ModelArts
ModelArts
- What's New
- Function Overview
- Product Bulletin
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Service Overview
- ModelArts Infographic
- What Is ModelArts?
- ModelArts Service Selection
- Advantages
- Use Cases
- Functions
- AI Development Basics
- Security
- Notes and Constraints
- Permissions Management
- ModelArts and Other Services
- Billing Description
- Quotas
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Billing
- Overview
- Billing Modes
- Billing Item (ModelArts Standard)
- Billing Item (ModelArts Studio)
- Billing Examples
- Changing the Billing Mode
- Renewal
- Bills
- About Arrears
- Stopping Billing
- Cost Management
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Billing FAQs
- How Do I View the ModelArts Jobs Being Billed?
- How Do I View ModelArts Expenditure Details?
- How Do I Stop Billing If I Do Not Use ModelArts?
- Billing FAQs About ModelArts Standard Data Management
- What Should I Do to Avoid Unnecessary Billing After I Label Datasets and Exit?
- How Are Training Jobs Billed?
- Why Does Billing Continue After All Projects Are Deleted?
- Historical Documents to Be Brought Offline
- Getting Started
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ModelArts User Guide (Standard)
- ModelArts Standard Usage
- ModelArts Standard Preparations
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ModelArts Standard Resource Management
- About ModelArts Standard Resource Pools
- Creating a Standard Dedicated Resource Pool
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Managing Standard Dedicated Resource Pools
- Viewing Details About a Standard Dedicated Resource Pool
- Resizing a Standard Dedicated Resource Pool
- Upgrading the Standard Dedicated Resource Pool Driver
- Rectifying a Faulty Node in a Standard Dedicated Resource Pool
- Modifying the Job Types Supported by a Standard Dedicated Resource Pool
- Migrating Standard Dedicated Resource Pools and Networks to Other Workspaces
- Configuring the Standard Dedicated Resource Pool to Access the Internet
- Using TMS Tags to Manage Resources by Group
- Managing Logical Subpools of a Standard Dedicated Resource Pool
- Releasing Standard Dedicated Resource Pools and Deleting the Network
- Managing Standard Dedicated Resource Pool Plug-ins
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Using Workflows for Low-Code AI Development
- What Is Workflow?
- Managing a Workflow
- Workflow Development Command Reference
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Using Notebook for AI Development and Debugging
- Application Scenarios
- Creating a Notebook Instance (Default Page)
- Creating a Notebook Instance (New Page)
- Managing Notebook Instances
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Using a Notebook Instance for AI Development Through JupyterLab
- Using JupyterLab to Develop and Debug Code Online
- Common Functions of JupyterLab
- Using Git to Clone the Code Repository in JupyterLab
- Creating a Scheduled Job in JupyterLab
- Uploading Files to JupyterLab
- Downloading a File from JupyterLab to a Local PC
- Using TensorBoard Visualization Jobs in JupyterLab
- Using Notebook Instances Remotely Through PyCharm
- Using Notebook Instances Remotely Through VS Code
- Using a Notebook Instance Remotely with SSH
- ModelArts CLI Command Reference
- Using MoXing Commands in a Notebook Instance
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Preparing and Processing Data
- Preparing Data
- Creating a ModelArts Dataset
- Importing Data to a ModelArts Dataset
- Labeling ModelArts Data
- Publishing ModelArts Data
- Analyzing ModelArts Data Characteristics
- Exporting Data from a ModelArts Dataset
- Getting Started: Creating an Object Detection Dataset
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Using ModelArts Standard to Train Models
- Model Training Process
- Preparing Model Training Code
- Preparing a Model Training Image
- Creating an Algorithm
- Creating a Production Training Job
- Creating a Production Training Job (New Version)
- Distributed Model Training
- Enabling Dynamic Route Acceleration for Training Jobs
- Incremental Model Training
- Automatic Model Tuning (AutoSearch)
- High Model Training Reliability
- Configuring Supernode Affinity Group Instances
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Managing Model Training Jobs
- Viewing Training Job Details
- Visualizing the Training Job Process
- Viewing the Resource Usage of a Training Job
- Viewing the Model Evaluation Result
- Viewing Training Job Events
- Viewing Training Job Logs
- Priority of a Training Job
- Using Cloud Shell to Debug a Production Training Job
- Saving the Image of a Debug Training Job
- Copying, Stopping, or Deleting a Training Job
- Managing Environment Variables of a Training Container
- Viewing Training Job Tags
- Managing Training Experiments
- Viewing Monitoring Metrics of a Training Job
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Using ModelArts Standard to Deploy Models for Inference and Prediction
- Overview
- Creating a Model
- Model Creation Specifications
- Deploying a Model as Real-Time Inference Jobs
- Deploying a Model as a Batch Inference Service
- Managing ModelArts Models
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Managing a Synchronous Real-Time Service
- Viewing Details About a Real-Time Service
- Viewing Events of a Real-Time Service
- Managing the Lifecycle of a Real-Time Service
- Modifying a Real-Time Service
- Viewing Performance Metrics of a Real-Time Service on Cloud Eye
- Integrating a Real-Time Service API into the Production Environment
- Configuring Auto Restart upon a Real-Time Service Fault
- Managing Batch Inference Jobs
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Creating a Custom Image for ModelArts Standard
- Applications of Custom Images
- Preset Images Supported by ModelArts
- Creating a Custom Image for a Notebook Instance
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Creating a Custom Image for Model Training
- Creating a Custom Training Image
- Creating a Custom Training Image Using a Preset Image
- Migrating Existing Images to ModelArts
- Creating a Custom Training Image (PyTorch + Ascend)
- Creating a Custom Training Image (PyTorch + CPU/GPU)
- Creating a Custom Training Image (MPI + CPU/GPU)
- Creating a Custom Training Image (Tensorflow + GPU)
- Creating a Custom Training Image (MindSpore + Ascend)
- Creating a Custom Image for Inference
- Monitoring ModelArts Standard Resources
- Using CTS to Audit ModelArts Standard
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ModelArts Studio (MaaS) User Guide
