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

Creating a Condition Phase to Control Branch Execution

Updated on 2024-10-29 GMT+08:00

Description

This phase is used for conditional branching in the execution of phases based on condition value comparison or metrics output by the preceding phase. The application scenarios are as follows:

You need to determine the subsequent process based on different input values. If you need to determine whether to retrain or register a model based on the model precision output by the training phase, you can use this phase to control the process.

Parameter Overview

You can use ConditionStep to create a condition phase. The following is an example of defining a ConditionStep.

Table 1 ConditionStep

Parameter

Description

Mandatory

Data Type

name

Name of a condition phase. The name contains a maximum of 64 characters, including only letters, digits, underscores (_), and hyphens (-). It must start with a letter and must be unique in a workflow.

Yes

str

conditions

List of conditions. The AND operation is used for multiple conditions.

Yes

Condition or condition list

if_then_steps

Steps to be executed if the calculation result of the condition expression is True.

No

str or str list

else_then_steps

Steps to be executed if the calculation result of the condition expression is False.

No

str or str list

title

Title for frontend-phase display.

No

str

description

Description of a condition phase.

No

str

depend_steps

Dependent phases.

No

Step or step list

Table 2 Condition

Parameter

Description

Mandatory

Data Type

condition_type

Condition type. The "==", ">", ">=", "in", "<", "<=", "!=", and "or" operators are supported.

Yes

ConditionTypeEnum

left

Left value of a condition expression.

Yes

int, float, str, bool, Placeholder, Sequence, Condition, MetricInfo

right

Right value of a condition expression

Yes

int, float, str, bool, Placeholder, Sequence, Condition, MetricInfo

Table 3 MetricInfo

Parameter

Description

Mandatory

Data Type

input_data

Metric input. Currently, only the output of JobStep is supported.

Yes

JobStep output

json_key

Key value corresponding to the metric information to be obtained

Yes

str

Description of the structure:

  • Condition object, which consists of the condition type, left value, and right value
    • The condition type is obtained from ConditionTypeEnum. The "==", ">", ">=", "in", "<", "<=", "!=", and "or" operators are supported. The following table describes the mapping.

      Enumeration

      Operator

      ConditionTypeEnum.EQ

      ==

      ConditionTypeEnum.GT

      >

      ConditionTypeEnum.GTE

      >=

      ConditionTypeEnum.IN

      in

      ConditionTypeEnum.LT

      <

      ConditionTypeEnum.LTE

      <=

      ConditionTypeEnum.NOT

      !=

      ConditionTypeEnum.OR

      or

    • The left and right values support the following types: integer, float, string, bool, placeholder, sequence, condition, and MetricInfo.
    • A condition phase supports a list of condition objects. The && operation is performed between multiple conditions.
  • if_then_steps and else_then_steps
    • if_then_steps indicates a list of phases that are ready for execution if conditions evaluate to true. In this case, steps in else_then_steps are skipped.
    • else_then_steps indicates a list of phases that are ready for execution if conditions evaluate to false. In this case, steps in if_then_steps are skipped.

Examples

Refer to simple or advanced examples as needed.

Simple Examples

  • Implemented using parameter configurations
    import modelarts.workflow as wf
    
    left_value = wf.Placeholder(name="left_value", placeholder_type=wf.PlaceholderType.BOOL, default=True)
    
    # Condition object
    condition = wf.steps.Condition(condition_type=wf.steps.ConditionTypeEnum.EQ, left=left_value, right=True) # Condition object, including the type, left value, and right value.
    
    # Condition phase
    condition_step = wf.steps.ConditionStep(
        name="condition_step_test", # Name of the condition phase. The name contains a maximum of 64 characters, including only letters, digits, underscores (_), and hyphens (-). It must start with a letter and must be unique in a workflow.
        conditions=condition, # Condition objects. The relationship between the conditions is &&.
        if_then_steps="job_step_1", # If conditions evaluate to true, job_step_1 is ready for execution, and job_step_2 is skipped.
        else_then_steps="job_step_2" # If conditions evaluate to false, job_step_2 is ready for execution, and job_step_1 is skipped.
    )
    
    # This phase is used only as an example. You need to supplement other fields as required.
    job_step_1 = wf.steps.JobStep(
        name="job_step_1",
        depend_steps=condition_step
    )
    
    # This phase is used only as an example. You need to supplement other fields as required.
    model_step_1 = wf.steps.ModelStep(
        name="model_step_1",
        depend_steps=job_step_1
    )
    
    # This phase is used only as an example. You need to supplement other fields as required.
    job_step_2 = wf.steps.JobStep(
        name="job_step_2",
        depend_steps=condition_step
    )
    
    # This phase is used only as an example. You need to supplement other fields as required.
    model_step_2 = wf.steps.ModelStep(
        name="model_step_2",
        depend_steps=job_step_2
    )
    
    workflow = wf.Workflow(
        name="condition-demo",
        desc="this is a demo workflow",
        steps=[condition_step, job_step_1, job_step_2, model_step_1, model_step_2]
    )
    
    NOTE:

    Scenario description: job_step_1 and job_step_2 indicate two training phases that depend on condition_step. condition_step parameters determine the subsequent phase execution.

