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ModelArts
ModelArts
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Visão geral de serviço
- Infográficos
- O que é o ModelArts?
- Funções
- Conhecimento básico
- Serviços relacionados
- Como acessar o ModelArts?
- Gerenciamento de permissões
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Segurança
- Responsabilidades compartilhadas
- Identificação e gerenciamento de ativos
- Autenticação de identidade e controle de acesso
- Proteção de dados
- Auditoria e registro em logs
- Resiliência de serviço
- Monitoramento de riscos
- Recuperação de falhas
- Gerenciamento de atualização
- Certificados
- Fronteira da segurança
- Cotas
- Preparações
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DevEnviron
- Introdução ao DevEnviron
- Cenários de aplicações
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Gerenciamento de instâncias do notebook
- Criação de uma instância de notebook
- Acesso a uma instância de notebook
- Pesquisa, inicialização, interrupção ou exclusão de uma instância de notebook
- Alteração de uma imagem de instância de notebook
- Alteração do flavor de uma instância de notebook
- Seleção de armazenamento no DevEnviron
- Montagem dinâmica de um sistema de arquivos paralelo do OBS
- Expansão dinâmica da capacidade de disco EVS
- Modificação da configuração de SSH para uma instância de notebook
- Exibição das instâncias do notebook de todos os usuários do IAM em uma conta de locatário
- Exibição de eventos do notebook
- Relatório de alarme do diretório de cache do notebook
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JupyterLab
- Processo de operação no JupyterLab
- Visão geral do JupyterLab e operações comuns
- Plug-in de parametrização de código
- Uso do SDK do ModelArts
- Uso do plug-in Git
- Treinamento de modelo visualizado
- Upload e download de dados no notebook
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IDE local
- Processo de operação em um IDE Local
- IDE local (PyCharm)
- IDE local (VS Code)
- IDE local (acesso usando SSH)
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Referência de comandos da CLI do ModelArts
- Visão geral da CLI do ModelArts
- (Opcional) Instalação local de ma-cli
- Completamento automático para comandos de ma-cli
- Autenticação de ma-cli
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Comando de criação de imagem de ma-cli
- Comando de criação de imagem de ma-cli
- Obtenção de um modelo de criação de imagem
- Carregamento de um modelo de criação de imagem
- Obtenção de imagens do ModelArts registradas
- Criação de uma imagem no notebook do ModelArts
- Obtenção de caches de criação de imagens no notebook do ModelArts
- Limpeza de caches de criação de imagens no notebook do ModelArts
- Registro de imagens do SWR com o gerenciamento de imagens do ModelArts.
- Cancelamento de registro de uma imagem registrada do gerenciamento de imagens do ModelArts
- Depuração de uma imagem do SWR em um ECS
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Uso do comando ma-cli ma-job para enviar um trabalho de treinamento do ModelArts
- Visão geral do comando ma-cli ma-job
- Obtenção de trabalhos de treinamento do ModelArts
- Envio de um trabalho de treinamento do ModelArts
- Obtenção de registros de trabalho de treinamento do ModelArts
- Obtenção de eventos de trabalho de treinamento do ModelArts
- Obtenção de mecanismos de IA do ModelArts para treinamento
- Obtenção de especificações de recursos do ModelArts para treinamento
- Interrupção de um trabalho de treinamento do ModelArts
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Uso do comando ma-cli dli-job para enviar um trabalho do Spark de DLI
- Visão geral
- Consulta de trabalhos do Spark de DLI
- Envio de um trabalho do Spark de DLI
- Consulta de logs de execução do Spark de DLI
- Consulta de filas do DLI
- Obtenção de recursos do grupo de DLI
- Upload de arquivos locais ou arquivos do OBS para um grupo do DLI
- Interrupção de um trabalho do Spark de DLI
- Uso de ma-cli para copiar dados do OBS
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Gerenciamento de recursos
- Pool de recursos
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Cluster elástico
- Atualizações abrangentes das funções de gerenciamento do pool de recursos do ModelArts
- Criação de um pool de recursos
- Exibição de detalhes sobre um pool de recursos
- Redimensionamento de um pool de recursos
- Definição de uma política de renovação
- Modificação da política de expiração
- Migração do espaço de trabalho
- Alteração de tipos de trabalho suportados por um pool de recursos
- Atualização de um driver de pool de recursos
- Exclusão de um pool de recursos
- Status anormal de um pool de recursos dedicados
- Rede do ModelArts
- Nós do ModelArts
- Logs de auditoria
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Monitoramento de recursos
- Visão geral
- Uso do Grafana para exibir as métricas de monitoramento do AOM
- Exibição de todas as métricas de monitoramento do ModelArts no console do AOM
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Contêineres do Docker com ModelArts
- Gerenciamento de imagens
- Uso de uma imagem predefinida
- Uso de imagens personalizadas em instâncias de notebook
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Uso de uma imagem personalizada para treinar modelos (treinamento de modelo)
- Visão geral
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Exemplo: criar uma imagem personalizada para treinamento
- Exemplo: criar uma imagem personalizada para treinamento (PyTorch + CPU/GPU)
- Exemplo: criar uma imagem personalizada para treinamento (MPI + CPU/GPU)
- Exemplo: criar uma imagem personalizada para treinamento (Horovod-PyTorch e GPUs)
- Exemplo: criar uma imagem personalizada para treinamento (MindSpore e GPUs)
- Exemplo: criar uma imagem personalizada para treinamento (TensorFlow e GPUs)
- Preparação de uma imagem de treinamento
- Criação de um algoritmo usando uma imagem personalizada
- Uso de uma imagem personalizada para criar um trabalho de treinamento baseado em CPU ou GPU
- Processo de solução de problemas
- Uso de uma imagem personalizada para criar aplicações de IA para implementação de inferência
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Perguntas frequentes
- Como acessar o SWR e carregar imagens para ele?
