable=unused-import
  File "C:\nxp\eIQ_Toolkit_v1.16.0\python\Lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 85, in <module>
    raise ImportError(
ImportError: Traceback (most recent call last):
  File "C:\nxp\eIQ_Toolkit_v1.16.0\python\Lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 70, in <module>
    from tensorflow.python._pywrap_tensorflow_internal import *
ImportError: DLL load failed while importing _pywrap_tensorflow_internal: DLL 占십깍옙화 占쏙옙틴占쏙옙 占쏙옙占쏙옙占쏙옙 占쏙옙 占쏙옙占쏙옙占싹댐옙.


Failed to load the native TensorFlow runtime.
See https://www.tensorflow.org/install/errors for some common causes and solutions.
If you need help, create an issue at https://github.com/tensorflow/tensorflow/issues and include the entire stack trace above this error message.

16:08:27.402 > eiq-trainer terminated
16:08:27.599 > [IMPORTER] Traceback (most recent call last):

16:08:27.622 > [IMPORTER]   File "C:\nxp\eIQ_Toolkit_v1.16.0\python\Lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 70, in <module>
    from tensorflow.python._pywrap_tensorflow_internal import *
ImportError: DLL load failed while importing _pywrap_tensorflow_internal: DLL 占십깍옙화 占쏙옙틴占쏙옙 占쏙옙占쏙옙占쏙옙 占쏙옙 占쏙옙占쏙옙占싹댐옙.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:\nxp\eIQ_Toolkit_v1.16.0\python\Lib\runpy.py", line 196, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "C:\nxp\eIQ_Toolkit_v1.16.0\python\Lib\runpy.py", line 86, in _run_code
    exec(code, run_globals)
  File "C:\nxp\eIQ_Toolkit_v1.16.0\python\Lib\site-packages\eiq\importer\server\__main__.py", line 11, in <module>

16:08:27.653 > [IMPORTER]     from eiq.importer.datasets.TFDS_importer import TFDSDataset
  File "C:\nxp\eIQ_Toolkit_v1.16.0\python\Lib\site-packages\eiq\importer\datasets\TFDS_importer.py", line 1, in <module>
    import tensorflow as tf
  File "C:\nxp\eIQ_Toolkit_v1.16.0\python\Lib\site-packages\tensorflow\__init__.py", line 40, in <module>
    from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow  # pylint: disable=unused-import
  File "C:\nxp\eIQ_Toolkit_v1.16.0\python\Lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 85, in <module>
    raise ImportError(
ImportError: Traceback (most recent call last):
  File "C:\nxp\eIQ_Toolkit_v1.16.0\python\Lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 70, in <module>
    from tensorflow.python._pywrap_tensorflow_internal import *
ImportError: DLL load failed while importing _pywrap_tensorflow_internal: DLL 占십깍옙화 占쏙옙틴占쏙옙 占쏙옙占쏙옙占쏙옙 占쏙옙 占쏙옙占쏙옙占싹댐옙.


Failed to load the native TensorFlow runtime.
See https://www.tensorflow.org/install/errors for some common causes and solutions.
If you need help, create an issue at https://github.com/tensorflow/tensorflow/issues and include the entire stack trace above this error message.

16:08:27.802 > eiq-importer terminated
[2025-09-03T07:08:29.389Z] Extension host terminating: eIQ Portal shutdown
[2025-09-03T07:08:29.392Z] Deactivated all extensions
[2025-09-03T07:08:29.392Z] Backing up to C:\Users\user\AppData\Roaming\eiqportal-nodejs\Config\state.sqlite.backup
[TRAINER] Invalid Path
[DATASTORE] Shutting down...
TRAINER STOP ERROR: FetchError: request to http://127.0.0.1:10821/validate/stop failed, reason: connect ECONNREFUSED 127.0.0.1:10821
Uninstalled console patch
16:08:29.415 > eiq-validator terminated
16:08:29.426 > modelrunner client terminated
16:08:29.436 > modelrunner terminated
16:08:29.444 > modeleditor terminated
[2025-09-03T07:08:29.523Z] Extension host with pid 8976 exiting with code 0
[2025-09-03T07:08:29.577Z] ExtensionHost terminated with code 0 and signal null.
[2025-09-03T07:08:29.578Z] Closed Socket.io logging server
16:08:30.395 > eiq-datastore terminated
eIQ Portal Shutting Down...

