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What are dynamic graphs?
A dynamic graph can be represented as an ordered list or an asynchronous stream of timed events, such as additions or deletions of nodes and edges¹. A social network like Twitter is a good illustration: when a person joins the platform, a new node is created. When they follow another person, a follow edge is created.
How do you describe a static graph?
The static bar graph means that the data which is represented pertains to one particular period or one particular year. As such, there is no transition in the year, and hence the name static. On the other hand, dynamic graphs portray the data over a period of time, for example, over a 40 year period.
What is computational graph?
A computational graph is defined as a directed graph where the nodes correspond to mathematical operations. Computational graphs are a way of expressing and evaluating a mathematical expression. The above computational graph has an addition node (node with “+” sign) with two input variables x and y and one output q.
What is PyTorch graph?
In PyTorch, the autograd package provides automatic differentiation to automate the computation of the backward passes in neural networks. The forward pass of your network defines the computational graph; nodes in the graph are Tensors and edges are functions that produced the output Tensors from input Tensors.
What’s the difference between static and dynamic graphs in deep learning?
What is the difference between dynamic graphs and static graphs in deep learning libraries? For the static graphs, you should first draw the graph completely and then inject data to run (define-and-run), while using dynamic graphs the graph structure is defined on-the-fly via the actual forward computation.
How are data flow graphs used in deep learning?
Computations over data-flow graphs is a popular trend for deep learning with neural networks, especially in the field of cheminformatics and understanding natural language. In most frameworks, such as TensorFlow, the graphs are static, which means the batch processing is only available for a set of data with the same type and shape.
Is it possible to batch process dynamic graphs?
Batch processing of dynamic graphs is a very common technique for a variety of applications, such as computer vision and natural language processing. However, due to the varieties of type and shapes between distinct data, batch processing with a static graph over such dataset is almost impossible with current frameworks and libraries.
How does dynamic batching work in deep learning?
Each distinct data has its own computation graphs, which consists of various operations at different depth in the current graph. Dynamic Batching will replace (Rewriting Process) the previous graph by computing all data of the same operation which occurs at the same depth.