- ModelArts Studio (MaaS) Usage
- Configuring ModelArts Studio (MaaS) Access Authorization
- Preparing ModelArts Studio (MaaS) Resources
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ModelArts Studio (MaaS) Real-Time Inference Services
- Viewing a Built-in Model in ModelArts Studio (MaaS)
- Deploying a Model Service in ModelArts Studio (MaaS)
- Managing My Services in ModelArts Studio (MaaS)
- Calling a Model Service in ModelArts Studio (MaaS)
- ModelArts Studio (MaaS) API Call Specifications
- Creating a Multi-Turn Dialogue in ModelArts Studio (MaaS)
- ModelArts Studio (MaaS) Management and Statistics
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ModelArts User Guide (Lite Server)
- Before You Start
- Provisioning Lite Server Resources (Old Version)
- Provisioning Lite Server Resources (New Version)
- Configuring Lite Server Resources
- Using Lite Server Resources
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Managing Lite Server Resources
- Viewing Lite Server Details
- Starting or Stopping the Lite Server
- Synchronizing the Lite Server Status
- Changing or Resetting the Lite Server OS
- Creating a Lite Server OS
- Lite Server Hot Standby Nodes
- Modifying a Lite Server Name
- Authorizing the Repair of Lite Server Nodes
- Releasing Lite Server Resources
- Lite Server Plug-in Management
- Managing Lite Server Supernodes
- Monitoring Lite Server Resources
- Managing CloudPond NPU Resources for Lite Server
- Using CTS to Audit Lite Server Operations
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ModelArts User Guide (Lite Cluster)
- Before You Start
- Enabling Lite Cluster Resources
- Configuring Lite Cluster Resources
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Using Lite Cluster Resources
- Using Snt9B for Distributed Training in a Lite Cluster Resource Pool
- Performing PyTorch NPU Distributed Training In a ModelArts Lite Resource Pool Using Ranktable-based Route Planning
- Using Snt9B for Inference in a Lite Cluster Resource Pool
- Using Ascend FaultDiag to Diagnose Logs in the ModelArts Lite Cluster Resource Pool
- Mounting an SFS Turbo File System to a Lite Cluster
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Managing Lite Cluster Resources
- Managing Lite Cluster Resources
- Managing Lite Cluster Resource Pools
- Managing Lite Cluster Node Pools
- Managing Lite Cluster Nodes
- Resizing a Lite Cluster Resource Pool
- Upgrading the Lite Cluster Resource Pool Driver
- Upgrading the Driver of a Lite Cluster Resource Pool Node
- Monitoring Lite Cluster Resources
- Releasing Lite Cluster Resources
- Lite Cluster Plug-in Management
- ModelArts User Guide (AI Gallery)
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Best Practices
- ModelArts Best Practices
- DeepSeek Inference Applications on MaaS
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Permissions Management
- Basic Concepts
- Permission Management Mechanisms
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Configuration Practices in Typical Scenarios
- Assigning Permissions to Individual Users for Using ModelArts
- Assigning Basic Permissions for Using ModelArts
- Separately Assigning Permissions to Administrators and Developers
- Assigning the Required Permissions
- Logging In to a Training Container Using Cloud Shell
- Prohibiting a User from Using a Public Resource Pool
- Authorizing ModelArts to Use SFS Turbo
- Assigning SFS Turbo Folder-Level Access Permissions to an IAM User
- Notebook
- Model Training
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Model Inference
- Enabling a ModelArts Standard Inference Service to Access the Internet
- E2E O&M Solution of ModelArts Inference Services
- Creating a Model Using a Custom Engine
- Using a Large Model to Create a Model on ModelArts Standard and Deploy It as a Real-Time Service
- Migrating a Third-Party Inference Framework to a Custom Inference Engine
- Enabling High-Speed Access to an Inference Service Through VPC Peering
- Full-Process Development of WebSocket Real-Time Services
- Creating a Custom Image and Using It to Create a Model
- Best Practices of Security Configuration
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API Reference
- Before You Start
- API Overview
- Calling APIs
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Development Environment Management
- Creating a Notebook Instance
- Querying Notebook Instances
- Querying All Notebook Instances
- Querying Details of a Notebook Instance
- Updating a Notebook Instance
- Deleting a Notebook Instance
- Saving a Running Instance as a Container Image
- Querying the List of Valid Specifications Supported by Notebook Instances
- Querying the List of Switchable Specifications Supported by Notebook Instances
- Querying the Available Duration of a Running Notebook Instance
- Prolonging a Notebook Instance
- Starting a Notebook Instance
- Stopping a Notebook Instance
- Obtaining the Notebook Instances with OBS Storage Mounted
- OBS Storage Mounting
- Obtaining Details About a Notebook Instance with OBS Storage Mounted
- Unmounting OBS Storage from a Notebook Instance
- Querying Supported Images
- Registering a Custom Image
- Querying the User Image List
- Obtaining Details of an Image
- Deleting an Image
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Training Management
- Creating an Algorithm
- Querying the Algorithm List
- Querying Algorithm Details
- Modifying an Algorithm
- Deleting an Algorithm
- Creating a Training Job
- Querying the Details About a Training Job
- Modifying the Description of a Training Job
- Deleting a Training Job
- Terminating a Training Job
- Querying the Logs of a Specified Task in a Given Training Job (Preview)
- Querying the Logs of a Specified Task in a Training Job (OBS Link)
- Querying the Running Metrics of a Specified Task in a Training Job
- Querying a Training Job List
- Obtaining the Events of a Training Job
- Obtaining the General Specifications Supported by a Training Job
- Obtaining the Preset AI Frameworks Supported by a Training Job
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App Authentication Management
- Obtaining the App List
- Creating Apps
- Obtaining App Details
- Deleting an App
- Adding an App Code
- Resetting an App Code
- Deleting an App Code
- Resetting an AppSecret
- Obtaining the List of APIs Bound to an App
- Registering an API and Authorizing the API to an App
- Deleting an API
- Authorizing an API to an App
- Updating API Authorization
- Canceling the Authorization of an API to an App
- Obtaining API Authorization Relationships
- Creates an API.
- API query
- Querying APIs and Apps
- Check whether the app exists.
