Help Center/ ModelArts/ ModelArts User Guide (Standard)/ Creating a Custom Image for ModelArts Standard/ Preset Images Supported by ModelArts/ Preset Dedicated Images in Notebook Instances
Updated on 2025-06-06 GMT+08:00
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Preset Dedicated Images in Notebook Instances

ModelArts DevEnviron provides Docker container images, which can run as preset containers. Certain preset images are built on common AI engine frameworks such as PyTorch, TensorFlow, and MindSpore. These images are named using the AI engines. Additionally, many common packages are preset in these images, relieving you from the package installation.

The images preset in ModelArts DevEnviron include:

  • Typical preset packages: AI engines based on standard Conda, data analysis software packages such as Pandas and Numpy, and tool software such as CUDA and cuDNN are included to meet your needs.
  • Preset Conda environments: A Conda environment and basic Conda Python (excluding any AI engine) are created for each preset image. The following figure shows the Conda environment for a preset MindSpore image.

    Select a Conda environment based on whether MindSpore is used for debugging.

  • Notebook: a web application that enables you to code on the GUI and combine the code, mathematical equations, and visualized content into a document.
  • JupyterLab plug-ins: enable flavor changing, case sharing to AI Gallery for communication, and instance stopping to improve user experience.
  • Remote SSH: allows you to remotely start and debug a notebook instance from a local PC.
  • Images preset in ModelArts DevEnviron: After these preset images support function development, the custom images created based on these preset images can be directly used for ModelArts training jobs.
Table 1 Preset x86 images

Engine

Image

PyTorch

pytorch_2.1.0-cuda_12.1-py_3.10.6-ubuntu_22.04-x86_64

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

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

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

Tensorflow

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

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

MindSpore

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

mindspore1.7.0-py3.7-ubuntu18.04

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

mindspore1.2.0-openmpi2.1.1-ubuntu18.04

No AI engine (base images dedicated for image customization)

conda3-cuda10.2-cudnn7-ubuntu18.04

conda3-ubuntu18.04

x86-powered PyTorch Base Images

PyTorch contains three types of images:

Image 1: pytorch_2.1.0-cuda_12.1-py_3.10.6-ubuntu_22.04-x86_64

Table 2 Information about the image

AI Engine

Whether to Use GPUs

(CUDA Version)

URL

Dependency

PyTorch 2.1

Yes

(CUDA 10.2)

swr.{region_id}.myhuaweicloud.com/atelier/pytorch_2_1:pytorch_2.1.0-cuda_12.1-py_3.10.6-ubuntu_22.04-x86_64-20250305173557-cb53968

PyPI package

Ubuntu package

torch 2.1.0+cu121

torchvision 0.16.0+cu121

torchaudio 2.1.0+cu121

ipykernel 6.7.0

ipython 8.33.0

jupyter-client 7.4.9

ma-cli 1.2.3

matplotlib 3.7.3

moxing-framework 2.2.10

numpy 1.24.2

opencv-python 4.8.0.76

pandas 2.2.2

Pillow 10.4.0

pip 22.0.4

psutil 6.0.0

PyYAML 6.0.2

scipy 1.12.0

scikit-learn 1.5.1

tornado 6.4.2

tensorboard 2.18.0

automake

build-essential

ca-certificates

cmake

cpp

curl

ffmpeg

g++

gcc

gfortran

git

git-lfs

grep

libcudnn7

libcudnn7-dev

libjpeg-dev:amd64

libjpeg8-dev:amd64

openssh-client

openssh-server

nginx

pandoc

python3

rpm

screen

tar

tmux

unzip

vim

wget

zip

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

Table 3 Information about the image

AI Engine

Whether to Use GPUs

(CUDA Version)

URL

Dependency

PyTorch 1.8

Yes

(CUDA 10.2)

swr.{region_id}.myhuaweicloud.com/atelier/pytorch_1_8:pytorch_1.8.0-cuda_10.2-py_3.7-ubuntu_18.04-x86_64-20220926104358-041ba2e

