Have you ever had computing jobs that are to be done periodically or on-demand but you do not want to install any third-party software or you do not have enough CPU power to do that. If that is the case, then AWS Batch would be the perfect solution for you. In this blog, you will learn what AWS Batch is, along with its features.
AWS Batch एक अमेज़न वेब सर्विस है इस पोस्ट में हम इसके बारे में अच्छे से जानेंगे What is AWS Batch in Hindi की AWS Batch किसे कहते है।
What is AWS Batch in Hindi?
AWS Batch helps you to run batch computing workloads on the AWS Cloud. Batch computing is a common way for developers, scientists, and engineers to access large amounts of compute resources. AWS Batch removes the undifferentiated heavy lifting of configuring and managing the required infrastructure, similar to traditional batch computing software. This service can efficiently provision resources in response to jobs submitted in order to eliminate capacity constraints, reduce compute costs, and deliver results quickly.
As a fully managed service, AWS Batch helps you to run batch computing workloads of any scale. AWS Batch automatically provisions compute resources and optimizes the workload distribution based on the quantity and scale of the workloads. With AWS Batch, there’s no need to install or manage batch computing software, so you can focus your time on analyzing results and solving problems.
AWS Batch is a set of batch management capabilities that enable developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. AWS Batch dynamically provisions the optimal quantity and different types of computing resources, such as CPU or memory-optimized compute resources, based on the volume and specific resource requirements of the batch jobs submitted.
With AWS Batch, there is no need to install and manage batch computing software or server clusters, instead, you focus on analyzing results and solving problems. AWS Batch plans, schedules, and executes your batch computing workloads using Amazon EC2, available with spot instance, and AWS compute resources with AWS Fargate or Fargate Spot.
Features of AWS Batch
The features of Amazon Batch are:
Dynamic Compute Resource Provisioning and Scaling
When AWS Batch is used with Farfate or Fargate spot, you will only need to set up a few concepts such as CE, job queue, and job definition. Now, you have a complete queue, scheduler, and compute architecture but you need not worry about managing a single piece of computing infrastructure.
AWS Batch with Fargate
When Fargate resources are used with AWS Batch, it allows you to have a completely serverless architecture for the batch jobs you need. Every batch receives the same exact amount of CPU and memory for the requests when you are dealing with Fargate. So, you will not have any wasted resource time and you also need not wait for any EC2 instance launches.
Priority-based Job Scheduling
One of the main features of AWS Batch is you can set up a number of queues with different priority levels. Unless the compute resources are available to execute the next job, the batch jobs are stored in queues. The AWS Batch scheduler is responsible for deciding when, where, and how to run the batch jobs that have already been submitted to a queue based on the resource requirements of the job.
Support for GPU Scheduling
AWS Batch supports GPU scheduling. It allows you to specify the number and type of accelerator that your jobs require as job definition input variables in AWS Batch. AWS Batch will scale up instances appropriate for your jobs based on the required number of GPUs and isolate the accelerators according to each job’s needs so that only the appropriate containers can access them.
Support for Popular Workflow Engines
AWS Batch supports and is integrated with the open-source and commercial workflows and languages such as Pegasus WMS, Luigi, Nextflow, Metaflow, Apache Airflow, and AWS Step Functions. This will enable you to use simple workflow languages to model your batch compute pipeline.
Integrated Monitoring and Logging
AWS Batch displays key operational metrics for your batch jobs in AWS Management Console. You can view metrics related to computing capacity as well as running, pending, and completed jobs. Logs for your jobs, e.g., STDERR and STDOUT, are available in AWS Management Console; the logs are also written Amazon CloudWatch Logs.
Support for Tightly-coupled HPC Workloads
AWS Batch supports multi-node parallel jobs, which enables you to run single jobs that span multiple EC2 instances. This feature lets you use AWS Batch to easily and efficiently run workloads such as large-scale, tightly-coupled high-performance computing (HPC) applications or distributed GPU model training.
Comparison between AWS Batch and AWS Lambda
AWS Batch
It allows developers, scientists, and engineers to run hundreds of thousands of batch computing operations on AWS quickly and easily. Based on the volume and specific resource requirements of the batch jobs submitted, it dynamically provisions the optimal quantity and kind of compute resources (e.g., CPU or memory optimized instances).
Pros
- Scalable
- Containerized
Cons
- More overhead than lambda
- Image management
AWS Lambda
AWS Lambda is a compute service that automatically maintains the underlying computing resources for you while running your code in response to events. You may use AWS Lambda to add custom logic to other AWS services or build your own back-end services that run on AWS scale, performance, and security.
Pros
- Stateless
- No deploy, no server, great sleep
- Easy to deploy
- Extensive API
- VPC Support
Cons
- Can’t execute ruby or go
- Can’t execute PHP w/o significant effort
Conclusion
इस पोस्ट में हमने AWS Batch अमेज़न वेब सर्विस के बारे में अच्छे से जाना।
