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Help Center/ ModelArts/ Service Overview/ Functions/ ModelArts Standard Functions/ Supported AI Frameworks in ModelArts Standard

Supported AI Frameworks in ModelArts Standard

Updated on 2025-02-07 GMT+08:00

For the development environment Notebook, training jobs, and model inference (model management and deployment), ModelArts Standard supports various AI frameworks and versions. Refer to the following sections.

Unified Image List

ModelArts provides a unified image for Arm+Ascend specifications, including MindSpore and PyTorch. These images are suitable for the Standard development environment, model training, and service deployment. Refer to the table below for more details.

For the URL of the images and the included dependencies, refer to ModelArts Unified Image List.

Table 1 MindSpore

Preset Image

Supported Chip

Application Scope

Applicable Region

mindspore_2.2.0-cann_7.0.1-py_3.9-euler_2.10.7-aarch64-snt9b

Ascend snt9b

Notebook, training, and inference deployment

CN-Hong Kong

Table 2 PyTorch

Preset Image

Supported Chip

Application Scope

Applicable Region

pytorch_2.1.0-cann_7.0.1-py_3.9-euler_2.10.7-aarch64-snt9b

Ascend snt9b

Notebook, training, and inference deployment

CN-Hong Kong

pytorch_1.11.0-cann_7.0.1-py_3.9-euler_2.10.7-aarch64-snt9b

Ascend snt9b

Notebook, training, and inference deployment

CN-Hong Kong

Development Environment Notebook

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

Table 3 Images supported by notebook of the new version

Image

Description

Supported Chip

Remote SSH

Online JupyterLab

pytorch1.8-cuda10.2-cudnn7-ubuntu18.04

CPU- or GPU-powered public image for general algorithm development and training, with built-in AI engine PyTorch 1.8

CPU/GPU

Yes

Yes

mindspore1.7.0-cuda10.1-py3.7-ubuntu18.04

CPU and GPU general algorithm development and training, preconfigured with AI engine MindSpore1.7.0 and cuda 10.1

CPU/GPU

Yes

Yes

mindspore1.7.0-py3.7-ubuntu18.04

CPU general algorithm development and training, preconfigured with AI engine MindSpore1.7.0

CPU

Yes

Yes

pytorch1.10-cuda10.2-cudnn7-ubuntu18.04

CPU and GPU general algorithm development and training, preconfigured with AI engine PyTorch1.10 and cuda10.2

CPU/GPU

Yes

Yes

tensorflow2.1-cuda10.1-cudnn7-ubuntu18.04

CPU- or GPU-powered public image for general algorithm development and training, with built-in AI engine TensorFlow 2.1

CPU/GPU

Yes

Yes

conda3-ubuntu18.04

Clean user customized base image only include conda

CPU

Yes

Yes

pytorch1.4-cuda10.1-cudnn7-ubuntu18.04

CPU- or GPU-powered public image for general algorithm development and training, with built-in AI engine PyTorch 1.4

CPU/GPU

Yes

Yes

tensorflow1.13-cuda10.0-cudnn7-ubuntu18.04

GPU-powered public image for general algorithm development and training, with built-in AI engine TensorFlow 1.13.1

GPU

Yes

Yes

conda3-cuda10.2-cudnn7-ubuntu18.04

Clean user customized base image include cuda10.2, conda

CPU

Yes

Yes

spark2.4.5-ubuntu18.04

CPU-powered algorithm development and training, preconfigured with PySpark 2.4.5 and can be attached to preconfigured Spark clusters including MRS and DLI

CPU

No

Yes

mindspore1.2.0-cuda10.1-cudnn7-ubuntu18.04

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

GPU

Yes

Yes

mindspore1.2.0-openmpi2.1.1-ubuntu18.04

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

CPU

Yes

Yes

Training Jobs

The supported AI engines and their corresponding versions for training are as follows when creating a training job.

