Help Center/ ModelArts/ FAQs/ General Issues/ Which AI Frameworks Does ModelArts Support?
Updated on 2024-06-15 GMT+08:00
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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

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 or GPU

Yes

Yes

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

CPU- or GPU-powered general algorithm development and training, preconfigured with AI engine MindSpore 1.7.0 and CUDA 10.1

CPU or GPU

Yes

Yes

mindspore1.7.0-py3.7-ubuntu18.04

CPU-powered general algorithm development and training, preconfigured with AI engine MindSpore 1.7.0

CPU

Yes

Yes

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

CPU- or GPU-powered general algorithm development and training, preconfigured with AI engine PyTorch 1.10 and CUDA 10.2

CPU or 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 or GPU

Yes

Yes

conda3-ubuntu18.04

Clean customized base image only includes 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 or 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 customized base image includes CUDA 10.2, 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 following table lists the AI engines.

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 2 AI engines supported by training jobs

Runtime Environment

System Architecture

System Version

AI Engine and Version

Supported CUDA or Ascend Version

TensorFlow

x86_64

Ubuntu18.04

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

cuda10.1

PyTorch

x86_64

Ubuntu18.04

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

cuda10.2

MPI

x86_64

Ubuntu18.04

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

Supported AI engines vary depending on regions.

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.

  • Runtime environments marked with recommended are unified runtime images, which will be used as mainstream base inference images. The installation packages of unified images are richer. For details, see 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 3 Supported AI engines and their runtime

Engine

Runtime

Note

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.
  • python3.6, python2.7, and tf2.1-python3.7 indicate that the model can run on both CPUs and GPUs. 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.
  • python2.7 and python3.6 can only be used to run models on CPUs.

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.
  • python2.7 and python3.6 can only be used to run models on CPUs.

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.
  • python2.7 and python3.6 can only be used to run models on CPUs.

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.
  • python2.7, python3.6, python3.7, pytorch1.4-python3.7, and pytorch1.5-python3.7 indicate that the model can run on both CPUs and GPUs.
  • The default runtime is python2.7.

MindSpore

aarch64 (recommended)

AArch64 can run only on Snt3 chips.
Parent topic: General Issues