Docker for the jetson family with cuda, cuddn, gstreamer, python and opencv

Hi is there an official nvidia arm docker container with the same spec like the nvidia jetson ubuntu os ?

i mean with cuda cuddn gstreamer python and opencv pre compiled ?

I am able to compile everything for my self… but why ?

Best regards

Hi @m.fischer, the l4t-base container has CUDA/cuDNN/TensorRT/GStreamer in it. The version of OpenCV from JetPack is in the latest l4t-ml container.

okay interesting everything works native like a charm.
with the l4t-base container I have to move opencv to complie darknet

  • ln -s /usr/include/opencv4/opencv2/ /usr/include/opencv2

cublas is also missing

  • /src/dark_cuda.h:61:5: error: ‘cublasHandle_t’ does not name a type
    cublasHandle_t blas_handle();

so on JetPack 4.5 every path is set perfekty, but sadly not on the container itself

Best regards Martin

Hi @m.fischer, are you building your container from Dockerfile? If so, set your default docker daemon to nvidia, and --runtime nvidia will get used during docker build operations (which will make the mounted CUDA/cuDNN files available during build-time)

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yes i am building directly on a jetson xavier with a fresh setup of Jetpack 4.5

this is my problem-> cublas for 10.1 is missing - #18 by phillip3m

but not nativ on the xavier!

this is my Dockerfile


RUN apt-get update && apt-get install -y jq

RUN ln -s /usr/include/opencv4/opencv2/ /usr/include/opencv2

ENV PATH=$PATH:/usr/local/cuda-10.2/bin
ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH/usr/local/cuda-10.2/lib64

RUN cp /usr/lib/aarch64-linux-gnu/ /usr/local/cuda-10.2/lib64/ &&
cp /usr/lib/aarch64-linux-gnu/ /usr/local/cuda-10.2/lib64/ &&
cp /usr/lib/aarch64-linux-gnu/ /usr/local/cuda-10.2/lib64/

#Start Darknet Install
RUN git clone /app

COPY Makefile Makefile

RUN make

#Install all the required packages for the python script
RUN pip3 install --upgrade pip

RUN chmod 777
CMD ["./"]

OK, to use the CUDA/cuDNN libraries/headers during docker build you probably need to set the default-runtime to nvidia, and reboot your system or restart your docker daemon.

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Successfully built 420bc36bf845


thank you for the support