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Workflow Runs in DNAWave

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Overview

DNAWave allows users to execute workflow runs on cloud platforms, providing scalable and flexible workflow execution. Workflows can be run on Amazon Web Services (AWS) using AWS Batch or on Google Cloud Platform (GCP) utilizing Google Batch API. Users can choose the appropriate platform based on their infrastructure preferences.

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Running a Workflow on AWS

To execute a workflow on AWS, follow these steps:
  1. Navigate to the Workflow Runs Page.
  2. Select AWS as the execution platform.
  3. Fill in the required fields:
    • Name: A descriptive name for the workflow run.
    • Description: An optional description.
    • Workflow: Select a predefined workflow.
    • Datasets: Choose datasets required for execution.
    • Execution Type: Immediate or Scheduled.
    • Compute Resources:
      • Priority level
      • Storage capacity
      • Retention mode (RETAIN or REMOVE)
      • Storage type (STATIC or DYNAMIC)
  4. Click Run Workflow to start execution.
  5. Monitor progress and logs in the Workflow Runs Dashboard.

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Running a Workflow on GCP

To execute a workflow on GCP, follow these steps:
  1. Navigate to the Workflow Runs Page.
  2. Select GCP as the execution platform.
  3. Fill in the required fields:
    • Name and Description.
    • Workflow and associated Datasets.
    • Execution Type (Immediate or Scheduled).
    • Compute Resources:
      • Task count and parallelism.
      • Region and machine type selection.
      • CPU cores and memory allocation.
  4. Click Run Workflow to initiate execution.
  5. Track the workflow status and logs in the Workflow Runs Dashboard.

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Monitoring Workflow Runs

Users can monitor workflow execution status in the Workflow Runs Dashboard, which provides:
  • Real-time status updates.
  • Execution logs and error tracking.
  • Workflow resource utilization details.

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Summary

DNAWave provides an intuitive interface for executing workflow runs on AWS and GCP, allowing users to choose the most suitable cloud infrastructure for their needs. Whether executing immediate or scheduled runs, the system ensures scalability and reliability in genomic workflows.