    Execution analysis:
    • If the default value of left_value is True, the calculation result of the condition logical expression is True. Then, job_step_1 is executed, job_step_2 is skipped, and all phases contained in the branches that use job_step_2 as the unique root node are skipped. That is, model_step_2 is skipped. Therefore, condition_step, job_step_1, and model_step_1 are executed.
    • If left_value is set to False, the calculation result of the condition logical expression is False. Then, job_step_2 is executed, job_step_1 is skipped, and all phases contained in the branches that use job_step_1 as the unique root node are skipped. That is, model_step_1 is skipped, and condition_step, job_step_2, and model_step_2 are executed.
  • Implemented by obtaining the metric information output by JobStep
    from modelarts import workflow as wf
    
    # Create an OutputStorage object to centrally manage training output directories.
    storage = wf.data.Storage(name="storage_name", title="title_info", with_execution_id=True, create_dir=True, description="description_info")  # The name field is mandatory, and the title and description fields are optional.
    
    # Define the input OBS object.
    obs_data = wf.data.OBSPlaceholder(name="obs_placeholder_name", object_type="directory")
    
    # Use JobStep to define a training phase, and use OBS to store the output.
    job_step = wf.steps.JobStep(
        name="training_job", # Name of a training phase. The name contains a maximum of 64 characters, including only letters, digits, underscores (_), and hyphens (-). It must start with a letter and must be unique in a workflow.
        title="Image classification training",  # Title, which defaults to the value of name.
        algorithm=wf.AIGalleryAlgorithm(
            subscription_id="subscription_id",  # Subscription ID of the subscribed algorithm
            item_version_id="item_version_id",  # Algorithm version ID. You can also enter the version number instead.
            parameters=[]
    
        ), # Algorithm used for training. An algorithm subscribed to in AI Gallery is used in this example. If the value of an algorithm hyperparameter does not need to be changed, you do not need to configure the hyperparameter in parameters. Hyperparameter values will be automatically filled.
        inputs=wf.steps.JobInput(name="data_url", data=obs_data),
        outputs=[
            wf.steps.JobOutput(name="train_url",obs_config=wf.data.OBSOutputConfig(obs_path=storage.join("directory_path"))),
            wf.steps.JobOutput(name="metrics", metrics_config=wf.data.MetricsConfig(metric_files=storage.join("directory_path/metrics.json", create_dir=False))) # Metric output path. Metric information is automatically output by the job script based on the specified data format. (In the example, the metric information needs to be output to the metrics.json file in the training output directory.)
        ],
        spec=wf.steps.JobSpec(
            resource=wf.steps.JobResource(
                flavor=wf.Placeholder(name="train_flavor", placeholder_type=wf.PlaceholderType.JSON, description="Training flavor")
            )
         )  # Training flavors
    )
    
    # Define a condition object.
    condition_lt = wf.steps.Condition(
        condition_type=wf.steps.ConditionTypeEnum.LT,
        left=wf.steps.MetricInfo(job_step.outputs["metrics"].as_input(), "accuracy"),
        right=0.5
    )
    
    condition_step = wf.steps.ConditionStep(
        name="condition_step_test", # Name of the condition phase. The name contains a maximum of 64 characters, including only letters, digits, underscores (_), and hyphens (-). It must start with a letter and must be unique in a workflow.
        conditions=condition_lt, # Condition objects. The relationship between the conditions is &&.
        if_then_steps="training_job_retrain", # If conditions evaluate to true, training_job_retrain is ready for execution, and model_registration is skipped.
        else_then_steps="model_registration", # If conditions evaluate to false, model_registration is ready for execution, and training_job_retrain is skipped.
        depend_steps=job_step
    )
    
    # Use JobStep to define a training phase, and use OBS to store the output.
    job_step_retrain = wf.steps.JobStep(
        name="training_job_retrain",  # Name of a training phase. The name contains a maximum of 64 characters, including only letters, digits, underscores (_), and hyphens (-). It must start with a letter and must be unique in a workflow.
        title="Image classification retraining",  # Title, which defaults to the value of name.
        algorithm=wf.AIGalleryAlgorithm(
            subscription_id="subscription_id",  # Subscription ID of the subscribed algorithm
            item_version_id="item_version_id",  # Algorithm version ID. You can also enter the version number instead.
            parameters=[]
    
