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Adam Van Etten
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docker/Dockerfile

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# inspired by sn4_baseline
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FROM nvidia/cuda:9.0-devel-ubuntu16.04
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FROM nvidia/cuda:9.2-devel-ubuntu16.04
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MAINTAINER avanetten
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# IF YOU WANT PROGRESS PRINTED TO TERMINAL
@@ -33,7 +32,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
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libcudnn7-dev=$CUDNN_VERSION-1+cuda9.0 && \
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apt-mark hold libcudnn7 && \
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rm -rf /var/lib/apt/lists/*
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# install requirements
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RUN apt-get update \
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&& apt-get install -y --no-install-recommends \
@@ -68,24 +67,18 @@ SHELL ["/bin/bash", "-c"]
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ENV PATH /opt/conda/bin:$PATH
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# install anaconda
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# https://repo.continuum.io/miniconda/
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RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-4.6.14-Linux-x86_64.sh -O ~/miniconda.sh && \
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RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-4.5.4-Linux-x86_64.sh -O ~/miniconda.sh && \
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/bin/bash ~/miniconda.sh -b -p /opt/conda && \
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rm ~/miniconda.sh && \
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/opt/conda/bin/conda clean -tipsy && \
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ln -s /opt/conda/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \
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echo ". /opt/conda/etc/profile.d/conda.sh" >> ~/.bashrc && \
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echo "conda activate base" >> ~/.bashrc
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# ENV TINI_VERSION v0.16.1
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ENV TINI_VERSION v0.18.0
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ADD https://github.com/krallin/tini/releases/download/${TINI_VERSION}/tini /usr/bin/tini
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RUN chmod +x /usr/bin/tini
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# use conda-forge instead of default channel
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RUN conda config --add channels conda-forge
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# && \
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# conda update conda
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# conda config --remove channels defaults && \
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# prepend pytorch and conda-forge before default channel
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RUN conda update conda && \
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conda config --prepend channels conda-forge
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# set up conda environment and add to $PATH
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RUN conda create -n simrdwn python=3.6 \
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RUN source activate simrdwn \
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&& conda install -n simrdwn \
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libgdal=2.4.1 \
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gdal=2.4.1 \
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geopandas=0.5.0 \
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opencv=4.1.0 \
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affine=2.2.2 \
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pyproj=1.9.6 \
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pyhamcrest=1.9.0 \
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cython=0.29.10 \
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contextlib2=0.5.5 \
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ncurses=6.1 \
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jupyter=1.0.0 \
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jupyterlab=0.35.6 \
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ipykernel=5.1.1 \
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matplotlib=3.1.0 \
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numpy=1.16.4 \
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pandas=0.24.2 \
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pillow=6.0.0 \
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pip=19.1.1 \
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scipy=1.3.0 \
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scikit-image=0.15.0 \
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scikit-learn=0.21.2 \
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shapely=1.6.4 \
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rtree=0.8.3 \
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testpath=0.4.2 \
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tqdm=4.32.1 \
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statsmodels=0.10.1 \
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gdal=2.4.2 \
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geopandas=0.6.3 \
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fiona \
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rasterio \
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awscli \
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affine \
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pyproj \
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pyhamcrest \
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cython \
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fiona \
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h5py \
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ncurses \
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jupyter \
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jupyterlab \
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ipykernel \
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libgdal \
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matplotlib \
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ncurses \
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numpy \
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statsmodels \
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pandas \
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pillow \
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pip \
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scipy \
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scikit-image \
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scikit-learn \
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shapely \
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rtree \
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testpath \
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tqdm \
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opencv \
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statsmodels \
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testpath \
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rtree \
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&& conda clean -p \
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&& conda clean -t \
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&& conda clean --yes --all
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# %% conda build purge-all \
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# Below is a hack to get around conda issues (2019-07-19)
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RUN source activate simrdwn \
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&& conda install -n simrdwn \
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-c conda-forge rasterio=1.0.24 \
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&& conda clean -p \
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&& conda clean -t \
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&& conda clean --yes --all
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# # Below is a hack to get around conda issues (2019-07-19)
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# RUN source activate simrdwn \
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# && conda install -n simrdwn \
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# -c conda-forge rasterio=1.0.24 \
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# && conda clean -p \
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# && conda clean -t \
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# && conda clean --yes --all
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# tf object detection api
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RUN git clone https://github.com/tensorflow/models.git /tensorflow/models
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WORKDIR /tensorflow/models/research/
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RUN git checkout r1.13.0
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#WORKDIR /tensorflow/models/research/
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#RUN protoc object_detection/protos/*.proto --python_out=.
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# WORKDIR /tensorflow/models/research/
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RUN source activate simrdwn && python -m ipykernel.kernelspec
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RUN python -m ipykernel.kernelspec
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# clone simrdwn
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RUN mkdir -p /opt/
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RUN git clone https://github.com/avanetten/simrdwn.git /opt/simrdwn/
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###################
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WORKDIR "/opt/simrdwn"
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# Set up our notebook config.
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WORKDIR /
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# TensorBoard
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# open ports for jupyterlab and tensorboard
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EXPOSE 8888 6006
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RUN ["/bin/bash"]
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# split weight file
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mkdir weight_split
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split -b 90m ave_dense_544_final.weights weight_split/
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# re-combine weight file
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cat weight_split/* > ave_dense_544_final.weights

results/weights_5class_vehicles/ave_dense_544_final.weights.zip

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