So basically, we have this fancy thing called AWS Neuron that lets us do some serious machine learning stuff on the cloud. And to make it even better, they introduced these Inf1 instances that are specifically designed for deep learning inferencing workloads.
But here’s where things get interesting: you can use Hugging Face TorchScript models with AWS Neuron! This means we can convert our pre-trained BertModel (which is a fancy way of saying “we already trained this model and now it knows how to do cool stuff”) into something that works on Inf1 instances.
So, let’s say you have some text data that you want to analyze using the BertModel. First, we need to import all the necessary libraries:
# Import the necessary libraries for using the BertModel
from transformers import BertModel, BertTokenizer, BertConfig
# Import the torch library for using neural networks
import torch
# Import the torch.neuron library for optimizing the model for Inf1 instances
import torch.neuron
# Create an instance of the BertModel class, which is a pre-trained model for text analysis
model = BertModel()
# Create an instance of the BertTokenizer class, which is used to tokenize text data for the model
tokenizer = BertTokenizer()
# Create an instance of the BertConfig class, which contains the configuration for the model
config = BertConfig()
# Load the pre-trained weights and configuration for the model
model = BertModel.from_pretrained('bert-base-uncased', config=config)
# Set the model to evaluation mode, which disables dropout and batch normalization layers
model.eval()
# Convert the model to a Neuron-optimized version for Inf1 instances
model = torch.neuron.trace(model)
# Create a sample text input for the model
input_text = "This is a sample input for the BertModel."
# Tokenize the input text using the BertTokenizer
input_ids = tokenizer.encode(input_text, add_special_tokens=True)
# Convert the input_ids to a torch tensor
input_ids = torch.tensor(input_ids)
# Use the model to generate embeddings for the input text
outputs = model(input_ids)
# Print the embeddings generated by the model
print(outputs)
Next, let’s load our pre-trained model and tokenize some text data using the `BertTokenizer`. We also need to convert this into a format that AWS Neuron can understand:
# Load pre-trained model using BertModel and tokenize text data using BertTokenizer
model = BertModel.from_pretrained("bert-base-uncased", torchscript=True) # Load pre-trained model from Hugging Face library and set torchscript to True for compatibility with AWS Neuron
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') # Load pre-trained tokenizer from Hugging Face library
# Convert text data into a format that AWS Neuron can understand
input_text = "This is some text data that we want to analyze using the BertModel."
input_ids = tokenizer(input_text, return_tensors='pt').to(device) # Tokenize input text using the pre-trained tokenizer and convert it into a PyTorch tensor for compatibility with AWS Neuron
Now, let’s trace our model and save it as a `.pt` file:
# Tracing the model using torch.jit.trace() function
traced_model = torch.jit.trace(model, [input_ids]) # torch.jit.trace() function takes in two parameters: the model to be traced and the input data. It returns a traced model that can be saved and used for inference.
# Saving the traced model as a .pt file
torch.jit.save(traced_model, "traced_bert.pt") # torch.jit.save() function takes in two parameters: the traced model and the file name to save it as. It saves the traced model as a .pt file, which can be loaded and used for inference later.
And that’s it! You can now load your traced model and use it for inference:
# Load the traced model using the torch.jit.load() function and assign it to the variable loaded_model
loaded_model = torch.jit.load("traced_bert.pt")
# Set the model to evaluation mode using the .eval() method
loaded_model.eval()
# Perform inference on the model by passing in the input_ids as an argument and assign the output to the variable output
output = loaded_model(input_ids)
And that’s how you can use Hugging Face TorchScript models with AWS Neuron! It might seem like a lot of work, but trust me it’s worth it for the performance boost and cost savings. Plus, who doesn’t love playing around with fancy machine learning stuff?