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Classroom Contents
Distributed Training and Gradient Compression - Lecture 14
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- 1 Intro
- 2 Problems of Distributed Training
- 3 Reduce Transfer Data Size Recall the workflow of Parameter-Server Based Distributed Training
- 4 Limitations of Sparse Communication
- 5 Optimizers with Momentum Repeat, update weights
- 6 Deep Gradient Compression
- 7 Comparison of Gradient Pruning Method
- 8 Latency Bottleneck
- 9 High Network Latency Slows Federated Lea
- 10 Conventional Algorithms Suffer from High La Vanilla Distributed Synchronous SGD
- 11 Delayed Gradient Averaging
- 12 DGA Accuracy Evaluation
- 13 Real-world Benchmark
- 14 Summary of Today's Lecture
- 15 References
