|Developer(s)||Google Brain Team|
|Initial release||November 9, 2015|
1.13.1 / February 25, 2019
|Written in||Python, C++, CUDA|
|Type||Machine learning library|
|License||Apache License 2.0|
TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks. It is used for both research and production at Google.:min 0:15/2:17 :p.2 :0:26/2:17
Starting in 2011, Google Brain built DistBelief as a proprietary machine learning system based on deep learning neural networks. Its use grew rapidly across diverse Alphabet companies in both research and commercial applications. Google assigned multiple computer scientists, including Jeff Dean, to simplify and refactor the codebase of DistBelief into a faster, more robust application-grade library, which became TensorFlow. In 2009, the team, led by Geoffrey Hinton, had implemented generalized backpropagation and other improvements which allowed generation of neural networks with substantially higher accuracy, for instance a 25% reduction in errors in speech recognition.
TensorFlow is Google Brain's second-generation system. Version 1.0.0 was released on February 11, 2017. While the reference implementation runs on single devices, TensorFlow can run on multiple CPUs and GPUs (with optional CUDA and SYCL extensions for general-purpose computing on graphics processing units). TensorFlow is available on 64-bit Linux, macOS, Windows, and mobile computing platforms including Android and iOS.
Its flexible architecture allows for the easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices.
TensorFlow computations are expressed as stateful dataflow graphs. The name TensorFlow derives from the operations that such neural networks perform on multidimensional data arrays, which are referred to as tensors. During the Google I/O Conference in June 2016, Jeff Dean stated that 1,500 repositories on GitHub mentioned TensorFlow, of which only 5 were from Google.
Tensor processing unit (TPU)
In May 2016, Google announced its Tensor Processing Unit (TPU), an application-specific integrated circuit (a hardware chip) built specifically for machine learning and tailored for TensorFlow. TPU is a programmable AI accelerator designed to provide high throughput of low-precision arithmetic (e.g., 8-bit), and oriented toward using or running models rather than training them. Google announced they had been running TPUs inside their data centers for more than a year, and had found them to deliver an order of magnitude better-optimized performance per watt for machine learning.
In May 2017, Google announced the second-generation, as well as the availability of the TPUs in Google Compute Engine. The second-generation TPUs deliver up to 180 teraflops of performance, and when organized into clusters of 64 TPUs, provide up to 11.5 petaflops.
In May 2018, Google announced the third-generation TPUs delivering up to 420 teraflops of performance and 128 GB HBM. Cloud TPU v3 Pods offer 100+ petaflops of performance and 32 TB HBM.
In July 2018, the Edge TPU was announced. Edge TPU is Google’s purpose-built ASIC chip designed to run TensorFlow Lite machine learning (ML) models on small client computing devices such as smartphones known as edge computing.
In May 2017, Google announced a software stack specifically for mobile development, TensorFlow Lite. In January 2019, TensorFlow team released a developer preview of the mobile GPU inference engine with OpenGL ES 3.1 Compute Shaders on Android devices and Metal Compute Shaders on iOS devices. In May 2019, Google announced that their TensorFlow Lite Micro (also known as TensorFlow Lite for Microcontrollers) and ARM's uTensor would be merging.
Pixel Visual Core (PVC)
In October 2017, Google released the Google Pixel 2 which featured their Pixel Visual Core (PVC), a fully programmable image, vision and AI processor for mobile devices. The PVC supports TensorFlow for machine learning (and Halide for image processing).
Google also released Colaboratory, which is a TensorFlow Jupyter notebook environment that requires no setup to use.
Machine Learning Crash Course (MLCC)
On March 1, 2018, Google released its Machine Learning Crash Course (MLCC). Originally designed to help equip Google employees with practical artificial intelligence and machine learning fundamentals, Google rolled out its free TensorFlow workshops in several cities around the world before finally releasing the course to the public.
Among the applications for which TensorFlow is the foundation, are automated image-captioning software, such as DeepDream. RankBrain now handles a substantial number of search queries, replacing and supplementing traditional static algorithm-based search results.
- "Credits". TensorFlow.org. Retrieved November 10, 2015.
- An Open Source Machine Learning Framework for Everyone: tensorflow/tensorflow, tensorflow, May 7, 2019, retrieved May 7, 2019
- "TensorFlow 2.0 Beta". TensorFlow. Retrieved June 8, 2019.
- "TensorFlow.js". Retrieved June 28, 2018.
TensorFlow.js has an API similar to the TensorFlow Python API, however it does not support all of the functionality of the TensorFlow Python API.