- Como configurar variáveis de ambiente para uma imagem?
- Como usar o Docker para iniciar uma imagem salva usando uma instância de notebook?
- Como configurar uma fonte de Conda em um ambiente de desenvolvimento de notebook?
- Quais são as versões de software suportadas para uma imagem personalizada?
- Histórico de modificações
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Melhores práticas
- Pool de recursos
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Cluster elástico
- Atualizações abrangentes das funções de gerenciamento do pool de recursos do ModelArts
- Criação de um pool de recursos
- Exibição de detalhes sobre um pool de recursos
- Redimensionamento de um pool de recursos
- Definição de uma política de renovação
- Modificação da política de expiração
- Migração do espaço de trabalho
- Alteração de tipos de trabalho suportados por um pool de recursos
- Atualização de um driver de pool de recursos
- Exclusão de um pool de recursos
- Status anormal de um pool de recursos dedicados
- Rede do ModelArts
- Nós do ModelArts
- Logs de auditoria
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Monitoramento de recursos
- Visão geral
- Uso do Grafana para exibir as métricas de monitoramento do AOM
- Exibição de todas as métricas de monitoramento do ModelArts no console do AOM
- No momento, o conteúdo não está disponível no seu idioma selecionado. Consulte a versão em inglês.
- What's New
- Function Overview
- Product Bulletin
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Billing
- Billing Modes
- Billing Item
- 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?
- Will I Be Charged for Uploading Datasets to ModelArts?
- What Should I Do to Avoid Unnecessary Billing After I Label Datasets and Exit?
- How Do I Stop Billing for a ModelArts ExeML Project?
- How Are Training Jobs Billed?
- Why Does Billing Continue After All Projects Are Deleted?
- 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 Free Nodes in a Standard Dedicated Resource Pool
- Releasing Standard Dedicated Resource Pools and Deleting the Network
- Using ExeML for Zero-Code AI Development
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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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Development Environments
- Application Scenarios
- Creating a Notebook Instance
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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 MindInsight Visualization Jobs in JupyterLab
- 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
- Managing Notebook Instances
- ModelArts CLI Command Reference
- Using Moxing Commands in a Notebook Instance
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Data Management
- Introduction to Data Preparation
- Getting Started
- Creating a Dataset
- Importing Data
- Data Analysis and Preview
- Labeling Data
- Publishing Data
- Exporting Data
- Introduction to Data Labeling
- Manual Labeling
- Auto Labeling
- Team Labeling
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Model Training
- Model Training Process
- Preparing Model Training Code
- Preparing a Model Training Image
- Creating a Debug Training Job
- Creating an Algorithm
- Creating a Production Training Job
- Distributed Model Training
- Incremental Model Training
- Automatic Model Tuning (AutoSearch)
- High Model Training Reliability
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Managing Model Training Jobs
- Viewing Training Job Details
- 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
- Rebuilding, Stopping, or Deleting a Training Job
- Managing Environment Variables of a Training Container
- Viewing Training Job Tags
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Inference Deployment
- 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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Image Management
- Application Scenarios 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
- Resource Monitoring
- Viewing Audit Logs
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ModelArts User Guide (Lite Server)
- Before You Start
- Enabling Lite Server Resources
- Configuring Lite Server Resources
- Using Lite Server Resources
- Managing Lite Server Resources
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ModelArts User Guide (Lite Cluster)
- Before You Start
- Enabling Lite Cluster Resources
- Configuring Lite Cluster Resources
- Using Lite Cluster Resources
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Managing Lite Server 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
- Managing Free Nodes in a Lite Cluster Resource Pool
- Monitoring Lite Cluster Resources
- Releasing Lite Cluster Resources
- ModelArts User Guide (AI Gallery)
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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 Details of a Notebook Instance
- Updating a Notebook Instance
- Deleting a Notebook Instance
- Saving a Running Instance as a Container Image
- Obtaining the Available Flavors
- Querying Flavors Available for a Notebook Instance
- 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
- Obtaining User Image Groups
- 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 General Specifications Supported by a Training Job
- Obtaining the Preset AI Frameworks Supported by a Training Job
- AI Application Management
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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
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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
- 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
- Querying the dedicated resource pool Job List
- Querying dedicated resource pool Job Statistics
- DevServer Management
- Authorization Management
- Managing DevEnviron Instances
- Use Cases
- Permissions Policies and Supported Actions
- Common Parameters
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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)
- Change History
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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
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General Issues
- What Is ModelArts?
