#
# This example Dockerfile illustrates a method to apply
# patches to the source code in NVIDIA's PyTorch
# container image and to rebuild PyTorch.  The RUN command
# included below will rebuild PyTorch in the same way as
# it was built in the original image.
#
# By applying customizations through a Dockerfile and
# `docker build` in this manner rather than modifying the
# container interactively, it will be straightforward to
# apply the same changes to later versions of the PyTorch
# container image.
#
# https://docs.docker.com/engine/reference/builder/
#
FROM nvcr.io/nvidia/pytorch:25.11-py3

# Bring in changes from outside container to /tmp
# (assumes my-pytorch-modifications.patch is in same directory as Dockerfile)
COPY my-pytorch-modifications.patch /tmp

# Change working directory to PyTorch source path
WORKDIR /opt/pytorch

# Apply modifications
RUN patch -p1 < /tmp/my-pytorch-modifications.patch

# Rebuild PyTorch
RUN cd pytorch && \
    USE_CUPTI_SO=1 \
    USE_KINETO=1 \
    CMAKE_PREFIX_PATH="/usr/local" \
    NCCL_ROOT="/usr" \
    USE_SYSTEM_NCCL=1 \
    USE_UCC=1 \
    USE_SYSTEM_UCC=1 \
    UCC_HOME="/opt/hpcx/ucc" \
    # UCC_DIR is for PyTorch to find ucc-config.cmake
    UCC_DIR="/opt/hpcx/ucc/lib/cmake/ucc" \
    UCX_HOME="/opt/hpcx/ucx" \
    UCX_DIR="/opt/hpcx/ucx/lib/cmake/ucx" \
    CFLAGS='-fno-gnu-unique' \
    DEFAULT_INTEL_MKL_DIR="/usr/local" \
    INTEL_MKL_DIR="/usr/local" \
    python setup.py install \
    && python setup.py clean

# Reset default working directory
WORKDIR /workspace