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Service Management
- Updating the Service Through the Patch Operation
- Obtaining Service Monitoring
- Obtaining Services
- Deploying Services
- Obtaining Supported Service Deployment Specifications
- Obtaining Service Details
- Updating Service Configurations
- Deleting a Service
- Updating a Single Property of a Model Service
- Obtaining Service Event Logs
- Obtaining Service Update Logs
- Adding a Resource Tag
- Deleting Resource Tags
- Obtaining Inference Service Tags
- Obtaining an Inference VPC Access Channel
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Resource Management
- Querying OS Configuration Parameters
- Querying a Plug-in Template
- Obtaining Nodes in a Resource Pool
- Deleting nodes in batches
- Querying a Trace List
- Creating Network Resources
- Obtaining Network Resources
- Obtaining a Network Resource
- Deleting a Network Resource
- Updating a Network Resource
- Querying the Real-Time Resource Usage
- Creating Resource Pools
- Obtaining Resource Pools
- Obtaining a Resource Pool
- Deleting a Resource Pool
- Updating a Resource Pool
- Monitoring a Resource Pool
- Resource Pool Statistics
- Obtaining Resource Specifications
- Obtain Jobs in a Resource Pool
- Querying dedicated resource pool Job Statistics
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DevServer Management
- Obtaining All DevServer Instances of a User
- Creating a DevServer Instance
- Obtaining DevServer Instance Details
- Deleting DevServer Instances
- Synchronizing the Status of All DevServer Instances of a User in Real Time
- Starting DevServer Instances
- Stopping DevServer Instances
- Creating DevServer Supernode Tags
- Deleting DevServer Supernode Tags
- Obtaining the DevServer Supernode Tags
- Reinstalling the OS Image of the DevServer Server
- Changing the OS Image of the DevServer Server
- Changing the OS Image of the DevServer Supernode Server
- Obtaining Details About All Supernode Instances of a User
- Deleting a DevServer Supernode Instance
- Restarting a DevServer Instance
- Starting a DevServer Supernode Server
- Stopping a DevServer Supernode Server
- Authorization Management
- Workspace Management
- Quota Management
- Resource Tag Management
- Node Pool Management
- Node Management
- AI Application Management
- Application Authentication Management
- Use Cases
- Permissions Policies and Supported Actions
- Appendix
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Historical APIs
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Data Management (Old Version)
- Querying the Dataset List
- Creating a Dataset
- Querying Details About a Dataset
- Modifying a Dataset
- Deleting a Dataset
- Obtaining Dataset Statistics
- Querying the Monitoring Data of a Dataset
- Querying the Dataset Version List
- Creating a Dataset Labeling Version
- Querying Details About a Dataset Version
- Deleting a Dataset Labeling Version
- Obtaining a Sample List
- Adding Samples in Batches
- Deleting Samples in Batches
- Obtaining Details About a Sample
- Obtaining Sample Search Condition
- Obtaining a Sample List of a Team Labeling Task by Page
- Obtaining Details About a Team Labeling Sample
- Querying the Dataset Label List
- Creating a Dataset Label
- Modifying Labels in Batches
- Deleting Labels in Batches
- Updating a Label by Label Names
- Deleting a Label and the Files that Only Contain the Label
- Updating Sample Labels in Batches
- Querying the Team Labeling Task List of a Dataset
- Creating a Team Labeling Task
- Querying Details About a Team Labeling Task
- Starting a Team Labeling Task
- Updating a Team Labeling Task
- Deleting a Team Labeling Task
- Creating a Team Labeling Acceptance Task
- Querying the Report of a Team Labeling Acceptance Task
- Updating Status of a Team Labeling Acceptance Task
- Querying Details About Team Labeling Task Statistics
- Querying Details About the Progress of a Team Labeling Task Member
- Querying the Team Labeling Task List by a Team Member
- Submitting Sample Review Comments of an Acceptance Task
- Reviewing Team Labeling Results
- Updating Labels of Team Labeling Samples in Batches
- Querying the Labeling Team List
- Creating a Labeling Team
- Querying Details About a Labeling Team
- Updating a Labeling Team
- Deleting a Labeling Team
- Sending an Email to a Labeling Team Member
- Querying the List of All Labeling Team Members
- Querying the List of Labeling Team Members
- Creating a Labeling Team Member
- Deleting Labeling Team Members in Batches
- Querying Details About Labeling Team Members
- Updating a Labeling Team Member
- Deleting a Labeling Team Member
- Querying the Dataset Import Task List
- Creating an Import Task
- Querying Details About a Dataset Import Task
- Querying the Dataset Export Task List
- Creating a Dataset Export Task
- Querying the Status of a Dataset Export Task
- Synchronizing a Dataset
- Querying the Status of a Dataset Synchronization Task
- Obtaining an Auto Labeling Sample List
- Querying Details About an Auto Labeling Sample
- Obtaining an Auto Labeling Task List by Page
- Starting Intelligent Tasks
- Obtaining Details About an Auto Labeling Task
- Stopping an Intelligent Task
- Querying the List of a Processing Task
- Creating a Processing Task
- Querying Details About a Processing Task
- Updating a Processing Task
- Deleting a Processing Task
- DevEnviron (Old Version)
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Training Management (Old Version)
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Training Jobs
- Creating a Training Job
- Querying a Training Job List
- Querying the Details About a Training Job Version
- Deleting a Version of a Training Job
- Obtaining Training Job Versions
- Creating a Version of a Training Job
- Stopping a Training Job
- Modifying the Description of a Training Job
- Deleting a Training Job
- Obtaining the Name of a Training Job Log File
- Querying a Built-in Algorithm
- Querying Training Job Logs
- Training Job Parameter Configuration
- Visualization Jobs
- Resource and Engine Specifications
- Job Statuses
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Training Jobs
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Data Management (Old Version)
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SDK Reference
- Before You Start
- SDK Overview
- Getting Started
- (Optional) Installing the ModelArts SDK Locally
- Session Authentication
- OBS Management
- Data Management
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Training Management (New Version)
- Training Jobs
- APIs for Resources and Engine Specifications
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Training Management (Old Version)
- Training Jobs
- Training Job Versions
- Training Job Parameter Configuration
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Visualization Jobs
- Creating a Visualization Job
- Querying the List of Visualization Job Objects
- Querying the List of Visualization Jobs
- Querying the Details About a Visualization Job
- Modifying the Description of a Visualization Job
- Stopping a Visualization Job
- Restarting a Visualization Job
- Deleting a Visualization Job
- Resource and Engine Specifications
- Job Statuses
- Model Management
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Service Management
- Service Management Overview
- Deploying a Local Service for Debugging
- Deploying a Real-Time Service
- Obtaining Details About a Service
- Testing an Inference Service
- Obtaining Services
- Obtaining Service Objects
- Updating Service Configurations
- Obtaining Service Monitoring Information
- Obtaining Service Logs
- Delete a Service
- Change History
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FAQs
- Permissions
- Storage
- Standard Workflow
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ModelArts Standard Data Preparation
- Is There a File Size Limit for Images to Be Added to a ModelArts Dataset?
