TensorFlow is an open-source machine learning framework created by Google. It enables users to use powerful algorithms to create machine learning models and neural networks that can be used for a variety of tasks such as image recognition, natural language processing, and predictive analytics. TensorFlow is widely used in research and industry, allowing developers to quickly develop machine learning models with minimal effort. With its intuitive APIs and extensive library of machine learning algorithms, TensorFlow makes it easy to quickly build complex machine learning systems. Additionally, its scalability allows users to run machine learning applications on everything from mobile devices up to distributed clusters. In short, TensorFlow is a powerful tool for creating machine learning applications that can be deployed anywhere. 

Cerebras software platform has been tightly co-designed with the WSE to be able to take full advantage of its computational resources while still allowing researchers to program using industry-standard Machine Learning frameworks like TensorFlow and PyTorch, without modification. It also provides a rich tool set for users to introspect and debug, and lower-level kernel APIs for extending the platform. 

All of this means exceptional deep learning performance, delivered in a truly plug-and-play configuration. Unlike clusters of graphics processing units, which can take weeks or months to set up, require extensive modifications to existing models, occupy dozens of datacenter racks and require complicated and proprietary InfiniBand to cluster, the CS-1 takes minutes to set up. Simply plug in the 100 Gigabit Ethernet links to a switch and you are ready to start training models at wafer-scale speed. Overcoming these technical hurdles across software and hardware with innovative solutions allowed Cerebras to solve the 70-year-old problem of wafer scale compute, for the first time in the history of chip design.