AWS created the Amazon Batch service to provide the best solution for professional developers, engineers, scientists, and scientists in order to efficiently run thousands of batch computing jobs. This dynamically allocates the best type and quantity of computing resources based on the volume of batch jobs and their specific resource requirements. AWS Batch does not require you to install or control batch compute software, nor server clusters, in order to run jobs. This allows you to concentrate on solving problems and analysing results. But what else is it capable of and how does it work?
You can find the answer to this question by following the blog. We will be covering the basics of AWS Batch Processing, including its features and cases, as well as how to get started.
What is Amazon Batch?
AWS Batch can be used to run batch computing workloads on AWS Cloud. Batch computing is used by engineers, scientists, and developers to access large numbers of computing resources. Amazon Batch also eliminates the tedious work of configuring and managing infrastructure, as well as the manual labor associated with traditional batch computing software. This service can efficiently provide resources for jobs that are submitted to it.
Capacity constraints eliminated
lowering compute costs
delivering results quickly.
Amazon Batch Components:
AWS Batch makes it easy to run batch jobs across multiple Availability Zones within a Region. AWS Batch allows you to create compute environments and associate them to a job queue. Next, you can create job definitions which specify which Docker containers are needed to run your jobs. Container images can be placed in or pulled from container registry, which may exist within or outside of AWS infrastructure.
1. Jobs
This is a unit of work you submit to AWS Batch. This includes a name and runs on AWS Fargate or Amazon EC2 resources within your computing environment. It uses parameters you specify in a job description. Jobs can also refer to other jobs by their name or ID. It can also be dependent on other jobs being completed.
2. Definitions of a Job
A job description describes the job’s running process. This is basically a blueprint of the resources that your job requires. This means that you can provide access to AWS resources to your job by adding an IAM role to your job. The job definition can also manage container properties, environment variables and mount points for persistent storage.
3. Job Queues
When you submit an AWS Batch job, it is sent to a job queue. The job stays there until it is scheduled onto a computing environment. You can also link multiple compute environments to a job queue, and assign priority values to these environments over job queues.
4. Compute Environment
A computing environment is a collection of managed and unmanaged compute resources that are used to run jobs. Managed to compute environments are useful in defining desired types of compute at different levels of detail. You can also create compute environments that use a particular type of EC2 instance. You can also specify the minimum, desired, or maximum number of vCPUs that the environment should have. You can also set the price for a Spot Instance.
What are the main features of AWS Batch
AWS Batch allows for you to package your code, identify their dependencies, then submit your batch job via the AWS Management Console, SDKs, CLIs or SDKs. AWS Batch has many features that make it easy to run these processes. Let’s look at them.
1. Dynamic resource provisioning and scaling
Fargate Spot or Fargate Spot with batch are all you need to set up. This creates a complete queue, scheduler, compute architecture, and manages it all automatically.
AWS Batch offers Managed Compute Environments, which dynamically scale compute resources based on the volume and resource requirements of submitted jobs.
Finally, you can now provision and manage your own compute resources within AWS Batch Unmanaged Comput Environments. This is a great option if you need to use different configurations than those offered by AWS Batch Managed Compute Environments.
2. AWS Batch with Fargate
AWS Batch with Fargate resources allows you to have a serverless architecture that runs your batch jobs. This ensures that every job gets the exact amount CPU and memory it needs. Fargate provides an additional layer of separation for Batch users. This means that AMI is not required to be managed or patched.
You don’t have to worry about maintaining two separate services while submitting Fargate-compatible jobs for Batch if you have workloads on EC2 and others on Fargate.
3. Support for HPC workloads that are tightly coupled
AWS Batch supports multi-node parallel jobs, which allow you to run single jobs that span multiple EC2 instances.
This feature allows you to use AWS Batch to efficiently run workloads such as large-scale, tightly-coupled High-Performance Computing applications (HPC) or distributed GPU model training.
AWS Batch supports Elastic Fabric Adapter as well. This is a network interface that allows you to run applications that require high levels of inter-node communications on AWS.
4. Simple job dependency modeling and job definitions
AWS Batch allows you to specify resource requirements such as vCPU and memory. AWS Identity and Access Management roles (IAM) roles, volume mounting points, container properties and environmen are all available.