- How Do I Import Local Labeled Data to ModelArts?
- Where Are the Data Labeling Results Stored in ModelArts?
- How Do I Download Labeling Results from ModelArts to a Local PC?
- Why Can't Team Members Receive Emails for a Team Labeling Task in ModelArts?
- How Is Data Distributed Between Team Members During Team Labeling in ModelArts?
- How Do I Merge Two Datasets in ModelArts?
- Why Are Images Displayed in Different Angles Under the Same Account in ModelArts?
- Do I Need to Train the Model Again Using the Data Newly Added After Auto Labeling Is Complete in ModelArts?
- How Do I Split an Image Dataset into Training and Validation Sets in ModelArts?
- Can I Customize Labels During Object Detection Labeling in ModelArts?
- What Should I Do If I Can't Find a New Dataset Version in ModelArts?
- How Do I Split a Dataset in ModelArts?
- How Do I Delete Images from a Dataset in ModelArts?
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ModelArts Standard Notebook
- Is the Keras Engine Supported by ModelArts Notebook Instances?
- How Do I Upload a File from a Notebook Instance to OBS or Download a File from OBS to a Notebook Instance in ModelArts?
- Where Is Data Uploaded from a ModelArts Notebook Instance?
- How Do I Copy Data from Notebook A to Notebook B in ModelArts?
- How Do I Rename an OBS File on a ModelArts Notebook Instance?
- How Do I Use the pandas Library to Process Data in OBS Buckets on a ModelArts Notebook Instance?
- How Do I Access the OBS Bucket of Another Account from a ModelArts Notebook Instance?
- What Is the Default Working Directory of JupyterLab on ModelArts Notebook Instances?
- How Do I Check the CUDA Version Used by a ModelArts Notebook Instance?
- How Do I Obtain the External IP Address of the Local Host from a ModelArts Notebook Instance?
- Is There a Proxy for ModelArts Notebook Instances? How Do I Disable It?
- How Do I Customize Engine IPython Kernel If the Built-in Engines of ModelArts Notebook Instances Do Not Meet My Requirements?
- What Should I Do If It Is Unstable to Install the Remote Plug-in on a ModelArts Notebook Instance?
- How Do I Connect to a Restarted ModelArts Notebook Instance?
- What Should I Do If the Source Code Cannot Be Accessed When I Use VS Code to Debug Code on a ModelArts Notebook Instance?
- How Do I View Remote Logs Using VS Code on a ModelArts Notebook Instance?
- How Do I Open the VS Code Configuration File settings.json on a ModelArts Notebook Instance?
- How Do I Set the Background Color of VS Code to Bean Green on a ModelArts Notebook Instance?
- How Do I Configure the Default Plug-in Remotely Installed for VS Code on a ModelArts Notebook Instance?
- How Do I Install a Local Plug-in Remotely or a Remote Plug-in Locally in ModelArts VS Code?
- How Do I Use Multiple Ascend Cards for Debugging on a ModelArts Notebook Instance?
- Why Are the Training Speeds Similar When Different Resource Flavors Are Used for Training on ModelArts Notebook Instances?
- How Do I Perform Incremental Training When Using MoXing on a ModelArts Notebook Instance?
- How Do I View the GPU Usage on a ModelArts Notebook Instance?
- How Can I Print the GPU Usage in Code on a ModelArts Notebook Instance?
- What Are the Relationships Among JupyterLab Directories, Terminal Files, and OBS Files on ModelArts Notebook Instances?
- How Do I Use ModelArts Datasets on a ModelArts Notebook Instance?
- pip and Common Commands
- What Are the Sizes of the /cache Directories for Resources with Varying Specifications on ModelArts Notebook Instances?
- What Is the Impact of Resource Overcommitment on ModelArts Notebook Instances?
- How Do I Install External Libraries in a Notebook Instance?
- How Do I Handle Unstable Internet Access Speed in ModelArts Notebook?
- Can I Use GDB in a Notebook Instance?
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ModelArts Standard Model Training
- What Should I Do If the Model Trained in ModelArts Is Underfitting?
- How Do I Obtain a Trained Model in ModelArts?
- How Do I Obtain RANK_TABLE_FILE for Distributed Training in ModelArts?
- How Do I Configure Input and Output Data for Model Training in ModelArts?
- How Do I Improve Training Efficiency While Reducing Interaction with OBS in ModelArts?
- How Do I Define Path Variables When Using MoXing to Copy Data in ModelArts?
- How Do I Create a Training Job That References a Third-Party Dependency Package in ModelArts?
- How Do I Install C++ Dependent Libraries During ModelArts Training?
- How Do I Check Whether a Folder Copy Is Complete During Job Training in ModelArts?
- How Do I Load Some Well Trained Parameters During Job Training in ModelArts?
- What Should I Do If I Cannot Access the Folder Using os.system ('cd xxx') During Training in ModelArts?
- How Do I Obtain the Dependency File Path from Training Code in ModelArts?
- How Do I Obtain the Actual File Path in a Training Container in ModelArts?
- What Are the Sizes of the /cache Directories for Resources with Varying Specifications in Training Jobs in ModelArts?
- Why Do Training Jobs Have Two Hyperparameter Directories /work and /ma-user in ModelArts?
- How Do I View the Resource Usage of a Training Job in ModelArts?
- How Do I Download a Well Trained Model in ModelArts or Migrate It to Another Account?
- What Should I Do If RuntimeError: Socket Timeout Is Displayed During Distributed Process Group Initialization using torchrun?
- What Should I Do If an Error Is Reported Indicating that the .so File in the $ANACONDA_DIR/envs/$DEFAULT_CONDA_ENV_NAME/lib Directory Cannot Be Found During Training?
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ModelArts Standard Inference Deployment
- How Do I Import a Keras .h5 Model to ModelArts?
- How Do I Edit the Installation Package Dependency Parameters in the Model Configuration File When Importing a Model to ModelArts?
- How Do I Change the Default Port When I Create a Real-Time Service Using a Custom Image in ModelArts?
- Does ModelArts Support Multi-Model Import?
- What Are the Restrictions on the Image Size for Importing AI Applications to ModelArts?
