Clusters, Hadoop, and Spark
ML System Optimization,
MLSysOps,
Clusters,
Hadoop,
Spark,
MapReduce,
Distributed K-Means,
Distributed CNN,
AI,
ML
Clusters, Hadoop, and Spark #
Scale-out frameworks distribute data and computation across networked machines. Their value comes from aggregate compute and storage, while their main costs are communication, scheduling, serialisation, and repeated synchronisation.
Course coverage:
- Cluster computing with Hadoop and Spark
- MapReduce k-means, mini-batch and streaming variants
- Convergence and communication reduction
- Distributed convolutional neural networks: data and model parallelism
- Layer or tensor slicing, mixed precision, pipelining, and distributed execution frameworks
Learning Objectives #
By the end of this page, you should be able to: