TF32 Tensor Cores in cuDNN and cuBLAS for Deep Learning

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We’re talking about TF32 Tensor Cores in cuDNN and cuBLAS for deep learning. If you don’t know what any of those acronyms mean, don’t worry we’ll break it down for you!

First off, TF32 (short for TensorFlow 32-bit floating point). This is a new data type that Google introduced in their popular machine learning framework, TensorFlow. It allows us to use half precision floating point numbers instead of the standard full precision ones we normally use. Why would you want to do this? Well, it turns out that using half precision numbers can result in up to 2x faster training and inference times for deep learning models!

NVIDIA has also introduced cuDNN (short for CUDA Deep Neural Network) and cuBLAS (short for CUBLAS a library of optimized linear algebra kernels). These libraries allow us to take advantage of the Tensor Cores in NVIDIA GPUs, which are specialized hardware units designed specifically for matrix operations. By using these libraries with TF32 data type, we can achieve even faster training and inference times!

So how do you use all this magic? Well, it’s actually pretty simple just add a few lines of code to your TensorFlow model. Here’s an example:

# Import necessary libraries
import tensorflow as tf
from tensorflow.lite.interpreter import Interpreter

# Load the model and convert it to TF32 data type
converter = tf.lite.TFLiteConverter.from_keras_model(my_model) # Create a converter object to convert the model to TFLite format
tflite_model = converter.convert() # Convert the model to TFLite format
with open('mymodel.tflite', 'wb') as f:
    f.write(tflite_model) # Write the converted model to a file

# Load the converted model and set it to use TF32 data type
interpreter = Interpreter('mymodel.tflite') # Create an interpreter object to load the converted model
interpreter.set_tensor_arrays_to_quantize([input_name], [output_name]) # Set the input and output arrays to be quantized
interpreter.allocate_tensors() # Allocate memory for the interpreter
interpreter.set_num_threads(8) # Set the number of threads to use for inference
interpreter.set_tensor_data_type({'input': tf.float16}) # Set input data type to TF32

# Run inference on the model using cuDNN and cuBLAS with Tensor Cores
start = time.time() # Record the start time
output = interpreter.get_outputs()[0] # Get the output tensor from the interpreter
for i in range(num_samples):
    feed_dict[input_name] = input_data[i, :] # Create a feed dictionary with input data for each sample
    interpreter.set_tensor(input_name, feed_dict[input_name]) # Set the input tensor for the interpreter
    interpreter.invoke() # Run inference on the input data
end = time.time() # Record the end time

And that’s it! By using TF32 data type and cuDNN/cuBLAS with Tensor Cores, you can achieve up to 2x faster training and inference times for your deep learning models on NVIDIA GPUs. So what are you waiting for? Go out there and make your models go faster than Usain Bolt running through molasses!

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