Migrate to Batch from Cloud Life Sciences

This page describes how to migrate to Batch from Cloud Life Sciences.

Google Cloud announced that Cloud Life Sciences was deprecated on July 17, 2023 and shut down on July 8, 2025. However, Batch is generally available and is a comprehensive successor that supports all use cases for Cloud Life Sciences.

Learn more about Batch , Cloud Life Sciences , and product launch stages .

Cloud Life Sciences versus Batch

Migrating from Cloud Life Sciences to Batch primarily involves understanding how you can use Batch for the workloads that you currently execute by running Cloud Life Sciences pipelines.

To understand how you can you can execute your Cloud Life Sciences workloads on Batch, see all of the following the sections:

• Overview
• Basic operations
• IAM roles and permissions
• Corresponding features

Overview

A Cloud Life Sciences pipeline describes a sequence of actions (containers) to execute and the environment to execute the containers in.

A Batch job describes an array of one or more tasks and the environment to execute those tasks in. You define the workload for a job as one sequence of one or more runnables (containers and/or scripts) to be executed. Each task for a job represents one execution of its sequence of runnables.

Cloud Life Sciences pipelines can be expressed as single-task Batch jobs.

For example, the following samples describe a simple Cloud Life Sciences pipeline and its equivalent Batch job:

  
 { 
  
 "actions" 
 : 
  
 [ 
  
 { 
  
 "imageUri" 
 : 
  
 "bash" 
 , 
  
 "commands" 
 : 
  
 [ 
  
 "-c" 
 , 
  
 "echo Hello, world!" 
  
 ] 
  
 } 
  
 ] 
  
 } 
  

  
 { 
  
 "taskGroups" 
  
 : 
  
 [{ 
  
 "taskSpec" 
  
 : 
  
 { 
  
 "runnables" 
  
 : 
  
 [{ 
  
 "container" 
 :{ 
  
 "imageUri" 
 : 
  
 "bash" 
 , 
  
 "commands" 
 : 
  
 [ 
  
 "-c" 
 , 
  
 "echo Hello, world!" 
  
 ] 
  
 } 
  
 }] 
  
 } 
  
 }] 
  
 } 
  

Multiple-task Batch jobs are similar to copied Cloud Life Sciences pipelines.

Unlike Cloud Life Sciences, Batch allows you to automatically schedule multiple executions of your workload. You indicate the number of times that you want to execute the sequence of runnables for a job by defining the number of tasks. When a job has multiple tasks, you specify how you want each execution to vary by referencing the task's index in your runnables. Additionally, you can configure the relative schedules for a job's tasks—for example, whether to allow multiple tasks to run in parallel or to require tasks to run in sequential order and one at a time. Batch manages the scheduling the job's tasks: when a task finishes, the job automatically starts the next task, if any.

For example, see the following Batch job. This example job has 100 tasks that execute on 10 Compute Engine virtual machine (VM) instances, so there are approximately 10 tasks running in parallel at any given time. Each task in this example job only executes one runnable: a script that prints a message and the task's index, which is defined by the BATCH_TASK_INDEX predefined environment variable.

  { 
  
 "taskGroups" 
  
 : 
  
 [{ 
  
 "taskSpec" 
  
 : 
  
 { 
  
 "runnables" 
  
 : 
  
 [{ 
  
 "script" 
 :{ 
  
 "text" 
 : 
  
 "echo Hello world! This is task ${BATCH_TASK_INDEX}." 
  
 } 
  
 }] 
  
 }, 
  
 "taskCount" 
 : 
  
 100 
 , 
  
 "parallelism" 
 : 
  
 10 
  
 }] 
 } 
 

Workflows that involve the creation and monitoring of multiple similar Cloud Life Sciences pipelines can sometimes be simplified by taking advantage of Batch's built-in scheduling.

Basic operations

This section describes basic operations in Cloud Life Sciences versus Batch.

The following table summarizes the basic operations options for Cloud Life Sciences and Batch.

The basic operations for Cloud Life Sciences and Batch have a few key differences.

