forked from mindspore-Ecosystem/mindspore
update auto install scripts
This commit is contained in:
parent
221246711a
commit
9b9d688c4c
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@ -1,5 +1,4 @@
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#!/bin/bash
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sudo apt-get --purge remove nvidia-*
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sudo apt-get --purge remove cuda-*
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sudo apt-get --purge remove cudnn-*
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@ -2,6 +2,7 @@
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set -ex
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MINDSPORE_VERSION=${MINDSPORE_VERSION:-1.5.0}
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PYTHON_VERSION=${PYTHON_VERSION:-3.7.5}
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#use huaweicloud mirror in China
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sudo sed -i "s@http://.*archive.ubuntu.com@http://repo.huaweicloud.com@g" /etc/apt/sources.list
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@ -14,14 +15,14 @@ sudo update-alternatives --install /usr/bin/python python /usr/bin/python3.7 100
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cd /tmp
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curl -O https://mirrors.tuna.tsinghua.edu.cn/anaconda/miniconda/Miniconda3-py37_4.10.3-Linux-x86_64.sh
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bash Miniconda3-py37_4.10.3-Linux-x86_64.sh
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bash Miniconda3-py37_4.10.3-Linux-x86_64.sh -b
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# add conda to PATH
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echo -e 'export PATH=~/miniconda3/bin/:$PATH' >> ~/.bash_profile
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echo -e '. ~/miniconda3/etc/profile.d/conda.sh' >> ~/.bash_profile
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source ~/.bash_profile
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conda init bash
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# setting up conda mirror
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# setting up conda mirror with qinghua source
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cat >~/.condarc <<END
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channels:
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- defaults
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@ -42,6 +43,25 @@ END
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#initialize conda env and install mindspore-cpu
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conda create -n py37 python=3.7.5 -y
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conda activate py37
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conda create -n ms_${PYTHON_VERSION} python=${PYTHON_VERSION} -y
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conda activate ms_${PYTHON_VERSION}
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conda install mindspore-cpu=${MINDSPORE_VERSION} -c mindspore -c conda-forge -y
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# check if it is the right mindspore version
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python -c "import mindspore;mindspore.run_check()"
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# check if it can be run with GPU
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cat > example.py <<END
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import numpy as np
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from mindspore import Tensor
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import mindspore.ops as ops
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import mindspore.context as context
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context.set_context(device_target="GPU")
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x = Tensor(np.ones([1,3,3,4]).astype(np.float32))
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y = Tensor(np.ones([1,3,3,4]).astype(np.float32))
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print(ops.add(x, y))
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END
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python example.py
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@ -4,29 +4,20 @@ set -ex
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MINDSPORE_VERSION=${MINDSPORE_VERSION:-1.5.0}
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PYTHON_VERSION=${PYTHON_VERSION:-3.7.5}
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MINDSPORE_VERSION=${MINDSPORE_VERSION:-1.5.0}
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CUDA_VERSION=${CUDA_VERSION:-8.0.5.39-1+cuda11.1}
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CUDA_VERSION=${CUDA_VERSION:-11.1.1-1}
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LIB_CUDA_VERSION=${LIB_CUDA_VERSION:-8.0.5.39-1+cuda11.1}
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DISTRIBUTED=${DISTRIBUTED:-false}
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CUDA_INSTALL_PATH=${CUDA_INSTALL_PATH:-cuda-11}
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CUDATOOLKIT_VERSION=${CUDATOOLKIT_VERSION:-11.1}
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CUDNN_VERSION=${CUDNN_VERSION:-8.0.5}
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#use huaweicloud mirror in China
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sudo sed -i "s@http://.*archive.ubuntu.com@http://repo.huaweicloud.com@g" /etc/apt/sources.list
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sudo sed -i "s@http://.*security.ubuntu.com@http://repo.huaweicloud.com@g" /etc/apt/sources.list
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sudo apt-get update
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# install python 3.7 and make it default
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sudo apt-get install gcc-7 libgmp-dev curl python3.7 -y
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sudo update-alternatives --install /usr/bin/python python /usr/bin/python3.7 100
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cd /tmp
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curl -O https://mirrors.tuna.tsinghua.edu.cn/anaconda/miniconda/Miniconda3-py37_4.10.3-Linux-x86_64.sh
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bash Miniconda3-py37_4.10.3-Linux-x86_64.sh