- "TensorFlow: Open source machine learning" "It is machine learning software being used for various kinds of perceptual and language understanding tasks" — Jeffrey Dean, minute 0:47 / 2:17 from YouTube clip
- Dean, Jeff; Monga, Rajat; et al. (November 9, 2015). "TensorFlow: Large-scale machine learning on heterogeneous systems" (PDF). TensorFlow.org. Google Research. Retrieved November 10, 2015.
- Metz, Cade (November 9, 2015). "Google Just Open Sourced TensorFlow, Its Artificial Intelligence Engine". Wired. Retrieved November 10, 2015.
- Perez, Sarah (November 9, 2015). "Google Open-Sources The Machine Learning Tech Behind Google Photos Search, Smart Reply And More". TechCrunch. Retrieved November 11, 2015.
- Oremus, Will (November 9, 2015). "What Is TensorFlow, and Why Is Google So Excited About It?". Slate. Retrieved November 11, 2015.
- Ward-Bailey, Jeff (November 25, 2015). "Google chairman: We're making 'real progress' on artificial intelligence". CSMonitor. Retrieved November 25, 2015.
- "Tensorflow Release 1.0.0".
- Metz, Cade (November 10, 2015). "TensorFlow, Google's Open Source AI, Points to a Fast-Changing Hardware World". Wired. Retrieved November 11, 2015.
- Machine Learning: Google I/O 2016 Minute 07:30/44:44 accessdate=2016-06-05
- TensorFlow (January 14, 2019). "What's coming in TensorFlow 2.0". Medium. Retrieved May 24, 2019.
- TensorFlow (May 9, 2019). "Introducing TensorFlow Graphics: Computer Graphics Meets Deep Learning". Medium. Retrieved May 24, 2019.
- Jouppi, Norm. "Google supercharges machine learning tasks with TPU custom chip". Google Cloud Platform Blog. Retrieved May 19, 2016.
- "Build and train machine learning models on our new Google Cloud TPUs". Google. May 17, 2017. Retrieved May 18, 2017.
- "Cloud TPU". Google Cloud. Retrieved May 24, 2019.
- "Cloud TPU machine learning accelerators now available in beta". Google Cloud Platform Blog. Retrieved February 12, 2018.
- Kundu, Kishalaya (July 26, 2018). "Google Announces Edge TPU, Cloud IoT Edge at Cloud Next 2018". Beebom. Retrieved February 2, 2019.
- "Google's new machine learning framework is going to put more AI on your phone".
- TensorFlow (January 16, 2019). "TensorFlow Lite Now Faster with Mobile GPUs (Developer Preview)". Medium. Retrieved May 24, 2019.
- "uTensor and Tensor Flow Announcement | Mbed". os.mbed.com. Retrieved May 24, 2019.
- "Colaboratory – Google". research.google.com. Retrieved November 10, 2018.
- "Machine Learning Crash Course with TensorFlow APIs". Google.
- "All symbols in TensorFlow | TensorFlow". TensorFlow. Retrieved February 18, 2018.
- "TensorFlow Version Compatibility | TensorFlow". TensorFlow. Retrieved May 10, 2018.
Some API functions are explicitly marked as "experimental" and can change in backward incompatible ways between minor releases. These include other languages
- "API Documentation". Retrieved June 27, 2018.
- "Swift for TensorFlow". Retrieved June 28, 2018.
Swift for TensorFlow is an early stage research project. It has been released to enable open source development and is not yet ready for general use by machine learning developers. The API is subject to change at any time.
- Icaza, Miguel de (February 17, 2018), TensorFlowSharp: TensorFlow API for .NET languages, retrieved February 18, 2018
- haskell: Haskell bindings for TensorFlow, tensorflow, February 17, 2018, retrieved February 18, 2018
- "malmaud/TensorFlow.jl". GitHub. Retrieved June 28, 2018.
- tensorflow: TensorFlow for R, RStudio, February 17, 2018, retrieved February 18, 2018
- Platanios, Anthony (February 17, 2018), tensorflow_scala: TensorFlow API for the Scala Programming Language, retrieved February 18, 2018
- rust: Rust language bindings for TensorFlow, tensorflow, February 17, 2018, retrieved February 18, 2018
- Mazare, Laurent (February 16, 2018), tensorflow-ocaml: OCaml bindings for TensorFlow, retrieved February 18, 2018
- "fazibear/tensorflow.cr". GitHub. Retrieved October 10, 2018.
- Byrne, Michael (November 11, 2015). "Google Offers Up Its Entire Machine Learning Library as Open-Source Software". Vice. Retrieved November 11, 2015.
- Woollaston, Victoria (November 10, 2015). "Google releases TensorFlow – Search giant makes its artificial intelligence software available to the public". DailyMail. Retrieved November 25, 2015.