Batch Shipyard Introduction
This page is the starting point for those unfamiliar with any of the Azure Batch service, Docker, or Singularity.
Azure Batch is a scalable job scheduling system leveraging the Microsoft Azure Cloud. Users can specify what their jobs are (e.g., executing a binary to process text data), when to run them, where to run them, and on what VM resources they are run on. The Azure Batch service takes care of the rest including: compute resource provisioning, task scheduling, automatic task recovery and retry on failure, automatic scaling of resources if specified, and many other complexities that exist at cloud-scale. There is no extra cost to use Azure Batch - Azure Batch is provided as a free value-added service on top of compute resources in Azure. Costs are incurred only for compute resources consumed and any assoicated datacenter data egress and storage costs, i.e., the same cost as if consuming Virtual Machines or Cloud Services directly. Additionally, Azure Batch provides the ability to run on Low Priority VMs which can dramatically lower costs for savings up to 80%!
Azure Batch can handle workloads on any point of the parallel and distributed processing spectrum, from embarassingly parallel workloads all the way to tightly-coupled message passing codes such as MPI jobs on Infiniband/RDMA.
Azure Batch has well-defined hierarchies of objects exposed to the user to schedule work on machines.
Azure Subscription --> Batch Account --> Compute Pool --> Compute Nodes
Batch accounts are provisioned from a valid Azure Subscription. With a Batch account, users can provision Compute Pools of varying type such as Windows or Linux. Pools are comprised of a target number of compute nodes which are identical VMs provisioned from the Azure cloud. Multiple Batch accounts can be provisioned per Azure Subscription, and multiple compute pools can be provisioned per Batch account. Please refer to this page for default service limits, including separate core quota limits that only apply to the Batch service.
Job --> Tasks
Task --> Subtasks (or tasklets)
Jobs are run on compute pools for which tasks are scheduled on to compute nodes, either individually or as part of a group within a multi-instance task (for which there are subtasks). Jobs can also be defined as part of a Job Schedule in which users can specify times for when a job should run or as part of any recurring schedule.
Files required as part of a task or generated as a side-effect of a task can be referenced using a compute job hierarchy or a compute node hierarchy (if the absolute file location is known). Files existing on compute nodes can be transferred to any accessible endpoint, including Azure Storage. Files may also be fetched from live compute nodes (i.e., nodes that have not yet been deleted).
The Docker ecosystem is a comprehensive suite of userland tooling and implementation of operating system-level virtualization where with the aid of the underlying OS kernel can enforce isolation between groups of running software. In contrast to hypervisor-based virtual machines, Docker is lightweight, leveraging a shared kernel for fast and consistent application deployments. More information about Docker can be found here.
- A Docker image contains all of the necessary software to run an application and exists only in read-only form. Can be thought of as a template for a container.
- Docker containers are instances of an image, with everything needed for the containerized application to run.
- Registries contain repositories of Docker images which can be later retrieved or updated.
Further in-depth treatment of Docker can be found here.
Singularity is an implementation of operating system-level virtualization similar to Docker, but is tailored for execution on shared HPC infrastructure typically found on-premises and supercomputing installations. Singularity provides environment encapsulation and does not require root privileges to execute containers. However, Singularity allows containers to utilize available hardware in the host systems such as GPUs and specialized networking within the privilege scope of the executing user. The ability to execute Singularity containers in Azure exactly as if they were executing against a cluster system in an organization provides true mobility of compute across on-premises to the cloud without having to modify any container image settings.
Containers and Azure Batch
By leveraging either the Docker and/or Singularity ecosystems including their respective packaging and tooling, users can spend less time hassling with the underlying infrastructure, VM application state consistency, potential dependency interaction side effects and spend more time on things that actually matter for their batch workloads: the job and task results themselves. And with Azure Batch, you can automatically scale your workload and only pay for the compute resources you use. Batch Shipyard provides a way to combine your containers with the power of scale-out cloud computing with ease!
Batch Shipyard Installation
Continue on to Batch Shipyard Installation.