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# add conda to PATH
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echo -e 'export PATH=~/miniconda3/bin/:$PATH' >> ~/.bash_profile
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echo -e '. ~/miniconda3/etc/profile.d/conda.sh' >> ~/.bash_profile
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source ~/.bash_profile
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echo -e 'export PATH=~/miniconda3/bin/:$PATH' >> ~/.bash_profile
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echo -e '. ~/miniconda3/etc/profile.d/conda.sh' >> ~/.bash_profile
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source ~/.bash_profile
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conda init bash
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# setting up conda mirror
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cat >~/.condarc <<END
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@ -49,68 +40,39 @@ END
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#initialize conda env and install mindspore-cpu
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conda create -n py37 python=3.7.5 -y
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conda activate py37
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conda create -n ms_${PYTHON_VERSION} python=${PYTHON_VERSION} -y
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conda activate ms_${PYTHON_VERSION}
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# install gmp 6.1.2, downloading gmp is slow
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echo "install gmp start"
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sudo apt-get install m4 -y
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cd /tmp
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curl -O https://gmplib.org/download/gmp/gmp-6.1.2.tar.xz
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xz -d gmp-6.1.2.tar.xz
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tar xvzf gmp-6.1.2.tar && cd gmp-6.1.2
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./configure --prefix=/usr/local/gmp-6.1.2
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make
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sudo make install
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echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/gmp-6.1.2/lib' >> ~/.bash_profile
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echo "install gmp success"
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# install cuda with apt-get
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echo "install cuda start"
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wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin
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sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600
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wget -c https://developer.download.nvidia.com/compute/cuda/11.1.1/local_installers/cuda-repo-ubuntu1804-11-1-local_11.1.1-455.32.00-1_amd64.deb
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sudo dpkg -i cuda-repo-ubuntu1804-11-1-local_11.1.1-455.32.00-1_amd64.deb
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sudo apt-key add /var/cuda-repo-ubuntu1804-11-1-local/7fa2af80.pub
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sudo apt-get update
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sudo apt-get -y install cuda
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# add cuda to path
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cat >> ~/.bash_profile <<END
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export PATH=/usr/local/cuda/bin:\$PATH
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export LD_LIBRARY_PATH=\$LD_LIBRARY_PATH:/usr/local/cuda/lib64
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END
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source ~/.bash_profile
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echo "cuda install success."
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# install cudnn
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cd /tmp
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wget https://developer.download.nvidia.cn/compute/cuda/repos/ubuntu1804/x86_64/libcudnn8_${CUDA_VERSION}_amd64.deb
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wget https://developer.download.nvidia.cn/compute/cuda/repos/ubuntu1804/x86_64/libcudnn8-dev_${CUDA_VERSION}_amd64.deb
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sudo dpkg -i libcudnn8_${CUDA_VERSION}_amd64.deb libcudnn8-dev_${CUDA_VERSION}_amd64.deb
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# Install CuDNN 8 and NCCL 2
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wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb
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sudo dpkg -i nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb
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sudo apt update
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sudo apt install -y libcudnn8=${CUDA_VERSION} libcudnn8-dev=${CUDA_VERSION} libnccl2=2.7.8-1+cuda11.1 libnccl2-dev=2.7.8-1+cuda11.1
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# optional (tensort for serving, openmpi for distributed training)
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if [[ "$DISTRIBUTED" == "false" ]]; then
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cd /tmp
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curl -O https://download.open-mpi.org/release/open-mpi/v4.0/openmpi-4.0.3.tar.gz
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tar xvzf openmpi-4.0.3.tar.gz
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cd openmpi-4.0.3
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./configure --prefix=/usr/local/openmpi
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make
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sudo make install
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echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/openmpi/lib' >> ~/.bash_profile
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echo 'export PATH=$PATH:/usr/local/openmpi/bin' >> ~/.bash_profile
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source ~/.bash_profile
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fi
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# echo "install gmp start"
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# sudo apt-get install m4 -y
