Add def file for tensorflow v2.7 container

workflows/sciml-bench/def_files/sciml-bench-cu11-tf27-mlnx.def

@@ -0,0 +1,88 @@
+Bootstrap: localimage
+From: ompi4-cu11-mlnx.sif
+
+%files
+# -----------------------------------------------------------------------------------
+    requirements.txt /sciml-benchmarks/requirements.txt
+    MANIFEST.in /sciml-benchmarks/MANIFEST.in
+    setup.py /sciml-benchmarks/setup.py
+    doc /sciml-benchmarks/doc
+    sciml_bench /sciml-benchmarks/sciml_bench
+
+
+%environment
+# -----------------------------------------------------------------------------------
+
+    export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
+    export LC_ALL=C
+    export HOROVOD_GPU_ALLREDUCE=NCCL
+    export HOROVOD_GPU_ALLGATHER=MPI
+    export HOROVOD_GPU_BROADCAST=MPI
+    #export HOROVOD_NCCL_HOME=/usr/local/cuda/nccl
+    #export HOROVOD_NCCL_INCLUDE=/usr/local/cuda/nccl/include
+    #export HOROVOD_NCCL_LIB=/usr/local/cuda/nccl/lib 
+    export PYTHON_VERSION=3.8
+    export TENSORFLOW_VERSION=2.7.0
+    export PYTORCH_VERSION=1.10.0+cu113
+
+%post
+# -----------------------------------------------------------------------------------
+# this will install all necessary packages and prepare the container
+
+# TensorFlow version is tightly coupled to CUDA and cuDNN so it should be selected carefully
+# Python 2.7 or 3.5 is supported by Ubuntu Xenial out of the box
+
+    export PYTHON_VERSION=3.8
+    export TENSORFLOW_VERSION=2.7.0
+    export PYTORCH_VERSION=1.10.0+cu113
+
+    echo "deb https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64 /" > /etc/apt/sources.list.d/nvidia-ml.list
+
+    apt-get -y update && apt-get install -y --allow-downgrades --allow-change-held-packages --no-install-recommends \
+        build-essential \
+        cmake \
+        git \
+        curl \
+        vim \
+        wget \
+        ca-certificates \
+        libjpeg-dev \
+        libpng-dev \
+        python${PYTHON_VERSION} \
+        python${PYTHON_VERSION}-dev
+
+    ln -sf /usr/bin/python${PYTHON_VERSION} /usr/bin/python
+
+    curl -O https://bootstrap.pypa.io/get-pip.py && \
+    python get-pip.py && \
+    rm get-pip.py
+
+# Install TensorFlow, Keras and PyTorch
+    pip install torch==${PYTORCH_VERSION} torchvision==0.11.1+cu113 torchaudio==0.10.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
+    pip install mxnet-cu112 tensorflow-gpu==${TENSORFLOW_VERSION} keras h5py filelock matplotlib scikit-learn
+    
+    ls /usr/local/
+    whereis nvcc
+    export PATH="/usr/local/cuda-11.5/bin:$PATH"
+    whereis nvcc
+
+# Install Horovod, temporarily using CUDA stubs
+    ldconfig /usr/local/cuda-11.4/targets/x86_64-linux/lib/stubs && \
+    HOROVOD_GPU_ALLREDUCE=NCCL HOROVOD_WITH_MXNET=1 HOROVOD_WITH_TENSORFLOW=1 HOROVOD_WITH_PYTORCH=1 pip install --no-cache-dir horovod && \
+    ldconfig
+
+# Set default NCCL parameters
+    echo NCCL_DEBUG=INFO >> /etc/nccl.conf && \
+    echo NCCL_SOCKET_IFNAME=^docker0 >> /etc/nccl.conf
+
+# Download examples
+    cd / && \
+    apt-get install -y --no-install-recommends subversion && \
+    svn checkout https://github.com/uber/horovod/trunk/examples && \
+    rm -rf /examples/.svn
+
+# Install sciml-bench
+    cd /sciml-benchmarks && pip install .
+
+%runscript
+    sciml-bench $@