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ML program as a computational graph
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Classroom Contents
Inside TensorFlow- Building ML Infra
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- 1 Intro
- 2 Big data and ML infra are similar
- 3 Speaker background
- 4 Why invest in ML infra?
- 5 Case study: Building a new TF runtime
- 6 ML program as a computational graph
- 7 An example ML program
- 8 Lifetime of an ML program
- 9 Vectorized normalization
- 10 A slight digression on Eager execution
- 11 ML infra and SQL query processing
- 12 (Random) scan-based access patterns
- 13 Beyond pure dataflow
- 14 ML and DB terminology mapping
- 15 Recall graph processing workflow
- 16 Expressing input pipelines
- 17 Decoupled API and execution
- 18 Challenge: Randomized transformations
- 19 Graph rewrites
- 20 Cost model and data stats
- 21 Constraint propagation
- 22 Storage/access optimizations
- 23 Push vs pull based execution
- 24 Distributed and parallel execution
- 25 ML infra is like data infra, with new twists
- 26 Let's collaborate
