anchor.diff: -FROM nvidia/cuda:10.0-cudnn7-devel-ubuntu16.04 AS nvidia
  +# 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
  +# 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
  +# 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}"
  -ENV PATH=/usr/local/nvidia/bin:/usr/local/cuda/bin:${PATH}
  +# ENV CUDA_VERSION=10.0.130
  +# ENV CUDA_PKG_VERSION=10-0=$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
  
positive.code: 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 && \
      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
  
negative.code: 
  # Create a tensorflow wheel for GPU/cuda
  ENV TF_NEED_CUDA=1
  ENV TF_CUDA_VERSION=10.1
  # 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
  