- What Are the Relationships Between ModelArts and Other Services?
- What Are the Differences Between ModelArts and DLS?
- How Do I Purchase or Enable ModelArts?
- How Do I Obtain an Access Key?
- How Do I Upload Data to OBS?
- What Do I Do If the System Displays a Message Indicating that the AK/SK Pair Is Unavailable?
- What Do I Do If a Message Indicating Insufficient Permissions Is Displayed When I Use ModelArts?
- How Do I Use ModelArts to Train Models Based on Structured Data?
- What Are Regions and AZs?
- How Do I View All Files Stored in OBS on ModelArts?
- Where Are Datasets of ModelArts Stored in a Container?
- Which AI Frameworks Does ModelArts Support?
- What Are the Functions of ModelArts Training and Inference?
- How Do I View an Account ID and IAM User ID?
- Can AI-assisted Identification of ModelArts Identify a Specific Label?
- How Does ModelArts Use Tags to Manage Resources by Group?
- Why Is the Job Still Queued When Resources Are Sufficient?
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Billing
- How Do I View the ModelArts Jobs Being Billed?
- How Do I View ModelArts Expenditure Details?
- Will I Be Charged for Uploading Datasets to ModelArts?
- What Should I Do to Avoid Unnecessary Billing After I Label Datasets and Exit?
- How Do I Stop Billing for a ModelArts ExeML Project?
- How Do I Stop Billing If I Do Not Use ModelArts?
- How Are Training Jobs Billed?
- Why Does Billing Continue After All Projects Are Deleted?
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ExeML (Old Version)
- Functional Consulting
- Preparing Data
- Creating a Project
- Labeling Data
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Training Models
- What Should I Do When the Train Button Is Unavailable After I Create an Image Classification Project and Label the Images?
- How Do I Perform Incremental Training in an ExeML Project?
- Can I Download a Model Trained Using ExeML?
- Why Does ExeML Training Fail?
- What Do I Do If an Image Error Occurred During Model Training Using ExeML?
- What Do I Do If Error ModelArts.0010 Occurred When I Use ExeML to Start Training as an IAM User?
- What Is the Training Speed of Each Parameter in ExeML Training Preference Settings?
- What Do I Do If ERROR:input key sound is not in model Occurred When I Use ExeML for Sound Classification Prediction?
- Deploying Models
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Data Management (Old Version)
- Are There Size Limits for Images to be Uploaded?
- What Do I Do If Images in a Dataset Cannot Be Displayed?
- How Do I Integrate Multiple Object Detection Datasets into One Dataset?
- What Do I Do If Importing a Dataset Failed?
- Can a Table Dataset Be Labeled?
- What Do I Do to Import Locally Labeled Data to ModelArts?
- Why Does Data Fail to Be Imported Using the Manifest File?
- Where Are Labeling Results Stored?
- How Do I Download Labeling Results to a Local PC?
- Why Cannot Team Members Receive Emails for a Team Labeling Task?
- Can Two Accounts Concurrently Label One Dataset?
- How Data Is Distributed Between Team Members During Team Labeling?
- Can I Delete an Annotator from a Labeling Team with a Labeling Task Assigned? What Is the Impact on the Labeling Result After Deletion? If the Annotator Cannot Be Deleted, Can I Separate the Annotator's Labeling Result?
- How Do I Define a Hard Example in Data Labeling? Which Samples Are Identified as Hard Examples?
- Can I Add Multiple Labeling Boxes to an Object Detection Dataset Image?
- How Do I Merge Two Datasets?
- Does Auto Labeling Support Polygons?
- What Do the Options for Accepting a Team Labeling Task Mean?
- Why Are Images Displayed in Different Angles Under the Same Account?
- Do I Need to Train Data Again If New Data Is Added After Auto Labeling Is Complete?
- Why Does the System Display a Message Indicating My Label Fails to Save on ModelArts?
- Can One Label By Identified Among Multiple Labels?
- Why Are Newly Added Images Not Automatically Labeled After Data Amplification Is Enabled?
- Why Cannot Videos in a Video Dataset Be Displayed or Played?
- Why All the Labeled Samples Stored in an OBS Bucket Are Displayed as Unlabeled in ModelArts After the Data Source Is Synchronized?
- How Do I Use Soft-NMS to Reduce Bounding Box Overlapping?
- Why ModelArts Image Labels Are Lost?
- How Do I Add Images to a Validation or Training Dataset?
- Can I Customize Labels for an Object Detection Dataset?
- What ModelArts Data Management Can Be Used for?
- Will My Old-Version Datasets Be Cleared After the Old Version Is Discontinued? The existing datasets and the ones newly created in the old version will be retained after the old version is discontinued.
- Why Is My New Dataset Version Unavailable in Versions?
- How Do I View the Size of a Dataset?
- How Do I View Labeling Details of a New Dataset?