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# cd /tmp
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# curl -O https://gmplib.org/download/gmp/gmp-6.1.2.tar.xz
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# xz -d gmp-6.1.2.tar.xz
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# tar xvzf gmp-6.1.2.tar && cd gmp-6.1.2
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# ./configure --prefix=/usr/local/gmp-6.1.2
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# make
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# sudo make install
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# echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/gmp-6.1.2/lib' >> ~/.bash_profile
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# install mindspore-gpu with conda
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conda install mindspore-gpu=${MINDSPORE_VERSION} cudatoolkit=${CUDATOOLKIT_VERSION} cudnn=${CUDNN_VERSION} -c mindspore -c conda-forge
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conda install mindspore-gpu=${MINDSPORE_VERSION} cudatoolkit=${CUDATOOLKIT_VERSION} -c mindspore -c conda-forge -y
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# check if it is the right mindspore version
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python -c "import mindspore;mindspore.run_check()"
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# check if it can be run with GPU
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cat > example.py <<END
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import numpy as np
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from mindspore import Tensor
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import mindspore.ops as ops
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import mindspore.context as context
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context.set_context(device_target="GPU")
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x = Tensor(np.ones([1,3,3,4]).astype(np.float32))
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y = Tensor(np.ones([1,3,3,4]).astype(np.float32))
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print(ops.add(x, y))
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END
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python example.py
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@ -5,14 +5,14 @@ PYTHON_VERSION=${PYTHON_VERSION:-3.7.5}
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MINDSPORE_VERSION=${MINDSPORE_VERSION:-1.5.0}
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CUDA_VERSION=${CUDA_VERSION:-8.0.5.39-1+cuda11.1}
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DISTRIBUTED=${DISTRIBUTED:-false}
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CUDA_INSTALL_PATH=${CUDA_INSTALL_PATH:-cuda-11}
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CUDA_VERSION=${CUDA_VERSION:-11.1.1-1}
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CUDA_INSTALL_PATH=${CUDA_INSTALL_PATH:-cuda-11.1}
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LIBNCCL2_VERSION=${LIBNCCL2_VERSION:-2.7.8-1+cuda11.1}
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ARCH=$(uname -m)
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if [[ "${PYTHON_VERSION}" == "3.7.5" ]]; then
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VERSION="${MINDSPORE_VERSION}-cp37-cp37m"
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else
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VERSION="${MINDSPORE_VERSION}-cp39-cp39m"
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fi
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declare -A version_map=()
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version_map["3.7.5"]="${MINDSPORE_VERSION}-cp37-cp37m"
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version_map["3.9.0"]="${MINDSPORE_VERSION}-cp39-cp39m"
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#use huaweicloud mirror in China
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sudo sed -i "s@http://.*archive.ubuntu.com@http://repo.huaweicloud.com@g" /etc/apt/sources.list
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@ -20,7 +20,7 @@ sudo sed -i "s@http://.*security.ubuntu.com@http://repo.huaweicloud.com@g" /etc/
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sudo apt-get update
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# install python 3.7 and make it default
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sudo apt-get install gcc-7 libgmp-dev curl python3.7 openssl ubuntu-drivers-common openssl -y
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sudo apt-get install gcc-7 libgmp-dev curl python3.7 openssl ubuntu-drivers-common openssl software-properties-common -y
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sudo update-alternatives --install /usr/bin/python python /usr/bin/python3.7 100
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cd /tmp
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@ -28,9 +28,7 @@ curl -O https://bootstrap.pypa.io/get-pip.py
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sudo python get-pip.py
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# add pip mirror
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mkdir ~/.pip
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mkdir -p ~/.pip
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cat > ~/.pip/pip.conf <<END
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[global]
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index-url = https://repo.huaweicloud.com/repository/pypi/simple
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@ -38,28 +36,55 @@ trusted-host = repo.huaweicloud.com
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timeout = 120
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END
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# install gmp 6.1.2, downloading gmp is slow
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# install nvidia driver if not presented
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# root@ecs-gpu-testing:~# ubuntu-drivers devices
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# == /sys/devices/pci0000:20/0000:20:00.0/0000:21:01.0 ==
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# modalias : pci:v000010DEd00001EB8sv000010DEsd000012A2bc03sc02i00
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# vendor : NVIDIA Corporation
