Restore cu128 Ray image alongside new cu129

Keep the existing 2.55.1-py312-cu128 image and Tekton pipeline
unchanged, adding the cu129 Training Hub variant as a separate image
rather than replacing cu128.

Co-authored-by: Cursor <cursoragent@cursor.com>

Author: Fiona-Waters

.tekton/ray-2.55.1-py312-cu128-push.yaml

@@ -0,0 +1,146 @@
+ARG PYTHON_VERSION=312
+ARG IMAGE_TAG=9.7-1778488949
+
+FROM registry.access.redhat.com/ubi9/python-${PYTHON_VERSION}:${IMAGE_TAG}
+
+ARG TARGETARCH
+
+LABEL name="ray-ubi9-py312-cu128" \
+      summary="CUDA 12.8 Python 3.12 image based on UBI9 for Ray" \
+      description="CUDA 12.8 Python 3.12 image based on UBI9 for Ray" \
+      io.k8s.display-name="CUDA 12.8 Python 3.12 base image for Ray" \
+      io.k8s.description="CUDA 12.8 Python 3.12 image based on UBI9 for Ray" \
+      authoritative-source-url="https://github.com/opendatahub-io/distributed-workloads"
+
+# Install CUDA base from:
+# https://gitlab.com/nvidia/container-images/cuda/-/blob/master/dist/12.8.0/ubi9/base/Dockerfile
+USER 0
+WORKDIR /opt/app-root/bin
+
+ENV NVIDIA_REQUIRE_CUDA="cuda>=12.8 brand=unknown,driver>=470,driver<471 brand=grid,driver>=470,driver<471 brand=tesla,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=vapps,driver>=470,driver<471 brand=vpc,driver>=470,driver<471 brand=vcs,driver>=470,driver<471 brand=vws,driver>=470,driver<471 brand=cloudgaming,driver>=470,driver<471 brand=unknown,driver>=535,driver<536 brand=grid,driver>=535,driver<536 brand=tesla,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=vapps,driver>=535,driver<536 brand=vpc,driver>=535,driver<536 brand=vcs,driver>=535,driver<536 brand=vws,driver>=535,driver<536 brand=cloudgaming,driver>=535,driver<536 brand=unknown,driver>=550,driver<551 brand=grid,driver>=550,driver<551 brand=tesla,driver>=550,driver<551 brand=nvidia,driver>=550,driver<551 brand=quadro,driver>=550,driver<551 brand=quadrortx,driver>=550,driver<551 brand=nvidiartx,driver>=550,driver<551 brand=vapps,driver>=550,driver<551 brand=vpc,driver>=550,driver<551 brand=vcs,driver>=550,driver<551 brand=vws,driver>=550,driver<551 brand=cloudgaming,driver>=550,driver<551 brand=unknown,driver>=560,driver<561 brand=grid,driver>=560,driver<561 brand=tesla,driver>=560,driver<561 brand=nvidia,driver>=560,driver<561 brand=quadro,driver>=560,driver<561 brand=quadrortx,driver>=560,driver<561 brand=nvidiartx,driver>=560,driver<561 brand=vapps,driver>=560,driver<561 brand=vpc,driver>=560,driver<561 brand=vcs,driver>=560,driver<561 brand=vws,driver>=560,driver<561 brand=cloudgaming,driver>=560,driver<561 brand=unknown,driver>=565,driver<566 brand=grid,driver>=565,driver<566 brand=tesla,driver>=565,driver<566 brand=nvidia,driver>=565,driver<566 brand=quadro,driver>=565,driver<566 brand=quadrortx,driver>=565,driver<566 brand=nvidiartx,driver>=565,driver<566 brand=vapps,driver>=565,driver<566 brand=vpc,driver>=565,driver<566 brand=vcs,driver>=565,driver<566 brand=vws,driver>=565,driver<566 brand=cloudgaming,driver>=565,driver<566"
