AWS Batch now supports Custom Logging Configurations, Swap Space, and Shared Memory
AWS Batch now supports new parameters when specifying AWS Batch job definitions, including custom logging configurations, swap space, and shared memory. AWS Batch enables 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 type of compute resources (e.g., GPU, CPU, or memory optimized instances) 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 that you use to run your jobs, allowing you to focus on analyzing results and solving problems.
AWS Batch now supports new parameters when specifying AWS Batch job definitions, including custom logging configurations, swap space, and shared memory. AWS Batch enables 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 type of compute resources (e.g., GPU, CPU, or memory optimized instances) 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 that you use to run your jobs, allowing you to focus on analyzing results and solving problems.
Customers can choose among several standard log drivers for their jobs and specify log driver options and secrets. This means that customers can, through an API parameter, have AWS Batch emit logs the way customers want to receive them, increasing visibility into their workloads. Simply specify the driver name, and pass in configurations options or secrets to the driver, and Batch will configure the jobs to communicate with that driver.
With the swap space parameter, you can now control the use of swap space for your jobs. Swap space enables applications to use more memory than they otherwise would be able to access, at the cost of higher latency and lower throughput of that memory access. Applications such as those with highly varying memory requirements, but less sensitivity to latency, may benefit from the use of swap memory.
AWS Batch now also supports the shm-size parameter, which allows you to specify the shared memory that a container can use. It enables memory-intensive containers to run faster by giving more access to allocated memory.
To get started using these new features with AWS Batch, get started here .