add CUDA arch compute_80

Author: hephaex

python-tensorflow/Dockerfile.1.15-py36-cuda11

@@ -0,0 +1,126 @@
+FROM lablup/common-base:20.08-py36-cuda11
+
+ARG TF_BUILD_VERSION=v1.15.3+nv20.07
+# Install the most recent bazel release.
+ENV BAZEL_VERSION 0.26.1
+
+# Set up Bazel.
+
+# Running bazel inside a `docker build` command causes trouble, cf:
+#   https://github.com/bazelbuild/bazel/issues/134
+# The easiest solution is to set up a bazelrc file forcing --batch.
+RUN echo "startup --batch" >>/etc/bazel.bazelrc
+# Similarly, we need to workaround sandboxing issues:
+#   https://github.com/bazelbuild/bazel/issues/418
+RUN echo "build --spawn_strategy=standalone --genrule_strategy=standalone" \
+    >>/etc/bazel.bazelrc
+
+WORKDIR /
+RUN mkdir /bazel && \
+    cd /bazel && \
+    curl -H "User-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.133 Safari/537.36" -fSsL -O https://github.com/bazelbuild/bazel/releases/download/$BAZEL_VERSION/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh && \
+    curl -H "User-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.133 Safari/537.36" -fSsL -o /bazel/LICENSE.txt https://raw.githubusercontent.com/bazelbuild/bazel/master/LICENSE && \
+    chmod +x bazel-*.sh && \
+    ./bazel-$BAZEL_VERSION-installer-linux-x86_64.sh && \
+    cd / && \
+    rm -f /bazel/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh
+
+# Download and build TensorFlow.
+WORKDIR /tensorflow
+
+# Download and build TensorFlow.
+# Enable checking out both tags and branches
+RUN export TAG_PREFIX="v" && \
+    echo ${TF_BUILD_VERSION} | grep -q ^${TAG_PREFIX}; \
+    if [ $? -eq 0 ]; then \
+        git clone --depth=1 https://github.com/nvidia/tensorflow.git . && \
+        git fetch --tags && \
+        git checkout ${TF_BUILD_VERSION}; \
+   else \
+        git clone --depth=1 --branch=${TF_BUILD_VERSION} https://github.com/nvidida/tensorflow.git . ; \
+    fi
+
+RUN yes "" | python3 configure.py
+RUN cp .bazelrc /root/.bazelrc
+
+ENV CI_BUILD_PYTHON ${PYTHON}
+ENV WHL_DIR=/tmp/pip3
+# Set bazel build parameters in .bazelrc in parameterized_docker_build.sh
+# Use --copt=-march values to get optimized builds appropriate for the hardware
+#   platform of your choice.
+# For ivy-bridge or sandy-bridge
+# --copt=-march="avx" \
+# For haswell, broadwell, or skylake
+# --copt=-march="avx2" \
+COPY .bazelrc /root/.mkl.bazelrc
+RUN echo "import /root/.mkl.bazelrc" >>/root/.bazelrc
+
+ENV TF_NEED_TENSORRT=1
+ENV TF_CUDA_COMPUTE_CAPABILITIES sm_37,sm_52,sm_60,sm_61,sm_70,sm_75,compute_70,compute_75,compute_80
+
+RUN tensorflow/tools/ci_build/builds/configured CPU  \
+      bazel --bazelrc=/root/.bazelrc build \
+        -c opt \
+        --copt=-msse4.1 \
+        --copt=-msse4.2 \
+	--copt=-mavx \
+	--copt=-mavx2 \
+	--copt=-mfma \
+	--copt=-mfpmath=both \ 
+	--copt=-O3 \
+	--copt=-Wformat \
+	--copt=-Wformat-security \
+	--copt=-fstack-protector \
+	--copt=-fPIC \
+	--copt=-fpic \
+	--config=opt \
+	--config=cuda \
+        --config=mkl \
+	--config=monolithic \
+        --config=gdr \ 
+        --config=verbs \
+#        --config=ngraph \
+        --config=numa \
+        --config=v1 \
+	--linkopt=-znoexecstack \
+	--linkopt=-zrelro \
+	--linkopt=-znow \
+	--linkopt=-fstack-protector \
+        --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" \
+      -k //tensorflow/tools/pip_package:build_pip_package && \
+    bazel-bin/tensorflow/tools/pip_package/build_pip_package "${WHL_DIR}" && \
+    python3 -m pip --no-cache-dir install --upgrade "${WHL_DIR}"/tensorflow-*.whl 
+RUN python3 -m pip --no-cache-dir install \
+        tensorboard==1.15 && \
+    rm -rf /root/.cache
+# Clean up Bazel cache when done.
+
+# Install Horovod, temporarily using CUDA stubs
+RUN HOROVOD_WITH_TENSORFLOW=1 HOROVOD_WITHOUT_PYTORCH=1 HOROVOD_WITHOUT_MXNET=1 \
+    pip install --no-cache-dir horovod==0.19.5 && \
+    ldconfig
+
+RUN python3 -m pip install --no-cache-dir \
+    	    mpi4py==3.0.3 \
+	    nni==1.6 \
+	    scikit-nni==0.2.1
+
+# Install ipython kernelspec
+Run python3 -m ipykernel install --display-name "TensorFlow 1.15 on Python 3.6 (CPU, MKL)" && \
+    cat /usr/local/share/jupyter/kernels/python3/kernel.json
+
+# Backend.AI specifics
+LABEL ai.backend.kernelspec="1" \
+      ai.backend.envs.corecount="OPENBLAS_NUM_THREADS,OMP_NUM_THREADS,NPROC" \
+      ai.backend.features="batch query uid-match user-input" \
+      ai.backend.base-distro="ubuntu16.04" \
+      ai.backend.resource.min.cpu="1" \
+      ai.backend.resource.min.mem="1g" \
+      ai.backend.resource.min.cuda.device=0 \
+      ai.backend.resource.min.cuda.shares=0 \
+      ai.backend.runtime-type="python" \
+      ai.backend.runtime-path="/usr/bin/python3" \
+      ai.backend.service-ports="ipython:pty:3000,jupyter:http:8080,jupyterlab:http:8090,vscode:http:8180,tensorboard:http:6006"
+
+WORKDIR /home/work
+# vim: ft=dockerfile