PyPI package

Ubuntu package

torch 1.8.0

torchvision 0.9.0

ipykernel 6.7.0

ipython 7.34.0

jupyter-client 7.4.4

ma-cli 1.2.3

matplotlib 3.5.1

modelarts 1.4.25

moxing-framework 2.1.0.5d9c87c8

numpy 1.19.5

opencv-python 4.1.2.30

pandas 1.1.5

Pillow 9.3.0

pip 21.0.1

psutil 5.8.0

PyYAML 5.1

scipy 1.5.2

scikit-learn 0.22.1

tornado 6.2

tensorboard 2.1.1

automake

build-essential

ca-certificates

cmake

cpp

curl

ffmpeg

g++

gcc

gfortran

git

git-lfs

grep

libcudnn7

libcudnn7-dev

libjpeg-dev:amd64

libjpeg8-dev:amd64

openssh-client

openssh-server

nginx

pandoc

python3

rpm

screen

tar

tmux

unzip

vim

wget

zip

Image 3: pytorch1.10-cuda10.2-cudnn7-ubuntu18.04

Table 4 Information about the image

AI Engine

Whether to Use GPUs

(CUDA Version)

URL

Dependency

Pytorch 1.10

Yes

(CUDA 10.2)

swr.{region_id}.myhuaweicloud.com/atelier/pytorch_1_10:pytorch_1.10.2-cuda_10.2-py_3.7-ubuntu_18.04-x86_64-20221008154718-2b3e39c

PyPI package

Ubuntu package

torch 1.10.2

torchvision 0.11.3

ipykernel 6.7.0

ipython 7.34.0

jupyter-client 7.4.4

ma-cli 1.2.3

matplotlib 3.5.1

modelarts 1.4.25

moxing-framework 2.1.0.5d9c87c8

numpy 1.19.5

opencv-python 4.1.2.30

pandas 1.1.5

Pillow 9.3.0

pip 21.0.1

psutil 5.8.0

PyYAML 5.1

scipy 1.5.2

scikit-learn 0.22.1

tornado 6.2

automake

build-essential

ca-certificates

cmake

cpp

curl

ffmpeg

g++

gcc

gfortran

git

git-lfs

grep

libcudnn7

libcudnn7-dev

libjpeg-dev:amd64

libjpeg8-dev:amd64

openssh-client

openssh-server

nginx

pandoc

python3

rpm

screen

tar

tmux

unzip

vim

wget

zip

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

Table 5 Information about the image

AI Engine

Whether to Use GPUs

(CUDA Version)

URL

Dependency

Pytorch 1.4

Yes

(CUDA 10.1)

swr.{region_id}.myhuaweicloud.com/atelier/pytorch_1_4:pytorch_1.4-cuda_10.1-py37-ubuntu_18.04-x86_64-20220926104017-041ba2e

PyPI package

Ubuntu package

torch 1.4.0

torchvision 0.5.0

ipykernel 6.7.0

ipython 7.34.0

jupyter-client 7.4.7

ma-cli 1.2.3

matplotlib 3.5.1

modelarts 1.4.25

moxing-framework 2.1.0.5d9c87c8

numpy 1.19.5

opencv-python 4.1.2.30

pandas 1.1.5

Pillow 6.2.0

pip 21.0.1

psutil 5.8.0

PyYAML 5.1

scipy 1.5.2

scikit-learn 0.22.1

tornado 6.2

tensorboard 2.1.1

automake

build-essential

ca-certificates

cmake

cpp

curl

ffmpeg

g++

gcc

gfortran

git

git-lfs

grep

libcudnn7

libcudnn7-dev

libjpeg-dev:amd64

libjpeg8-dev:amd64

openssh-client

openssh-server

nginx

pandoc

python3

rpm

screen

tar

tmux

unzip

vim

wget

zip

x86-powered TensorFlow Base Images

TensorFlow contains two types of images:

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

Table 6 Information about the image

AI Engine

Whether to Use GPUs

(CUDA Version)

URL

Dependency

Tensorflow 2.1

Yes

(CUDA 10.1)

swr.{region_id}.myhuaweicloud.com/atelier/tensorflow_2_1:tensorflow_2.1.0-cuda_10.1-py_3.7-ubuntu_18.04-x86_64-20220926144607-041ba2e

