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docker-cuda: move recipe to reusable makefile target deps-cuda #1055

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Jun 9, 2023
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docker-cuda: move recipe to reusable makefile target deps-cuda #1055

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d76409e
docker-cuda: move recipe to reusable makefile target deps-cuda
bertsky Jun 8, 2023
2538979
make deps-cuda: Set MAMBA_ROOT_PREFIX to CONDA_PREFIX
kba Jun 9, 2023
79ad301
:memo: changelog
kba Jun 9, 2023
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55 changes: 8 additions & 47 deletions Dockerfile.cuda
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Original file line number Diff line number Diff line change
Expand Up @@ -4,58 +4,19 @@ FROM $BASE_IMAGE
ENV MAMBA_EXE=/usr/local/bin/conda
ENV MAMBA_ROOT_PREFIX=/conda
ENV PATH=$MAMBA_ROOT_PREFIX/bin:$PATH
ENV CONDA_EXE=$MAMBA_EXE
ENV CONDA_PREFIX=$MAMBA_ROOT_PREFIX
ENV CONDA_SHLVL='1'

WORKDIR $MAMBA_ROOT_PREFIX

RUN curl -Ls https://micro.mamba.pm/api/micromamba/linux-64/latest | tar -xvj bin/micromamba
RUN mv bin/micromamba $MAMBA_EXE
RUN hash -r
RUN mkdir -p $CONDA_PREFIX/lib $CONDA_PREFIX/include
RUN echo $CONDA_PREFIX/lib >> /etc/ld.so.conf.d/conda.conf
# Get CUDA toolkit, including compiler and libraries with dev.
# The nvidia channels do not provide (recent) cudnn (needed for Torch, TF etc):
#RUN conda install -c nvidia/label/cuda-11.8.0 cuda && conda clean -a
# The conda-forge channel has cudnn but no cudatoolkit-dev anymore,
# so let's combine nvidia and conda-forge (will be same lib versions, no waste of space):
RUN conda install -c nvidia/label/cuda-11.8.0 \
cuda-nvcc \
cuda-cccl \
&& conda clean -a \
&& find $CONDA_PREFIX -name "*_static.a" -delete
# cuda-cudart-dev \
# cuda-libraries-dev \
#RUN conda install -c conda-forge \
# cudatoolkit=11.8.0 \
# cudnn=8.8.* && \
# conda clean -a && \
# find $CONDA_PREFIX -name "*_static.a" -delete
# Since Torch will pull in the CUDA libraries (as Python pkgs) anyway,
# let's jump the shark and pull these via NGC index directly,
# but then share them with the rest of the system so native compilation/linking
# works, too:
RUN pip3 install nvidia-pyindex \
&& pip3 install nvidia-cudnn-cu11==8.6.0.163 \
nvidia-cublas-cu11 \
nvidia-cusparse-cu11 \
nvidia-cusolver-cu11 \
nvidia-curand-cu11 \
nvidia-cufft-cu11 \
nvidia-cuda-runtime-cu11 \
nvidia-cuda-nvrtc-cu11 \
&& for pkg in cudnn cublas cusparse cusolver curand cufft cuda_runtime cuda_nvrtc; do \
for lib in /usr/local/lib/python3.8/site-packages/nvidia/$pkg/lib/lib*.so.*; do \
base=$(basename $lib); \
ln -s $lib $CONDA_PREFIX/lib/$base.so; \
ln -s $lib $CONDA_PREFIX/lib/${base%.so.*}.so; \
done \
&& ln -s /usr/local/lib/python3.8/site-packages/nvidia/$pkg/include/* $CONDA_PREFIX/include/; \
done \
&& ldconfig
# gputil/nvidia-smi would be nice, too – but that drags in Python as a conda dependency...
WORKDIR /build

COPY Makefile .

