Help Center/ ModelArts/ Service Overview/ Basic Knowledge/ Common Concepts of ModelArts
Updated on 2024-07-11 GMT+08:00

Common Concepts of ModelArts

ExeML

ExeML is the process of automating model design, parameter tuning, and model training, model compression, and model deployment with the labeled data. The process is code-free and does not require developers to have experience in model development. A model can be built in three steps: labeling data, training a model, and deploying the model.

Device-Edge-Cloud

Device-Edge-Cloud indicates devices, intelligent edge nodes, and the public cloud.

Inference

Inference is the process of deriving a new judgment from a known judgment according to a certain strategy. In AI, machines simulate human intelligence, and complete inference based on neural networks.

Real-Time Inference

Real-time inference specifies a web service that provides an inference result for each inference request.

Batch Inference

Batch inference specifies a batch job that processes batch data for inference.

Ascend Chip

The Ascend chips are a series of Huawei-developed AI chips with high computing performance and low power consumption.

Resource Pool

ModelArts provides large-scale computing clusters for model development, training, and deployment. There are two types of resource pools: public resource pool and dedicated resource pool. The public resource pool is provided by default. Dedicated resource pools are created separately and used exclusively.

AI Gallery

The AI market provides common models and algorithms. You can also share your own models, algorithms, or datasets with other users or make them publicly available.

MoXing

A lightweight distributed framework developed by the ModelArts team and built on deep learning engines such as TensorFlow, PyTorch, MXNet, and MindSpore. It improves performance of these engines and makes them easier to use. MoXing contains many components. MoXing Framework is a basic common component that can be used to access OBS. It is decoupled from AI engines and can be used in all ModelArts-supported AI engines such as TensorFlow, PyTorch, MXNet, and MindSpore.

MoXing Framework provides common data file operations in OBS, such as reading, writing, listing, creating folders for, querying, moving, copying, and deleting data files.

You can call MoXing APIs in ModelArts notebook instances without downloading or installing the SDKs. Therefore, MoXing is more convenient than ModelArts and OBS SDKs.