PyPI package

Ubuntu package

tensorflow 2.1.0

ipykernel 6.7.0

ipython 7.34.0

jupyter-client 7.4.4

ma-cli 1.2.3

matplotlib 3.5.1

modelarts 1.4.25

moxing-framework 2.1.0.5d9c87c8

numpy 1.19.5

opencv-python 4.1.2.30

pandas 1.1.5

Pillow 9.3.0

pip 21.0.1

psutil 5.8.0

PyYAML 5.1

scipy 1.5.2

scikit-learn 0.22.1

tornado 6.2

tensorboard 2.1.1

automake

build-essential

ca-certificates

cmake

cpp

curl

ffmpeg

g++

gcc

gfortran

git

git-lfs

grep

libcudnn7

libcudnn7-dev

libjpeg-dev:amd64

libjpeg8-dev:amd64

openssh-client

openssh-server

nginx

python3

rpm

screen

tar

tmux

unzip

vim

wget

zip

Image 2: tensorflow1.13-cuda10.0-cudnn7-ubuntu18.04

Table 7 Information about the image

AI Engine

Whether to Use GPUs

(CUDA Version)

URL

Dependency

Tensorflow 1.13-gpu

Yes

(CUDA 10.0)

swr.{region_id}.myhuaweicloud.com/atelier/tensorflow_1_13:tensorflow_1.13-cuda_10.0-py_3.7-ubuntu_18.04-x86_64-20220926104358-041ba2e

PyPI package

Ubuntu package

tensorflow-gpu 1.13.1

ipykernel 6.7.0

ipython 7.34.0

jupyter-client 7.4.6

ma-cli 1.2.3

matplotlib 3.5.2

modelarts 1.4.25

moxing-framework 2.0.1.rc0.ffd1c0c8

numpy 1.17.0

opencv-python 4.1.2.30

pandas 1.1.5

Pillow 6.2.0

pip 21.0.1

psutil 5.8.0

PyYAML 5.1

scipy 1.2.2

scikit-learn 0.22.1

tornado 6.2

automake

build-essential

ca-certificates

cmake

cpp

curl

ffmpeg

g++

gcc

gfortran

git

git-lfs

grep

libcudnn7

libcudnn7-dev

libjpeg-dev:amd64

libjpeg8-dev:amd64

openssh-client

openssh-server

nginx

python3

rpm

screen

tar

tmux

unzip

vim

wget

zip

x86-powered MindSpore Base Images

MindSpore contains four types of images:

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

Table 8 Information about the image

AI Engine

Whether to Use GPUs

(CUDA Version)

URL

Dependency

MindSpore-gpu 1.7.0

Yes

(CUDA 10.1)

swr.{region_id}.myhuaweicloud.com/atelier/mindspore_1_7_0:mindspore_1.7.0-cuda_10.1-py_3.7-ubuntu_18.04-x86_64-20220926104017-041ba2e

PyPI package

Ubuntu package

mindspore-gpu 1.7.0

ipykernel 6.7.0

ipython 7.34.0

jupyter-client 7.4.4

ma-cli 1.2.3

matplotlib 3.5.1

modelarts 1.4.25

moxing-framework 2.1.0.5d9c87c8

numpy 1.19.5

pandas 1.1.5

Pillow 9.3.0

pip 21.0.1

psutil 5.8.0

PyYAML 5.1

scipy 1.5.2

scikit-learn 0.22.1

tornado 6.2

mindinsight 1.7.0

mindvision 0.1.0

automake

build-essential

ca-certificates

cmake

cpp

curl

ffmpeg

g++

gcc

gfortran

git

git-lfs

grep

libcudnn7

libcudnn7-dev

libjpeg-dev:amd64

libjpeg8-dev:amd64

openssh-client

openssh-server

nginx

python3

rpm

screen

tar

tmux

unzip

vim

wget

zip

Image 2: mindspore1.7.0-py3.7-ubuntu18.04

Table 9 Information about the image

AI Engine

Whether to Use GPUs

(CUDA Version)

URL

Dependency

MindSpore 1.7.0

No

swr.{region_id}.myhuaweicloud.com/atelier/mindspore_1_7_0:mindspore_1.7.0-cpu-py_3.7-ubuntu_18.04-x86_64-20220926104017-041ba2e

