Batch Shipyard

Batch Shipyard is a tool to help provision and execute batch processing and HPC Docker workloads on Azure Batch compute pools. No experience with the Azure Batch SDK is needed; run your Dockerized tasks with easy-to-understand configuration files!

Additionally, Batch Shipyard provides the ability to provision and manage entire standalone remote file systems (storage clusters) in Azure, independent of any integrated Azure Batch functionality.

Batch Shipyard is now integrated directly into Azure Cloud Shell and you can execute any Batch Shipyard workload using your web browser or the Microsoft Azure Android and iOS app.

Major Features

• Automated Docker Host Engine installation tuned for Azure Batch compute nodes
• Automated deployment of required Docker images to compute nodes
• Accelerated Docker image deployment at scale to compute pools consisting of a large number of VMs via private peer-to-peer distribution of Docker images among the compute nodes
• Comprehensive data movement support: move data easily between locally accessible storage systems, remote filesystems, Azure Blob or File Storage, and compute nodes
• Support for Docker Registries including Azure Container Registry and other Internet-accessible public and private registries
• Standalone Remote Filesystem Provisioning with integration to auto-link these filesystems to compute nodes with support for
  • NFS
  • GlusterFS distributed network file system
• Automatic shared data volume support
  • Remote Filesystems as provisioned by Batch Shipyard
  • Azure File via SMB
  • GlusterFS provisioned directly on compute nodes
• Seamless integration with Azure Batch job, task and file concepts along with full pass-through of the Azure Batch API to containers executed on compute nodes
• Support for Low Priority Compute Nodes
• Support for pool autoscale and autopool to dynamically scale and control computing resources on-demand
• Support for Task Factories with the ability to generate tasks based on parametric (parameter) sweeps, randomized input, file enumeration, replication, and custom Python code-based generators
• Support for deploying Batch compute nodes into a specified Virtual Network
• Transparent support for GPU-accelerated Docker applications on Azure N-Series VM instances
• Support for multi-instance tasks to accommodate Dockerized MPI and multi-node cluster applications on compute pools with automatic job completion and Docker task termination
• Transparent assist for running Docker containers utilizing Infiniband/RDMA for MPI on HPC low-latency Azure VM instances:
  • A-Series: STANDARD_A8, STANDARD_A9
  • H-Series: STANDARD_H16R, STANDARD_H16MR
  • N-Series: STANDARD_NC24R
• Support for Azure Batch task dependencies allowing complex processing pipelines and DAGs with Docker containers
• Support for job schedules and recurrences for automatic execution of tasks at set intervals
• Support for live job and job schedule migration between pools
• Automatic setup of SSH users to all nodes in the compute pool and optional tunneling to Docker Hosts on compute nodes
• Support for credential management through Azure KeyVault
• Support for execution on an Azure Function App environment
• Support for custom host images

Installation

Azure Cloud Shell

Batch Shipyard is now integrated into Azure Cloud Shell with no installation required. Simply request a Cloud Shell session and type shipyard to invoke the CLI.

Local Installation

Installation is typically an easy two-step process. The CLI is also available as a Docker image: alfpark/batch-shipyard:latest-cli. Please see the installation guide for more information regarding installation and requirements.

Documentation and Recipes

Please refer to the Batch Shipyard Documentation on Read the Docs.

Visit the Batch Shipyard Recipes section for various sample Docker workloads using Azure Batch and Batch Shipyard.

Batch Shipyard Compute Node OS Support

Batch Shipyard is currently compatible with supported Marketplace Linux VMs and Linux custom images supported by Azure Batch.

Change Log

Please see the Change Log for project history.

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Please see this project's Code of Conduct and Contributing guidelines.