Installing MindSpore GPU Nightly by pip
MindSpore Nightly is a preview version which includes latest features and bugfixes, not fully supported and tested. Install MindSpore Nightly version if you wish to try out the latest changes on MindSpore.
This document describes how to quickly install MindSpore Nightly by pip in a Linux system with a GPU environment.
For details about how to install third-party dependency software when confirming the system environment information, see the third-party dependency software installation section in the Experience source code compilation and install the MindSpore GPU version on Linux provided by the community. Thanks to the community member Flying penguin for sharing.
Installing MindSpore and dependencies
The following table lists the system environment and third-party dependencies required to install MindSpore.
software |
version |
description |
Ubuntu |
18.04 |
OS for compiling and running MindSpore |
CUDA |
10.1 or 11.1 |
parallel computing architecture for MindSpore GPU |
cuDNN |
7.6.x or 8.0.x |
deep neural network acceleration library used by MindSpore GPU |
Python |
3.7-3.9 |
Python environment that MindSpore depends on |
GCC |
7.3.0~9.4.0 |
C++ compiler for compiling MindSpore |
gmp |
6.1.2 |
multiple precision arithmetic library used by MindSpore |
Open MPI |
4.0.3 |
high performance message passing library used by MindSpore (optional, required for single-node/multi-GPU and multi-node/multi-GPU training) |
TensorRT |
7.2.2 |
high performance deep learning inference SDK used by MindSpore (optional, required for serving inference) |
The following describes how to install the third-party dependencies.
Installing CUDA
MindSpore GPU supports CUDA 10.1 and CUDA 11.1. NVIDIA officially shows a variety of installation methods. For details, please refer to CUDA download page and CUDA installation guide.
The following only shows instructions for installing by runfile on Linux systems.
Before installing CUDA, you need to run the following commands to install related dependencies.
sudo apt-get install linux-headers-$(uname -r) gcc-7
The minimum required GPU driver version of CUDA 10.1 is 418.39. The minimum required GPU driver version of CUDA 11.1 is 450.80.02. You may run nvidia-smi
command to confirm the GPU driver version. If the driver version does not meet the requirements, you should choose to install the driver during the CUDA installation. After installing the driver, you need to reboot your system.
Run the following command to install CUDA 11.1 (recommended).
wget https://developer.download.nvidia.com/compute/cuda/11.1.1/local_installers/cuda_11.1.1_455.32.00_linux.run
sudo sh cuda_11.1.1_455.32.00_linux.run
echo -e "export PATH=/usr/local/cuda-11.1/bin:\$PATH" >> ~/.bashrc
echo -e "export LD_LIBRARY_PATH=/usr/local/cuda-11.1/lib64:\$LD_LIBRARY_PATH" >> ~/.bashrc
source ~/.bashrc
Or install CUDA 10.1 with the following command.
wget https://developer.download.nvidia.com/compute/cuda/10.1/Prod/local_installers/cuda_10.1.243_418.87.00_linux.run
sudo sh cuda_10.1.243_418.87.00_linux.run
echo -e "export PATH=/usr/local/cuda-10.1/bin:\$PATH" >> ~/.bashrc
echo -e "export LD_LIBRARY_PATH=/usr/local/cuda-10.1/lib64:\$LD_LIBRARY_PATH" >> ~/.bashrc
source ~/.bashrc
When the default path /usr/local/cuda has an installation package, the LD_LIBRARY_PATH environment variable does not work. The reason is that MindSpore uses DT_RPATH to support startup without environment variables, reducing user settings. DT_RPATH has a higher priority than the LD_LIBRARY_PATH environment variable.
Installing cuDNN
After completing the installation of CUDA, Log in and download the corresponding cuDNN installation package from cuDNN page. If CUDA 10.1 was previously installed, download cuDNN v7.6.x for CUDA 10.1. If CUDA 11.1 was previously installed, download cuDNN v8.0.x for CUDA 11.1. Note that download the tgz compressed file. Assuming that the downloaded cuDNN package file is named cudnn.tgz
and the installed CUDA version is 11.1, execute the following command to install cuDNN.
tar -zxvf cudnn.tgz
sudo cp cuda/include/cudnn.h /usr/local/cuda-11.1/include
sudo cp cuda/lib64/libcudnn* /usr/local/cuda-11.1/lib64
sudo chmod a+r /usr/local/cuda-11.1/include/cudnn.h /usr/local/cuda-11.1/lib64/libcudnn*
If a different version of CUDA have been installed or the CUDA installation path is different, just replace /usr/local/cuda-11.1
in the above command with the currently installed CUDA path.
Installing Python
Python can be installed in multiple ways.
-
Install Python with Conda.
Install Miniconda:
cd /tmp
curl -O https://mirrors.tuna.tsinghua.edu.cn/anaconda/miniconda/Miniconda3-py37_4.10.3-Linux-$(arch).sh
bash Miniconda3-py37_4.10.3-Linux-$(arch).sh -b
cd -
. ~/miniconda3/etc/profile.d/conda.sh
conda init bash
After the installation is complete, you can set up Tsinghua source acceleration download for Conda, and see here.
