AI is transforming industries across a wide spectrum of industries, and we’re only scratching the surface of AI’s possibilities. Some industrial advancements go unnoticed, yet the advantages of AI combined with deep learning have far-reaching implications.
PaddlePaddle is China’s first and only open-source DL platform. It’s like other popular AI frameworks like Google’s TensorFlow and Facebook’s PyTorch, giving software developers of all skill levels the tools, services, and resources they need to quickly embrace and apply deep learning at scale.
More than 1.9 million developers are using PaddlePaddle, and industries all over China are using the platform to create specialized applications for their industries, ranging from the automotive industry’s acceleration of autonomous vehicles to the healthcare industry’s applications for fighting covid-19.
Baidu’s PaddlePaddle is geared for production situations, and it focuses on parallel processing in a distributed environment to speed up forward and backward passes. In layman’s terms, a cluster of GPUs may be utilized to accelerate deep learning on PaddlePaddle. So, in principle, everyone should use PaddlePaddle instead of TensorFlow since faster training and inference is beneficial to everyone, including researchers.
Faster than TensorFlow
People seem to overlook the fact that deep learning frameworks quicker than TensorFlow are nothing new; for example, GoogleNet V1 is the most relevant because of its extensive usage of 3×3 convolution kernels, which are now the most popular kernel types. As you can see, Nervana’s Neon library outperforms it by over 2X. It’s also worth noting that Neon can grow to many GPUs. Frameworks aren’t novel in and of itself; MXNet, for example, contains a lot of multi-GPU and multi-node optimizations, as does Samsung’s Veles. The reality is that several frameworks are quicker and more scalable than TensorFlow.
However, because of its network impact, TensorFlow still reigns supreme and will do so for the foreseeable future. TensorFlow is widely used by Google, thus anytime a new article from Google Brain is released, it utilizes it, and this trickles down to independent researchers who use their TensorFlow implementations. There are a lot of tutorials and support for TensorFlow. Finally, TensorFlow has an impressive number of writers and is almost certain to never be deprecated.
Features and innovations
PaddlePaddle now has developed over 200 pretraining models, some of which include open-source code to aid in the quick creation of industrial applications.
It uses a programmable approach to construct neural networks, making AI creation easier while reducing the technical burden on consumers. It offers expressive programming, as well as developing flexibility, allowing developers to create software that meets a variety of needs while maintaining good runtime efficiency. Algorithms can create neural networks that perform better than those created by humans.
Additionally, PaddlePaddle has also achieved advancements in the training of ultra-large-scale deep neural networks. Its platform, which is the first of its type in the world, allows users to train deep neural networks with over 100 billion features and trillions of parameters from data sources spread across hundreds of nodes. Oppo, a Chinese smartphone manufacturer, is one of the benefactors, having increased the training efficacy of their recommendation system by 80% using PaddlePaddle.
PaddlePaddle is not only interoperable with existing open-source frameworks for model training, but it also speeds up deep neural network inference on a range of CPUs and hardware platforms.