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Concept: Mirrored vs. per-replica values
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Classroom Contents
Inside TensorFlow - tf.distribute.Strategy
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- 1 Intro
- 2 A class with multiple implementations
- 3 Data parallelism
- 4 Parameter servers and workers
- 5 Central Storage
- 6 Mirrored Variables
- 7 All-reduce algorithm
- 8 Ring all-reduce
- 9 Hierarchical all-reduce
- 10 OneDevice Strategy
- 11 Parallel input preprocessing: coming
- 12 What changes when you switch strategies?
- 13 # Training with Keras
- 14 # Training with Estimator
- 15 Concept: Mirrored vs. per-replica values
- 16 Support computations following this pattern
- 17 setup
- 18 loss, optimizer
- 19 # Custom training loop, part 3: each replica
- 20 Concept: Modes
- 21 all replicas
- 22 outer loop
- 23 Default Strategy
- 24 # Average loss using the global batch size
- 25 # Optimizer implementation, part 1
- 26 merge_call(fn, args) is our secret weapon
- 27 # Optimizer implementation, part 2
- 28 Concept: Replica vs. variable locality
- 29 One standard pattern for updating state
- 30 # Example: Mean metric
- 31 Questions?
