Installing MindSpore in GPU by pip

• Installing MindSpore in GPU by pip
  • System Environment Information Confirmation
  • Installing MindSpore
  • Installation Verification
  • Version Update
  • Installing MindInsight
  • Installing MindArmour
  • Installing MindSpore Hub

This document describes how to quickly install MindSpore by pip in a Linux system with a GPU environment.

System Environment Information Confirmation

• Confirm that Ubuntu 18.04 is installed with 64-bit operating system.
• Confirm that GCC 7.3.0 is installed.
• Confirm that CUDA 10.1 is installed.
  • If CUDA is installed in a non-default path, after installing CUDA, environment variable PATH(e.g. export PATH=/usr/local/cuda-${version}/bin:$PATH) and LD_LIBRARY_PATH(e.g. export LD_LIBRARY_PATH=/usr/local/cuda-${version}/lib64:$LD_LIBRARY_PATH) need to be set. Please refer to CUDA installation guide for detailed post installation actions.
• Confirm that cuDNN 7.6.X is installed.
• Confirm that OpenMPI 3.1.5 is installed. (optional, required for single-node/multi-GPU and multi-node/multi-GPU training)
• Confirm that NCCL 2.7.6-1 is installed. (optional, required for single-node/multi-GPU and multi-node/multi-GPU training)
• Confirm that gmp 6.1.2 is installed.
• Confirm that Python 3.7.5 is installed.
  • If you didn't install Python or you have installed other versions, please download the Python 3.7.5 64-bit from Python or Huaweicloud to install.

Installing MindSpore

pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/{version}/MindSpore/gpu/ubuntu_x86/cuda-10.1/mindspore_gpu-{version}-cp37-cp37m-linux_{arch}.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple

Of which,

• When the network is connected, dependency items are automatically downloaded during .whl package installation. (For details about other dependency items, see requirements.txt). In other cases, you need to manually install dependency items.
• {version} denotes the version of MindSpore. For example, when you are downloading MindSpore 1.0.1, {version} should be 1.0.1.
• {arch} denotes the system architecture. For example, the Linux system you are using is x86 architecture 64-bit, {arch} should be x86_64. If the system is ARM architecture 64-bit, then it should be aarch64.

Installation Verification

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.tensor_add(x, y))

• The outputs should be the same as:

[[[ 2.  2.  2.  2.],
    [ 2.  2.  2.  2.],
    [ 2.  2.  2.  2.]],

    [[ 2.  2.  2.  2.],
    [ 2.  2.  2.  2.],
    [ 2.  2.  2.  2.]],

    [[ 2.  2.  2.  2.],
    [ 2.  2.  2.  2.],
    [ 2.  2.  2.  2.]]]

It means MindSpore has been installed successfully.

Version Update

Using the following command if you need update MindSpore version:

pip install --upgrade mindspore-gpu

Installing MindInsight

If you need to analyze information such as model scalars, graphs, computation graphs and model traceback, you can install MindInsight.

For more details, please refer to MindInsight.

Installing MindArmour

If you need to conduct AI model security research or enhance the security of the model in you applications, you can install MindArmour.

For more details, please refer to MindArmour.

Installing MindSpore Hub

If you need to access and experience MindSpore pre-trained models quickly, you can install MindSpore Hub.

For more details, please refer to MindSpore Hub.