PyPI package

Ubuntu package

mindspore 1.7.0

ipykernel 6.7.0

ipython 7.34.0

jupyter-client 7.4.6

ma-cli 1.2.3

matplotlib 3.5.1

modelarts 1.4.25

moxing-framework 2.1.0.5d9c87c8

numpy 1.19.5

pandas 1.1.5

Pillow 9.3.0

pip 21.0.1

psutil 5.8.0

PyYAML 5.1

scipy 1.5.2

scikit-learn 0.22.1

tornado 6.2

mindinsight 1.7.0

mindvision 0.1.0

automake

build-essential

ca-certificates

cmake

cpp

curl

ffmpeg

g++

gcc

gfortran

git

git-lfs

grep

libjpeg-dev:amd64

libjpeg8-dev:amd64

openssh-client

openssh-server

nginx

python3

rpm

screen

tar

tmux

unzip

vim

wget

zip

Image 3: mindspore1.2.0-cuda10.1-cudnn7-ubuntu18.04

Table 10 Information about the image

AI Engine

Whether to Use GPUs

(CUDA Version)

URL

Dependency

Mindspore-gpu 1.2.0

Yes

(CUDA 10.1)

swr.{region_id}.myhuaweicloud.com/atelier/mindspore_1_2_0:mindspore_1.2.0-py_3.7-cuda_10.1-ubuntu_18.04-x86_64-20220926104106-041ba2e

PyPI package

Ubuntu package

mindspore-gpu 1.2.0

ipykernel 6.7.0

ipython 7.34.0

jupyter-client 7.4.6

ma-cli 1.2.3

matplotlib 3.5.1

modelarts 1.4.25

moxing-framework 2.1.0.5d9c87c8

numpy 1.19.5

pandas 1.1.5

Pillow 6.2.0

pip 21.0.1

psutil 5.8.0

PyYAML 5.1

scipy 1.5.2

scikit-learn 0.22.1

tornado 6.2

mindinsight 1.2.0

automake

build-essential

ca-certificates

cmake

cpp

curl

ffmpeg

g++

gcc

gfortran

git

git-lfs

grep

libcudnn7

libcudnn7-dev

libjpeg-dev:amd64

libjpeg8-dev:amd64

openssh-client

openssh-server

nginx

python3

rpm

screen

tar

tmux

unzip

vim

wget

zip

Image 4: mindspore1.2.0-openmpi2.1.1-ubuntu18.04

Table 11 Information about the image

AI Engine

Whether to Use GPUs

(CUDA Version)

URL

Dependency

Mindspore 1.2.0

No

swr.{region_id}.myhuaweicloud.com/atelier/mindspore_1_2_0:mindspore_1.2.0-py_3.7-ubuntu_18.04-x86_64-20220926104106-041ba2e

PyPI package

Ubuntu package

mindspore 1.2.0

ipykernel 6.7.0

ipython 7.34.0

jupyter-client 7.4.6

ma-cli 1.2.3

matplotlib 3.5.1

modelarts 1.4.25

moxing-framework 2.1.0.5d9c87c8

numpy 1.19.5

pandas 1.1.5

Pillow 6.2.0

pip 21.0.1

psutil 5.8.0

PyYAML 5.1

scipy 1.5.2

scikit-learn 0.22.1

tornado 6.2

mindinsight 1.2.0

automake

build-essential

ca-certificates

cmake

cpp

curl

ffmpeg

g++

gcc

gfortran

git

git-lfs

grep

libjpeg-dev:amd64

libjpeg8-dev:amd64

openssh-client

openssh-server

nginx

python3

rpm

screen

tar

tmux

unzip

vim

wget

zip

x86-powered Custom Dedicated Base Images

ModelArts provides the following notebook base images powered by custom images (x86): conda3-cuda10.2-cudnn7-ubuntu18.04 and conda3-ubuntu18.04. These images do not have AI engines or related software packages. The image size is only 2 GB to 5 GB. You can use these images as base images and install your desired engine and dependency packages, improving scalability. In addition, these images are preset with some configurations required for starting the development environment. You can use these images after installing required software packages, without the need for any adaptations.

Such images are the most basic ones and have no component installed. They are small enough to facilitate image customization. If you need to use the OBS SDK, use ModelArts SDK instead to copy files. For details, see Transferring Files.

