Which AI Frameworks Does ModelArts Support?

Supported AI frameworks and versions of ModelArts vary slightly based on the development environment, training jobs, and model inference (model management and deployment). The following describes the AI frameworks supported by each module.

Development Environment

Notebook instances in the development environment support different AI engines and versions based on specific work environments (that is, different Python versions). After creating a notebook instance in the corresponding work environment, create a file based on the corresponding version in Table AI engines. ModelArts notebook instances support multiple engines. That is, a notebook instance can use all supported engines. Different engines can be switched quickly and conveniently.

Table 1 AI engines

Work Environment

Built-in AI Engine and Version

Supported Chip

Multi-Engine 1.0 (Python 3, Recommended)

MXNet-1.2.1

CPU/GPU

PySpark-2.3.2

CPU

Pytorch-1.0.0

GPU

TensorFlow-1.13.1

CPU/GPU

TensorFlow-1.8

CPU/GPU

XGBoost-Sklearn

CPU

Multi-Engine 1.0 (Python2)

Caffe-1.0.0

CPU/GPU

MXNet-1.2.1

CPU/GPU

PySpark-2.3.2

CPU

PyTorch1.0.0

GPU

TensorFlow-1.13.1

CPU/GPU

TensorFlow-1.8

CPU/GPU

XGBoost-Sklearn

CPU

Multi-Engine 2.0 (Python3)

Pytorch-1.4.0

GPU

R-3.6.1

CPU/GPU

TensorFlow-2.1.0

CPU/GPU

Training Jobs

Supported AI engines and versions when creating training jobs are as follows:

Table 2 AI engines supported by training jobs

Environment

Supported Chip

System Architecture

System Version

AI Engine and Version

Supported CUDA Version

TensorFlow

CPU and GPU

x86_64

Ubuntu 16.04

TF-1.13.1-python3.6

CUDA 10.0

TF-1.8.0-python3.6

CUDA 9.0

TF-2.1.0-python3.6

CUDA 10.1

Caffe

CPU and GPU

x86_64

Ubuntu 16.04

Caffe-1.0.0-python2.7

CUDA 8.0

Spark_MLlib

CPU

x86_64

Ubuntu 16.04

Spark-2.3.2-python3.6

N/A

XGBoost-Sklearn

CPU

x86_64

Ubuntu 16.04

Scikit_Learn-0.18.1-python3.6

N/A

PyTorch

CPU and GPU

x86_64

Ubuntu 16.04

PyTorch-1.3.0-python3.6

CUDA 10.0

PyTorch-1.0.0-python3.6

CUDA 9.0

MXNet

CPU/GPU

x86_64

Ubuntu16.04

MXNet-1.2.1-python3.6

CUDA 9.0

Model Inference

For imported models and model inference is completed on ModelArts, supported engines and their runtime are as follows:

Table 3 Supported AI engines and their runtime

Engine

Runtime

Precautions

TensorFlow

python3.6

python2.7

tf1.13-python2.7-gpu

tf1.13-python2.7-cpu

tf1.13-python3.6-gpu

tf1.13-python3.6-cpu

tf1.13-python3.7-cpu

tf1.13-python3.7-gpu

tf2.1-python3.7

  • 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.

MXNet

python3.7

python3.6

  • MXNet 1.2.1 is used in python3.6 and python3.7.

  • python3.6 and python3.7 indicate that the model can run on both CPUs and GPUs.

  • The default runtime is python3.6.

Caffe

python3.6

python3.7

python3.6-gpu

python3.7-gpu

python3.6-cpu

python3.7-cpu

  • Caffe 1.0.0 is used in python3.6, python3.7, python3.6-gpu, python3.7-gpu, python3.6-cpu, and python3.7-cpu.

  • python 3.6 and python3.7 can only be used to run models on CPUs. For other runtime values, if the suffix contains cpu or gpu, the model can run only on CPUs or GPUs. Use the runtime of python3.6-gpu, python3.7-gpu, python3.6-cpu, or python3.7-cpu.

  • The default runtime is python3.6.

Spark_MLlib

python3.6

  • Spark_MLlib 2.3.2 is used in python3.6.

  • python 3.6 can only be used to run models on CPUs.

Scikit_Learn

python3.6

  • Scikit_Learn 0.18.1 is used in python3.6.

  • python 3.6 can only be used to run models on CPUs.

XGBoost

python3.6

  • XGBoost 0.80 is used in python3.6.

  • python 3.6 can only be used to run models on CPUs.

PyTorch

python3.6

python3.7

pytorch1.4-python3.7

  • PyTorch 1.0 is used in python3.6 and python3.7.

  • python3.6, python3.7, and pytorch1.4-python3.7 indicate that the model can run on both CPUs and GPUs.

  • The default runtime is python3.6.

last updated: 2024-05-18 01:55