 

 

[링크 : https://blog.naver.com/daebarq/222109221303]

[링크 : https://stackoverflow.com/questions/67259900/tensorflow-installation-on-windows-dll-load-failed-while-importing-pywrap-tens]

[링크 : https://blog.naver.com/se0_y/221986584579]

[링크 : https://learn.microsoft.com/en-us/cpp/windows/latest-supported-vc-redist?view=msvc-170]

[링크 : https://github.com/tensorflow/tensorflow/issues/22512]

 

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embeded/i.mx 8m plus2025. 9. 3. 11:06

vainfo

eiq 에서 libva 에러가 났는데

wayland에서 x.org로 바꾸니 에러가 발생하지 않는다. 중요한(?) 에러는 아니었는 듯.

 

$ cat /proc/cpuinfo 
model name : Intel(R) Core(TM) i7-10510U CPU @ 1.80GHz

$ vainfo
libva info: VA-API version 1.14.0
libva info: Trying to open /usr/lib/x86_64-linux-gnu/dri/iHD_drv_video.so
libva info: Found init function __vaDriverInit_1_14
libva info: va_openDriver() returns 0
vainfo: VA-API version: 1.14 (libva 2.12.0)
vainfo: Driver version: Intel iHD driver for Intel(R) Gen Graphics - 22.3.1 ()
vainfo: Supported profile and entrypoints
      VAProfileMPEG2Simple            : VAEntrypointVLD
      VAProfileMPEG2Main              : VAEntrypointVLD
      VAProfileH264Main               : VAEntrypointVLD
      VAProfileH264Main               : VAEntrypointEncSliceLP
      VAProfileH264High               : VAEntrypointVLD
      VAProfileH264High               : VAEntrypointEncSliceLP
      VAProfileJPEGBaseline           : VAEntrypointVLD
      VAProfileJPEGBaseline           : VAEntrypointEncPicture
      VAProfileH264ConstrainedBaseline: VAEntrypointVLD
      VAProfileH264ConstrainedBaseline: VAEntrypointEncSliceLP
      VAProfileVP8Version0_3          : VAEntrypointVLD
      VAProfileHEVCMain               : VAEntrypointVLD
      VAProfileHEVCMain10             : VAEntrypointVLD
      VAProfileVP9Profile0            : VAEntrypointVLD
      VAProfileVP9Profile2            : VAEntrypointVLD

 

G3460 + nvidia 1060

$ cat /proc/cpuinfo 
model name : Intel(R) Pentium(R) CPU G3460 @ 3.50GHz

$ cat log 
$ vainfo
libva info: VA-API version 1.14.0
libva info: Trying to open /usr/lib/x86_64-linux-gnu/dri/nvidia_drv_video.so
libva info: va_openDriver() returns -1
vaInitialize failed with error code -1 (unknown libva error),exit

 

$ LIBVA_DRIVER_NAME=iHD vainfo 
libva info: VA-API version 1.14.0
libva info: User environment variable requested driver 'iHD'
libva info: Trying to open /usr/lib/x86_64-linux-gnu/dri/iHD_drv_video.so
libva info: Found init function __vaDriverInit_1_14
DRM_IOCTL_I915_GEM_APERTURE failed: Invalid argument
Assuming 131072kB available aperture size.
May lead to reduced performance or incorrect rendering.
get chip id failed: -1 [22]
param: 4, val: 0
libva error: /usr/lib/x86_64-linux-gnu/dri/iHD_drv_video.so init failed
libva info: va_openDriver() returns 18
vaInitialize failed with error code 18 (invalid parameter),exit

 