- What Are the Differences Between Real-Time Services and Batch Services in ModelArts?
- Why Can't I Select Ascend Snt3 Resources When Deploying Models in ModelArts?
- Can I Locally Deploy Models Trained on ModelArts?
- What Is the Maximum Size of a ModelArts Real-Time Service Prediction Request Body?
- How Do I Prevent Python Dependency Package Conflicts in a Custom Prediction Script When Deploying a Real-Time Service in ModelArts?
- How Do I Speed Up Real-Time Service Prediction in ModelArts?
- Can a New-Version AI Application Still Use the Original API in ModelArts?
- What Is the Format of a Real-Time Service API in ModelArts?
- How Do I Fill in the Request Header and Request Body When a ModelArts Real-Time Service Is Running?
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ModelArts Standard Images
- How Do I Use the Image Customized by a User Under a Different Tenant Account to Create a Notebook Instance?
- How Do I Log In to SWR and Upload Images to It?
- How Do I Configure Environment Variables for an Image in a Dockerfile?
- How Do I Start a Container Using a Docker Image?
- How Do I Configure a Conda Source on a ModelArts Notebook Instance?
- What Are the Software Version Requirements for a Custom Image?
- Why Is an Image Reported as Larger Than 35 GB When I'm Saving It But Its Size Is Displayed as 13 GB in SWR?
- How Do I Prevent the Save Failure of a Custom Image Larger Than 35 GB?
- How Do I Reduce the Size of the Target Image Created on the Local PC or ECS?
- Will an Oversized Image Become Smaller If I Uninstall and Reinstall Its Packages?
- What Do I Do If Error "ModelArts.6787" Is Reported When I Register an Image in ModelArts?
- How Do I Set the Default Kernel?
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ModelArts Standard Dedicated Resource Pools
- Can I Use ECSs to Create a Dedicated Resource Pool for ModelArts?
- Can I Deploy Multiple Services on One Dedicated Resource Pool Node in ModelArts?
- What Are the Differences Between Public Resource Pools and Dedicated Resource Pools in ModelArts?
- Why Does a Job in ModelArts Stay in the Pending State?
- Why Can I View the Deleted Dedicated Resource Pools That Failed to Be Created on the ModelArts Console?
- How Do I Add a VPC Peering Connection Between a Dedicated Resource Pool and an SFS in ModelArts?
- ModelArts Studio (MaaS)
- API/SDK
-
History
- How Do I Upload Data to OBS?
- Which AI Frameworks Does ModelArts Support?
- How Does ModelArts Use Tags to Manage Resources by Group?
- How Do I View ModelArts Expenditure Details?
- What Do I Do If the VS Code Window Is Not Displayed?
- What Do I Do If a Remote Connection Failed After VS Code Is Opened?
- What Do I Do If Error Message "Could not establish connection to xxx" Is Displayed During a Remote Connection?
- What Do I Do If Error Message "Bad owner or permissions on C:\Users\Administrator/.ssh/config" or "Connection permission denied (publickey)" Is Displayed?
- What Do I Do If Error Message "ssh: connect to host xxx.pem port xxxxx: Connection refused" Is Displayed?
- What Do I Do If Error Message "no such identity: C:/Users/xx /test.pem: No such file or directory" Is Displayed?
- What Are the Precautions for Switching Training Jobs from the Old Version to the New Version?
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Troubleshooting
- General Issues
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DevEnviron
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Environment Configuration Faults
- Disk Space Used Up
- An Error Is Reported When Conda Is Used to Install Keras 2.3.1 in Notebook
- Error "HTTP error 404 while getting xxx" Is Reported During Dependency Installation in a Notebook
- The numba Library Has Been Installed in a Notebook Instance and Error "import numba ModuleNotFoundError: No module named 'numba'" Is Reported
- Failed to Save Files in JupyterLab
- "Server Connection Error" Is Displayed After the Kernelgateway Process Is Stopped
- SSH Access Is Occasionally Denied, and the Error Message "Not allowed at this time" Is Displayed
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Instance Faults
- Failed to Create a Notebook Instance and JupyterProcessKilled Is Displayed in Events
- Failed to Access a Notebook Instance
- An Error Is Displayed Indicating No Space Left After the pip install Command Is Executed
- Code Can Be Run But Cannot Be Saved and Error Message "save error" Is Displayed
- A Request Timeout Error Is Reported When the Open Button of a Notebook Instance Is Clicked
- ModelArts.6333 Error Occurs
- What Can I Do If a Message Is Displayed Indicating that the Token Does Not Exist or Is Lost When I Open a Notebook Instance?
-
Code Running Failures
- An Error Occurs When You Run Code on a Notebook Instance Because No File Is Found in /tmp
- Notebook Instance Failed to Run Code
- "dead kernel" Is Displayed and the Instance Breaks Down When Training Code Is Run
- cudaCheckError Occurs During Training
- What Do I Do If Insufficient Space Is Displayed in DevEnviron?
- Notebook Instance Breaks Down When opencv.imshow Is Used
- Path of a Text File Generated in the Windows OS Cannot Be Found on a Notebook Instance
- What Do I Do If No Kernel Is Displayed After a Notebook File Is Created?
- JupyterLab Plug-in Faults
-
Failures to Access the Development Environment Through VS Code
- VS Code Window Is Not Displayed
- Remote Connection Failed After VS Code Is Opened
- Failed to Connect to the Development Environment Via VS Code
- Error Message "Could not establish connection to xxx" Is Displayed During a Remote Connection
- Connection to a Remote Development Environment Remains in the "Setting up SSH Host xxx: Downloading VS Code Server locally" State for More Than 10 Minutes
- What Do I Do If the Connection to a Remote Development Environment Remains in the State of "Setting up SSH Host xxx: Copying VS Code Server to host with scp" for More Than 10 Minutes?
- Connection to a Remote Development Environment Remains in the State of "ModelArts Remote Connect: Connecting to instance xxx..." for More Than 10 Minutes
- Remote Connection Is in the Retry State
- Error Message "The VS Code Server failed to start" Is Displayed
- Error Message "Permissions for 'x:/xxx.pem' are too open" Is Displayed
- Error Message "Bad owner or permissions on C:\Users\Administrator/.ssh/config" Is Displayed
- Error Message "Connection permission denied (publickey)" Is Displayed
- What Do I Do If Error Message "ssh: connect to host xxx.pem port xxxxx: Connection refused" Is Displayed?