+ENV NV_CUDA_CUDART_VERSION=12.8.57-1
+
+RUN NVIDIA_GPGKEY_SUM=d0664fbbdb8c32356d45de36c5984617217b2d0bef41b93ccecd326ba3b80c87 && \
+    if [ "${TARGETARCH}" = "arm64" ]; then NVARCH=sbsa; else NVARCH=x86_64; fi && \
+    curl -fsSL https://developer.download.nvidia.com/compute/cuda/repos/rhel9/${NVARCH}/D42D0685.pub | sed '/^Version/d' > /etc/pki/rpm-gpg/RPM-GPG-KEY-NVIDIA && \
+    echo "$NVIDIA_GPGKEY_SUM  /etc/pki/rpm-gpg/RPM-GPG-KEY-NVIDIA" | sha256sum -c --strict -
+
+ENV CUDA_VERSION=12.8.0
+
+COPY cuda.repo-* ./
+COPY NGC-DL-CONTAINER-LICENSE /
+
+RUN if [ "${TARGETARCH}" = "arm64" ]; then \
+        cp cuda.repo-arm64 /etc/yum.repos.d/cuda.repo; \
+    else \
+        cp cuda.repo-x86_64 /etc/yum.repos.d/cuda.repo; \
+    fi
+
+# For libraries in the cuda-compat-* package: https://docs.nvidia.com/cuda/eula/index.html#attachment-a
+RUN yum upgrade -y && yum install -y \
+    cuda-cudart-12-8-${NV_CUDA_CUDART_VERSION} \
+    cuda-compat-12-8 \
+    && yum clean all \
+    && rm -rf /var/cache/yum/*
+
+# nvidia-docker 1.0
+RUN echo "/usr/local/nvidia/lib" >> /etc/ld.so.conf.d/nvidia.conf && \
+    echo "/usr/local/nvidia/lib64" >> /etc/ld.so.conf.d/nvidia.conf
+
+ENV PATH=/usr/local/nvidia/bin:/usr/local/cuda/bin:${PATH}
+ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
+
+# nvidia-container-runtime
+ENV NVIDIA_VISIBLE_DEVICES=all
+ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
+
+# Install CUDA runtime from:
+# https://gitlab.com/nvidia/container-images/cuda/-/blob/master/dist/12.8.0/ubi9/runtime/Dockerfile
+ENV NV_CUDA_LIB_VERSION=12.8.0-1
+ENV NV_NVTX_VERSION=12.8.55-1
+ENV NV_LIBNPP_VERSION=12.3.3.65-1
+ENV NV_LIBNPP_PACKAGE=libnpp-12-8-${NV_LIBNPP_VERSION}
+ENV NV_LIBCUBLAS_VERSION=12.8.3.14-1
+ENV NV_LIBNCCL_PACKAGE_NAME=libnccl
+ENV NV_LIBNCCL_PACKAGE_VERSION=2.25.1-1
+ENV NV_LIBNCCL_VERSION=2.25.1
+ENV NCCL_VERSION=2.25.1
+ENV NV_LIBNCCL_PACKAGE=${NV_LIBNCCL_PACKAGE_NAME}-${NV_LIBNCCL_PACKAGE_VERSION}+cuda12.8
+
+RUN yum install -y \
+    cuda-libraries-12-8-${NV_CUDA_LIB_VERSION} \
+    cuda-nvtx-12-8-${NV_NVTX_VERSION} \
+    ${NV_LIBNPP_PACKAGE} \
+    libcublas-12-8-${NV_LIBCUBLAS_VERSION} \
+    ${NV_LIBNCCL_PACKAGE} \
+    && yum clean all \
+    && rm -rf /var/cache/yum/*
