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 models using meta models from training jobs, OBS, or container images, and centrally manage all iterated and debugged models.

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 a model 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 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 a model for service deployment.

Supported AI Engines for ModelArts Inference

If you import a model from OBS to ModelArts, the following AI engines and versions are supported.

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	tf1.13-python3.7-cpu tf1.13-python3.7-gpu tensorflow_2.1.0-cuda_10.1-py_3.7-ubuntu_18.04-x86_64	The default runtime environment is tensorflow_2.1.0-cuda_10.1-py_3.7-ubuntu_18.04-x86_64. Default start command: sh /home/mind/run.sh
Spark_MLlib	python2.7 python3.6	Spark_MLlib 2.3.2 is used in python2.7 and python3.6. 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 python3.6	Scikit_Learn 0.18.1 is used in python2.7 and python3.6. 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 python3.6	XGBoost 0.80 is used in python2.7 and python3.6. 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	python3.7 pytorch_1.8.0-cuda_10.2-py_3.7-ubuntu_18.04-x86_64	The default runtime environment is pytorch_1.8.0-cuda_10.2-py_3.7-ubuntu_18.04-x86_64. Default start command: sh /home/mind/run.sh