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.
|
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 and GPU general algorithm development and training, preconfigured with AI engine TensorFlow2.1 |
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 |
|
pytorch_1.11.0-cann_7.0.1-py_3.9-euler_2.10.7-aarch64-snt9b |
Ascend_snt9b and Arm-powered public image for algorithm development and training, with built-in AI engine PyTorch 1.11 |
Ascend_snt9b |
Yes |
Yes |
|
mindspore_2.2.0-cann_7.0.1-py_3.9-euler_2.10.7-aarch64-snt9b |
Ascend_snt9b and Arm-powered public image for algorithm development and training, with built-in AI engine MindSpore |
Ascend_snt9b |
Yes |
Yes |
|
pytorch_2.1.0-cann_7.0.1-py_3.9-euler_2.10.7-aarch64-snt9b |
Ascend_snt9b and Arm-powered public image for algorithm development and training, with built-in AI engine PyTorch 2.1 |
Ascend_snt9b |
Yes |
Yes |
Training Jobs
The following table lists the AI engines.
<Training engine name_version>-[cpu | <cuda_version | cann_version >]-<py_version>-<OS name_version>-< x86_64 | aarch64>
|
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>
- Each preset AI engine has its default model start command. Do not modify it unless necessary.
|
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) |
|
|
Spark_MLlib |
python2.7 (unavailable soon) python3.6 (unavailable soon) |
|
|
Scikit_Learn |
python2.7 (unavailable soon) python3.6 (unavailable soon) |
|
|
XGBoost |
python2.7 (unavailable soon) python3.6 (unavailable soon) |
|
|
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) |
|
|
MindSpore |
aarch64 (recommended) |
AArch64 can run only on Snt3 chips.
|
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