anchor.diff:  ENV LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/local/nvidia/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs"
   ENV NVIDIA_VISIBLE_DEVICES=all
   ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
  -ENV NVIDIA_REQUIRE_CUDA="cuda>=10.0"
  -RUN apt-get update && apt-get install -y --no-install-recommends \
  -      cuda-cupti-$CUDA_PKG_VERSION \
  -      cuda-cudart-$CUDA_PKG_VERSION \
  -      cuda-cudart-dev-$CUDA_PKG_VERSION \
  -      cuda-libraries-$CUDA_PKG_VERSION \
  -      cuda-libraries-dev-$CUDA_PKG_VERSION \
  -      cuda-nvml-dev-$CUDA_PKG_VERSION \
  -      cuda-minimal-build-$CUDA_PKG_VERSION \
  -      cuda-command-line-tools-$CUDA_PKG_VERSION \
  -      libcudnn7=7.5.0.56-1+cuda10.0 \
  -      libcudnn7-dev=7.5.0.56-1+cuda10.0 \
  -      libnccl2=2.4.2-1+cuda10.0 \
  -      libnccl-dev=2.4.2-1+cuda10.0 && \
  -    ln -s /usr/local/cuda-10.0 /usr/local/cuda && \
  +ENV NVIDIA_REQUIRE_CUDA="cuda>=10.1"
  +# RUN apt-get update && apt-get install -y --no-install-recommends \
  +#       cuda-cupti-$CUDA_PKG_VERSION \
  +#       cuda-cudart-$CUDA_PKG_VERSION \
  +#       cuda-cudart-dev-$CUDA_PKG_VERSION \
  +#       cuda-libraries-$CUDA_PKG_VERSION \
  +#       cuda-libraries-dev-$CUDA_PKG_VERSION \
  +#       cuda-nvml-dev-$CUDA_PKG_VERSION \
  +#       cuda-minimal-build-$CUDA_PKG_VERSION \
  +#       cuda-command-line-tools-$CUDA_PKG_VERSION \
  +#       libcudnn7=7.5.0.56-1+cuda10.0 \
  +#       libcudnn7-dev=7.5.0.56-1+cuda10.0 \
  +#       libnccl2=2.4.2-1+cuda10.0 \
  +#       libnccl-dev=2.4.2-1+cuda10.0 && \
  +#     ln -s /usr/local/cuda-10.0 /usr/local/cuda && \
  +#     ln -s /usr/local/cuda/lib64/stubs/libcuda.so /usr/local/cuda/lib64/stubs/libcuda.so.1
  +RUN conda install -y cudatoolkit=10.1.243 cudnn=7.6.5 && \
  +    conda install -y install nccl=2.5.7.1 -c conda-forge && \
       ln -s /usr/local/cuda/lib64/stubs/libcuda.so /usr/local/cuda/lib64/stubs/libcuda.so.1
   
   # Work to upgrade to Python 3.7 can be found on this branch: https://github.com/Kaggle/docker-python/blob/upgrade-py37/Dockerfile
  
positive.code: 
  # Create a tensorflow wheel for GPU/cuda
  ENV TF_NEED_CUDA=1
  ENV TF_CUDA_VERSION=10.0
  # 3.7 is for the K80 and 6.0 is for the P100, 7.5 is for the T4: https://developer.nvidia.com/cuda-gpus
  ENV TF_CUDA_COMPUTE_CAPABILITIES=3.7,6.0,7.5
  ENV TF_CUDNN_VERSION=7
  
negative.code: # FROM nvidia/cuda:10.0-cudnn7-devel-ubuntu16.04 AS nvidia
  FROM continuumio/anaconda3:2019.03
  
  # Avoid interactive configuration prompts/dialogs during apt-get.
  ENV DEBIAN_FRONTEND=noninteractive
  
  # This is necessary to for apt to access HTTPS sources
  # RUN apt-get update && \
  #     apt-get install apt-transport-https
  
  # Cuda support
  # COPY --from=nvidia /etc/apt/sources.list.d/cuda.list /etc/apt/sources.list.d/
  # COPY --from=nvidia /etc/apt/sources.list.d/nvidia-ml.list /etc/apt/sources.list.d/
  # COPY --from=nvidia /etc/apt/trusted.gpg /etc/apt/trusted.gpg.d/cuda.gpg
  
  # Ensure the cuda libraries are compatible with the GPU image.
  # TODO(b/120050292): Use templating to keep in sync.
  # ENV CUDA_VERSION=10.0.130
  # ENV CUDA_PKG_VERSION=10-0=$CUDA_VERSION-1
  # LABEL com.nvidia.volumes.needed="nvidia_driver"
  # LABEL com.nvidia.cuda.version="${CUDA_VERSION}"
  # TODO(rosbo): Is this needed?
  # ENV PATH=/usr/local/nvidia/bin:/usr/local/cuda/bin:${PATH}
  # The stub is useful to us both for built-time linking and run-time linking, on CPU-only systems.
  # When intended to be used with actual GPUs, make sure to (besides providing access to the host
  # CUDA user libraries, either manually or through the use of nvidia-docker) exclude them. One
  