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.
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: almost 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: about 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: about 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 / about 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: over 2 years ago
I remember as part of the AWS certification exams this use was explicitly mentioned. https://aws.amazon.com/batch/. - Source: Hacker News / over 2 years ago
Https://aws.amazon.com/batch/ is the go-to for HPC, or maybe I'm misunderstanding your requirements. Source: almost 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 / almost 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 / about 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: over 3 years ago
I think AWS batch is what you're looking for. Source: over 3 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: about 4 years ago
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