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- 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 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>
Runtime Environment |
Supported Chip |
System Architecture |
System Version |
AI Engine and Version |
Supported CUDA or Ascend Version |
|---|---|---|---|---|---|
TensorFlow |
CPU or GPU |
x86_64 |
Ubuntu 18.04 |
tensorflow_2.1.0-cuda_10.1-py_3.7-ubuntu_18.04-x86_64 |
CUDA 10.1 |
PyTorch |
CPU or GPU |
x86_64 |
Ubuntu 18.04 |
pytorch_1.8.0-cuda_10.2-py_3.7-ubuntu_18.04-x86_64 |
CUDA 10.2 |
MPI |
GPU |
x86_64 |
Ubuntu 18.04 |
mindspore_1.3.0-cuda_10.1-py_3.7-ubuntu_1804-x86_64 |
CUDA 10.1 |
Horovod |
GPU |
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 |
Runtime Environment |
Supported Chip |
System Architecture |
System Version |
AI Engine and Version |
Supported CUDA or Ascend Version |
|---|---|---|---|---|---|
TensorFlow |
CPU or GPU |
x86_64 |
Ubuntu 16.04 |
TF-1.8.0-python2.7 |
- |
TF-1.8.0-python3.6 |
- |
||||
TF-1.13.1-python2.7 |
- |
||||
TF-1.13.1-python3.6 |
- |
||||
TF-2.1.0-python3.6 |
- |
||||
MXNet |
CPU or GPU |
x86_64 |
Ubuntu 16.04 |
MXNet-1.2.1-python2.7 |
- |
MXNet-1.2.1-python3.6 |
- |
||||
Spark_MLlib |
CPU |
x86_64 |
Ubuntu 16.04 |
Spark-2.3.2-python3.6 |
- |
Spark-2.3.2-python2.7 |
- |
||||
Ray |
CPU or GPU |
x86_64 |
Ubuntu 16.04 |
RAY-0.7.4-python3.6 |
- |
PyTorch |
CPU or GPU |
x86_64 |
Ubuntu 16.04 |
PyTorch-1.0.0-python2.7 |
- |
PyTorch-1.0.0-python3.6 |
- |
||||
PyTorch-1.3.0-python2.7 |
- |
||||
PyTorch-1.3.0-python3.6 |
- |
||||
PyTorch-1.4.0-python3.6 |
- |
||||
Caffe |
CPU or GPU |
x86_64 |
Ubuntu 16.04 |
Caffe-1.0.0-python2.7 |
CUDA 8.0 |
MindSpore-GPU |
GPU |
x86_64 |
Ubuntu 18.04 |
MindSpore-1.1.0-python3.7 |
- |
MindSpore-1.2.0-python3.7 |
- |
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>
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. |
General Issues FAQs
- What Is ModelArts?
- What Are the Relationships Between ModelArts and Other Services?
- What Are the Differences Between ModelArts and DLS?
- How Do I Purchase or Enable ModelArts?
- How Do I Obtain an Access Key?
- How Do I Upload Data to OBS?
- What Do I Do If the System Displays a Message Indicating that the AK/SK Pair Is Unavailable?
- What Do I Do If a Message Indicating Insufficient Permissions Is Displayed When I Use ModelArts?
- How Do I Use ModelArts to Train Models Based on Structured Data?
- What Are Regions and AZs?
- How Do I View All Files Stored in OBS on ModelArts?
- Where Are Datasets of ModelArts Stored in a Container?
- Which AI Frameworks Does ModelArts Support?
- What Are the Functions of ModelArts Training and Inference?
- How Do I View an Account ID and IAM User ID?
- Can AI-assisted Identification of ModelArts Identify a Specific Label?
- How Does ModelArts Use Tags to Manage Resources by Group?
- How Do I View All ModelArts Monitoring Metrics in AOM?
- Why Is the Job Still Queued When Resources Are Sufficient?
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