+
+# Set this flag so that libraries can find the location of CUDA
+ENV XLA_FLAGS=--xla_gpu_cuda_data_dir=/usr/local/cuda
+
+# Install CUDA devel from:
+# https://gitlab.com/nvidia/container-images/cuda/-/blob/master/dist/12.8.0/ubi9/devel/Dockerfile
+ENV NV_CUDA_LIB_VERSION=12.8.0-1
+# ARM64 doesn't have nvprof package - set in runtime
+ENV NV_NVPROF_VERSION=12.8.57-1
+ENV NV_NVPROF_DEV_PACKAGE=cuda-nvprof-12-8-${NV_NVPROF_VERSION}
+ENV NV_CUDA_CUDART_DEV_VERSION=12.8.57-1
+ENV NV_NVML_DEV_VERSION=12.8.55-1
+ENV NV_LIBCUBLAS_DEV_VERSION=12.8.3.14-1
+ENV NV_LIBNPP_DEV_VERSION=12.3.3.65-1
+ENV NV_LIBNPP_DEV_PACKAGE=libnpp-devel-12-8-${NV_LIBNPP_DEV_VERSION}
+ENV NV_LIBNCCL_DEV_PACKAGE_NAME=libnccl-devel
+ENV NV_LIBNCCL_DEV_PACKAGE_VERSION=2.25.1-1
+ENV NCCL_VERSION=2.25.1
+ENV NV_LIBNCCL_DEV_PACKAGE=${NV_LIBNCCL_DEV_PACKAGE_NAME}-${NV_LIBNCCL_DEV_PACKAGE_VERSION}+cuda12.8
+ENV NV_CUDA_NSIGHT_COMPUTE_VERSION=12.8.0-1
+ENV NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE=cuda-nsight-compute-12-8-${NV_CUDA_NSIGHT_COMPUTE_VERSION}
+
+RUN yum install -y \
+    make \
+    findutils \
+    cuda-command-line-tools-12-8-${NV_CUDA_LIB_VERSION} \
+    cuda-libraries-devel-12-8-${NV_CUDA_LIB_VERSION} \
+    cuda-minimal-build-12-8-${NV_CUDA_LIB_VERSION} \
+    cuda-cudart-devel-12-8-${NV_CUDA_CUDART_DEV_VERSION} \
+    cuda-nvml-devel-12-8-${NV_NVML_DEV_VERSION} \
+    libcublas-devel-12-8-${NV_LIBCUBLAS_DEV_VERSION} \
+    ${NV_LIBNPP_DEV_PACKAGE} \
+    ${NV_LIBNCCL_DEV_PACKAGE} \
+    ${NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE} \
+    && if [ "${TARGETARCH}" != "arm64" ]; then \
+        yum install -y ${NV_NVPROF_DEV_PACKAGE}; \
+    fi \
+    && yum clean all \
+    && rm -rf /var/cache/yum/*
+
+ENV LIBRARY_PATH=/usr/local/cuda/lib64/stubs
+
+# Install CUDA devel cudnn from:
+# https://gitlab.com/nvidia/container-images/cuda/-/blob/master/dist/12.8.0/ubi9/devel/cudnn/Dockerfile
+ENV NV_CUDNN_VERSION=9.7.0.66-1
+ENV NV_CUDNN_PACKAGE=libcudnn9-cuda-12-${NV_CUDNN_VERSION}
+ENV NV_CUDNN_PACKAGE_DEV=libcudnn9-devel-cuda-12-${NV_CUDNN_VERSION}
+
+LABEL com.nvidia.cudnn.version="${NV_CUDNN_VERSION}"
+
+RUN yum install -y \
+    ${NV_CUDNN_PACKAGE} \
+    ${NV_CUDNN_PACKAGE_DEV} \
+    && yum clean all \
+    && rm -rf /var/cache/yum/*
+
+# Install Python packages
+
+# Install micropipenv to deploy packages from Pipfile.lock
+RUN pip install --no-cache-dir -U "micropipenv[toml]"
+
+# Install Python dependencies from Pipfile.lock file
+COPY Pipfile.lock ./
+
+RUN micropipenv install && rm -f ./Pipfile.lock
+
+# Restore user workspace
+USER 1001
+WORKDIR /opt/app-root/src