- What Do I Do If Error Message "ssh: connect to host ModelArts-xxx port xxx: Connection timed out" Is Displayed?
- Error Message "Load key "C:/Users/xx/test1/xxx.pem": invalid format" Is Displayed
- Error Message "An SSH installation couldn't be found" or "Could not establish connection to instance xxx: 'ssh' ..." Is Displayed
- Error Message "no such identity: C:/Users/xx /test.pem: No such file or directory" Is Displayed
- Error Message "Host key verification failed" or "Port forwarding is disabled" Is Displayed
- Error Message "Failed to install the VS Code Server" or "tar: Error is not recoverable: exiting now" Is Displayed
- Error Message "XHR failed" Is Displayed During VS Code's Connection to a Remote Notebook Instance
- VS Code Connection Automatically Disconnected If No Operation Is Performed for a Long Time
- Remote Connection Takes a Long Time After VS Code Is Automatically Upgraded
- Error Message "Connection reset" Is Displayed During an SSH Connection
- Notebook Instance Is Frequently Disconnected or Stuck After It Is Connected with MobaXterm Using SSH
- Error Message "Missing GLIBC, Missing required dependencies" Is Displayed When VS Code Is Used to Connect to a Development Environment
- Error Message Is Displayed Indicating That ms-vscode-remote.remot-sdh Is Uninstalled Due to a Reported Issue When VSCode-huawei Is Used
- Instance Directory in VS Code Does Not Match That on the Cloud When VS Code Is Used to Connect to an Instance
-
Custom Image Faults
- Faults of Custom Images on Notebook Instances
- What If the Error Message "there are processes in 'D' status, please check process status using'ps -aux' and kill all the 'D' status processes" or "Buildimge,False,Error response from daemon,Cannot pause container xxx" Is Displayed When I Save an Image?
- What Do I Do If Error "container size %dG is greater than threshold %dG" Is Displayed When I Save an Image?
- What Do I Do If Error "too many layers in your image" Is Displayed When I Save an Image?
- What Do I Do If Error "The container size (xG) is greater than the threshold (25G)" Is Reported When I Save an Image?
- Error Message "BuildImage,True,Commit successfully|PushImage,False,Task is running." Is Displayed When an Image Is Saved
- No Kernel Is Displayed After a Notebook Instance Created Using a Custom Image Is Started
- Some Extra Packages Are Found in the Conda Environment Built Using a Custom Image
- Failed to Create a Custom Image Using ma-cli and an Error Is Displayed Indicating that the File Does Not Exist
- Error Message "Unexpected error from cudaGetDeviceCount" Is Displayed When Torch Is Used
- Unable to Access a Notebook Instance Created Using an Old Image
- Other Faults
-
Environment Configuration Faults
-
Training Jobs
-
OBS Operation Issues
- Failed to Read Files
- Error Message Is Displayed Repeatedly When a TensorFlow-1.8 Job Is Connected to OBS
- TensorFlow Stops Writing TensorBoard to OBS When the Size of Written Data Reaches 5 GB
- Error "Unable to connect to endpoint" Error Occurs When a Model Is Saved
- Error Message "BrokenPipeError: Broken pipe" Is Displayed When OBS Data Is Copied
- Error Message "ValueError: Invalid endpoint: obs.xxxx.com" Is Displayed in Logs
- Error Message "errorMessage:The specified key does not exist" Displayed in Logs
-
In-Cloud Migration Adaptation Issues
- Failed to Import a Module
- Error Message "No module named .*" Is Displayed in Training Job Logs
- Failed to Install a Third-Party Package
- Failed to Download the Code Directory
- Error Message "No such file or directory" Is Printed in Training Job Logs
- Failed to Find the .so File During Training
- ModelArts Training Job Failed to Parse Parameters and an Error Is Displayed in the Log
- Training Output Path Is Used by Another Job
- Error Message "RuntimeError: std:exception" Is Displayed for a PyTorch 1.0 Engine
- Error Message "retCode=0x91, [the model stream execute failed]" Displayed in MindSpore Logs
- Error Occurred When Pandas Reads Data from an OBS File If MoXing Is Used to Adapt to an OBS Path
- Error Message "Please upgrade numpy to >= xxx to use this pandas version" Is Displayed in Logs
- Reinstalled CUDA Version Does Not Match the One in the Target Image
- Error ModelArts.2763 Occurred During Training Job Creation
- Error Message "AttributeError: module '***' has no attribute '***'" Is Displayed Training Job Logs
- System Container Exits Unexpectedly
-
Hard Faults Due to Space Limit
- Downloading Files Timed Out or No Space Left for Reading Data
- Insufficient Container Space for Copying Data
- Error Message "No space left" Displayed When a TensorFlow Multi-node Job Downloads Data to /cache
- Size of the Log File Has Reached the Limit
- Error Message "write line error" Is Displayed in Logs
- Error Message "No space left on device" Is Displayed in Logs
- Training Job Failed Due to OOM
- Insufficient Disk Space
- Internet Access Issues
- Permission Issues
-
GP Issues
- Error Message "No CUDA-capable device is detected" Is Displayed in Logs
- Error Message "RuntimeError: connect() timed out" Is Displayed in Logs
- Error Message "cuda runtime error (10) : invalid device ordinal at xxx" Is Displayed in Logs
- Error Message "RuntimeError: Cannot re-initialize CUDA in forked subprocess" Is Displayed in Logs
- No GP Detected in a Training Job
-
Service Code Issues
- Error Message "pandas.errors.ParserError: Error tokenizing data. C error: Expected .* fields" Is Displayed in Logs
- Error Message "max_pool2d_with_indices_out_cuda_frame failed with error code 0" Is Displayed in Logs
- Training Job Failed with Error Code 139
- Debugging Training Code in a Development Environment
- Error Message "'(slice(0, 13184, None), slice(None, None, None))' is an invalid key" Is Displayed in Logs
- Error Message "DataFrame.dtypes for data must be int, float or bool" Is Displayed in Logs
- Error Message "CUDNN_STATUS_NOT_SUPPORTED" Is Displayed in Logs
- Error Message "Out of bounds nanosecond timestamp" Is Displayed in Logs
- Error Message "Unexpected keyword argument passed to optimizer" Is Displayed in Logs
