Alluxio Data Orchestration for Machine Learning
summary
Alluxio adds a POSIX layer with a JNI-based FUSE implementation, a distributed caching layer and a distributedLoad job to prefetch datasets; engineers optimize listing and metadata for massive small-file workloads and add retries and replication for stability. Production results include 80% speedup for a Microsoft cluster with 400 jobs and >1 TB datasets, Alibaba seeing throughput jump from ~300 MB/s to up to 6 GB/s or reported 5x improvement on ImageNet-style small-file benchmarks, and multi-hundred-terabyte deployments handling billions of files.
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chapters (30)
- 0:00Intro
- 2:00Users
- 3:34Agenda
- 4:09Training
- 4:38Frostbite
- 4:50Multiple machines
- 5:31Summary
- 6:15GPU cluster
- 8:00Cloud training data solutions
- 10:33Architecture
- 11:21Posix
- 15:21Load Data
- 16:53Cached Data
- 18:39Copy Data
- 20:17Retry Mechanism
- 21:41Solution
- 22:54Use Cases
- 23:33Microsoft Case
- 26:42Alibaba Case Results
- 27:28Momo Case
- 30:07Lasso Case
- 30:48Recap
- 32:13Questions
- 32:52Roadmap
- 35:07Open Source vs Enterprise
- 37:55Collaboration
- 38:29Technical Question
- 40:32Capacity bottleneck
- 42:28Clarification
- 43:22Upcoming features
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