Dockerfile for NGC images

Autonomous Machines Jetson & Embedded Systems Jetson AGX Xavier
1

Hi,
We’re checking with options on how to customize the’nvcr.io/nvidia/tensorflow:20.03-tf2-py3’ image for our specific CUDA requirements. Is it possible to find the Dockerfile for this image?

3

Hi,

Do you want a docker image that can run on Jetson platform?
If yes, there are only two available images currently.

nvidia:l4t-base : with CUDA toolkit installed only.
nvidia:deepstream-l4t : with CUDA, cuDNN, TensorRT and Deepstream installed.

Thanks.

4

Hi AastaLLL,
Is it possible to get their Dockerfiles?

5

You can use docker image history --no-trunc or docker history --no-trunc to get the history of how an image was built. You can then build a dockerfile from that. You can also use docker image save to get filesystem images. Other docker image commands might be useful which you can find with docker image help.

Here is a Dockerfile for a ubuntu image with nvidia’s apt repos enabled. You can then install whatever cuda packages you want with apt-get, however some of the nvidia-l4t packages will not install for the moment (without ugly hacks). I will put up a proper repo later this week, but for now, here it is (use it under the MIT license).

FROM ubuntu:bionic

# This determines what <SOC> gets filled in in the nvidia apt sources list:
# valid choices: t210, t186, t194
ARG SOC="t210"
# because Nvidia has no keyserver for Tegra currently, we DL the whole BSP tarball, just for the apt key.
ARG BSP_URI="https://developer.nvidia.com/embedded/dlc/r32-3-1_Release_v1.0/t210ref_release_aarch64/Tegra210_Linux_R32.3.1_aarch64.tbz2"
ARG BSP_SHA512="13c4dd8e6b20c39c4139f43e4c5576be4cdafa18fb71ef29a9acfcea764af8788bb597a7e69a76eccf61cbedea7681e8a7f4262cd44d60cefe90e7ca5650da8a"

WORKDIR /tmp
# install apt key and configure apt sources
RUN apt-get update && apt-get install -y --no-install-recommends \
        ca-certificates \
        wget \
    && BSP_SHA512_ACTUAL="$(wget --https-only -nv --show-progress --progress=bar:force:noscroll -O- ${BSP_URI} | tee bsp.tbz2 | sha512sum -b | cut -d ' ' -f 1)" \
    && [ ${BSP_SHA512_ACTUAL} = ${BSP_SHA512} ] \
    && echo "Extracting bsp.tbz2" \
    && tar --no-same-permissions -xjf bsp.tbz2 \
    && cp Linux_for_Tegra/nv_tegra/jetson-ota-public.key /etc/apt/trusted.gpg.d/jetson-ota-public.asc \
    && chmod 644 /etc/apt/trusted.gpg.d/jetson-ota-public.asc \
    && echo "deb https://repo.download.nvidia.com/jetson/common r32 main" > /etc/apt/sources.list.d/nvidia-l4t-apt-source.list \
    && echo "deb https://repo.download.nvidia.com/jetson/${SOC} r32 main" >> /etc/apt/sources.list.d/nvidia-l4t-apt-source.list \
    && rm -rf * \
    && apt-get purge -y --autoremove \
        wget \
    && rm -rf /var/lib/apt/lists/*

in my case I needed

        cuda-compiler-10-0 \
        cuda-minimal-build-10-0 \
        cuda-libraries-dev-10-0 \

and that was enough.

You can purge the cuda dependencies once you’re done building with them, so your layer might look like this:

RUN apt-get update && apt-get install -y --no-install-recommends \
        cuda-thing-10-0 \
        other-cuda-thing-10-0 \
    && build_your_thing.sh \
    && apt-get purge -y --autoremove \
        cuda-thing-10-0 \
        other-cuda-thing-10-0 \
    && rm -rf /var/lib/apt/lists/*

When you docker run with --runtime nvidia it will add bind mount /usr/local/cuda from the host, though I have been told this behavior will change, so you may wish to leave the cuda runtime dependencies in the image (-dev packages can still go).