forked from mindspore-Ecosystem/mindspore
134 lines
6.1 KiB
Bash
134 lines
6.1 KiB
Bash
#!/bin/bash
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set -ex
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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=${CUDA_VERSION:-8.0.5.39-1+cuda11.1}
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DISTRIBUTED=${DISTRIBUTED:-false}
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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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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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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 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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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 -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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trusted-host = repo.huaweicloud.com
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timeout = 120
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END
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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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# 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=${CUDA_VERSION}
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# add cuda to path
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cat >> ~/.bash_profile <<END
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export PATH=/usr/local/cuda/bin:\$PATH
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export LD_LIBRARY_PATH=\$LD_LIBRARY_PATH:/usr/local/cuda/lib64
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END
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source ~/.bash_profile
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echo "cuda install success."
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sudo apt-get install -y libcudnn8=${CUDA_VERSION} libcudnn8-dev=${CUDA_VERSION} libnccl2=${LIBNCCL2_VERSION} libnccl-dev=${LIBNCCL2_VERSION}
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# optional (tensort for serving, openmpi for distributed training)
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# uncomment this to compile openmpi
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# cd /tmp
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# curl -O https://download.open-mpi.org/release/open-mpi/v4.0/openmpi-4.0.3.tar.gz
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# tar xvzf openmpi-4.0.3.tar.gz
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# cd openmpi-4.0.3
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# ./configure --prefix=/usr/local/openmpi
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# make
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# sudo make install
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# echo 'export LD_LIBRARY_PATH=$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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# reference this to install tensorrt https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html#downloading
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echo "install mindspore gpu ${MINDSPORE_VERSION}"
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pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/${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
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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 |