'Machine Learning Magic: How to Speed Up Offline Inference for Large Datasets' by Alluxio machinelearning ml
The architecture consists of four parts: the job scheduler , training/inference job, data storage , and Alluxio. Alluxio is the cache layer of the entire system.
For each inference task, we provide two mount points, one for data read and one for data write. Each mount point has its own FUSE daemon.The system can be configured separately for different read and write scenarios to make task execution more efficient. The first optimization is the flush enhancement. We have received feedback from our users that they have lost the output results after the job was finished. After investigating this issue, we finally solved it by implementing the flush function in the FUSE daemon. When a job is finished, the system will automatically call the flush function. By optimizing the flush function, we have prevented the loss of output data.
When a user submits a job to OpenPAI, the job scheduler will schedule it. In the case of running tasks in the cluster, these will have to wait a period of time. Meanwhile, OpenPAI can send a prefetch command to Alluxio master, which will cache the data. Therefore, the workload has already been cached before the job runs. As a result, OpenPAI will schedule the job to run directly on its own node.According to the test results, Alluxio’s optimization greatly improves the job’s running speed.
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