Machine learning (ML) models keep getting larger and more complex. Whereas before
models used to be represented by static data-flow graphs, they are now implemented via …

Executing a family of Deep Neural Networks (DNNs) training jobs on the same or similar
datasets in parallel is typical in current deep learning scenarios. It is time-consuming and …

Dynamic control flow is an important technique often used to design expressive and efficient
deep learning computations for applications such as text parsing, machine translation …

Real-world deep learning programs are often developed with dynamic programming
languages like Python, which usually have complex features, such as built-in functions and …

Deep learning has increasingly begun to be used across a wide range of computing
applications. Dynamism—the property where the execution of a computation differs in some …

Although eager-mode frameworks are more convenient, they are less efficient today as
operations are dispatched to the hardware one at a time. This execution model precludes …