The built-in training engines are named in the following format:
<Training engine name_version>-[cpu | <cuda_version | cann_version >]-<py_version>-<OS name_version>-< x86_64 | aarch64>
Table 4 AI engines supported by training jobs

Runtime Environment

CPU Architecture

OS Version

AI Engine and Version

Supported CUDA or Ascend Version

TensorFlow

x86_64

Ubuntu 18.04

tensorflow_2.1.0-cuda_10.1-py_3.7-ubuntu_18.04-x86_64

cuda10.1

PyTorch

x86_64

Ubuntu 18.04

pytorch_1.8.0-cuda_10.2-py_3.7-ubuntu_18.04-x86_64

CUDA 10.2

MPI

x86_64

Ubuntu 18.04

mindspore_1.3.0-cuda_10.1-py_3.7-ubuntu_1804-x86_64

CUDA 10.1

Horovod

x86_64

Ubuntu 18.04

horovod_0.20.0-tensorflow_2.1.0-cuda_10.1-py_3.7-ubuntu_18.04-x86_64

CUDA 10.1

horovod_0.22.1-pytorch_1.8.0-cuda_10.2-py_3.7-ubuntu_18.04-x86_64

CUDA 10.2

NOTE:

Supported AI engines vary depending on regions.

Supported AI Engines for Inference

If you import a preset image from a template or OBS to create a model, you can select the AI engines and versions in the table below.

NOTE:
  • Runtime environments marked as recommended is sourced from unified images, which will be the mainstream inference base image in the future. The unified image contains more comprehensive installation packages. For details, see Base Inference Images.
  • Images of the old version will be discontinued. Use unified images instead.
  • The base images to be removed are no longer maintained.
  • The naming convention for the unified runtime image is as follows: <AI engine name and version> - <Hardware and version: CPU or CUDA or CANN> - <Python version> - <OS version> - <CPU architecture>.
Table 5 Supported AI engines and their runtime environments

Engine

Runtime Environment

Remarks

TensorFlow

python3.6

python2.7 (unavailable soon)

tf1.13-python3.6-gpu

tf1.13-python3.6-cpu

tf1.13-python3.7-cpu

tf1.13-python3.7-gpu

tf2.1-python3.7 (unavailable soon)

tensorflow_2.1.0-cuda_10.1-py_3.7-ubuntu_18.04-x86_64 (recommended)

  • TensorFlow 1.8.0 is used in python2.7 and python3.6.
  • The model can run on both CPUs and GPUs when using python3.6, python2.7, or tf2.1-python3.7. For other runtime values, if the suffix contains cpu or gpu, the model can run only on CPUs or GPUs.
  • The default runtime is python2.7.

Spark_MLlib

python2.7 (unavailable soon)

python3.6 (unavailable soon)

  • Spark_MLlib 2.3.2 is used in python2.7 and python3.6.
  • The default runtime is python2.7.
  • python 2.7 and python 3.6 can only be used to run models applicable to CPU.

Scikit_Learn

python2.7 (unavailable soon)

python3.6 (unavailable soon)

  • Scikit_Learn 0.18.1 is used in python2.7 and python3.6.
  • The default runtime is python2.7.
  • python 2.7 and python 3.6 can only be used to run models applicable to CPU.

XGBoost

python2.7 (unavailable soon)

python3.6 (unavailable soon)

  • XGBoost 0.80 is used in python2.7 and python3.6.
  • The default runtime is python2.7.
  • python 2.7 and python 3.6 can only be used to run models applicable to CPU.

PyTorch

python2.7 (unavailable soon)

python3.6

python3.7

pytorch1.4-python3.7

pytorch1.5-python3.7 (unavailable soon)

pytorch_1.8.0-cuda_10.2-py_3.7-ubuntu_18.04-x86_64 (recommended)

  • PyTorch 1.0 is used in python2.7, python3.6, and python3.7.
  • The model can run on both CPUs and GPUs when using python2.7, python3.6, python3.7, pytorch1.4-python3.7, or pytorch1.5-python3.7.
  • The default runtime is python2.7.

MindSpore

aarch64 (recommended)

aarch64 can only be used to run models on Snt3 chips.

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