AIStore: scalable storage for AI applications
AIStore is a lightweight object storage system with the capability to linearly scale-out with each added storage node and a special focus on petascale deep learning.
AIStore (AIS for short) is a built from scratch, lightweight storage stack tailored for AI apps. AIS consistently shows balanced I/O distribution and linear scalability across arbitrary numbers of clustered servers, producing performance charts that look as follows:
The picture above comprises 120 HDDs.
The ability to scale linearly with each added disk was, and remains, one of the main incentives behind AIStore. Much of the development is also driven by the ideas to offload dataset transformation and other I/O intensive stages of the ETL pipelines.
AIS runs natively on Kubernetes and features open format - thus, the freedom to copy or move your data from AIS at any time using the familiar Linux
For AIStore white paper and design philosophy, for introduction to large-scale deep learning and the most recently added features, please see AIStore Overview (where you can also find six alternative ways to work with existing datasets). Videos and animated presentations can be found at videos.
Finally, getting started with AIS takes only a few minutes.
There is a vast spectrum of possible deployments - primarily due to the fact that the essential prerequisites boil down to having Linux with a disk. This results in a practically unlimited set of options from all-in-one (AIS gateway + AIS target) docker container to a petascale bare-metal cluster of any size, and from a single development VM or workstation to multiple racks of high-end servers.
The table below contains a few concrete examples:
| Deployment option | Targeted audience and objective | | --- | ---| | Local playground | AIS developers and development, Linux or Mac OS | | Minimal production-ready deployment | This option utilizes preinstalled docker image and is targeting first-time users or researchers (who could immediately start training their models on smaller datasets) | | Easy automated GCP/GKE deployment | Developers, first-time users, AI researchers | | Large-scale production deployment | Requires Kubernetes and is provided (documented, automated) via a separate repository: ais-k8s |
Further, there's the capability referred to as global namespace. Simply put, as long as there’s HTTP connectivity, AIS clusters can be easily interconnected to “see” - i.e., list, read, write, cache, evict - each other's datasets. This ad-hoc capability, in turn, makes it possible to start small and gradually/incrementally build high-performance shared storage comprising petabytes.
For detailed discussion on supported deployments, please refer to Getting Started.
Alex Aizman (NVIDIA)