- How Do I Export Labeled Data?
- Why Cannot I Find My Newly Created Dataset?
- What Do I Do If the Database Quota Is Incorrect?
- How Do I Split a Dataset?
- How Do I Delete a Dataset Image?
- Why Is There No Sample in the ModelArts Dataset Downloaded from AI Gallery and Then an OBS Bucket?
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Notebook (New Version)
- Constraints
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Data Upload or Download
- How Do I Upload a File from a Notebook Instance to OBS or Download a File from OBS to a Notebook Instance?
- How Do I Upload Local Files to a Notebook Instance?
- How Do I Import Large Files to a Notebook Instance?
- Where Will the Data Be Uploaded to?
- How Do I Download Files from a Notebook Instance to a Local Computer?
- How Do I Copy Data from Development Environment Notebook A to Notebook B?
- What Can I Do If a File Fails to Be Uploaded to a Notebook Instance?
- Failed to View the Local Mount Point of a Dynamically Mounted OBS Parallel File System in JupyterLab of a Notebook Instance
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Data Storage
- How Do I Rename an OBS File?
- Do Files in /cache Still Exist After a Notebook Instance is Stopped or Restarted? How Do I Avoid a Restart?
- How Do I Use the pandas Library to Process Data in OBS Buckets?
- How Do I Access the OBS Bucket of Another Account from a Notebook Instance?
- What Is the Default Working Directory on JupyterLab?
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Environment Configurations
- How Do I Check the CUDA Version Used by a Notebook Instance?
- How Do I Enable the Terminal Function in DevEnviron of ModelArts?
- How Do I Install External Libraries in a Notebook Instance?
- How Do I Obtain the External IP Address of My Local PC?
- How Can I Resolve Abnormal Font Display on a ModelArts Notebook Accessed from iOS?
- Is There a Proxy for Notebook? How Do I Disable It?
- Adding a Custom IPython Kernel in a Notebook Instance
-
Notebook Instances
- What Do I Do If I Cannot Access My Notebook Instance?
- What Should I Do When the System Displays an Error Message Indicating that No Space Left After I Run the pip install Command?
- What Do I Do If "Read timed out" Is Displayed After I Run pip install?
- What Do I Do If the Code Can Be Run But Cannot Be Saved, and the Error Message "save error" Is Displayed?
- Why Is a Request Timeout Error Reported When I Click the Open Button of a Notebook Instance?
- When the SSH Tool Is Used to Connect to a Notebook Instance, Server Processes Are Cleared, but the GPU Usage Is Still 100%
-
Code Execution
- What Do I Do If a Notebook Instance Won't Run My Code?
- Why Does the Instance Break Down When dead kernel Is Displayed During Training Code Running?
- What Do I Do If cudaCheckError Occurs During Training?
- What Should I Do If DevEnviron Prompts Insufficient Space?
- Why Does the Notebook Instance Break Down When opencv.imshow Is Used?
- Why Cannot the Path of a Text File Generated in Windows OS Be Found In a Notebook Instance?
- What Do I Do If Files Fail to Be Saved in JupyterLab?
-
VS Code
- What Do I Do If Installing a Remote Plug-in Failed?
- What Do I Do If a Restarted Notebook Instance Can Be Connected Only After I Locally Delete known_hosts?
- What Do I Do If the Source Code Cannot Be Accessed When I Use VS Code for Debugging?
- What Do I Do If a Message Is Displayed Indicating an Incorrect Username or Email Address When I Use VS Code to Submit Code?
- How Can I View Remote Logs in VS Code?
- How Can I Open the VS Code Configuration File settings.json?
- How Can I Change the VS Code Background Color to Light Green?
- How Can I Set the Default Remote Plug-in in VS Code?
- How Can I Install a Local Plug-in on the Remote End or a Remote Plug-in on the Local End Through VS Code?
-
Failures to Access the Development Environment Through VS Code
- 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?
- Basic Problems Causing the Failures to Access the Development Environment Through VS Code
- What Do I Do If Error Message "Could not establish connection to xxx" Is Displayed During a Remote Connection?
- What Do I Do If the Connection to a Remote Development Environment Remains in "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: Downloading VS Code Server locally" for More Than 10 Minutes?
- What Do I Do If the Connection to a Remote Development Environment Remains in the State of "ModelArts Remote Connect: Connecting to instance xxx..." for More Than 10 Minutes?
- What Do I Do If a Remote Connection Is in the Retry State?
- What Do I Do If Error Message "The VS Code Server failed to start" Is Displayed?
- What Do I Do If Error Message "Permissions for 'x:/xxx.pem' are too open" Is Displayed?
- 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 "ssh: connect to host ModelArts-xxx port xxx: Connection timed out" Is Displayed?
- What Do I Do If Error Message "Load key "C:/Users/xx/test1/xxx.pem": invalid format" Is Displayed?
- What Do I Do If Error Message "An SSH installation couldn't be found" or "Could not establish connection to instance xxx: 'ssh' ..." 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 Do I Do If Error Message "Host key verification failed" or "Port forwarding is disabled" Is Displayed?
