TensorFlow Phases:
Development Phase: This is when you train the mode. Training is usually done on Desktop or laptop.
Run Phase or Inference Phase: Once training is done Tensorflow can be run on many different platforms. Users can run it on
Users can train it on multiple machines then he can run it on a different machine once he has the trained model.
The model can be trained and used on GPUs as well as CPUs. Deep learning relies on a lot of matrix multiplication. TensorFlow is very fast at computing the matrix multiplication because it is written in C++. Although it is implemented in C++, TensorFlow can be accessed and controlled by other languages mainly, Python. Finally, a significant feature of TensorFlow is the TensorBoard. The TensorBoard enables to monitor graphically and visually what TensorFlow is doing.
N.B.: GPUs were initially designed for video games. In late 2010, Stanford researchers found that GPU was also very good at matrix operations and algebra so that it makes them very fast for doing these kinds of calculations.
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