G3460 

$ vainfo
libva info: VA-API version 1.14.0
libva info: Trying to open /usr/lib/x86_64-linux-gnu/dri/iHD_drv_video.so
libva info: Found init function __vaDriverInit_1_14
libva error: /usr/lib/x86_64-linux-gnu/dri/iHD_drv_video.so init failed
libva info: va_openDriver() returns 1
libva info: Trying to open /usr/lib/x86_64-linux-gnu/dri/i965_drv_video.so
libva info: Found init function __vaDriverInit_1_10
libva info: va_openDriver() returns 0
vainfo: VA-API version: 1.14 (libva 2.12.0)
vainfo: Driver version: Intel i965 driver for Intel(R) Haswell Desktop - 2.4.1
vainfo: Supported profile and entrypoints
      VAProfileMPEG2Simple            : VAEntrypointVLD
      VAProfileMPEG2Simple            : VAEntrypointEncSlice
      VAProfileMPEG2Main              : VAEntrypointVLD
      VAProfileMPEG2Main              : VAEntrypointEncSlice
      VAProfileH264ConstrainedBaseline: VAEntrypointVLD
      VAProfileH264ConstrainedBaseline: VAEntrypointEncSlice
      VAProfileH264Main               : VAEntrypointVLD
      VAProfileH264Main               : VAEntrypointEncSlice
      VAProfileH264High               : VAEntrypointVLD
      VAProfileH264High               : VAEntrypointEncSlice
      VAProfileH264MultiviewHigh      : VAEntrypointVLD
      VAProfileH264MultiviewHigh      : VAEntrypointEncSlice
      VAProfileH264StereoHigh         : VAEntrypointVLD
      VAProfileH264StereoHigh         : VAEntrypointEncSlice
      VAProfileVC1Simple              : VAEntrypointVLD
      VAProfileVC1Main                : VAEntrypointVLD
      VAProfileVC1Advanced            : VAEntrypointVLD
      VAProfileNone                   : VAEntrypointVideoProc
      VAProfileJPEGBaseline           : VAEntrypointVLD

[링크 : https://github.com/elFarto/nvidia-vaapi-driver/issues/272]

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embeded/i.mx 8m plus2025. 9. 3. 10:41

ubuntu nvidia 드라이버 설치

 

sudo add-apt-repository ppa:graphics-drivers/ppa
ubuntu-drivers devices
sudo ubuntu-drivers autoinstall

 

$ ubuntu-drivers devices
== /sys/devices/pci0000:00/0000:00:01.0/0000:01:00.0 ==
modalias : pci:v000010DEd00001C03sv000010DEsd00000000bc03sc00i00
vendor   : NVIDIA Corporation
model    : GP106 [GeForce GTX 1060 6GB]
driver   : nvidia-driver-550 - distro non-free
driver   : nvidia-driver-470 - distro non-free
driver   : nvidia-driver-450-server - distro non-free
driver   : nvidia-driver-565 - third-party non-free
driver   : nvidia-driver-575 - distro non-free recommended
driver   : nvidia-driver-580 - third-party non-free
driver   : nvidia-driver-470-server - distro non-free
driver   : nvidia-driver-575-server - distro non-free
driver   : nvidia-driver-570 - third-party non-free
driver   : nvidia-driver-545 - distro non-free
driver   : nvidia-driver-535 - distro non-free
driver   : nvidia-driver-418-server - distro non-free
driver   : nvidia-driver-390 - distro non-free
driver   : nvidia-driver-570-server - distro non-free
driver   : nvidia-driver-535-server - distro non-free
driver   : xserver-xorg-video-nouveau - distro free builtin

 

sudo apt-get install nvidia-driver-580

 

리부팅 이후

$ nvidia-smi
Wed Sep  3 10:53:56 2025       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 580.76.05              Driver Version: 580.76.05      CUDA Version: 13.0     |
+-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA GeForce GTX 1060 6GB    Off |   00000000:01:00.0  On |                  N/A |
| 42%   38C    P8              6W /  120W |     172MiB /   6144MiB |      1%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|    0   N/A  N/A             976      G   /usr/lib/xorg/Xorg                       73MiB |
|    0   N/A  N/A            1271      G   /usr/bin/gnome-shell                     86MiB |
+-----------------------------------------------------------------------------------------+

