Help Center/ ModelArts/ ModelArts User Guide (Standard)/ Inference Deployment/ Creating an AI Application/ Creation Methods
Updated on 2024-10-29 GMT+08:00
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Creation Methods

AI development and optimization require frequent iterations and debugging. Modifications in datasets, training code, or parameters affect the quality of models. If the metadata of the development process cannot be centrally managed, the optimal model may fail to be reproduced.

With ModelArts, you can create AI applications using meta models from training jobs, OBS, or container images, and centrally manage all iterated and debugged AI applications.

Constraints

  • After deploying a model in an ExeML project, it is automatically added to the AI application list. ExeML-generated AI applications can only be deployed, not downloaded.
  • All users can create AI applications and manage AI application versions at no cost.

Meta Model Sources

  • Importing a Meta Model from a Training Job: Create a training job in ModelArts to train a model. After obtaining a desired model, use it to create an AI application for service deployment.
  • Importing a Meta Model from OBS: If you use a mainstream framework to develop and train a model locally, you can upload the model to an OBS bucket based on the model package specifications, import the model from OBS to ModelArts, and use the model to create an AI application for service deployment.
  • Importing a Meta Model from a Container Image: If an AI engine is not supported by ModelArts, you can use it to build a model, import the model to ModelArts as a custom image, and use the image to create an AI application for service deployment.

Supported AI Engines for ModelArts Inference

If you import a model from OBS to ModelArts and use it to create an AI application, the following AI engines and versions are supported.

  • Runtime environments marked with recommended are from unified images, which will be used as mainstream base images for inference. Unified images provide comprehensive installation packages. For details, see Preset Dedicated Images for Inference.
  • Images of the old version will be discontinued. Use unified images instead.
  • The base images to be removed are no longer maintained.
  • A runtime environment of a unified image is named in the following format: <AI engine 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.
Table 1 Supported AI engines, their runtime environments, and default start commands

Engine

Runtime Environment

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.
  • The model can run on both CPUs and GPUs when using python3.6, python2.7, or tf2.1-python3.7. If the runtime environment has a suffix of cpu or gpu, the model can only run on CPUs or GPUs respectively.
  • The default runtime environment is python2.7.
  • Default start command: sh /home/mind/run.sh

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 environment is python2.7.
  • python2.7 and python3.6 can only be used to run models on CPUs.
  • Default start command: bash /home/work/predict/bin/run.sh

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 environment is python2.7.
  • python2.7 and python3.6 can only be used to run models on CPUs.
  • Default start command: bash /home/work/predict/bin/run.sh

XGBoost

python2.7 (unavailable soon)

python3.6 (unavailable soon)
  • XGBoost 0.80 is used in python2.7 and python3.6.
  • The default runtime environment is python2.7.
  • python2.7 and python3.6 can only be used to run models on CPUs.
  • Default start command: bash /home/work/predict/bin/run.sh

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.
  • The model can run on both CPUs and GPUs when using python2.7, python3.6, python3.7, pytorch1.4-python3.7, or pytorch1.5-python3.7.
  • The default runtime environment is python2.7.
  • Default start command: sh /home/mind/run.sh

MindSpore

aarch64 (recommended)

aarch64 can only be used to run models on Snt3 chips.

  • Default start command: sh /home/mind/run.sh