ModelArts Best Practices
This document provides ModelArts samples concerning a variety of scenarios and AI engines to help you quickly understand the process and operations of using ModelArts for AI development.
LLM Training and Inference
|
Sample |
Scenario |
Description |
|---|---|---|
|
Adapting Mainstream Open-Source Models to AscendFactory NPU Training |
Pre-training, SFT full-parameter fine-tuning training, and LoRA fine-tuning training |
Describes the training process of mainstream open-source models, such as DeepSeek, Llama, Qwen3, and Qwen-VL series, based on ModelArts. The AscendFactory framework and AI Compute Service NPUs are used for training. |
|
Adapting Mainstream Open-Source Models for NPU Inference on Ascend-vLLM with PyTorch |
Inference deployment, inference performance test, inference accuracy test, and inference model quantization |
Describes the inference deployment process of mainstream open-source models, such as Llama, Qwen3, and Qwen-VL series, based on ModelArts. The Ascend-vLLM framework and AI Compute Service NPUs are used for inference. |
Image Generation Model Training and Inference
|
Sample |
Scenario |
Description |
|---|---|---|
|
Adapting Stable Diffusion for NPU Inference with Diffusers/ComfyUI and Lite Server (6.5.907) |
SD1.5, SDXL, SD3.5, and Hunyuan model inference |
Describes the inference process of mainstream image generation models based on ModelArts Lite Server. AI Compute Service NPUs are used for inference. After the inference service is started, it can be used in image generation scenarios. |
|
Stable Diffusion XL Inference Guide Based on ModelArts Notebook (6.5.907) |
SDXL model inference |
Describes the inference process of mainstream image generation models based on ModelArts Standard Notebook. AI Compute Service NPUs are used for inference. After the inference service is started, it can be used in image generation scenarios. |
Video Generation Model Training and Inference
|
Sample |
Scenario |
Description |
|---|---|---|
|
Inference Guide for Wan Series Video Generation Models Adapted to NPU via Lite Server |
Wan series model inference |
Describes the inference process of Wan series models based on ModelArts Lite Server. The PyTorch framework and AI Compute Service NPUs are used for inference. |
Configuring ModelArts Standard Permissions
|
Sample |
Function |
Scenario |
Description |
|---|---|---|---|
|
IAM permission configuration and management |
Permission assignment for IAM users |
Assign specific ModelArts operation permissions to the IAM users under a Huawei Cloud account. This prevents exceptions from occurring due to permissions when the IAM users access ModelArts. |
ModelArts Standard Model Training
|
Sample |
Image |
Function |
Scenario |
Description |
|---|---|---|---|---|
|
Building a Handwritten Digit Recognition Model with ModelArts Standard |
PyTorch |
Algorithm customization |
Handwritten digit recognition |
Use your customized algorithm to train a handwritten digit recognition model and deploy the model for prediction. |
ModelArts Standard Inference Deployment
|
Sample |
Function |
Scenario |
Description |
|---|---|---|---|
|
Migrating a Third-Party Inference Framework to a Custom Inference Engine |
Third-party frameworks Inference deployment |
- |
ModelArts allows the deployment of third-party inference frameworks. This section describes how to migrate TF Serving and Triton to a custom inference engine. |
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