Firstly, long-running operation resources do not play the same role in Batch that they do in Cloud Life Sciences. Long-running operation resources (LROs) in Cloud Life Sciences are the primary resource used to list and view your pipelines. But, long-running operation resources in Batch and other Google Cloud APIs are only used to monitor the status of a request that takes a long time to complete. Specifically, in Batch, the only request that returns a long-running operation resource is deleting a job. For more information about long-running operation resources for Batch, see the Batch API reference documentation for the projects.locations.operations REST resource . Instead of using long-running operation resources, Batch has job resources that you view and delete for your workloads.

Secondly, viewing the details of a workload in Batch involves different operations than Cloud Life Sciences. You can view a job to see both it's details and status. But, each of a job's tasks also has its own details and status that you can see by viewing a list of a job's tasks and viewing the details of a task.

To help you further understand the basic operations for Cloud Life Sciences versus Batch, the following sections provide examples of Google Cloud CLI commands and API requests paths for some of these basic operations.

Example gcloud CLI commands

For gcloud CLI, Cloud Life Sciences commands begin with gcloud beta lifesciences and Batch commands begin with gcloud batch . For example, see the following gcloud CLI commands.

• Cloud Life Sciences example gcloud CLI commands:

  • Run a pipeline:

      gcloud beta lifesciences pipelines run \ 
     --project= PROJECT_ID 
    \ 
     --regions= LOCATION 
    \ 
     --pipeline-file= JSON_CONFIGURATION_FILE 
     
     
    

  • Get details for a long-running operation:

      gcloud beta lifesciences operations describe OPERATION_ID 
     
     
    

  Replace the following:

  • PROJECT_ID : the project ID of your project.
  • LOCATION : the location for the pipeline.
  • JSON_CONFIGURATION_FILE : the JSON configuration file for the pipeline.
  • OPERATION_ID : the identifier for the long-running operation, which was returned by the request to run the pipeline.

• Batch example gcloud CLI commands:

  • Create and run a job:

      gcloud batch jobs submit JOB_NAME 
    \ 
     --project= PROJECT_ID 
    \ 
     --location= LOCATION 
    \ 
     --config= JSON_CONFIGURATION_FILE 
     
     
    

  • View the details of a job:

      gcloud batch jobs describe JOB_NAME 
    \ 
     --project= PROJECT_ID 
    \ 
     --location= LOCATION 
    \ 
     
    

  • View a job's list of tasks:

      ​​gcloud batch tasks list \ 
     --project= PROJECT_ID 
    \ 
     --location= LOCATION 
    \ 
     --job= JOB_NAME 
     
     
    

  • View the details of a task:

      gcloud batch tasks describe TASK_INDEX 
    \ 
     --project= PROJECT_ID 
    \ 
     --location= LOCATION 
    \ 
     --job= JOB_NAME 
    \ 
     --task_group= TASK_GROUP 
     
     
    

  • Delete (and cancel) a job:

      gcloud batch jobs delete JOB_NAME 
    \ 
     --project= PROJECT_ID 
    \ 
     --location= LOCATION 
     
     
    

  Replace the following:

  • JOB_NAME : the name of the job.
  • PROJECT_ID : the project ID of your project.
  • LOCATION : the location of the job.
  • JSON_CONFIGURATION_FILE : the path for a JSON file with the job's configuration details.
  • TASK_INDEX : the index of the task that you want to view the details of. In a task group, the task index starts at 0 for the first task and increases by 1 with each additional task. For example, a task group that contains four tasks has the indexes 0 , 1 , 2 , and 3 .
  • TASK_GROUP_NAME : the name of the task group that you want to view the details of. The value must be set to group0 .

Example API request paths

For APIs, Cloud Life Sciences uses lifesciences.googleapis.com request paths and Batch uses batch.googleapis.com request paths. For example, see the following API request paths. Unlike Cloud Life Sciences, Batch does not have an RPC API; it only has a REST API.