- What Do I Do If Error Message "Failed to install the VS Code Server" or "tar: Error is not recoverable: exiting now" Is Displayed?
- What Do I Do If Error Message "XHR failed" Is Displayed When a Remote Notebook Instance Is Accessed Through VS Code?
- What Do I Do for an Automatically Disconnected VS Code Connection If No Operation Is Performed for a Long Time?
- What Do I Do If It Takes a Long Time to Set Up a Remote Connection After VS Code Is Automatically Upgraded?
- What Do I Do If Error Message "Connection reset" Is Displayed During an SSH Connection?
- What Can I Do If a Notebook Instance Is Frequently Disconnected or Stuck After I Use MobaXterm to Connect to the Notebook Instance in SSH Mode?
- Using Custom Images in Notebook
-
Others
- How Do I Use Multiple Ascend Cards for Debugging in a Notebook Instance?
- Why Is the Training Speed Similar When Different Notebook Flavors Are Used?
- How Do I Perform Incremental Training When Using MoXing?
- How Do I View GPU Usage on the Notebook?
- How Can I Obtain GPU Usage Through Code?
- Which Real-Time Performance Indicators of an Ascend Chip Can I View?
- Does the System Automatically Stop or Delete a Notebook Instance If I Do Not Enable Automatic Stop?
- What Are the Relationships Between Files Stored in JupyterLab, Terminal, and OBS?
- How Do I Use the Datasets Created on ModelArts in a Notebook Instance?
- pip and Common Commands
- What Are Sizes of the /cache Directories for Different Notebook Specifications in DevEnviron?
- How Do I Isolate IAM Users for Using Development Environments?
- What Is the Impact of Resource Overcommitment on Notebook Instances?
-
Training Jobs
-
Functional Consulting
- What Are the Format Requirements for Algorithms Imported from a Local Environment?
- What Are the Solutions to Underfitting?
- What Are the Precautions for Switching Training Jobs from the Old Version to the New Version?
- How Do I Obtain a Trained ModelArts Model?
- How Do I Set the Runtime Environment of the AI Engine Scikit_Learn 0.18.1?
- Must the Hyperparameters Optimized Using a TPE Algorithm Be Categorical?
- What Is TensorBoard Used for in Model Visualization Jobs?
- How Do I Obtain RANK_TABLE_FILE on ModelArts for Distributed Training?
- How Do I Obtain the CUDA and cuDNN Versions of a Custom Image?
- How Do I Obtain a MoXing Installation File?
- In a Multi-Node Training, the TensorFlow PS Node Functioning as a Server Will Be Continuously Suspended. How Does ModelArts Determine Whether the Training Is Complete? Which Node Is a Worker?
- How Do I Install MoXing for a Custom Image of a Training Job?
- An IAM User Cannot Select an Existing SFS Turbo File System When Using a Dedicated Resource Pool to Create a Training Job
- Reading Data During Training
-
Compiling the Training Code
- How Do I Create a Training Job When a Dependency Package Is Referenced by the Model to Be Trained?
- What Is the Common File Path for Training Jobs?
- How Do I Install a Library That C++ Depends on?
- How Do I Check Whether a Folder Copy Is Complete During Job Training?
- How Do I Load Some Well Trained Parameters During Job Training?
- How Do I Obtain Training Job Parameters from the Boot File of the Training Job?
- Why Can't I Use os.system ('cd xxx') to Access the Corresponding Folder During Job Training?
- How Do I Invoke a Shell Script in a Training Job to Execute the .sh File?
- How Do I Obtain the Dependency File Path to be Used in Training Code?
- What Is the File Path If a File in the model Directory Is Referenced in a Custom Python Package?
-
Creating a Training Job
- What Can I Do If the Message "Object directory size/quantity exceeds the limit" Is Displayed When I Create a Training Job?
- What Are Sizes of the /cache Directories for Different Resource Specifications in the Training Environment?
- Is the /cache Directory of a Training Job Secure?
- Why Is a Training Job Always Queuing?
- What Determines the Hyperparameter Directory (/work or /ma-user) When Creating a Training Job?
- Managing Training Job Versions
-
Viewing Job Details
- How Do I Check Resource Usage of a Training Job?
- How Do I Access the Background of a Training Job?
- Is There Any Conflict When Models of Two Training Jobs Are Saved in the Same Directory of a Container?
- Only Three Valid Digits Are Retained in a Training Output Log. Can the Value of loss Be Changed?
- Can a Trained Model Be Downloaded or Migrated to Another Account? How Do I Obtain the Download Path?
-
Functional Consulting
-
Service Deployment
-
Model Management
-
Importing Models
- How Do I Import the .h5 Model of Keras to ModelArts?
- How Do I Edit the Installation Package Dependency Parameters in a Model Configuration File When Importing a Model?
- How Do I Change the Default Port to Create a Real-Time Service Using a Custom Image?
- Does ModelArts Support Multi-Model Import?
