What is the difference between CuDNNLSTM and LSTM in Keras?

In Keras, LSTM and CuDNNLSTM are two distinct implementations, primarily differing in their underlying architecture and runtime efficiency.

1. Basic Differences:

   • LSTM: is the standard implementation of the Long Short-Term Memory (LSTM) network, compatible with various backends (such as TensorFlow and Theano) and supports both CPU and GPU execution.
   • CuDNNLSTM: is implemented using NVIDIA's CuDNN library, specifically optimized for efficient operation on NVIDIA GPUs. CuDNN (CUDA Deep Neural Network library) is NVIDIA's GPU-accelerated library designed for deep neural networks.

2. Performance:

   • CuDNNLSTM typically runs faster than the standard LSTM in environments with NVIDIA GPUs due to CuDNN's highly optimized hardware-specific implementation.
   • LSTM is more commonly used in environments without GPUs or with non-NVIDIA GPUs, but generally offers lower performance compared to CuDNNLSTM.

3. Use Cases:

   • If your model requires deployment across diverse hardware platforms (including GPU-less systems) or if you are using a non-NVIDIA GPU, LSTM provides greater flexibility.
   • If your environment includes an NVIDIA GPU and you prioritize high runtime performance, CuDNNLSTM can significantly enhance efficiency.

4. Code Implementation: In Keras, the code for both implementations is similar, but CuDNNLSTM typically omits parameters like activation or recurrent_activation that require adjustment in LSTM, as it defaults to specific activation functions and optimization configurations.

   Example:

   python
   # Using LSTM
   from keras.layers import LSTM
   lstm_layer = LSTM(50, return_sequences=True, input_shape=(10, 64))
   
   # Using CuDNNLSTM
   from keras.layers import CuDNNLSTM
   cudnn_lstm_layer = CuDNNLSTM(50, return_sequences=True, input_shape=(10, 64))

Summary: The choice between these implementations depends on your specific requirements, such as cross-platform compatibility or faster model training speed. With appropriate hardware support, CuDNNLSTM offers a more efficient solution.