What are the benefits of mixed precision training in deep learning?
Mixed precision training allows models to use both 16-bit and 32-bit floating point types, which can speed up training and reduce memory usage.
How does it improve training speed?
By using 16-bit precision for most calculations, it reduces the amount of data processed, leading to faster computations on compatible hardware.
Are there any downsides to using mixed precision?
Yes, potential downsides include numerical instability and the need for careful tuning to ensure model accuracy is maintained.
Which frameworks support mixed precision training?
Popular frameworks like TensorFlow and PyTorch have built-in support for mixed precision training, making it easier to implement.
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