Alluxio Data Orchestration for Machine Learning

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.

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Speed up large-scale ML/DL offline inference job with Alluxio

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.

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Accelerate Analytics and ML in the Hybrid Cloud Era

Many companies we talk to have on premises data lakes and use the cloud(s) to burst compute. Many are now establishing new object data lakes as well. As a result, running analytics such as Hive, Spark, Presto and machine learning are experiencing sluggish response times with data and compute in multiple locations. We also know there is an immense and growing data management burden to support these workflows.

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Accelerate Analytics and ML in the Hybrid Cloud Era

Many companies we talk to have on premises data lakes and use the cloud(s) to burst compute. Many are now establishing new object data lakes as well. As a result, running analytics such as Hive, Spark, Presto and machine learning are experiencing sluggish response times with data and compute in multiple locations. We also know there is an immense and growing data management burden to support these workflows.

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Accelerate Analytics and ML in the Hybrid Cloud Era

Alluxio Tech Talk *

In this talk, we will walk through what Alluxio’s Data Orchestration for the hybrid cloud era is and how it solves the performance and data management challenges we see.

Accelerate Analytics and ML in the Hybrid Cloud Era

Many companies we talk to have on premises data lakes and use the cloud(s) to burst compute. Many are now establishing new object data lakes as well. As a result, running analytics such as Hive, Spark, Presto and machine learning are experiencing sluggish response times with data and compute in multiple locations. We also know there is an immense and growing data management burden to support these workflows.

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Accelerate Analytics and ML in the Hybrid Cloud Era

Many companies we talk to have on premises data lakes and use the cloud(s) to burst compute. Many are now establishing new object data lakes as well. As a result, running analytics such as Hive, Spark, Presto and machine learning are experiencing sluggish response times with data and compute in multiple locations. We also know there is an immense and growing data management burden to support these workflows.

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Speeding Up I/O for Machine Learning

Alluxio Global Online Meetup *

This talk will guide the audience on how Alluxio can greatly simplify the data preparation phase in with remote and possibly multiple data sources. We will share the lessons and benchmark from Bill Zhao an engineer led in Apple when building a Machine Learning platform using Tensorflow, NFS, DC/OS and Alluxio.