Image 1: conda3-cuda10.2-cudnn7-ubuntu18.04

Table 12 Information about the image

AI Engine

Whether to Use GPUs

(CUDA Version)

URL

Dependency

None

Yes

(CUDA 10.2)

swr.{region_id}.myhuaweicloud.com/atelier/user_defined_base:cuda_10.2-ubuntu_18.04-x86_64-20221008154718-2b3e39c

PyPI package

Ubuntu package

ipykernel 6.7.0

ipython 7.34.0

jupyter-client 7.4.9

ma-cli 1.2.3

matplotlib 3.5.2

modelarts 1.4.25

moxing-framework 2.1.6.879ab2f4

numpy 1.21.6

pandas 1.3.5

Pillow 9.5.0

pip 20.3.3

psutil 5.9.4

PyYAML 6.0

scipy 1.7.3

tornado 6.2

automake

build-essential

ca-certificates

cmake

cpp

curl

g++

gcc

gfortran

grep

libcudnn7

libcudnn7-dev

nginx

python3

rpm

tar

unzip

vim

wget

zip

Image 2: conda3-ubuntu18.04

Table 13 Information about the image

AI Engine

Whether to Use GPUs

(CUDA Version)

URL

Dependency

None

No

swr.{region_id}.myhuaweicloud.com/atelier/user_defined_base:ubuntu_18.04-x86_64-20221008154718-2b3e39c

Example:

CN North-Beijing4

swr.cn-north-4.myhuaweicloud.com/atelier/user_defined_base:ubuntu_18.04-x86_64-20221008154718-2b3e39c

CN East-Shanghai1

swr.cn-east-3.myhuaweicloud.com/atelier/user_defined_base:ubuntu_18.04-x86_64-20221008154718-2b3e39c

CN South-Guangzhou

swr.cn-south-1.myhuaweicloud.com/atelier/user_defined_base:ubuntu_18.04-x86_64-20221008154718-2b3e39c

PyPI package

Ubuntu package

ipykernel 6.7.0

ipython 7.34.0

jupyter-client 7.4.9

ma-cli 1.2.3

matplotlib 3.5.2

modelarts 1.4.25

moxing-framework 2.1.6.879ab2f4

numpy 1.21.6

pandas 1.3.5

Pillow 9.5.0

pip 20.3.3

psutil 5.9.4

PyYAML 6.0

scipy 1.7.3

tornado 6.2

automake

build-essential

ca-certificates

cmake

cpp

curl

g++

gcc

gfortran

grep

nginx

python3

rpm

tar

unzip

vim

wget

zip

Arm-powered MindSpore Base Images

Arm-powered MindSpore contains three types of images:

Image 1: mindspore_1.10.0-cann_6.0.1-py_3.7-euler_2.8.3

Table 14 Information about the image

AI Engine

URL

Dependency

Mindspore-Ascend 1.10.0

{region_id}.myhuaweicloud.com/atelier/mindspore_1_10_ascend:mindspore_1.10.0-cann_6.0.1-py_3.7-euler_2.8.3-aarch64-d910-20230303173945-815d627

Example:

CN North-Beijing4

swr.cn-north-4.myhuaweicloud.com/atelier/mindspore_1_10_ascend:mindspore_1.10.0-cann_6.0.1-py_3.7-euler_2.8.3-aarch64-d910-20230303173945-815d627

PyPI package

YUM package

mindspore-ascend 1.10.0

ipykernel 6.7.0

ipython 7.34.0

jupyter-client 7.4.5

ma-cli 1.2.3

matplotlib 3.5.1

modelarts 1.4.25

moxing-framework 2.0.1.rc0.ffd1c0c8

numpy 1.21.2

pandas 1.1.3

Pillow 9.4.0

pip 21.0.1

psutil 5.7.0

PyYAML 5.3.1

scipy 1.5.4

scikit-learn 0.24.0

tornado 6.2

mindinsight 1.9.0

cmake

cpp

curl

ffmpeg

g++

gcc

git

grep

python3

rpm

tar

unzip

wget

zip

Image 2: mindspore_1.9.0-cann_6.0.0-py_3.7-euler_2.8.3

Table 15 Information about the image

AI Engine

URL

Dependency

MindSpore 1.9.0

swr.{region_id}.myhuaweicloud.com/atelier/mindspore_1_9_ascend:mindspore_1.9.0-cann_6.0.0-py_3.7-euler_2.8.3-aarch64-d910-20221116111529

