Introduction to Model Management¶
AI model development and optimization require frequent iterations and debugging. Changes in datasets, training code, or parameters may affect the quality of models. If the metadata of the development process cannot be managed in a unified manner, the optimal model may fail to be reproduced.
ModelArts model management allows you to import models generated with all training versions to manage all iterated and debugged models in a unified manner.
Usage Restrictions¶
In an automatic learning project, after a model is deployed, the model is automatically uploaded to the model management list. However, models generated by automatic learning cannot be downloaded and can be used only for deployment and rollout.
Methods of Importing a Model¶
Importing from Trained Models: You can create a training job on ModelArts and complete model training. After obtaining a satisfactory model, import the model to the Model Management page for model deployment.
Importing from a Template: Because the configurations of models with the same functions are similar, ModelArts integrates the configurations of such models into a common template. By using this template, you can easily and quickly import models without compiling the config.json configuration file.
Importing from a Container Image: For AI engines that are not supported by ModelArts, you can import the model you compile to ModelArts using custom images.
Importing from OBS: If you use a frequently-used framework to develop and train a model locally, you can import the model to ModelArts for model deployment.
Model Management Functions¶
Supported Function | Description |
|---|---|
Import the trained models to ModelArts for unified management. You can import models using four methods. The following provides the operation guide for each method. | |
To facilitate source tracing and repeated model tuning, ModelArts provides the model version management function. You can manage models based on versions. |