Currently, I am using the default 64 as the batch size for the seq2seq tensorflow model. What is the maximum batch size , layer size etc I can go with a single Titan X GPU with 12 GB RAM with Haswell-E xeon 128GB RAM. The input data is converted to embeddings. Following are some helpful parameters I am using , it seems the cell input size is 1024:
encoder_inputs: a list of 2D Tensors [batch_size x cell.input_size].
decoder_inputs: a list of 2D Tensors [batch_size x cell.input_size].
tf.app.flags.DEFINE_integer("size", 1024, "Size of each model layer.")
So based on my hardware what is the maximum batch size , layers, input size I can go? Currently the GPU shows that 99% memory is occupied.