You can run at-scale parallel batch processing jobs automatically and efficiently complete resource scheduling, task provisioning, and data loading.
Easy to Use
With available job scheduling functions, you can use cloud services to develop your business without the need to dive into complex programming.
Batch fully manages resource and process scheduling in batch computing.
You only need to pay for the cloud resources you use, freeing up your capital investments in software, hardware, and labor-intensive O&M for revenue-generating endeavors.
The volume of data that must be processed in gene sequencing continues to mushroom as efforts and mapping become more refined. The increased precision and clarity means more computing power is needed, otherwise systems become strained from the pressure. With Batch, you can cost-effectively make use of in-cloud computing resources to process massive volumes of data.
The convenient process control in the accessible data processing software and running environment allows you to process large quantity of tasks in parallel for accelerated analysis and computing timetables.
Batch works seamlessly with cloud storage services like OBS, SFS, and EVS to meet the particulars of your storage needs.
You can use cloud resources without any software and hardware investment and only pay for the resources you use.
You can use Batch to deploy offline training tasks in batches based on deep learning frameworks TensorFlow, Caffe, MXNet, and much more.
Delivers one-stop closed-loop services covering computing resources, network, storage, and job management, allowing you to focus on your business rather than your mashup.
Provides general job management utilities for unified deployment of various deep learning frameworks. All that means you can provision deep learning tasks with just a small amount of code integration.
Batch is capable of running large quantities of jobs in parallel, removing the concerns over inadequate compute resources to handle the rendering tasks involved with production of videos, animations, and special effects.
Supports running of numerous jobs in parallel to help you speed up the rendering process.
Allows you to migrate services to the cloud without any special programming requirements. Multiple cloud storage services can be used for storing data.
Allows you to start using resources right away in pay-per-use mode without having to install and manage computing software and server clusters.
Batch gives you immediate access to cloud resources so you can quickly perform transcoding on massive volumes of multimedia data without having to invest huge amounts upfront into hardware. Get what you need now and step away from the ensuring complexities in managing the host of software to run everything.
Use the cloud resources you need without any software and hardware investment and only pay for the resources you use.
A large amount of data can be processed in parallel to improve transcoding efficiency.
You can specify job priority, execution time, and Directed Acyclic Graph (DAG)-based dependencies among multiple task groups in a job.
Resources are dynamically allocated to fulfill job requirements. You can manage and allocate heterogeneous resources, such as the memory, CPU, and GPU.
Batch works seamlessly with cloud storage services, such as OBS and SFS.
You can obtain task logs in real time and customize log storage paths.
Batch informs you when you need to purchase more Spot instances so you are not paying for more computing power than you need. This function is being rolled out soon.