- Restrictions on the Size of an Image for Importing an AI Application
-
Importing Models
-
Service Deployment
-
Functional Consulting
- What Types of Services Can Models Be Deployed as on ModelArts?
- What Are the Differences Between Real-Time Services and Batch Services?
- Why Cannot I Select Ascend 310 Resources?
- Can Models Trained on ModelArts Be Deployed Locally?
- What Is the Maximum Size of a Prediction Request Body?
- Can Real-Time Services Be Billed on a Yearly/Monthly Basis?
- How Do I Select Compute Node Specifications for Deploying a Service?
- What Is the CUDA Version for Deploying a Service on GPUs?
-
Real-Time Services
- What Do I Do If a Conflict Occurs in the Python Dependency Package of a Custom Prediction Script When I Deploy a Real-Time Service?
- How Do I Speed Up Real-Time Prediction?
- Can a New-Version AI Application Still Use the Original API?
- What Is the Format of a Real-Time Service API?
- How Do I Check Whether an Error Is Caused by a Model When a Real-Time Service Is Running But Prediction Failed?
- How Do I Fill in the Request Header and Request Body of an Inference Request When a Real-Time Service Is Running?
- Why Cannot I Access the Obtained Inference Request Address from the Initiator Client?
- What Do I Do If Error ModelArts.3520 Is Reported During Service Deployment?
- Why Did My Service Deployment Fail with Proper Deployment Timeout Configured?
-
Functional Consulting
-
Model Management
-
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?
- How Is a Node Newly Added to a Dedicated Resource Pool Billed?
- What Are the Differences Between a Public Resource Pool and a Dedicated Resource Pool?
- How Do I Log In to a Dedicated Resource Pool Node Through SSH?
- How Are Training Jobs Queued?
- What Do I Do If Resources Are Insufficient for Staring a New Real-Time Service After I Stop a Real-Time Service in a Dedicated Resource Pool?
- Can a Public Resource Pool Be Used for Network Connection Between ModelArts and the Authentication Service for Running Algorithms?
- Why Is a Dedicated Resource Pool That Fails to Be Created Still Displayed on the Console After It Is Deleted?
- How Do I Add a VPC Peering Connection Between a Dedicated Resource Pool and an SFS?
-
API/SDK
- Can ModelArts APIs or SDKs Be Used to Download Models to a Local PC?
- What Installation Environments Do ModelArts SDKs Support?
- Does ModelArts Use the OBS API to Access OBS Files over an Intranet or the Internet?
- How Do I Obtain a Job Resource Usage Curve After I Submit a Training Job by Calling an API?
- How Do I View the Old-Version Dedicated Resource Pool List Using the SDK?
-
Using PyCharm Toolkit
- What Should I Do If an Error Occurs During Toolkit Installation?
- What Should I Do If an Error Occurs When I Edit a Credential in PyCharm Toolkit?
- Why Cannot I Start Training?
- What Should I Do If Error "xxx isn't existed in train_version" Occurs When a Training Job Is Submitted?
- What Should I Do If Error "Invalid OBS path" Occurs When a Training Job Is Submitted?
- What Should I Do If Error "NoSuchKey" Occurs When PyCharm Toolkit Is Used to Submit a Training Job?
- What Should I Do If an Error Occurs During Service Deployment?
- How Do I View Error Logs of PyCharm Toolkit?
- How Do I Use PyCharm ToolKit to Create Multiple Jobs for Simultaneous Training?
- What Should I Do If "Error occurs when accessing to OBS" Is Displayed When PyCharm ToolKit Is Used?
-
General Issues
-
Troubleshooting
- General Issues
- ExeML
-
DevEnviron
-
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
- What Do I Do If Files Fail to Be Saved in JupyterLab?
- Instance Faults
- Code Running Failures
- JupyterLab Plug-in Faults
-
Failures to Access the Development Environment Through VS Code
- 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 I Failed to Access the Development Environment Through VS Code?
- What Do I Do If Error Message "Could not establish connection to xxx" Is Displayed During a Remote Connection?
- What Do I Do If the Connection to a Remote Development Environment Remains in "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: Downloading VS Code Server locally" for More Than 10 Minutes?
- What Do I Do If a Remote Connection Is in the Retry State?
- What Do I Do If Error Message "The VS Code Server failed to start" Is Displayed?
- What Do I Do If 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?
- What Do I Do If Error Message "Load key "C:/Users/xx/test1/xxx.pem": invalid format" Is Displayed?
- What Do I Do If Error Message "An SSH installation couldn't be found" or "Could not establish connection to instance xxx: 'ssh' ..." 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 Do I Do If Error Message "Host key verification failed" or "Port forwarding is disabled" Is Displayed?
- What Do I Do If Error Message "Failed to install the VS Code Server" or "tar: Error is not recoverable: exiting now" Is Displayed?
- What Do I Do If Error Message "XHR failed" Is Displayed When a Remote Notebook Instance Is Accessed Through VS Code?
- What Do I Do for an Automatically Disconnected VS Code Connection If No Operation Is Performed for a Long Time?
- What Do I Do If It Takes a Long Time to Set Up a Remote Connection After VS Code Is Automatically Upgraded?
