Hi all,
I am having a problem with my custom Docker container on the newest JetPack 4.6.1. The container is basically a JetPack installation on top of nvcr.io/nvidia/l4t-base:r32.4.4 image and it includes CUDA 10.0, cuDNN 7.6.3, TensorRT 5.1.6. The reason I’m using this old image is that the inference performance is too bad after TensorFlow 1.13.1 with newer TensorRT versions.
I don’t have any problems when deploying this image on the Jetsons with up to JetPack version 4.5.x but having the problem below once I deploy it on JetPack 4.6.1:
# python3
Python 3.6.9 (default, Dec 8 2021, 21:08:43)
[GCC 8.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorrt
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python3.6/dist-packages/tensorrt/__init__.py", line 1, in <module>
from .tensorrt import *
ImportError: /usr/lib/aarch64-linux-gnu/libnvinfer.so.5: undefined symbol: NvMediaDlaGetMaxOutstandingRequests
I can not even find anything about NvMediaDlaGetMaxOutstandingRequests on the internet except NVIDIA Drive platform resources. Docker containers supposed be a isolated environment from the host resources. I know --runtime nvidia maps some host components to the container but I can not get it working even I don’t use the flag.
What is changed on JetPack 4.6.1 that could effect this? Thanks in advance.
This is the beginning of my Dockerfile related to installation of JetPack components:
FROM nvcr.io/nvidia/l4t-base:r32.4.4
WORKDIR /root
ENV DEBIAN_FRONTEND noninteractive
# Essential Ubuntu-base installations
RUN apt update && \
apt install -y --no-install-recommends \
build-essential \
cmake \
make \
gcc \
g++ \
pkg-config \
unzip \
yasm \
git \
checkinstall \
python3-pip \
python3-dev \
python3-testresources \
python3-cffi \
wget \
gnupg2 \
libgail-common \
libgail18 \
libgtk2.0-0 \
libgtk2.0-bin \
libgtk2.0-common \
libtbb2 \
libv4l-dev \
v4l-utils && \
rm -rf /var/lib/apt/lists/*
# Clean the CUDA-10.2 resources under /usr/local
RUN cd /usr/local/ && \
rm -rf cuda && \
rm -rf cuda-10.2
COPY packages /jp43_packages
# Install JetPack 4.3 CUDA and CUDA-X libraries
RUN cd /jp43_packages && \
dpkg -i cuda-repo-l4t-10-0-local-10.0.326_1.0-1_arm64.deb && \
apt-key add /var/cuda-repo-10-0-local-10.0.326/7fa2af80.pub && \
apt update && \
apt install -y --no-install-recommends \
cuda-cusparse-10-0 \
cuda-cupti-10-0 \
cuda-cusolver-10-0 \
cuda-cufft-10-0 \
cuda-cublas-10-0 \
cuda-cublas-dev-10-0 \
cuda-compiler-10-0 \
cuda-cudart-10-0 \
cuda-tools-10-0 \
cuda-curand-10-0 \
cuda-curand-dev-10-0 \
cuda-nvcc-10-0 \
cuda-libraries-10-0 && \
ln -s /usr/local/cuda-10.0 /usr/local/cuda && \
ln -s /usr/local/cuda-10.0/targets/aarch64-linux/lib/libcurand.so.10.0.326 /usr/local/cuda-10.0/targets/aarch64-linux/lib/libcurand.so.10 && \
rm -rf /var/lib/apt/lists/*
ENV PATH="/usr/local/cuda-10.0/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin"
RUN cd /jp43_packages && \
dpkg -i \
libcudnn7_7.6.3.28-1+cuda10.0_arm64.deb \
libcudnn7-dev_7.6.3.28-1+cuda10.0_arm64.deb \
libnvinfer5_5.1.6-1+cuda10.0_arm64.deb \
libnvinfer-dev_5.1.6-1+cuda10.0_arm64.deb \
libnvinfer-samples_5.1.6-1+cuda10.0_all.deb \
python3-libnvinfer_5.1.6-1+cuda10.0_arm64.deb \
python3-libnvinfer-dev_5.1.6-1+cuda10.0_arm64.deb \
uff-converter-tf_5.1.6-1+cuda10.0_arm64.deb \
graphsurgeon-tf_5.1.6-1+cuda10.0_arm64.deb \
tensorrt_5.1.6.1-1+cuda10.0_arm64.deb && \
dpkg -i \
OpenCV-4.1.1-2-gd5a58aa75-aarch64-libs.deb \
OpenCV-4.1.1-2-gd5a58aa75-aarch64-dev.deb \
OpenCV-4.1.1-2-gd5a58aa75-aarch64-python.deb && \
rm -rf /var/lib/apt/lists/*