RUN make deps-cuda

WORKDIR /data

RUN rm -fr /build

CMD ["/usr/local/bin/ocrd", "--help"]

55 changes: 54 additions & 1 deletion Makefile
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Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,8 @@ help:
@echo ""
@echo " Targets"
@echo ""
@echo " deps-ubuntu Dependencies for deployment in an ubuntu/debian linux"
@echo " deps-cuda Dependencies for deployment with GPU support via Conda"
@echo " deps-ubuntu Dependencies for deployment in an Ubuntu/Debian Linux"
@echo " deps-test Install test python deps via pip"
@echo " install (Re)install the tool"
@echo " install-dev Install with pip install -e"
Expand Down Expand Up @@ -47,6 +48,58 @@ help:
# pip install command. Default: $(PIP_INSTALL)
PIP_INSTALL = $(PIP) install

deps-cuda: CONDA_EXE ?= /usr/local/bin/conda
deps-cuda: export CONDA_PREFIX ?= /conda
deps-cuda: PYTHON_PREFIX != $(PYTHON) -c 'import sysconfig; print(sysconfig.get_paths()["purelib"])'
deps-cuda:
curl -Ls https://micro.mamba.pm/api/micromamba/linux-64/latest | tar -xvj bin/micromamba
Comment on lines +53 to +55
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These lines cause build warnings in the latest code (where line numbers changed):

Makefile:63: warning: overriding commands for target `deps-cuda'
Makefile:61: warning: ignoring old commands for target `deps-cuda'

mv bin/micromamba $(CONDA_EXE)
# Install Conda system-wide (for interactive / login shells)
echo 'export MAMBA_EXE=$(CONDA_EXE) MAMBA_ROOT_PREFIX=$(CONDA_PREFIX) CONDA_PREFIX=$(CONDA_PREFIX) PATH=$(CONDA_PREFIX)/bin:$$PATH' >> /etc/profile.d/98-conda.sh
mkdir -p $(CONDA_PREFIX)/lib $(CONDA_PREFIX)/include
echo $(CONDA_PREFIX)/lib >> /etc/ld.so.conf.d/conda.conf
# Get CUDA toolkit, including compiler and libraries with dev,
# however, the Nvidia channels do not provide (recent) cudnn (needed for Torch, TF etc):
#conda install -c nvidia/label/cuda-11.8.0 cuda && conda clean -a
#
# The conda-forge channel has cudnn and cudatoolkit but no cudatoolkit-dev anymore (and we need both!),
# so let's combine nvidia and conda-forge (will be same lib versions, no waste of space),
# but omitting cuda-cudart-dev and cuda-libraries-dev (as these will be pulled by pip for torch anyway):
conda install -c nvidia/label/cuda-11.8.0 \
cuda-nvcc \
cuda-cccl \
&& conda clean -a \
&& find $(CONDA_PREFIX) -name "*_static.a" -delete
#conda install -c conda-forge \
# cudatoolkit=11.8.0 \
# cudnn=8.8.* && \
#conda clean -a && \
#find $(CONDA_PREFIX) -name "*_static.a" -delete
#
# Since Torch will pull in the CUDA libraries (as Python pkgs) anyway,
# let's jump the shark and pull these via NGC index directly,
# but then share them with the rest of the system so native compilation/linking
# works, too:
$(PIP) install nvidia-pyindex \
&& $(PIP) install nvidia-cudnn-cu11==8.6.0.163 \
nvidia-cublas-cu11 \
nvidia-cusparse-cu11 \
nvidia-cusolver-cu11 \
nvidia-curand-cu11 \
nvidia-cufft-cu11 \
nvidia-cuda-runtime-cu11 \
nvidia-cuda-nvrtc-cu11 \
&& for pkg in cudnn cublas cusparse cusolver curand cufft cuda_runtime cuda_nvrtc; do \
for lib in $(PYTHON_PREFIX)/nvidia/$$pkg/lib/lib*.so.*; do \
base=`basename $$lib`; \
ln -s $$lib $(CONDA_PREFIX)/lib/$$base.so; \
ln -s $$lib $(CONDA_PREFIX)/lib/$${base%.so.*}.so; \
done \
&& ln -s $(PYTHON_PREFIX)/nvidia/$$pkg/include/* $(CONDA_PREFIX)/include/; \
done \
&& ldconfig
# gputil/nvidia-smi would be nice, too – but that drags in Python as a conda dependency...

# Dependencies for deployment in an ubuntu/debian linux
deps-ubuntu:
apt-get install -y python3 imagemagick libgeos-dev
Expand Down