PyPI package

YUM package

mindspore-ascend 1.9.0

ipykernel 6.7.0

ipython 7.34.0

jupyter-client 7.4.5

ma-cli 1.2.3

matplotlib 3.5.1

modelarts 1.4.25

moxing-framework 2.0.1.rc0.ffd1c0c8

numpy 1.21.2

pandas 1.1.3

Pillow 9.3.0

pip 22.3.1

psutil 5.7.0

PyYAML 5.3.1

scipy 1.5.4

scikit-learn 0.24.0

tornado 6.2

mindinsight 1.9.0

cmake

cpp

curl

ffmpeg

g++

gcc

git

grep

python3

rpm

tar

unzip

wget

zip

Image 3: mindspore1.7.0-cann5.1.0-py3.7-euler2.8.3

Table 16 Information about the image

AI Engine

URL

Dependency

Mindspore-Ascend 1.7.0

swr.{region_id}.myhuaweicloud.com/atelier/mindspore_1_7_0:mindspore_1.7.0-cann_5.1.0-py_3.7-euler_2.8.3-aarch64-d910-20220906

PyPI package

YUM package

mindspore-ascend 1.7.0

ipykernel 5.3.4

ipython 7.34.0

jupyter-client 7.3.4

ma-cli 1.2.3

matplotlib 3.5.1

modelarts 1.4.25

moxing-framework 2.0.1.rc0.ffd1c0c8

numpy 1.21.2

pandas 1.1.3

Pillow 9.2.0

pip 22.1.2

psutil 5.7.0

PyYAML 5.3.1

scipy 1.5.4

scikit-learn 0.24.0

tornado 6.2

mindinsight 1.7.0

cmake

cpp

curl

ffmpeg

g++

gcc

git

grep

python3

rpm

tar

unzip

wget

zip

Arm-powered TensorFlow Base Images

Arm-powered TensorFlow contains two types of images:

Image 1: tensorflow1.15-mindspore1.7.0-cann5.1.0-euler2.8-aarch64

Table 17 Information about the image

AI Engine

URL

Dependency

Mindspore-Ascend 1.7.0

swr.{region_id}.myhuaweicloud.com/atelier/notebook2.0-mul-kernel-arm-ascend-cp37:5.0.1-c81-20220726

PyPI package

YUM package

mindspore-ascend 1.7.0

ipykernel 6.7.0

ipython 7.29.0

jupyter-client 7.0.6

ma-cli 1.2.3

matplotlib 3.1.2

modelarts 1.4.25

moxing-framework 2.0.0.rc2.4b57a67b

numpy 1.17.5

pandas 1.1.3

Pillow 7.0.0

pip 21.2.4

psutil 5.7.0

PyYAML 5.3.1

scipy 1.5.4

scikit-learn 0.24.0

tornado 6.1

mindinsight 1.7.0

cmake

cpp

curl

ffmpeg

g++

gcc

git

grep

python3

rpm

tar

unzip

wget

zip

Image 2: tensorflow1.15-cann5.1.0-py3.7-euler2.8.3

Table 18 Information about the image

AI Engine

Whether to Use Ascend

(CANN Version)

URL

Dependency

Tensorflow 1.15

Yes

(CANN 5.1)

swr.{Region ID}.{Region domain name}./atelier/

tensorflow_1_15_ascend:tensorflow_1.15-cann_5.1.0-py_3.7-euler_2.8.3-aarch64-d910-20220906

PyPI package

YUM package

tensorflow 1.15.0

tensorboard 1.15.0

ipykernel 5.3.4

ipython 7.34.0

jupyter-client 7.3.4

ma-cli 1.2.3

matplotlib 3.5.1

modelarts 1.4.25

moxing-framework 2.0.1.rc0.ffd1c0c8

numpy 1.17.5

pandas 0.24.2

Pillow 9.2.0

pip 22.1.2

psutil 5.7.0

PyYAML 5.3.1

scipy 1.3.3

scikit-learn 0.20.0

tornado 6.2

ca-certificates.noarch

cmake

cpp

curl

gcc-c++

gcc

gdb

grep

nginx

python3

rpm

tar

unzip

vim

wget

zip