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.
- 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
- Docker Private Registry support
- Azure Container Registry
- Any internet accessible Docker container registry
- Self-hosted private registry backed to Azure Storage with automated private registry instance creation on compute nodes
- Standalone Remote Filesystem Provisioning with integration to auto-link these filesystems to compute nodes with support for
- Automatic shared data volume support
- Remote Filesystems as provisioned by Batch Shipyard
- Azure File Docker Volume Driver installation and share setup for SMB/CIFS backed to Azure Storage
- 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 Azure Batch task dependencies allowing complex processing pipelines and DAGs with Docker containers
- 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:
- 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
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.
Installation is typically an easy two-step process. The CLI is also available as a Docker image: alfpark/batch-shipyard:cli-latest. Please see the installation guide for more information regarding installation and requirements.
Please refer to the Batch Shipyard Guide for a complete primer on concepts, usage and a quickstart guide.
Please visit the Batch Shipyard Recipes for various sample Docker workloads using Azure Batch and Batch Shipyard after you have completed the introductory sections of the Batch Shipyard Guide.
Batch Shipyard is currently compatible with supported Marketplace Linux VMs and Linux custom images supported by Azure Batch.
See the CHANGELOG.md file.
Please see this project's Code of Conduct and Contributing guidelines.