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# driver : nvidia-driver-418 - third-party non-free
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# driver : nvidia-driver-450 - third-party non-free
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# driver : nvidia-driver-460 - third-party non-free
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# driver : nvidia-driver-450-server - distro non-free
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# driver : nvidia-driver-460-server - distro non-free
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# driver : nvidia-driver-440 - third-party non-free
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# driver : nvidia-driver-418-server - distro non-free
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# driver : nvidia-driver-465 - third-party non-free
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# driver : nvidia-driver-470 - third-party non-free recommended #pick the latest one
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# driver : nvidia-driver-410 - third-party non-free
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# driver : nvidia-driver-470-server - distro non-free
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# driver : nvidia-driver-455 - third-party non-free
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# driver : xserver-xorg-video-nouveau - distro free builtin
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# sudo apt-get install nvidia-driver-470 -y
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# nvidia-smi # run this to check the driver is working
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#root@ecs-testing:~# nvidia-smi
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#Thu Dec 30 21:06:13 2021
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#+-----------------------------------------------------------------------------+
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#| NVIDIA-SMI 460.73.01 Driver Version: 460.73.01 CUDA Version: 11.2 |
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#|-------------------------------+----------------------+----------------------+
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#| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
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#| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
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#| | | MIG M. |
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#|===============================+======================+======================|
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#| 0 Tesla T4 Off | 00000000:21:01.0 Off | 0 |
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#| N/A 61C P0 29W / 70W | 0MiB / 15109MiB | 0% Default |
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#| | | N/A |
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#+-------------------------------+----------------------+----------------------+
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#
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#+-----------------------------------------------------------------------------+
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#| Processes: |
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#| GPU GI CI PID Type Process name GPU Memory |
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#| ID ID Usage |
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#|=============================================================================|
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#| No running processes found |
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#+-----------------------------------------------------------------------------+
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sudo apt-get install m4 -y
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cd /tmp
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curl -O https://gmplib.org/download/gmp/gmp-6.1.2.tar.xz
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xz -d gmp-6.1.2.tar.xz
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tar xvzf gmp-6.1.2.tar && cd gmp-6.1.2
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./configure --prefix=/usr/local/gmp-6.1.2
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make
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sudo make install
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echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/gmp-6.1.2/lib' >> ~/.bash_profile
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# install cuda with linux.run
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# another option is to use https://developer.download.nvidia.com/compute/cuda/11.1.0/local_installers/cuda_11.1.0_455.23.05_linux.run
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wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin
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sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600
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wget -c https://developer.download.nvidia.com/compute/cuda/11.1.1/local_installers/cuda-repo-ubuntu1804-11-1-local_11.1.1-455.32.00-1_amd64.deb
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sudo dpkg -i cuda-repo-ubuntu1804-11-1-local_11.1.1-455.32.00-1_amd64.deb
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sudo apt-key add /var/cuda-repo-ubuntu1804-11-1-local/7fa2af80.pub
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# install cuda/cudnn/nccl2 with apt-get
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# another option is to use linux.run https://developer.download.nvidia.com/compute/cuda/11.1.0/local_installers/cuda_11.1.0_455.23.05_linux.run
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sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
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sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/ /"
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sudo add-apt-repository "deb https://developer.download.nvidia.cn/compute/machine-learning/repos/ubuntu1804/x86_64/ /"
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sudo apt-get update
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sudo apt-get -y install cuda
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sudo apt-get -y install cuda=${CUDA_VERSION}
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# add cuda to path
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cat >> ~/.bash_profile <<END