Create a virtual environment, taking Python 3.7.5 as an example:
conda create -n mindspore_py37 python=3.7.5 -y
conda activate mindspore_py37
-
Or install Python via APT with the following command.
sudo apt-get update
sudo apt-get install software-properties-common -y
sudo add-apt-repository ppa:deadsnakes/ppa -y
sudo apt-get install python3.7 python3.7-dev python3.7-distutils python3-pip -y
# set new installed Python as default
sudo update-alternatives --install /usr/bin/python python /usr/bin/python3.7 100
# install pip
python -m pip install pip -i https://repo.huaweicloud.com/repository/pypi/simple
sudo update-alternatives --install /usr/bin/pip pip ~/.local/bin/pip3.7 100
pip config set global.index-url https://repo.huaweicloud.com/repository/pypi/simple
To install other Python versions, just change 3.7
in the command.
Run the following command to check the Python version.
python --version
Installing GCC and gmp
Run the following commands to install GCC and gmp.
sudo apt-get install gcc-7 libgmp-dev -y
To install a later version of GCC, run the following command to install GCC 8.
sudo apt-get install gcc-8 -y
Or install GCC 9 (Note that GCC 9 is not compatible with CUDA 10.1).
sudo apt-get install software-properties-common -y
sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt-get update
sudo apt-get install gcc-9 -y
Installing Open MPI-optional
You may compile and install Open MPI by the following command.
curl -O https://download.open-mpi.org/release/open-mpi/v4.0/openmpi-4.0.3.tar.gz
tar xzf openmpi-4.0.3.tar.gz
cd openmpi-4.0.3
./configure --prefix=/usr/local/openmpi-4.0.3
make
sudo make install
echo -e "export PATH=/usr/local/openmpi-4.0.3/bin:\$PATH" >> ~/.bashrc
echo -e "export LD_LIBRARY_PATH=/usr/local/openmpi-4.0.3/lib:\$LD_LIBRARY_PATH" >> ~/.bashrc
source ~/.bashrc
cd -
Installing TensorRT-optional
After completing the installation of CUDA and cuDNN, download TensorRT 7.2.2 for CUDA 11.1 from TensorRT download page, and note to download installation package in TAR format. Suppose the downloaded file is named TensorRT-7.2.2.3.Ubuntu-18.04.x86_64-gnu.cuda-11.1.cudnn8.0.tar.gz
, install TensorRT with the following command.
tar xzf TensorRT-7.2.2.3.Ubuntu-18.04.x86_64-gnu.cuda-11.1.cudnn8.0.tar.gz
cd TensorRT-7.2.2.3
echo -e "export TENSORRT_HOME=$PWD" >> ~/.bashrc
echo -e "export LD_LIBRARY_PATH=\$TENSORRT_HOME/lib:\$LD_LIBRARY_PATH" >> ~/.bashrc
source ~/.bashrc
cd -
Installing MindSpore
For CUDA 11.1:
pip install mindspore-cuda11-dev -i https://pypi.tuna.tsinghua.edu.cn/simple
Of which,
- Currently, MindSpore GPU Nightly only provides CUDA11 version.
- When the network is connected, dependency items are automatically downloaded during .whl package installation. (For details about the dependency, see required_package in setup.py .) In other cases, you need to install it by yourself. When running models, you need to install additional dependencies based on requirements.txt specified for different models in ModelZoo. For details about common dependencies, see requirements.txt.
- pip will be installing the latest version of MindSpore GPU Nightly automatically. If you wish to specify the version to be installed, please refer to the instruction below regarding to version update, and specify version manually.
Installation Verification
Before running MindSpore GPU version, please make sure that installation path of nvcc has been added to PATH
and LD_LIBRARY_PATH
environment variabels. If you have not done so, please follow the example below, based on CUDA11 installed in default location:
export PATH=/usr/local/cuda-11.1/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-11.1/lib64:$LD_LIBRARY_PATH
If a different version of CUDA have been installed or the CUDA installation path is different, replace /usr/local/cuda-11.1
in the above command with the currently installed CUDA path.
i:
python -c "import mindspore;mindspore.run_check()"
The outputs should be the same as:
MindSpore version: __version__
The result of multiplication calculation is correct, MindSpore has been installed successfully!
It means MindSpore has been installed successfully.
ii:
import numpy as np
import mindspore as ms
import mindspore.ops as ops
ms.set_context(device_target="GPU")
x = ms.Tensor(np.ones([1,3,3,4]).astype(np.float32))
y = ms.Tensor(np.ones([1,3,3,4]).astype(np.float32))
print(ops.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
Use the following command if you need to update the MindSpore version:
pip install --upgrade mindspore-cuda11-dev=={version}
Of which,
- When updating to a release candidate (rc) version,
{version}
should be specified manually, e.g. 1.6.0rc1.dev20211125; When you want to upgrade to the latest version automatically, =={version}
could be removed.
Note: Currently MindSpore GPU nightly is only available in the CUDA11 version. If you still want to use the CUDA10 version, please refer to the source code compilation guide to compile it yourself on the environment where CUDA10 is installed.