Which AI Frameworks Does ModelArts Support?

The AI frameworks and versions supported by ModelArts vary slightly based on the development environment notebook, training jobs, and model inference (AI application management and deployment). The following describes the AI frameworks supported by each module.

Development Environment Notebook

The image and versions supported by development environment notebook instances vary based on runtime environments.

Table 1 Images supported by notebook of the new version

Image

Description

Supported Chip

Remote SSH

Online JupyterLab

mindspore1.7.0-cann5.1.0-py3.7-euler2.8.3

Ascend+ARM algorithm development and training. MindSpore is preset in the AI engine.

Ascend 910

Yes

Yes

mindstudio5.0.rc1-ascend910-cann5.1.rc1-euler2.8.3-aarch64

Ascend+ARM algorithm development and training. MindSpore is preset in the AI engine.

Ascend 910

Yes

No

mindspore1.8.0-cann5.1.2-py3.7-euler2.8.3

Ascend+ARM algorithm development and training. MindSpore is preset in the AI engine.

Ascend 910

Yes

Yes

tensorflow1.15-cann5.1.0-py3.7-euler2.8.3

Ascend+ARM algorithm development and training. TensorFlow is preset in the AI engine.

Ascend 910

Yes

Yes

mindspore_2.0.0-cann_6.3.0-py_3.7-euler_2.8.3

Ascend- and Arm-powered public image for algorithm development and training, with built-in AI engine MindSpore

Ascend 910

Yes

Yes

pytorch_1.11.0-cann_6.3.0-py_3.7-euler_2.8.3

Ascend- and Arm-powered public image for algorithm development and training, with built-in AI engine PyTorch

Ascend 910

Yes

Yes

tensorflow1.15-mindspore1.7.0-cann5.1.0-euler2.8-aarch64

Ascend+ARM algorithm development and training. TensorFlow and MindSpore are preset in the AI engine.

Ascend 910

Yes

Yes

tensorflow_1.15.0-cann_6.3.0-py_3.7-euler_2.8.3

Ascend+ARM algorithm development and training. MindSpore is preset in the AI engine.

Ascend 910

Yes

Yes

tensorflow1.15.0-cann5.1.2-py3.7-euler2.8.3

Ascend+ARM algorithm development and training. MindSpore is preset in the AI engine.

Ascend 910

Yes

Yes

Table 2 Images supported by notebook of the old version

Runtime Environment

Built-in AI Engine and Version

Supported Chip

Ascend-Powered-Engine 1.0 (Python3)

MindSpore 1.2.0

Ascend 910

MindSpore 1.1.1

Ascend 910

TensorFlow 1.15.0

Ascend 910

Training Jobs

The built-in training engines in the new version are named in the following format:

<Training engine name_version>-[cpu | <cuda_version | cann_version >]-<py_version>-<OS name_version>-< x86_64 | aarch64>
Table 3 AI engines supported by training jobs of the new version

Runtime Environment

Supported Chip

System Architecture

System Version

AI Engine and Version

Supported CUDA or Ascend Version

Ascend-Powered-Engine

Ascend 910

aarch64

Euler 2.8

mindspore_2.0.0-cann_6.3.0-py_3.7-euler_2.8.3-aarch64

cann_6.3.0

PyTorch

Ascend 910

aarch64

Euler 2.8

pytorch_1.11.0-cann_6.3.0-py_3.7-euler_2.8.3-aarch64

cann_6.3.0

TensorFlow

Ascend 910

aarch64

Euler 2.8

tensorflow_1.15.0-cann_6.3.0-py_3.7-euler_2.8.3-aarch64

cann_6.3.0

Supported AI Engines for ModelArts Inference

If you import a model from a template or OBS to create an AI application, the following AI engines and versions are supported.

Note

  • Runtime environments marked with recommended are unified runtime images, which will be used as mainstream base inference images.

  • Images of the old version will be discontinued. Use unified images.

  • The base images to be removed are no longer maintained.

  • Naming a unified runtime image: <AI engine name and version> - <Hardware and version: CPU, CUDA, or CANN> - <Python version> - <OS version> - <CPU architecture>

Table 4 Supported AI engines and their runtime

Engine

Runtime

TensorFlow

tensorflow_1.15.0-cann_6.3.0-py_3.7-euler_2.8.3-aarch64

MindSpore

mindspore_2.0.0-cann_6.3.0-py_3.7-euler_2.8.3-aarch64

PyTorch

pytorch_1.11.0-cann_6.3.0-py_3.7-euler_2.8.3-aarch64