TensorFlow Framework

Context:
- It can (typically) make use of Data Flow Graphs.
- It can (typically) support TensorFlow Tensors (n-dimensional arrays).
- It can (typically) support TensorFlow Graphs (to descrine computations).
- It can (often) be a Deep Learning Framework.
- It can include TensorFlow API's such as tf.contrib.learn ML System [1].
- It can be used to specify a TensorFlow Program (to develop complex machine learning systems).
- It can be managed by a Google Project.
- ...
Example(s):
- TensorFlow 2 family, such as:
  - TensorFlow v2.13.0 (~2023-08-03) [2].
  - ...
- TensorFlow 1 family, such as:
  - TensorFlow v1.6.0 (~2018-02-28) [3].
  - TensorFlow v0.12 (~2016-12-19) [4]
  - TensorFlow v0.8 (~2016-04-13).
- https://github.com/tensorflow/tensorflow/releases
- TensorFlow Demo: http://playground.tensorflow.org
- ...
Counter-Example(s):
- PyTorch.
- MXNet.
- Torch ML Framework.
- Caffe Framework.
- MindSpore Framework.
See: Distributed TensorFlow, Keras, DeepMind, Google Brain, TensorFlow Serving, TensorFlow Cluster.

References

Software	Creator	Software license	Open source	Platform	Written in	Interface	OpenMP support	OpenCL support	CUDA support	Automatic differentiation	Has pretrained models	Recurrent nets	Convolutional nets	RBM/DBNs	Parallel execution (multi node)
TensorFlow	Google Brain team	Apache 2.0	Yes	Linux, Mac OS X, Windows^[1]	C++, Python	Python, C/C++, Java, Go	No	No^[2]^[3]	Yes	Yes^[4]	Yes^[5]	Yes	Yes	Yes	Yes

https://www.tensorflow.org/versions/r0.10/get_started/basic_usage.html#overview
- QUOTE: TensorFlow is a programming system in which you represent computations as graphs. Nodes in the graph are called ops (short for operations). An op takes zero or more Tensors, performs some computation, and produces zero or more Tensors. A Tensor is a typed multi-dimensional array. For example, you can represent a mini-batch of images as a 4-D array of floating point numbers with dimensions [batch, height, width, channels].
  A TensorFlow graph is a description of computations. To compute anything, a graph must be launched in a Session. A Session places the graph ops onto Devices, such as CPUs or GPUs, and provides methods to execute them. These methods return tensors produced by ops as numpy ndarray objects in Python, and as tensorflow::Tensor instances in C and C++.

https://www.tensorflow.org/versions/r0.10/get_started/basic_usage.html#tensors
- QUOTE: TensorFlow programs use a tensor data structure to represent all data -- only tensor are passed between operations in the computation graph. You can think of a TensorFlow tensor as an n-dimensional array or list. A tensor has a static type, a rank, and a shape. To learn more about how TensorFlow handles these concepts, see the Rank, Shape, and Type reference.

(Wikipedia, 2016) ⇒ https://en.wikipedia.org/wiki/TensorFlow Retrieved:2016-5-2.
- TensorFlow is an open source software library for machine learning in various kinds of perceptual and language understanding tasks.^[6] It is a second-generation API which is currently used for both research and production by 50 different teams in dozens of commercial Google products, such as speech recognition, Gmail, Google Photos, and Search. These teams had previously used DistBelief, a first-generation API. TensorFlow was originally developed by the Google Brain team for Google's research and production purposes and later released under the Apache 2.0 open source license on November 9, 2015.

http://googleresearch.blogspot.com/2016/04/deepmind-moves-to-tensorflow.html
- QUOTE: With Google’s recent open source release of TensorFlow, we initiated a project to test its suitability for our research environment. Over the last six months, we have re-implemented more than a dozen different projects in TensorFlow to develop a deeper understanding of its potential use cases and the tradeoffs for research. Today we are excited to announce that DeepMind will start using TensorFlow for all our future research. We believe that TensorFlow will enable us to execute our ambitious research goals at much larger scale and an even faster pace, providing us with a unique opportunity to further accelerate our research programme.