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Merge pull request #881 from Kaggle/tf2.3.1-whl
Build TensorFlow 2.3.1 wheel.
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tensorflow-whl/CHANGELOG.md

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* `2.1.0-py36`: TensorFlow 2.1.0 with Python 3.6
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* `2.1.0-py36-2`: TensorFlow 2.1.0 with CUDA 10.1
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* `2.1.0-py37`: TensorFlow 2.1.0 with Python 3.7
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* `2.1.0-py37-2`: TensorFlow 2.1.0 with Python 3.7 & DLVM base image.
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* `2.1.0-py37-3`: TensorFlow 2.1.0 with Python 3.7, DLVM base image, tensorflow-gcs-config.
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* `2.2.0-py37`: TensorFlow 2.2.0 with Python 3.7.
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* `2.2.0-py37-2`: TensorFlow 2.2.0 with Python 3.7 & TFA.
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* `2.3.0-py37`: TensorFlow 2.3.0 with Python.
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* `2.1.0-py37-2`: TensorFlow 2.1.0 with Python 3.7 & DLVM base image
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* `2.1.0-py37-3`: TensorFlow 2.1.0 with Python 3.7, DLVM base image, tensorflow-gcs-config
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* `2.2.0-py37`: TensorFlow 2.2.0 with Python 3.7
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* `2.2.0-py37-2`: TensorFlow 2.2.0 with Python 3.7 & TFA
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* `2.3.0-py37`: TensorFlow 2.3.0 with Python 3.7
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* `2.3.1-py37`: TensorFlow 2.3.1 with Python 3.6

tensorflow-whl/Dockerfile

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FROM nvidia/cuda:10.1-cudnn7-devel-ubuntu18.04 AS nvidia
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FROM gcr.io/deeplearning-platform-release/base-cpu:latest
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FROM nvidia/cuda:10.2-cudnn7-devel-ubuntu18.04 AS nvidia
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FROM gcr.io/deeplearning-platform-release/base-cpu:m46
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# Avoid interactive configuration prompts/dialogs during apt-get.
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ENV DEBIAN_FRONTEND=noninteractive
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# Ensure the cuda libraries are compatible with the GPU image.
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# TODO(b/120050292): Use templating to keep in sync.
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ENV CUDA_MAJOR_VERSION=10
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ENV CUDA_MINOR_VERSION=1
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ENV CUDA_PATCH_VERSION=243
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ENV CUDA_MINOR_VERSION=2
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ENV CUDA_PATCH_VERSION=89
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ENV CUDA_VERSION=$CUDA_MAJOR_VERSION.$CUDA_MINOR_VERSION.$CUDA_PATCH_VERSION
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ENV CUDA_PKG_VERSION=$CUDA_MAJOR_VERSION-$CUDA_MINOR_VERSION=$CUDA_VERSION-1
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LABEL com.nvidia.volumes.needed="nvidia_driver"
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RUN cd /usr/local/src && \
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git clone https://github.com/tensorflow/tensorflow && \
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cd tensorflow && \
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git checkout tags/v2.3.0 && \
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git checkout tags/v2.3.1 && \
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pip install keras_applications --no-deps && \
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pip install keras_preprocessing --no-deps
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