Elastic GPU Service:Install AIACC-Training

Supported frameworks

Alibaba Cloud provides AIACC-Training software packages for different versions of deep learning frameworks. The following table lists the versions of the frameworks that are supported by AIACC-Training.

Method 1: Install AIACC-Training in an existing AI software environment

If an AI training environment for deep learning is deployed, you can install AIACC-Training in automatic or manual mode. Before you install AIACC-Training, make sure that your environment meets the following requirements:

• Python 3 and pip are installed.

• A deep learning framework such as PyTorch, TensorFlow, or MXNet is installed.

wget https://ali-perseus-release.oss-cn-huhehaote.aliyuncs.com/install_AIACC-Training.sh && bash install_AIACC-Training.sh

Method 2: Install a Conda environment that contains AIACC-Training

Conda is an open source system that is used to manage software packages and environments. This system is supported on different platforms. You can create a Conda environment that contains AIACC-Training by running a single command. In the Conda environment, CUDA Toolkit, Python 3, deep learning frameworks, and the latest AIACC-Training software are installed. This helps you quickly build and manage different deep learning frameworks and framework versions. This also helps you significantly improve training performance by using AIACC-Training.

1. Visit the Conda official website to download and install the latest version of Miniconda. For more information, see Miniconda.

2. Run the following command based on the required framework version and environment to create a Conda environment that contains AIACC-Training:

   conda env create -f https://ali-perseus-release.oss-cn-huhehaote.aliyuncs.com/conda/latest/${framework}_${framework_version}_cu${cuda_version}_py${python_version}.yaml

   In this example, PyTorch 1.7.1, CUDA 11.0, and Python 3.6 are used. Sample code:

   cuda_version=11.0  
   framework=torch    
   framework_version=1.7.1
   python_version=36
   conda env create -f https://ali-perseus-release.oss-cn-huhehaote.aliyuncs.com/conda/latest/${framework}_${framework_version}_cu${cuda_version}_py${python_version}.yaml

   Parameters:

   • ${cuda_version}: the CUDA version. Specify the full version that contains periods (.). The version cannot be later than the CUDA version that is already installed on the GPU-accelerated instance.

   • ${framework}: the type of the deep learning framework. If you use TensorFlow, MXNet, or PyTorch, set the parameter to tensorflow, mxnet, or torch.

   • ${framework_version}: the version of the deep learning framework. For example, if you use PyTorch 1.7.1, add framework_version=1.7.1 to the command.

   • ${python_version}: the Python version. You must remove periods (.) from the version. For example, if you use Python 3.6, add python_version=36 to the command.

   Important

   If the system prompts that the URL of the Conda environment cannot be found, the specified framework version is not supported. For more information, see Supported frameworks.

Method 3: Install a Docker image that is configured with AIACC-Training

You can download a Docker image that is configured with AIACC-Training. In the Docker image, CUDA, Python 3, deep learning frameworks, and the latest AIACC-Training software are installed. This helps you quickly deploy deep learning environments and manage different CUDA environments. This also helps you significantly improve training performance by using AIACC-Training.

docker pull registry.cn-beijing.aliyuncs.com/cto_office/perseus-training:${os_type}-cu${cuda_version}-${framework}${framework_version}-py${python_version}-latest

os_type=centos7          
cuda_version=11.0        
framework=tf             
framework_version=2.4.0
python_version=36        
docker pull registry.cn-beijing.aliyuncs.com/cto_office/perseus-training:${os_type}-cu${cuda_version}-${framework}${framework_version}-py${python_version}-latest