On-Demand Videos
Unlock the full performance of your AI/ML infrastructure on Oracle Cloud Infrastructure (OCI).
Join Oracle's Master Principal Cloud Architect Xinghong He and Alluxio's VP of Technology Bin Fan for an in-depth technical session exploring how modern tiered caching, optimized storage integration, and smart deployment choices can deliver sub-millisecond latency and up to 5× faster data access on OCI — at scale.
You'll learn about:
- Architectural insights: How Alluxio’s tiered caching architecture works with OCI Object Storage and BM.DenseIO compute instances to eliminate data access bottlenecks.
- Benchmark-proven results: See real MLPerf Storage 2.0 and Warp benchmark outcomes demonstrating sub-millisecond latency and dramatic throughput gains.
- Deployment strategies: Compare deployment options — dedicated mode for peak performance vs. co-located mode for cost-efficient scale.
- Practical, actionable guidance: Implementation best practices you can apply directly to your AI/ML workloads on OCI.
Fireworks AI is a leading inference cloud provider for Generative AI, powering real-time inference and fine-tuning services for customers' applications that require minimal latency, high throughput, and high concurrency. Their GPU infrastructure spans 10+ clouds and 15+ regions, serving enterprises and developers deploying production AI workloads at scale.
With model sizes reaching 70GB+, Fireworks AI faced critical challenges: eliminating cold start delays, managing highly concurrent model downloads across GPU clusters, reducing tens of thousands in annual cloud egress costs, and automating manual pipeline management that consumed 4+ hours weekly. They chose Alluxio as their solution to scale with their hyper-growth without requiring dedicated infrastructure resources.
In this tech talk, Akram Bawayah, Software Engineer at Fireworks AI, and Bin Fan, VP of Technology at Alluxio, share how Fireworks AI uses Alluxio to power their multi-cloud inference infrastructure.
They discuss:
- How Fireworks AI uses Alluxio in its high-performance model distribution system to deliver fast, reliable inference across multiple clouds
- How implementing Alluxio distributed caching achieved 1TB/s+ model deployment throughput, reducing model loading from hours to minutes while significantly cutting cloud egress costs
- How to simplify infrastructure operations and seamlessly scale model distribution across multi-cloud GPU environments
In this talk, Eric Wang, Senior Staff Software Engineer introduces Uber’s open-source generative end-to-end ML lifecycle management platform: Michelangelo.
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ALLUXIO DAY V 2021
August 27, 2021
ALLUXIO DAY V 2021
August 27, 2021
ALLUXIO WEBINAR
With data lakes expanding from on-prem to the cloud as well as increasing use of new object data stores, data platform teams are challenged with providing consistent, high-throughput access to distributed data sources for analytics and AI/ML applications. In today’s hybrid cloud and multi-cloud era, data-intensive applications such as Presto, Spark, Hive, and Tensorflow are suffering more sluggish response times and increased complexity with the growing separation of data and compute.
Join Alluxio’s distributed systems experts as they explore today’s data access challenges and open source data orchestration solutions for modernizing your data platform.
In this tech talk, you’ll learn:
- How data access and throughput challenges are hindering large-scale analytics and AI/ML applications
- How a data orchestration layer can simplify distributed data access and improve performance
- Real-world production use cases and example journeys for architecting a modern data platform
ALLUXIO DAY IV 2021
June 24, 2021
Driven by strong interests from our open-source community, the core team of Alluxio started to re-design an efficient and transparent way for users to leverage data orchestration through the POSIX interface. We have introduced a new JNI-based FUSE implementation to support POSIX data access, as well as many improvements in relevant data operations like more efficient distributedLoad, optimizations on listing or calculating directories with a massive amount of files, which are common in model training.
ALLUXIO DAY IV 2021
June 24, 2021
Today’s analytics workloads demand real-time access to expansive amounts of data. This session demonstrates how Alluxio’s data orchestration platform, running on Intel Optane persistent memory, accelerates access to this data and uncovers its valuable business insights faster.
ALLUXIO DAY IV 2021
June 24, 2021
RaptorX is an internal project name aiming to boost query latency significantly beyond what vanilla Presto is capable of. For this session, we introduce the hierarchical cache work including Alluxio data cache, fragment result cache, etc. Cache is the key building block for RaptorX. With the support of the cache, we are able to boost query performance by 10X. This new architecture can beat performance oriented connectors like Raptor with the added benefit of continuing to work with disaggregated storage.
ALLUXIO DAY IV 2021
June 24, 2021
Nowadays it is not straightforward to integrate Alluxio with popular query engines like Presto on existing Hive data. Solutions proposed by the community like Alluxio Catalog Service or Transparent URI brings unnecessary pressure on Alluxio masters when querying files should not be cached. This talk covers TikTok’s approach on adopting Alluxio for the cache layer without introducing additional services.
ALLUXIO DAY IV 2021
June 24, 2021
Alluxio has an excellent metrics system and supports various kinds of metrics, e.g. an embedded JSON sink and the prometheus sink. Users and developers can easily create a custom sink of Alluxio by implementing the Sink interface.
Also, Alluxio provides a metrics page in web UI to display some key information of Alluxio, such as bytes throughput and storage space. However, if you want a more flexible and universal monitoring, additional work is required.
ALLUXIO DAY III 2021
April 27, 2021
Data Lake Analytics(DLA) is a large scale serverless data federation service on Alibaba Cloud. One of its serverless analytics engine is based on Presto. The DLA Presto engine supports a variety of data sources and is widely used in different application scenarios in the cloud. In this session, we will talk about the system architecture of DLA Presto engine, as well as the challenges and solutions. In particular, we will introduce the use of alluxio local cache to solve performance issues on OSS data sources caused by access delay and OSS bandwidth limitation. We will discuss the principle of alluxio local cache and some improvements we have made.
ALLUXIO DAY III 2021
April 27, 2021
Increasingly powerful compute accelerators and large training dataset have made the storage layer a potential bottleneck in deep learning training/inference.
Offline inference job usually consumes and produces tens of tera-bytes data while running more than 10 hours.
For a large-scale job, it usually causes high IO pressure, increase job failure rate, and bring many challenges for system stability.
We adopt alluxio which acts as an intermediate storage tier between the compute tier and cloud storage to optimize IO throughput of deep learning inference job.
For the production workload, the performance improves 18% and we seldom see job failure because of storage issue.
ALLUXIO DAY III 2021
April 27, 2021
At Aspect Analytics we intend to use Dask, a distributed computation library for Python, to deal with MSI data stored as large tensors. In this talk we explore using Alluxio and Alluxio FUSE as a data consolidation and caching layer for some of our bioinformatics workflows.
ALLUXIO DAY III 2021
April 27, 2021
Alluxio’s capabilities as a Data Orchestration framework have encouraged users to onboard more of their data-driven applications to an Alluxio powered data access layer. Driven by strong interests from our open-source community, the core team of Alluxio started to re-design an efficient and transparent way for users to leverage data orchestration through the POSIX interface. This effort has a lot of progress with the collaboration with engineers from Microsoft, Alibaba and Tencent. Particularly, we have introduced a new JNI-based FUSE implementation to support POSIX data access, created a more efficient way to integrate Alluxio with FUSE service, as well as many improvements in relevant data operations like more efficient distributedLoad, optimizations on listing or calculating directories with a massive amount of files, which are common in model training. We will also share our engineering lessons and roadmap in future releases to support Machine Learning applications.