        ), # Algorithm used for training. An algorithm subscribed to in AI Gallery is used in this example. If the value of an algorithm hyperparameter does not need to be changed, you do not need to configure the hyperparameter in parameters. Hyperparameter values will be automatically filled.
        inputs=wf.steps.JobInput(name="data_url", data=obs_data),
        outputs=[
            wf.steps.JobOutput(name="train_url",obs_config=wf.data.OBSOutputConfig(obs_path=storage.join("directory_path_retrain"))),
            wf.steps.JobOutput(name="metrics", metrics_config=wf.data.MetricsConfig(metric_files=storage.join("directory_path_retrain/metrics.json", create_dir=False))) # Metric output path. Metric information is automatically output by the job script based on the specified data format. (In the example, the metric information needs to be output to the metrics.json file in the training output directory.)
        ],
        spec=wf.steps.JobSpec(
            resource=wf.steps.JobResource(
                flavor=wf.Placeholder(name="train_flavor_retrain", placeholder_type=wf.PlaceholderType.JSON, description="Training flavor")
            )
         ),  # Training flavors
        depend_steps=condition_step
    )
    
    # Define model name parameters.
    model_name = wf.Placeholder(name="placeholder_name", placeholder_type=wf.PlaceholderType.STR)
    
    model_step = wf.steps.ModelStep(
        name="model_registration", # Name of the model registration phase. The name contains a maximum of 64 characters, including only letters, digits, underscores (_), and hyphens (-). It must start with a letter and must be unique in a workflow.
        title="Model Registration",  # Title
        inputs=wf.steps.ModelInput(name='model_input', data=job_step.outputs["train_url"].as_input()),  # job_step output is used as the input.
        outputs=wf.steps.ModelOutput(name='model_output', model_config=wf.steps.ModelConfig(model_name=model_name, model_type="TensorFlow")),  # ModelStep outputs
        depend_steps=condition_step,
    )
    
    workflow = wf.Workflow(
        name="condition-demo",
        desc="this is a demo workflow",
        steps=[job_step, condition_step, job_step_retrain, model_step],
        storages=storage
    )

    In this example, ConditionStep obtains the accuracy output by job_step and compares it with the preset value to determine whether to retrain or register the model. When the accuracy output by job_step is less than the threshold 0.5, the calculation result of condition_lt is True. In this case, job_step_retrain runs and model_step skips. Otherwise, job_step_retrain skips and model_step runs.

    NOTE:

    For details about the format requirements of the metric file generated by job_step, see Creating a Training Job Phase. In the condition phase, only the metric data whose type is float can be used as the input.

    The following is an example of the metrics.json file:

    [
        {
            "key": "loss",
            "title": "loss",
            "type": "float",
            "data": {
                "value": 1.2
            }
        },
        {
            "key": "accuracy",
            "title": "accuracy",
            "type": "float",
            "data": {
                "value": 0.8
            }
        }   
    ]

Advanced Examples

import modelarts.workflow as wf

left_value = wf.Placeholder(name="left_value", placeholder_type=wf.PlaceholderType.BOOL, default=True)
condition1 = wf.steps.Condition(condition_type=wf.steps.ConditionTypeEnum.EQ, left=left_value, right=True)

internal_condition_1 = wf.steps.Condition(condition_type=wf.steps.ConditionTypeEnum.GT, left=10, right=9)
internal_condition_2 = wf.steps.Condition(condition_type=wf.steps.ConditionTypeEnum.LT, left=10, right=9)

# The result of condition2 is internal_condition_1 || internal_condition_2.
condition2 = wf.steps.Condition(condition_type=wf.steps.ConditionTypeEnum.OR, left=internal_condition_1, right=internal_condition_2)

condition_step = wf.steps.ConditionStep(
    name="condition_step_test", # Name of the condition phase. The name contains a maximum of 64 characters, including only letters, digits, underscores (_), and hyphens (-). It must start with a letter and must be unique in a workflow.
    conditions=[condition1, condition2], # Condition objects. The relationship between the conditions is &&.
    if_then_steps=["job_step_1"], # If conditions evaluate to true, job_step_1 is ready for execution, and job_step_2 is skipped.
    else_then_steps=["job_step_2"] # If conditions evaluate to false, job_step_2 is ready for execution, and job_step_1 is skipped.
)

# This phase is used only as an example. You need to supplement other fields as required.
job_step_1 = wf.steps.JobStep(
    name="job_step_1",
    depend_steps=condition_step
)

# This phase is used only as an example. You need to supplement other fields as required.
job_step_2 = wf.steps.JobStep(
    name="job_step_2",
    depend_steps=condition_step
)

workflow = wf.Workflow(
    name="condition-demo",
    desc="this is a demo workflow",
    steps=[condition_step, job_step_1, job_step_2],
)

ConditionStep supports nested condition phases. You can flexibly design tit based on different scenarios.

NOTE:

The condition phase can only support two branches, which is very limiting. You can use the new branch function to replace the ConditionStep capability without creating new phases. For details, see Configuring Phase Parameters to Control Branch Execution.

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

Feedback

Feedback

Feedback

0/500

Selected Content

Submit selected content with the feedback