Table of contents
Scalability
AWS Batch automatically provisions the optimal quantity and type of compute resources based on the volume and specific resource requirements of the batch jobs submitted.
Cost-Effectiveness
By using AWS Batch, you only pay for the resources you consume, and it provides integration with Spot Instances which can significantly lower costs.
No Infrastructure Management
AWS Batch removes the need to manage server clusters or other infrastructure, allowing users to focus entirely on jobs and workloads.
Flexible Job Definitions
Users can easily specify job definitions to model their machine learning, batch processing, or other computational tasks, allowing for flexibility in resource allocation.
Integration with AWS Services
AWS Batch integrates with various AWS services like Amazon CloudWatch, AWS Lambda, and AWS IAM to provide a comprehensive and secure batch processing solution.
We have collected here some useful links to help you find out if AWS Batch is good.
Check the traffic stats of AWS Batch on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of AWS Batch on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of AWS Batch's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of AWS Batch on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
The latest comments about AWS Batch on Reddit. This can help you find out how popualr the product is and what people think about it.
Compute: This is the big one. It's the cost of running EC2 instances with GPUs (like the g5 or p4 series) for model training and deployment. It also includes the compute for services like Amazon SageMaker and AWS Batch. - Source: dev.to / about 2 months ago
After moving off Jenkins, I moved everything to AWS Batch with Fargate. This works quite well, but it is proving to be a little expensive, as I have to pay for:. Source: over 2 years ago
If you're looking for more control over your infrastructure and want to run a full computing environment, EC2 might be the right choice for you. With EC2, you have complete control over the operating system, network, and storage, which can be useful if you need to install custom software or use specific hardware configurations. Additionally, EC2 + Batch processing provide a wider range of instance types, including... Source: over 2 years ago
AWS Batch is the equivalent of a university cluster you submit to with slurm/sge/lsf/etc. But does not use those schedulers as AWS has their own. Source: over 2 years ago
Developers frequently use batch computing to access significant amounts of processing power. You may perform batch computing workloads in the AWS Cloud with the aid of AWS Batch, a fully managed service provided by AWS. It is a powerful solution that can plan, schedule, and execute containerized batch or machine learning workloads across the entire spectrum of AWS compute capabilities, including Amazon ECS, Amazon... - Source: dev.to / over 2 years ago
As others mentioned, you *can*. It might be easier with AWS Batch (https://aws.amazon.com/batch/) depending on what you're trying to do. Source: almost 3 years ago
I remember as part of the AWS certification exams this use was explicitly mentioned. https://aws.amazon.com/batch/. - Source: Hacker News / about 3 years ago
Https://aws.amazon.com/batch/ is the go-to for HPC, or maybe I'm misunderstanding your requirements. Source: about 3 years ago
Instead, break up the workflow - run each step on its own compute instance with resources right sized for each step. There are a lot of AWS instance types to choose from, and if you connect a workflow engine to AWS Batch, AWS Batch will manage picking the right one for you based on CPU and memory (and GPU) requirements. - Source: dev.to / over 3 years ago
Another alternative is to use AWS Batch with spot instances in conjunction with AWS Step Functions leveraging the service integration Run a Job (.sync) pattern. After calling AWS Batch submitJob, the workflow pauses. When the job is complete, Step Functions progresses to the next state. - Source: dev.to / over 3 years ago
Check out AWS Batch, it will provide the instances for you and remove them once the job is done. And if you're looking for a framework that allows you to quickly move back and forth between local and cloud, check out Ploomber. Source: over 3 years ago
I would suggest the AWS genomics CLI and AWS Batch as good features to learn. Both benefit from knowledge of nextflow so I would start there and work towards to the cloud. There's already a website with loads of common bioinformatics workflows already implemented. Source: over 3 years ago
If you're looking for something a bit more managed, check out Batch. It's basically a managed AWS service that does a lot of what I describe, kind of the same, but kind of differently. Batch has its own workflow peculiarities, but you may prefer dealing with those rather than dealing with something custom hacked together to behave like it. Source: almost 4 years ago
I think AWS batch is what you're looking for. Source: about 4 years ago
Sounds like Batch could be what you are after; if you can package your software into a docker container and separate the parts of the pipeline needing different resources into different jobs and run them in separate queues with appropriately sized instances in their compute environments. I’d explore wrapping up the jobs in a Nextflow script too. Source: over 4 years ago
AWS Batch has earned a robust reputation in the realm of cloud-based batch computing, particularly for its scalability and flexibility in handling large compute jobs. As a fully managed service from Amazon Web Services, it simplifies the complexities traditionally associated with configuring and managing infrastructure for batch workloads. Specifically, AWS Batch is renowned for its ability to efficiently plan, schedule, and execute containerized batch or machine learning workloads across AWS's extensive computing landscape, which includes services like Amazon ECS, Amazon EKS, AWS Fargate, and an array of EC2 instance types, including GPU instances.
Among its notable strengths, AWS Batch is praised for its scalability, making it an ideal choice for engineers needing considerable processing power for large-scale tasks. It is akin to submitting jobs to a university cluster using traditional schedulers like slurm or sge, albeit with AWS's proprietary scheduling system. This capability has made it particularly attractive to researchers in fields such as bioinformatics, where leveraging substantial computational resources for data-intensive tasks is common.
However, AWS Batch is not without its challenges. Cost considerations emerge frequently in discussions among users. While it offers significant capabilities, some users report it can be expensive, especially when utilized with AWS Fargate. This has led some to explore alternative strategies or services to manage their workflows more economically.
AWS Batch's integration with other AWS services contributes significantly to its flexibility and appeal. The ability to connect with AWS Step Functions, for instance, allows for smooth orchestration of workflows, facilitating a modular and scalable approach to batch processing. This integration is particularly useful in scenarios involving complex processing pipelines, where each task can be right-sized according to its specific resource requirements.
From a development and operational perspective, AWS Batch aligns with modern trends of containerization and automation. The service efficiently spins up and deallocates compute resources as required, relieving users of the need to manually manage infrastructure. This characteristic is especially valued in scenarios where developers aim to bridge local and cloud environments seamlessly.
In conclusion, AWS Batch is highly regarded in the domain of cloud-based batch processing, notably for its breadth of functionality and ability to streamline large-scale compute tasks. While it provides a powerful and managed solution that removes much of the operational burden, cost and specific workflow requirements might prompt some users to seek complementary tools or services. Overall, for organizations heavily invested in the AWS ecosystem and those seeking to enhance their capacity for data processing and analysis, AWS Batch remains a leading choice in cloud computing solutions.
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Is AWS Batch good? This is an informative page that will help you find out. Moreover, you can review and discuss AWS Batch here. The primary details have not been verified within the last quarter, and they might be outdated. If you think we are missing something, please use the means on this page to comment or suggest changes. All reviews and comments are highly encouranged and appreciated as they help everyone in the community to make an informed choice. Please always be kind and objective when evaluating a product and sharing your opinion.