- What Do I Do If Error Message "Connection reset" Is Displayed During an SSH Connection?
- What Can I Do If a Notebook Instance Is Frequently Disconnected or Stuck After I Use MobaXterm to Connect to the Notebook Instance in SSH Mode?
- What Do I Do If Error Message "Missing GLIBC, Missing required dependencies" Is Displayed When I Access the Development Environment Through VS Code?
- What Do I Do If an Error Message Is Displayed Indicating That ms-vscode-remote.remot-sdh Is Uninstalled Due To a Reported Issue When Using VSCode-huawei?
- Instance Directory in VS Code Does Not Match That on the Cloud When VS Code Is Used to Connect to an Instance
-
Save an Image Failures
- Troubleshooting for Custom Images in 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?
- 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
- Other Faults
-
Environment Configuration Faults
-
Training Jobs
-
OBS Operation Issues
- Error in File Reading
- 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" Displayed When OBS Data Is Copied
- Error Message "ValueError: Invalid endpoint: obs.xxxx.com" 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 .*" 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" Displayed 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" 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" 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 '***'" 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" Displayed in Logs
- Error Message "No space left on device" Displayed in Logs
- Training Job Failed Due to OOM
- Common Issues Related to Insufficient Disk Space and Solutions
- Internet Access Issues
- Permission Issues
-
GPU Issues
- Error Message "No CUDA-capable device is detected" Displayed in Logs
- Error Message "RuntimeError: connect() timed out" Displayed in Logs
- Error Message "cuda runtime error (10) : invalid device ordinal at xxx" Displayed in Logs
- Error Message "RuntimeError: Cannot re-initialize CUDA in forked subprocess" Displayed in Logs
- No GPU Is Found for a Training Job
-
Service Code Issues
- Error Message "pandas.errors.ParserError: Error tokenizing data. C error: Expected .* fields" Displayed in Logs
- Error Message "max_pool2d_with_indices_out_cuda_frame failed with error code 0" Displayed in Logs
- Training Job Failed with Error Code 139
- Debugging Training Code in the Cloud Environment If a Training Job Failed
- Error Message "'(slice(0, 13184, None), slice(None, None, None))' is an invalid key" Displayed in Logs
- Error Message "DataFrame.dtypes for data must be int, float or bool" Displayed in Logs
- Error Message "CUDNN_STATUS_NOT_SUPPORTED" Displayed in Logs
- Error Message "Out of bounds nanosecond timestamp" Displayed in Logs
- Error Message "Unexpected keyword argument passed to optimizer" Displayed in Logs
- Error Message "no socket interface found" 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'" 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 Process Exits Unexpectedly
- Stopped Training Job Process
-
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
- Obtaining the Directory Structure in the Target Image When Importing an AI Application Through OBS
- Failed to Obtain Certain Logs on the ModelArts Log Query Page
- Failed to Download a pip Package When an AI Application Is Created Using OBS
- Failed to Use a Custom Image to Create an AI application
- Insufficient Disk Space Is Displayed When a Service Is Deployed After an AI Application Is Imported
- Error Occurred When a Created AI Application Is Deployed as a Service
- Invalid Runtime Dependency Configured in an Imported Custom Image
- Garbled Characters Displayed in an AI Application Name Returned When AI Application Details Are Obtained Through an API
- The Model or Image Exceeded the Size Limit for AI Application Import
- 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?
-
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
- Videos
- User Guide (ExeML)
-
Workflows
- MLOps Overview
- What Is Workflow?
- How to Use a Workflow?
-
How to Develop a Workflow?
- Concepts
- Parameter Configuration
- Unified Storage
- Phase Type
- Branch Control
- Data Selection Among Multiple Inputs
- Creating a Workflow
- Debugging a Workflow
- Publishing a Workflow
- Advanced Capabilities
- FAQs
-
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)
-
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
-
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)
-
User Guide for Senior AI Engineers (To Be Offline)
- Operation Guide
- Data Management (Old Version to Be Terminated)
- Training Management (Old Version )
- Resource Pools (Old Version to Be Terminated)
- Custom Images
- Permissions Management
- Audit Logs
- Change History
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- Recurso 2: imagens predefinidas prontas para uso com configurações otimizadas e suporte a mecanismos de IA convencionais
- Recurso 3: JupyterLab, uma ferramenta de desenvolvimento e depuração interativa on-line
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Introdução às ferramentas de desenvolvimento
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Introdução às ferramentas de desenvolvimento
Atualizado em 2024-09-18 GMT+08:00
Este documento descreve as funções do notebook DevEnviron da nova versão.
O desenvolvimento de software é um processo de reduzir os custos do desenvolvedor e melhorar a experiência de desenvolvimento. No desenvolvimento de IA, o ModelArts dedica-se a melhorar a experiência de desenvolvimento de IA e simplificar o processo de desenvolvimento. O DevEnviron do ModelArts usa recursos da nuvem nativa e integra a cadeia de ferramentas de desenvolvimento para fornecer uma melhor experiência de IA na nuvem para desenvolvimento, exploração e ensino de IA.
