TensorFlow is an open-source deep learning framework developed by the Google Brain team and released publicly in November 2015. It provided a way to define neural networks as computational graphs of tensor operations and to train them efficiently across CPUs, GPUs, and Google's own TPU chips, and it became one of the two dominant deep learning frameworks of the late 2010s, alongside PyTorch.
History
TensorFlow succeeded DistBelief, an earlier, less flexible internal system Google had used for large-scale neural network training. Its 2015 open-source release, followed by a 1.0 stable release in 2017, made Google's large-scale deep learning tooling available to outside researchers and companies for the first time and helped popularize deep learning as a mainstream engineering discipline beyond academic labs. TensorFlow's original design used a define-and-run static computation graph: a user first built the full graph of operations, then executed it, an approach that enabled aggressive performance optimization and easy deployment but made debugging and iterating on model architecture more cumbersome than the dynamic graph approach PyTorch introduced in 2016. Google responded with TensorFlow 2.0 in 2019, which made eager, dynamic execution the default and integrated Keras, a higher-level, more user-friendly API, as its primary interface.
Ecosystem
Beyond the core training library, TensorFlow's ecosystem included TensorFlow Lite for mobile and edge AI deployment, TensorFlow.js for running models in a web browser, TensorFlow Extended for production machine learning pipelines, and TensorBoard, a widely used visualization tool for monitoring training. Keras, originally an independent project, became tightly integrated with TensorFlow as its recommended high-level API, lowering the barrier for newcomers to define and train models without writing low-level graph code directly.
Decline in research share and current status
Despite TensorFlow's early dominance, PyTorch's more researcher-friendly dynamic execution model drove a steady shift in academic research toward PyTorch through the late 2010s and early 2020s, and by around 2022 the majority of new papers at major machine learning conferences used PyTorch rather than TensorFlow. TensorFlow retained a stronger footprint in some production and mobile deployment contexts, partly due to its maturity and Google-backed tooling, and remained widely used in industry systems built before the shift, as well as within Google itself. As of the mid-2020s, TensorFlow is generally regarded as the framework that helped establish large-scale neural network training as mainstream engineering practice and that trained many influential early deep learning systems, even as PyTorch and, for very large-scale training, Google's own JAX library became more prominent choices for new large language model and foundation model development within Google and across the field.