TensorFlow is an open-source interface and implementation for executing machine learning algorithms on a wide range of heterogeneous systems, from mobile devices to large-scale distributed systems. It supports various algorithms, including deep neural networks, and has been used in multiple fields such as speech recognition, computer vision, and natural language processing. The paper introduces the TensorFlow interface and its implementation, which was released under the Apache 2.0 license in November 2015. TensorFlow's flexibility and performance make it suitable for both research and production environments. The system supports parallelism through replication and parallel execution of core model dataflow graphs, allowing for efficient training and deployment of large-scale machine learning models. The paper also discusses the programming model, basic concepts, single-device and distributed execution, fault tolerance, extensions like gradient computation and partial execution, and optimizations such as common subexpression elimination and lossy compression. Additionally, it covers tools like TensorBoard for visualization and performance tracing.TensorFlow is an open-source interface and implementation for executing machine learning algorithms on a wide range of heterogeneous systems, from mobile devices to large-scale distributed systems. It supports various algorithms, including deep neural networks, and has been used in multiple fields such as speech recognition, computer vision, and natural language processing. The paper introduces the TensorFlow interface and its implementation, which was released under the Apache 2.0 license in November 2015. TensorFlow's flexibility and performance make it suitable for both research and production environments. The system supports parallelism through replication and parallel execution of core model dataflow graphs, allowing for efficient training and deployment of large-scale machine learning models. The paper also discusses the programming model, basic concepts, single-device and distributed execution, fault tolerance, extensions like gradient computation and partial execution, and optimizations such as common subexpression elimination and lossy compression. Additionally, it covers tools like TensorBoard for visualization and performance tracing.