Compute
Elastic Cloud Server
Huawei Cloud Flexus
Bare Metal Server
Auto Scaling
Image Management Service
Dedicated Host
FunctionGraph
Cloud Phone Host
Huawei Cloud EulerOS
Networking
Virtual Private Cloud
Elastic IP
Elastic Load Balance
NAT Gateway
Direct Connect
Virtual Private Network
VPC Endpoint
Cloud Connect
Enterprise Router
Enterprise Switch
Global Accelerator
Management & Governance
Cloud Eye
Identity and Access Management
Cloud Trace Service
Resource Formation Service
Tag Management Service
Log Tank Service
Config
OneAccess
Resource Access Manager
Simple Message Notification
Application Performance Management
Application Operations Management
Organizations
Optimization Advisor
IAM Identity Center
Cloud Operations Center
Resource Governance Center
Migration
Server Migration Service
Object Storage Migration Service
Cloud Data Migration
Migration Center
Cloud Ecosystem
KooGallery
Partner Center
User Support
My Account
Billing Center
Cost Center
Resource Center
Enterprise Management
Service Tickets
HUAWEI CLOUD (International) FAQs
ICP Filing
Support Plans
My Credentials
Customer Operation Capabilities
Partner Support Plans
Professional Services
Analytics
MapReduce Service
Data Lake Insight
CloudTable Service
Cloud Search Service
Data Lake Visualization
Data Ingestion Service
GaussDB(DWS)
DataArts Studio
Data Lake Factory
DataArts Lake Formation
IoT
IoT Device Access
Others
Product Pricing Details
System Permissions
Console Quick Start
Common FAQs
Instructions for Associating with a HUAWEI CLOUD Partner
Message Center
Security & Compliance
Security Technologies and Applications
Web Application Firewall
Host Security Service
Cloud Firewall
SecMaster
Anti-DDoS Service
Data Encryption Workshop
Database Security Service
Cloud Bastion Host
Data Security Center
Cloud Certificate Manager
Edge Security
Situation Awareness
Managed Threat Detection
Blockchain
Blockchain Service
Web3 Node Engine Service
Media Services
Media Processing Center
Video On Demand
Live
SparkRTC
MetaStudio
Storage
Object Storage Service
Elastic Volume Service
Cloud Backup and Recovery
Storage Disaster Recovery Service
Scalable File Service Turbo
Scalable File Service
Volume Backup Service
Cloud Server Backup Service
Data Express Service
Dedicated Distributed Storage Service
Containers
Cloud Container Engine
SoftWare Repository for Container
Application Service Mesh
Ubiquitous Cloud Native Service
Cloud Container Instance
Databases
Relational Database Service
Document Database Service
Data Admin Service
Data Replication Service
GeminiDB
GaussDB
Distributed Database Middleware
Database and Application Migration UGO
TaurusDB
Middleware
Distributed Cache Service
API Gateway
Distributed Message Service for Kafka
Distributed Message Service for RabbitMQ
Distributed Message Service for RocketMQ
Cloud Service Engine
Multi-Site High Availability Service
EventGrid
Dedicated Cloud
Dedicated Computing Cluster
Business Applications
Workspace
ROMA Connect
Message & SMS
Domain Name Service
Edge Data Center Management
Meeting
AI
Face Recognition Service
Graph Engine Service
Content Moderation
Image Recognition
Optical Character Recognition
ModelArts
ImageSearch
Conversational Bot Service
Speech Interaction Service
Huawei HiLens
Video Intelligent Analysis Service
Developer Tools
SDK Developer Guide
API Request Signing Guide
Terraform
Koo Command Line Interface
Content Delivery & Edge Computing
Content Delivery Network
Intelligent EdgeFabric
CloudPond
Intelligent EdgeCloud
Solutions
SAP Cloud
High Performance Computing
Developer Services
ServiceStage
CodeArts
CodeArts PerfTest
CodeArts Req
CodeArts Pipeline
CodeArts Build
CodeArts Deploy
CodeArts Artifact
CodeArts TestPlan
CodeArts Check
CodeArts Repo
Cloud Application Engine
MacroVerse aPaaS
KooMessage
KooPhone
KooDrive

Creation Methods

Updated on 2024-12-26 GMT+08:00

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.

Constraints

  • After deploying a model in an ExeML project, it is automatically added to the model list. ExeML-generated models can only be deployed, not downloaded.
  • All users can create models and manage model 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 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.

NOTE:
  • 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

We use cookies to improve our site and your experience. By continuing to browse our site you accept our cookie policy. Find out more

Feedback

Feedback

Feedback

0/500

Selected Content

Submit selected content with the feedback