- Error Message "no socket interface found" Is Displayed in Logs
- Error Message "Runtimeerror: Dataloader worker (pid 46212) is killed by signal: Killed BP" Displayed in Logs
- Error Message "AttributeError: 'NoneType' object has no attribute 'dtype'" Displayed in Logs
- Error Message "No module name 'unidecode'" Is Displayed in Logs
- Distributed TensorFlow Cannot Use tf.variable
- When MXNet Creates kvstore, the Program Is Blocked and No Error Is Reported
- ECC Error Occurs in the Log, Causing Training Job Failure
- Training Job Failed Because the Maximum Recursion Depth Is Exceeded
- Training Using a Built-in Algorithm Failed Due to a bndbox Error
- Training Job Status Is Reviewing Job Initialization
- Training Job Process Exits Unexpectedly
- Stopped Training Job Process
- Training Job Suspensions
-
Running a Training Job Failed
- Troubleshooting a Training Job Failure
- An NCCL Error Occurs When a Training Job Fails to Be Executed
- Troubleshooting Process
- A Training Job Created Using a Custom Image Is Always in the Running State
- Failed to Find the Boot File When a Training Job Is Created Using a Custom Image
- Running a Job Failed Due to Persistently Rising Memory Usage
- Training Jobs Created in a Dedicated Resource Pool
- Training Performance Issues
-
OBS Operation Issues
-
Inference Deployment
-
Model Management
- Failed to Create a Model
- Suspended Account or Insufficient Permission to Import Models
- Failed to Build an Image or Import a File During Model Creation
- Failed to Obtain the Directory Structure in the Target Image When Creating a Model Through OBS
- Failed to Obtain Certain Logs on the ModelArts Log Query Page
- Failed to Download a pip Package When a Model Is Created Using OBS
- Failed to Use a Custom Image to Create a Model
- Insufficient Disk Space Is Displayed When a Service Is Deployed After a Model Is Imported
- Error Occurred When a Created Model Is Deployed as a Service
- Invalid Runtime Dependency Configured in an Imported Custom Image
- Garbled Characters Displayed in a Model Name Returned When Model Details Are Obtained Through an API
- Failed to Import a Model Due to Oversized Model or Image
- A Single Model File to Be Imported Exceeds the Size Limit (5 GB)
- Creating a Model Failed Due to Image Building Timeout
-
Service Deployment
- Error Occurred When a Custom Image Model Is Deployed as a Real-Time Service
- Alarm Status of a Deployed Real-Time Service
- Failed to Start a Service
- Failed to Pull an Image When a Service Is Deployed, Started, Upgraded, or Modified
- Image Restarts Repeatedly When a Service Is Deployed, Started, Upgraded, or Modified
- Container Health Check Fails When a Service Is Deployed, Started, Upgraded, or Modified
- Resources Are Insufficient When a Service Is Deployed, Started, Upgraded, or Modified
- Error Occurred When a CV2 Model Package Is Used to Deploy a Real-Time Service
- Service Is Consistently Being Deployed
- A Started Service Is Intermittently in the Alarm State
- Failed to Deploy a Service and Error "No Module named XXX" Occurred
- Insufficient Permission to or Unavailable Input/Output OBS Path of a Batch Service
- Error "No CUDA runtime is found" Occurred When a Real-Time Service Is Deployed
- What Can I Do if the Memory Is Insufficient?
- ModelArts.3520 The Number of Real-Time Services Cannot Exceed 11
- Exit Code 127 Is Reported During Service Deployment
- "pod has unbound immediate PersistentVolumeClaims" Is Displayed During Service Deployment
-
Service Prediction
- Service Prediction Failed
- Error "APIG.XXXX" Occurred in a Prediction Failure
- Error ModelArts.4206 Occurred in Real-Time Service Prediction
- Error ModelArts.4302 Occurred in Real-Time Service Prediction
- Error ModelArts.4503 Occurred in Real-Time Service Prediction
- Error MR.0105 Occurred in Real-Time Service Prediction
- Method Not Allowed
- Request Timed Out
- Error Occurred When an API Is Called for Deploying a Model Created Using a Custom Image
- Error "DL.0105" Occurred During Real-Time Inference
-
Model Management
-
MoXing
- Error Occurs When MoXing Is Used to Copy Data
- How Do I Disable the Warmup Function of the Mox?
- Pytorch Mox Logs Are Repeatedly Generated
- Failed to Perform Local Fine Tuning on the Checkpoint Generated by moxing.tensorflow
- Copying Data Using MoXing Is Slow and the Log Is Repeatedly Printed in a Training Job
- Failed to Access a Folder Using MoXing and Read the Folder Size Using get_size
-
APIs or SDKs
- "ERROR: Could not install packages due to an OSError" Occurred During ModelArts SDK Installation
- Error Occurred During Service Deployment After the Target Path to a File Downloaded Through a ModelArts SDK Is Set to a File Name
- A Training Job Created Using an API Is Abnormal
- Execution of a huaweicloud.com API Times Out
- Resource Pool
- Lite Cluster
- Videos
-
More Documents
- Preparations (To Be Offline)
-
DevEnviron
- Introduction to DevEnviron
- Application Scenarios
-
Managing Notebook Instances
- Creating a Notebook Instance
- Accessing a Notebook Instance
- Searching for, Starting, Stopping, or Deleting a Notebook Instance
- Changing a Notebook Instance Image
- Changing the Flavor of a Notebook Instance
- Selecting Storage in DevEnviron
- Dynamically Mounting an OBS Parallel File System
- Dynamically Expanding EVS Disk Capacity
- Modifying the SSH Configuration for a Notebook Instance
- Viewing the Notebook Instances of All IAM Users Under One Tenant Account
- Viewing Notebook Events
- Notebook Cache Directory Alarm Reporting
-
JupyterLab
- Operation Process in JupyterLab
- JupyterLab Overview and Common Operations
- Code Parametrization Plug-in
- Using ModelArts SDK
- Using the Git Plug-in
- Visualized Model Training
- Uploading and Downloading Data in Notebook
-
Local IDE
- Operation Process in a Local IDE
- Local IDE (PyCharm)
- Local IDE (VS Code)
- Local IDE (Accessed Using SSH)
-
ModelArts CLI Command Reference
- ModelArts CLI Overview
- (Optional) Installing ma-cli Locally
- Autocompletion for ma-cli Commands
- ma-cli Authentication
-
ma-cli Image Building Command
- ma-cli Image Building Command
- Obtaining an Image Creation Template