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@ -67,35 +92,43 @@ export PATH=/usr/local/cuda/bin:\$PATH
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export LD_LIBRARY_PATH=\$LD_LIBRARY_PATH:/usr/local/cuda/lib64
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END
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source ~/.bash_profile
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echo "cuda install success."
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# install cudnn
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cd /tmp
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wget https://developer.download.nvidia.cn/compute/cuda/repos/ubuntu1804/x86_64/libcudnn8_${CUDA_VERSION}_amd64.deb
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wget https://developer.download.nvidia.cn/compute/cuda/repos/ubuntu1804/x86_64/libcudnn8-dev_${CUDA_VERSION}_amd64.deb
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sudo dpkg -i libcudnn8_${CUDA_VERSION}_amd64.deb libcudnn8-dev_${CUDA_VERSION}_amd64.deb
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# Install CuDNN 8 and NCCL 2
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wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb
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sudo dpkg -i nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb
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sudo apt update
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sudo apt install -y libcudnn8=${CUDA_VERSION} libcudnn8-dev=${CUDA_VERSION} libnccl2=2.7.8-1+cuda11.1 libnccl2-dev=2.7.8-1+cuda11.1
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sudo apt-get install -y libcudnn8=${CUDA_VERSION} libcudnn8-dev=${CUDA_VERSION} libnccl2=${LIBNCCL2_VERSION} libnccl-dev=${LIBNCCL2_VERSION}
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# optional (tensort for serving, openmpi for distributed training)
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if [[ "$DISTRIBUTED" == "false" ]]; then
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cd /tmp
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curl -O https://download.open-mpi.org/release/open-mpi/v4.0/openmpi-4.0.3.tar.gz
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tar xvzf openmpi-4.0.3.tar.gz
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cd openmpi-4.0.3
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./configure --prefix=/usr/local/openmpi
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make
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sudo make install
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echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/openmpi/lib' >> ~/.bash_profile
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echo 'export PATH=$PATH:/usr/local/openmpi/bin' >> ~/.bash_profile
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source ~/.bash_profile
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fi
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||||
# uncomment this to compile openmpi
|
||||
# cd /tmp
|
||||
# curl -O https://download.open-mpi.org/release/open-mpi/v4.0/openmpi-4.0.3.tar.gz
|
||||
# tar xvzf openmpi-4.0.3.tar.gz
|
||||
# cd openmpi-4.0.3
|
||||
# ./configure --prefix=/usr/local/openmpi
|
||||
# make
|
||||
# sudo make install
|
||||
# echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/openmpi/lib' >> ~/.bash_profile
|
||||
# echo 'export PATH=$PATH:/usr/local/openmpi/bin' >> ~/.bash_profile
|
||||
# source ~/.bash_profile
|
||||
# reference this to install tensorrt https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html#downloading
|
||||
|
||||
# install mindspore gpu
|
||||
echo "install mindspore gpu ${MINDSPORE_VERSION}"
|
||||
pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/${MINDSPORE_VERSION}/MindSpore/gpu/${ARCH}/${CUDA_INSTALL_PATH}/mindspore_gpu-${version_map["$PYTHON_VERSION"]}-linux_${ARCH}.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple
|
||||
|
||||
pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/${MINDSPORE_VERSION}/MindSpore/gpu/${CUDA_INSTALL_PATH}/mindspore-${VERSION}-linux_${ARCH}.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple
|
||||
|
||||
# check if it is the right mindspore version
|
||||
python -c "import mindspore;mindspore.run_check()"
|
||||
|
||||
# check if it can be run with GPU
|
||||
|
||||
cat > example.py <<END
|
||||
import numpy as np
|
||||
from mindspore import Tensor
|
||||
import mindspore.ops as ops
|
||||
import mindspore.context as context
|
||||
|
||||
context.set_context(device_target="GPU")
|
||||
x = Tensor(np.ones([1,3,3,4]).astype(np.float32))
|
||||
y = Tensor(np.ones([1,3,3,4]).astype(np.float32))
|
||||
print(ops.add(x, y))
|
||||
END
|
||||
|
||||
python example.py
|
||||
|
|
@ -6,12 +6,8 @@ PYTHON_VERSION=${PYTHON_VERSION:-3.7.5}
|
|||
MINDSPORE_VERSION=${MINDSPORE_VERSION:-1.5.0}
|
||||
ARCH=`uname -m`
|
||||
|
||||
if [[ "${PYTHON_VERSION}" == "3.7.5" ]]; then
|
||||
VERSION="${MINDSPORE_VERSION}-cp37-cp37m"
|
||||
else
|
||||
VERSION="${MINDSPORE_VERSION}-cp39-cp39"
|
||||
fi
|
||||
|
||||
declare -A version_map=()
|
||||
version_map["3.7.5"]="${MINDSPORE_VERSION}-cp37-cp37m"
|
||||
|
||||
#use huaweicloud mirror in China
|
||||
sudo sed -i "s@http://.*archive.ubuntu.com@http://repo.huaweicloud.com@g" /etc/apt/sources.list
|
||||
|
|
@ -26,4 +22,23 @@ cd /tmp
|
|||
curl -O https://bootstrap.pypa.io/get-pip.py
|
||||
sudo python get-pip.py
|
||||
|
||||
pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/${MINDSPORE_VERSION}/MindSpore/cpu/${ARCH}/mindspore-${VERSION}-linux_${ARCH}.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple
|
||||
pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/${MINDSPORE_VERSION}/MindSpore/cpu/${ARCH}/mindspore-${version_map["$PYTHON_VERSION"]}-linux_${ARCH}.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple
|
||||
|
||||
# check if it is the right mindspore version
|
||||
python -c "import mindspore;mindspore.run_check()"
|
||||
|
||||
# check if it can be run with GPU
|
||||
|
||||
cat > example.py <<END
|
||||
import numpy as np
|
||||
from mindspore import Tensor
|
||||
import mindspore.ops as ops
|
||||
import mindspore.context as context
|
||||
|
||||
context.set_context(device_target="GPU")
|
||||
x = Tensor(np.ones([1,3,3,4]).astype(np.float32))
|
||||
y = Tensor(np.ones([1,3,3,4]).astype(np.float32))
|
||||
print(ops.add(x, y))
|
||||
END
|
||||
|
||||
python example.py
|
||||
Loading…
Reference in New Issue