Notebook de ModelArts para colaboração perfeita na nuvem e no local
- Plug-ins de JupyterLab na nuvem, IDE local e ModelArts para desenvolvimento e depuração remotos, adaptados às suas necessidades
- Ambiente de desenvolvimento em nuvem com recursos de computação de IA, armazenamento em nuvem e mecanismos de IA integrados
- Ambiente de tempo de execução personalizado salvo como uma imagem para treinamento e inferência
Recurso 1: desenvolvimento remoto, permitindo acesso remoto ao notebook a partir de um IDE local
O notebook da nova versão oferece desenvolvimento remoto. Depois de ativar o SSH remoto, você pode acessar remotamente o ambiente de desenvolvimento do notebook de ModelArts para depurar e executar código de um IDE local.
Devido a recursos locais limitados, os desenvolvedores que usam um IDE local executam e depuram o código normalmente em um servidor de CPU ou GPU compartilhado entre os membros da equipe. Construir e manter o servidor de CPU ou GPU é caro.
As instâncias de notebook de ModelArts estão prontas para uso com vários mecanismos e flavors internos para você selecionar. Você pode usar um ambiente de contêiner dedicado. Somente após configurações simples, você pode acessar remotamente o ambiente para executar e depurar o código do IDE local.
O notebook de ModelArts pode ser considerado como uma extensão de um ambiente de desenvolvimento local. As operações como leitura de dados, treinamento e salvamento de arquivos são as mesmas realizadas em um ambiente local.
O notebook de ModelArts permite que você use recursos na nuvem com os hábitos de codificação locais inalterados.
Um IDE local suporta o código do Visual Studio (VS), o PyCharm e o SSH.
Recurso 2: imagens predefinidas prontas para uso com configurações otimizadas e suporte a mecanismos de IA convencionais
Os mecanismos de IA e as versões pré-configuradas em cada imagem são fixos. Ao criar uma instância de notebook, especifique um mecanismo de IA e uma versão, incluindo o tipo de chip.
O DevEnviron do ModelArts fornece um grupo de imagens predefinidas, incluindo imagens PyTorch, TensorFlow e MindSpore. Você pode usar uma imagem predefinida para iniciar a instância do notebook. Após o desenvolvimento na instância, envie um trabalho de treinamento sem qualquer adaptação.
As versões de imagem predefinidas no ModelArts são determinadas com base no feedback do usuário e na estabilidade da versão. Se o seu desenvolvimento pode ser feito usando as versões predefinidas no ModelArts, por exemplo, o MindSpore 1.5, use imagens predefinidas. Essas imagens foram totalmente verificadas e têm muitos pacotes de instalação comumente usados embutidos. Elas estão prontas para usar, aliviando você de configurar o ambiente.
As imagens predefinidas no DevEnviron do ModelArts são:
- Pacotes predefinidos comuns: mecanismos de IA comuns, como PyTorch e MindSpore, baseados no Conda padrão, pacotes de software de análise de dados comuns, como Pandas e Numpy, e software de ferramenta comum, como CUDA e CUDNN, atendendo aos requisitos comuns de desenvolvimento de IA.
- Ambientes Conda predefinidos: um ambiente Conda e Conda Python básico (excluindo qualquer mecanismo de IA) são criados para cada imagem predefinida. A figura a seguir mostra o ambiente Conda para o MindSpore predefinido.
Selecione um ambiente Conda com base em se o mecanismo de IA é usado para depuração.
- Notebook: uma aplicação da Web que permite codificar na GUI e combinar o código, as equações matemáticas e o conteúdo visualizado em um documento.
- Plug-ins do JupyterLab: permitem mudança de flavor, compartilhamento de casos na Galeria de IA para comunicação e interrupção de instâncias para melhorar a experiência do usuário.
- SSH remoto: permite depurar remotamente uma instância de notebook a partir de um PC local.
- Depois que as imagens predefinidas no DevEnviron de ModelArts suportam o desenvolvimento, os trabalhos de treinamento podem ser executados no ModelArts.
- Para simplificar as operações, o notebook de ModelArts da nova versão não suporta alternância entre mecanismos de IA em uma instância de notebook.
- Os mecanismos de IA variam de acordo com as regiões. Para obter detalhes sobre os mecanismos de IA disponíveis em uma região, consulte os mecanismos de IA exibidos no console de gerenciamento.
Recurso 3: JupyterLab, uma ferramenta de desenvolvimento e depuração interativa on-line
O ModelArts integra JupyterLab de código aberto para desenvolvimento e depuração interativos on-line. Você pode usar o notebook no console de gerenciamento do ModelArts para compilar e depurar código e treinar modelos baseados no código, sem necessidade de instalação ou configuração de ambiente.
O JupyterLab é um ambiente de desenvolvimento interativo. É o produto da próxima geração do Jupyter Notebook. O JupyterLab permite compilar notebooks, operar terminais, editar texto Markdown, ativar interação e visualizar arquivos e imagens CSV.
Tópico principal: Conhecimento básico
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