- Loading an Image Creation Template
- Obtaining Registered ModelArts Images
- Creating an Image in ModelArts Notebook
- Obtaining Image Creation Caches in ModelArts Notebook
- Clearing Image Creation Caches in ModelArts Notebook
- Registering SWR Images with ModelArts Image Management
- Deregistering a Registered Image from ModelArts Image Management
- Debugging an SWR Image on an ECS
-
Using the ma-cli ma-job Command to Submit a ModelArts Training Job
- ma-cli ma-job Command Overview
- Obtaining ModelArts Training Jobs
- Submitting a ModelArts Training Job
- Obtaining ModelArts Training Job Logs
- Obtaining ModelArts Training Job Events
- Obtaining ModelArts AI Engines for Training
- Obtaining ModelArts Resource Specifications for Training
- Stopping a ModelArts Training Job
- Using the ma-cli dli-job Command to Submit a DLI Spark Job
- Using ma-cli to Copy OBS Data
-
Model Development (To Be Offline)
- Introduction to Model Development
- Preparing Data
- Preparing Algorithms
-
Performing a Training
- Creating a Training Job
- Viewing Training Job Details
- Viewing Training Job Events
- Training Job Logs
- Cloud Shell
- Viewing the Resource Usage of a Training Job
- Evaluation Results
- Viewing Training Tags
- Viewing Fault Recovery Details
- Viewing Environment Variables of a Training Container
- Stopping, Rebuilding, or Searching for a Training Job
- Releasing Training Job Resources
- Advanced Training Operations
- Distributed Training
- Automatic Model Tuning (AutoSearch)
-
Image Management
- Image Management
- Using a Preset Image
-
Using Custom Images in Notebook Instances
- Constraints on Custom Images in Notebook Instances
- Registering an Image in ModelArts
- Creating a Custom Image
- Saving a Notebook Instance as a Custom Image
- Creating and Using a Custom Image in Notebook
- Creating a Custom Image on an ECS and Using It in Notebook
- Troubleshooting for Custom Images in Notebook Instances
-
Using a Custom Image to Train Models (Model Training)
- Overview
-
Example: Creating a Custom Image for Training
- Example: Creating a Custom Image for Training (PyTorch + CPU/GPU)
- Example: Creating a Custom Image for Training (MPI + CPU/GPU)
- Example: Creating a Custom Image for Training (Horovod-PyTorch and GPUs)
- Example: Creating a Custom Image for Training (MindSpore and GPUs)
- Example: Creating a Custom Image for Training (TensorFlow and GPUs)
- Preparing a Training Image
- Creating an Algorithm Using a Custom Image
- Using a Custom Image to Create a CPU- or GPU-based Training Job
- Troubleshooting Process
- Using a Custom Image to Create AI applications for Inference Deployment
-
FAQs
- How Can I Log In to SWR and Upload Images to It?
- How Do I Configure Environment Variables for an Image?
- How Do I Use Docker to Start an Image Saved Using a Notebook Instance?
- How Do I Configure a Conda Source in a Notebook Development Environment?
- What Are Supported Software Versions for a Custom Image?
- Why Does an Error Occur When I Try to Save an Image That Is Reported as Larger Than 35 GB, Even Though It Is Only Displayed as 13 GB in SWR?
- How Do I Ensure That an Image Can be Saved Correctly Without Being Too Large?
- How Do I Reduce the Size of an Image Created Locally or on ECS?
- Will an Image Be Smaller If I Uninstall and Repackage It or Simply Delete Existing Datasets from the Image?
- What Do I Do If Error "ModelArts.6787" Is Reported When I Register an Image on ModelArts?
- Modification History
-
Model Inference (To Be Offline)
- Introduction to Inference
- Managing AI Applications
-
Deploying an AI Application as a Service
- Deploying AI Applications as Real-Time Services
- Deploying AI Applications as Batch Services
- Upgrading a Service
- Starting, Stopping, Deleting, or Restarting a Service
- Viewing Service Events
-
Inference Specifications
- Model Package Specifications
-
Model Templates
- Introduction to Model Templates
-
Templates
- TensorFlow-based Image Classification Template
- TensorFlow-py27 General Template
- TensorFlow-py36 General Template
- MXNet-py27 General Template
- MXNet-py36 General Template
- PyTorch-py27 General Template
- PyTorch-py36 General Template
- Caffe-CPU-py27 General Template
- Caffe-GPU-py27 General Template
- Caffe-CPU-py36 General Template
- Caffe-GPU-py36 General Template
- Arm-Ascend Template
- Input and Output Modes
- Examples of Custom Scripts
- ModelArts Monitoring on Cloud Eye
-
Resource Management
- Resource Pool
-
Elastic Cluster
- Comprehensive Upgrades to ModelArts Resource Pool Management Functions
- Creating a Resource Pool
- Viewing Details About a Resource Pool
- Resizing a Resource Pool
- Setting a Renewal Policy
- Modifying the Expiration Policy
- Migrating the Workspace
- Changing Job Types Supported by a Resource Pool
- Upgrading a Resource Pool Driver
- Deleting a Resource Pool
- Abnormal Status of a Dedicated Resource Pool
- ModelArts Network
- ModelArts Nodes
- Audit Logs
- Monitoring Resources
-
Data Preparation and Analytics
- Introduction to Data Preparation
- Getting Started
- Creating a Dataset
- Importing Data
- Data Analysis and Preview
- Labeling Data
- Publishing Data
- Exporting Data
- Data Labeling (To Be Offline)
- General Reference
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DevServer Management
- Obtaining All DevServer Instances of a User
- Creating a DevServer Instance
- Obtaining DevServer Instance Details
- Deleting DevServer Instances
- Synchronizing the Status of All DevServer Instances of a User in Real Time
- Starting DevServer Instances
- Stopping DevServer Instances
- Creating DevServer Supernode Tags
- Deleting DevServer Supernode Tags
- Obtaining the DevServer Supernode Tags
- Reinstalling the OS Image of the DevServer Server
- Changing the OS Image of the DevServer Server
- Changing the OS Image of the DevServer Supernode Server
- Obtaining Details About All Supernode Instances of a User
- Deleting a DevServer Supernode Instance
- Restarting a DevServer Instance
- Starting a DevServer Supernode Server
- Stopping a DevServer Supernode Server
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