Building Generative AI Applications Using Amazon Bedrock and SageMaker JumpStart

in

You know, manually coding every line and praying to the tech gods that your model doesn’t crash midway through training?

Introducing a new era of AI development: one where you can build generative models with ease using pre-trained checkpoints and customizable pipelines. No more spending hours upon hours tweaking hyperparameters or worrying about data preparation Bedrock and SageMaker JumpStart have got your back!

So, how does it work? Let’s break it down:

1. First, you need to head over to the Amazon SageMaker console and create a new notebook instance. This is where all the magic happens you can write code, run experiments, and train models using your favorite framework (PyTorch, TensorFlow, etc.).

2. Next, you’ll want to install Bedrock and its dependencies using pip:

# Install Bedrock and its dependencies using pip
pip install bedrock-sagemaker==0.13.4

3. Once that’s done, you can load a pre-trained checkpoint for your chosen model (GPT2, BERT, etc.) and start fine-tuning it on your own data:

# Import necessary libraries
import sagemaker as sm

# Load the pre-trained GPT2 checkpoint
gpt2 = BedrockPipeline.load('gpt2') # Load the pre-trained GPT2 model using BedrockPipeline

# Define a custom pipeline for fine-tuning on your data
pipeline = gpt2.add_transform( # Add a transform to the GPT2 model
    sm.Transform( # Create a SageMaker Transform object
        input_name='text', # Specify the input data column name
        output_name='cleaned_text', # Specify the output data column name
        source=my_data, # Specify the input data source
        transform=preprocess # Specify the preprocessing function to be applied to the input data
    )
).fit() # Fit the pipeline to the data

# Train the model using SageMaker's training job API
training_job = pipeline.train({'text': my_data}) # Train the model using the specified input data column name and data source

4. And that’s it! Once your model is trained and ready to go, you can use it for generating new text based on a given prompt:

# Load the fine-tuned GPT2 checkpoint
# Changed variable name from "gpt2" to "model" for clarity
model = BedrockPipeline.load('my_model')

# Generate some new text using your custom pipeline
# Added type annotation for "generated_text" variable
generated_text: str = model.transform({'cleaned_text': my_prompt})['output']
# Added type annotation for "my_prompt" variable
my_prompt: str = "This is my prompt"
# Added explanation for the purpose of the code segment
# This code segment loads the fine-tuned GPT2 model and uses it to generate new text based on a given prompt. The "transform" method takes in a dictionary with the key "cleaned_text" and the value of the prompt, and returns a dictionary with the key "output" and the generated text as the value. The "generated_text" variable is annotated as a string, while the "my_prompt" variable is annotated as a string and given a sample prompt for demonstration purposes.

So, what are you waiting for? Head over to Amazon SageMaker and start building generative AI applications with Bedrock and JumpStart today! And if you need any help along the way, don’t hesitate to reach out we’re here to make your life easier.

SICORPS