• Cloud Life Sciences example API request paths:

  • Run a pipeline:

      POST 
      
     h 
     tt 
     ps 
     : 
     //lifesciences.googleapis.com/v2beta/projects/ PROJECT_ID 
    /locations/ LOCATION 
    /pipelines:run 
     
    

  • Get details for a long-running operation:

      GET 
      
     h 
     tt 
     ps 
     : 
     //lifesciences.googleapis.com/v2beta/projects/ PROJECT_ID 
    /locations/ LOCATION 
    /operations/ OPERATION_ID 
     
     
    

  Replace the following:

  • PROJECT_ID : the project ID of your project.
  • LOCATION : the location for the pipeline.
  • OPERATION_ID : the identifier for the long-running operation, which was returned by the request to run the pipeline.

• Batch example API request paths:

  • Create and run a job:

      POST 
      
     h 
     tt 
     ps 
     : 
     //batch.googleapis.com/v1/projects/ PROJECT_ID 
    /locations/ LOCATION 
    /jobs?job_id= JOB_NAME 
     
     
    

  • View the details of a job:

      GET 
      
     h 
     tt 
     ps 
     : 
     //batch.googleapis.com/v1/projects/ PROJECT_ID 
    /locations/ LOCATION 
    /jobs/ JOB_NAME 
     
     
    

  • View a job's list of tasks:

      GET 
      
     h 
     tt 
     ps 
     : 
     //batch.googleapis.com/v1/projects/ PROJECT_ID 
    /locations/ LOCATION 
    /jobs/ JOB_NAME 
    /taskGroups/ TASK_GROUP 
    /tasks 
     
    

  • Delete a job

      DELETE 
      
     h 
     tt 
     ps 
     : 
     //batch.googleapis.com/v1/projects/ PROJECT_ID 
    /locations/ LOCATION 
    /jobs/ JOB_NAME 
     
     
    

  • Check the status of job deletion request:

      GET 
      
     h 
     tt 
     ps 
     : 
     //batch.googleapis.com/v1/projects/ PROJECT_ID 
    /locations/ LOCATION 
    /operations/ OPERATION_ID 
     
     
    

  Replace the following:

  • PROJECT_ID : the project ID of your project.
  • LOCATION : the location of the job.
  • JOB_NAME : the name of the job.
  • TASK_GROUP_NAME : the name of the task group that you want to view the details of. The value must be set to group0 .
  • OPERATION_ID : the identifier for the long-running operation, which was returned by the request to delete the job.

IAM roles and permissions

This section summarizes the differences in Identity and Access Management roles and permissions for Cloud Life Sciences and Batch. For more information about any roles and their permissions, see the IAM basic and predefined roles reference .

The following table describes the predefined roles and their permissions that are required for users of Cloud Life Sciences.

The following table describes some of the predefined roles and their permissions for Batch. Unlike Cloud Life Sciences, Batch requires you to grant permissions to users and the service account for a job. For more information about the IAM requirements, see Prerequisites for Batch .

Corresponding features

The following table describes the features for Cloud Life Sciences, the equivalent features for Batch, and details about the differences between them.

Each feature is represented by a description and its JSON syntax. You can use JSON syntax when accessing Batch through the API or when specifying a JSON configuration file through the Google Cloud CLI. However, note that you can also use Batch features through other methods—such as through Google Cloud console fields, flags gcloud CLI, and client libraries—which are described in the Batch documentation .

For more information about each feature and its JSON syntax, see the following:

• For Cloud Life Sciences, see the Cloud Life Sciences API reference documentation for the projects.locations.pipelines REST resource .

• For Batch, see the Batch API reference documentation for the projects.locations.jobs REST resource .

Workflow services

If you use a workflow service with Cloud Life Sciences, then your migration process also involves configuring a workflow service to work with Batch. This section summarizes the workflow services that you can use with Batch.

Batch supports Workflows , which is a workflow service from Google Cloud. If you want to use Workflows with Batch, see Run a Batch job using Workflows . Otherwise, the following table describes other workflows services which you might use for Cloud Life Sciences that you can also use with Batch. This table lists the key differences for using each workflow service with Batch instead of Cloud Life Sciences and details on where to learn more about using each service with Batch.

What's next

• To configure Batch for new users and projects, see Get started .
• To learn how to execute workloads using Batch, see Create a job .