[링크 : https://2dudwns.tistory.com/20]

 

cuda 설치도 패키지로..!

sudo apt-get install nvidia-cuda-toolkit
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embeded/ARM2025. 8. 28. 14:18

SVE(Scalable Vector Extension)

armv7 까지는 NEON 이고 armv8 부터는 SVE 라고 이름이 달라지는 듯

 

Scalable Vector Extension (SVE) is a vector extension the A64 instruction set of the Armv8-A architecture. Armv9-A builds on SVE with the SVE2 extension. Unlike other SIMD architectures, SVE and SVE2 do not define the size of the vector registers, but constrains it to a range of possible values, from a minimum of 128 bits up to a maximum of 2048 in 128-bit wide units. Therefore, any CPU vendor can implement the extension by choosing the vector register size that better suits the workloads the CPU is targeting. The design of SVE and SVE2 guarantees that the same program can run on different implementations of the instruction set architecture without the need to recompile the code.

[링크 : https://developer.arm.com/Architectures/Scalable%20Vector%20Extensions]

[링크 : https://developer.arm.com/Architectures/SVE]

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embeded/i.mx 8m plus2025. 8. 26. 15:08

eiq 에러들

 

FATAL:gpu_data_manager_impl_private.cc(415)] GPU process isn't usable. Goodbye.

 

It is Electron issue, when Electron-libs don't match your system --in-process-gpu option helps as workaround.
Also --disable-gpu-sandbox or --no-sandbox options helps too

[링크 : https://www.reddit.com/r/archlinux/comments/xf5pkt/how_to_fix_gpu_data_manager_impl_privatecc415_gpu/?tl=ko]

[링크 : https://github.com/Automattic/simplenote-electron/issues/3096]

[링크 : https://stackoverflow.com/questions/65679630/fatalgpu-data-manager-impl-private-cc439-gpu-process-isnt-usable-goodbye]

 

$ sudo ./eiq-portal 
[37727:0826/154023.382498:FATAL:electron_main_delegate.cc(252)] Running as root without --no-sandbox is not supported. See https://crbug.com/638180.
Trace/breakpoint trap
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embeded/i.mx 8m plus2025. 8. 26. 11:53

eiq 학습 시도

cpu로 일단 굴리는 중 (g4400t야 힘내!!)

cpu라 학습도 느리고, 1000번 정도 돌린걸로는 0.1 정도 밖에 안되서

다시 0.9를 넘길때 까지 무한 뺑뻉이 돌리는 중

 

현재까진 윈도우에서만 되고

ubuntu 22.04 + eiq 1.16 실패

ubuntu 22.04 + eiq 1.16 실패.

 

전찬리에 실패중!

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embeded/i.mx 8m plus2025. 8. 22. 16:36

nvidia tao toolkit

eIQ의 TAO가 먼가 해서 눌렀더니

 

전이학습이 가능한 솔루션이라..

NVIDIA TAO Toolkit은 어떤 솔루션인가요?

AI/머신 러닝 모델을 처음부터 만들려면 엄청난 양의 데이터와 수많은 데이터 과학자가 필요합니다. 하지만 이제는 기존 신경망 모델에서 학습된 기능을 추출하여 새로운 맞춤형 모델에 적용하는 주요 기법인 transfer learning을 통해 모델 개발을 가속할 수 있습니다.

TensorFlow와 PyTorch를 기반으로 하는 NVIDIA TAO Toolkit은 AI/딥 러닝 프레임워크의 복잡성을 없애 모델 훈련 프로세스를 가속하는 NVIDIA TAO 프레임워크의 로코드 버전입니다. TAO Toolkit을 사용하면 AI 전문 지식이나 대규모 훈련 데이터 세트 없이도 전이 학습의 이점을 활용해 자체 데이터로 NVIDIA의 사전 훈련된 모델을 미세 조정하고 추론에 맞춰 최적화할 수 있습니다.

[링크 : https://developer.nvidia.com/ko-kr/tao-toolkit]

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