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. It's an elastic cluster that can grow and shrink at runtime and can be ad-hoc deployed, with or without Kubernetes, anywhere from a single Linux machine to a bare-metal cluster of any size.
AIS consistently shows balanced I/O distribution and linear scalability across arbitrary numbers of clustered nodes. The ability to scale linearly with each added disk was, and remains, one of the main incentives. Much of the initial design was also driven by the ideas to offload custom dataset transformations (often referred to as ETL). And finally, since AIS is a software system that aggregates Linux machines to provide storage for user data, there's the requirement number one: reliability and data protection.
put-copies
and ec-put
jobs, respectively.prefetch
, download
, copy or transform datasets, and many more.For easy usage, management, and monitoring, there's also:
$ ais
bucket etl help log create dsort stop blob-download
object job advanced performance download evict cp rmo
cluster auth storage remote-cluster prefetch get rmb wait
config show archive alias put ls start search
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 tar(1)
, scp(1)
, rsync(1)
and similar.
For developers and data scientists, there's also:
For the original 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.
AIS deployment options, as well as intended (development vs. production vs. first-time) usages, are all summarized here.
Since prerequisites boil down to, essentially, having Linux with a disk the deployment options range from all-in-one container to a petascale bare-metal cluster of any size, and from a single VM to multiple racks of high-end servers. But practical use cases require, of course, further consideration and may include:
Option | 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 via a separate repository: ais-k8s |
Further, there's the capability referred to as global namespace: given HTTP(S) connectivity, AIS clusters can be easily interconnected to "see" each other's datasets. Hence, the idea to start "small" to gradually and incrementally build high-performance shared capacity.
For detailed discussion on supported deployments, please refer to Getting Started.
For performance tuning and preparing AIS nodes for bare-metal deployment, see performance.
AIStore supports multiple ways to populate itself with existing datasets, including (but not limited to):
The on-demand "way" is maybe the most popular, whereby users just start running their workloads against a remote bucket with AIS cluster positioned as an intermediate fast tier.
But there's more. In v3.22, we introduce blob downloader, a special facility to download very large remote objects (BLOBs).
Generally, AIStore (cluster) requires at least some sort of deployment procedure. There are standalone binaries, though, that can be built from source or, alternatively, installed directly from GitHub:
$ ./scripts/install_from_binaries.sh --help
The script installs aisloader and CLI from the most recent, or the previous, GitHub release. For CLI, it'll also enable auto-completions (which is strongly recommended).
AIS is one of the PyTorch Iterable Datapipes.
Specifically, TorchData library provides:
to list and, respectively, load data from AIStore.
Further references and usage examples - in our technical blog at https://aiatscale.org/blog:
Since AIS natively supports a number of remote backends, you can also use (PyTorch + AIS) to iterate over Amazon S3 and Google Cloud buckets, and more.
This repo includes SGL and Slab allocator intended to optimize memory usage, Streams and Stream Bundles to multiplex messages over long-lived HTTP connections, and a few other sub-packages providing rather generic functionality.
With a little effort, they all could be extracted and used outside.
aisnode
command line
MIT
Alex Aizman (NVIDIA)