This version deeply optimizes the function, performance, and experience of the imperative programming mode (dynamic graph), and further strengthens the basic functions of the framework. It also significantly optimizes the performance of the native inference library, provides a lightweight inference engine Paddle Lite to achieve a great coverage of hardware support, rcomprehensively upgrades Paddle Serving, and has a powerful and simple service-oriented deployment capability. This version further enriches and improves the corresponding development kits and utility components, continues to improve the function and experience of the existing kits and components, and releases a new image classification kit,i.e., and Paddle quantum machine learning framework.
This version deeply optimizes the function, performance, and experience of the imperative programming mode (dynamic graph), and further strengthens the basic functions of the framework. It also significantly optimizes the performance of the native inference library, provides a lightweight inference engine Paddle Lite to achieve a great coverage of hardware support, rcomprehensively upgrades Paddle Serving, and has a powerful and simple service-oriented deployment capability. This version further enriches and improves the corresponding development kits and utility components, continues to improve the function and experience of the existing kits and components, and releases a new image classification kit, i.e., PaddleClas, and Paddle quantum machine learning framework.
**Training framework:** Deeply optimizes the function, performance, and experience of imperative programming (dynamic graph) and especially enhances the capability of converting dynamic graph to static graph. Supports to convert data-dependent control flow into static graph to save and deploy, or train under static graph mode. Further optimizes the function of Data Loader and the usage of gradient clipping. Fixes problems for declarative programming mode such as fetching tensors with different lengths between multi-cards. The combination of mixed precision and recomputation shows good results in large-batch training. Adds a number of APIs and ComplexVariable and supports complex number tensor expressions and common complex number operations.
**Training framework:** Deeply optimizes the function, performance, and experience of imperative programming (dynamic graph) and especially enhances the capability of converting dynamic graph to static graph. Supports to convert data-dependent control flow into static graph to save and deploy, or train under static graph mode. Further optimizes the function of Data Loader and the usage of gradient clipping. Fixes problems for declarative programming mode such as fetching tensors with different lengths between multi-cards. The combination of mixed precision and recomputation shows good results in large-batch training. Adds a number of APIs and ComplexVariable and supports complex number tensor expressions and common complex number operations.
