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Author: mikebo93

chapter_computatioinal_graph/Chapter_Summary.md

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+# Chapter Summary
+
+1.  The computational graph technology is introduced to machine learning
+    frameworks in order to achieve a trade-off between programming
+    flexibility and computational efficiency.
+
+2.  A computational graph contains tensors (as units of data) and
+    operators (as units of operations).
+
+3.  A computational graph represents the computational logic and status
+    of a machine learning model and offers opportunities for
+    optimizations.
+
+4.  A computational graph is a directed acyclic graph. Operators in the
+    graph are directly or indirectly dependent on or independent of each
+    other, without circular dependencies.
+
+5.  Control flows, represented by conditional control and loop control,
+    determines how data flows in a computational graph.
+
+6.  Computational graphs come in two types: static and dynamic.
+
+7.  Static graphs support easy model deployment, offering high
+    computational efficiency and low memory footprint at the expense of
+    debugging performance.
+
+8.  Dynamic graphs provide computational results on the fly, which
+    increases programming flexibility and makes debugging easy for model
+    optimization and iterative algorithm improvement.
+
+9.  We can appropriately schedule the execution of operators based on
+    their dependencies reflected in computational graphs.
+
+10. For operators that run independently, we can consider concurrent
+    scheduling to achieve parallel computing. For operators with
+    computational dependencies, schedule them to run in serial.
+
+11. Specific training tasks of a computational graph can run
+    synchronously or asynchronously. The asynchronous mechanism
+    effectively improves the hardware efficiency and shortens the
+    training time.