Dockerfile.pytorch-py38

FROM nvcr.io/nvidia/l4t-pytorch:r32.5.0-pth1.6-py3 AS pytorch-builder
ENV DEBIAN_FRONTEND=noninteractive

# https://qengineering.eu/install-pytorch-on-jetson-nano.html
RUN apt-get update && apt-get install -y --no-install-recommends \
        python3.8 python3.8-dev \
        ninja-build git cmake clang \
        libopenmpi-dev libomp-dev ccache \
        libopenblas-dev libblas-dev libeigen3-dev

RUN python3.8 -m pip install --upgrade pip
RUN python3.8 -m pip install setuptools wheel mock pillow scikit-build cython
# download PyTorch 1.8.1 with all its libraries
RUN git clone -b lts/release/1.8 \
        --depth 1 --recursive  --recurse-submodules --shallow-submodules \
        https://github.com/pytorch/pytorch.git
WORKDIR pytorch
RUN python3.8 -m pip install -r requirements.txt
COPY pytorch-1.8-jetson.patch .
RUN patch -p1 < pytorch-1.8-jetson.patch

ENV BUILD_CAFFE2_OPS=OFF
ENV USE_FBGEMM=OFF
ENV USE_FAKELOWP=OFF
ENV BUILD_TEST=OFF
ENV USE_MKLDNN=OFF
ENV USE_NNPACK=OFF
ENV USE_XNNPACK=OFF
ENV USE_QNNPACK=OFF
ENV USE_PYTORCH_QNNPACK=OFF
ENV USE_CUDA=ON
ENV USE_CUDNN=ON
ENV TORCH_CUDA_ARCH_LIST="5.3;6.2;7.2"
ENV USE_NCCL=OFF
ENV USE_SYSTEM_NCCL=OFF
ENV USE_OPENCV=OFF
#ENV MAX_JOBS=2
# set path to ccache
ENV PATH=/usr/lib/ccache:$PATH
# set clang compiler
ENV CC=clang
ENV CXX=clang++
# create symlink to cublas
# ln -s /usr/lib/aarch64-linux-gnu/libcublas.so /usr/local/cuda/lib64/libcublas.so
# start the build
# RUN python3.8 setup.py bdist_wheel