Mis on eelistatavam arvutusgraafiku vaatenurgast: dünaamiline või staatiline?

The choice between a dynamic and a static computation graph depends on the task and the requirements for flexibility and performance.

• Static graph (e.g., TensorFlow 1.x) is built once before execution. This allows for optimizing calculations, planning them in advance, and efficiently utilizing resources. Static graphs are suitable for production environments and tasks with a fixed model structure.

• Dynamic graph (e.g., PyTorch, TensorFlow Eager) is built 'on the fly' during code execution. This provides greater flexibility, easier debugging, and the ability to naturally use conditional operators and loops.

The preference depends on the situation:

• For research and prototyping, a dynamic graph is often more convenient.
• For industrial use and optimization, a static graph is preferable.

Therefore, there is no universal answer, but dynamic graphs offer flexibility, while static graphs provide performance and optimization.