Unlock the Power of OpenAI: Design Your ML Pipeline with GPT-3.5
Machine learning (ML) has revolutionized the tech world by providing an ability to “learn” from data. At the forefront of this revolution is OpenAI’s GPT-3.5, an advanced version of the transformer-based language model. This article aims to guide you through the creation of an end-to-end ML pipeline, leveraging GPT-3.5 for automating several routine ML tasks, with the final goal of deploying our model on AWS SageMaker.
Disclaimer: You need a paid subscription to OpenAI for this tutorial but it would not be costly!
Introduction
Before we begin, it is essential to understand why we are using GPT-3.5. This model is the latest in a line of generative pretraining transformers (GPT) that have set the benchmark for natural language processing tasks. The core strength of GPT-3.5 lies in its capability to generate human-like text, giving the model an uncanny ability to understand context and provide relevant responses.
In this guide, we will primarily focus on three critical areas: Extract, Transform, Load (ETL), model training, and model serving. Our aim is to generate a synthetic dataset using GPT-3.5, train an ML model on this data, and finally deploy the model using AWS SageMaker.
Setting Up The Environment & OpenAI
To begin with, we need to set up our Python environment. This requires installing the necessary Python libraries and configuring the OpenAI API for our use.
# Install the necessary libraries
!pip install openai pandas numpy sklearn awscli boto3 sagemaker
# Import the required libraries
import openai
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import boto3
import sagemakerThe next step is to authenticate with the OpenAI API. This requires an API key that you receive upon signing up on the OpenAI platform.
openai.api_key = 'your-api-key'Please replace ‘your-api-key’ with your actual OpenAI API key. This allows your Python script to interact with the OpenAI platform and utilize GPT-3.5’s capabilities.
Data
The ETL phase is an essential part of any data-driven project. This phase involves gathering data (extract), modifying it into a form suitable for our operations (transform), and finally storing it in a suitable location for further use (load).
However, in our case, instead of extracting data from a database or a data warehouse, we will be using GPT-3.5 to generate our synthetic dataset. This underlines one of the key uses of the model — creating datasets when there is no pre-existing data or augmenting existing datasets to improve model training. The primary advantage here is the control we have over the data generation process, allowing us to create precisely the kind of dataset we need.
# Generate synthetic data using GPT-3.5
response = openai.Completion.create(
engine="text-davinci-003.5",
prompt="Generate a dataset of 1000 examples for binary classification with two features, 'age' and 'income'. The label should be 'can afford luxury car'.",
max_tokens=5000
)
# Extract the text
data_text = response.choices[0].text.strip()
# Transform the data into a structured format
data_lines = data_text.split("\n")
data_dict = {'age': [], 'income': [], 'can_afford_luxury_car': []}
for line in data_lines:
age, income, can_afford = line.split(",")
data_dict['age'].append(float(age))
data_dict['income'].append(float(income))
data_dict['can_afford_luxury_car'].append(int(can_afford))
# Load into a DataFrame
df = pd.DataFrame(data_dict)In the above Python code, we are sending a prompt to the GPT-3.5 model to create a dataset for a binary classification problem. The problem involves determining whether individuals of varying ages and income levels can afford a luxury car. The prompt is formulated to be clear and direct, specifying the number of examples and the kind of data we require.
GPT-3.5 responds to this prompt and returns a large text string containing our dataset, which is then extracted, transformed into a structured format, and loaded into a Pandas DataFrame. Pandas is a Python library offering data structures and data analysis tools that are ideal for this kind of data manipulation.
Model Training
Once our dataset is ready, we move on to the model training phase. This is where we create our ML model and train it on our synthetic data. The goal here is to allow the model to ‘learn’ from this data and make accurate predictions when presented with new, unseen data.
# Split data into training and test set
train_df, test_df = train_test_split(df, test_size=0.2)
# Train the model with GPT-3.5
training_prompt = f"Train a binary classification model with the following training data:\n{train_df.to_csv(index=False)}"
response = openai.Completion.create(
engine="text-davinci-003.5",
prompt=training_prompt,
max_tokens=500
)
# Extract the model
model_text = response.choices[0].text.strip()Here, we first split our dataset into a training set and a test set. The training set is used to train our ML model, while the test set is used to evaluate the model’s performance on unseen data.
Next, we prompt GPT-3.5 to train a binary classification model with our training data. The model, once trained, can be used to predict whether a new individual can afford a luxury car based on their age and income.
Model Deployment
With our trained ML model at hand, we are ready to deploy it on AWS SageMaker. The final step of our pipeline involves taking our model from a local environment to a cloud-based one, enabling it to be accessed and used for predictions over the internet.
# Convert DataFrame to CSV and save
train_df.to_csv('train.csv', index=False, header=False)
test_df.to_csv('test.csv', index=False, header=False)
# Upload the dataset to S3
sagemaker_session = sagemaker.Session()
bucket = sagemaker_session.default_bucket()
prefix = 'gpt-3.5-demo'
train_location = sagemaker_session.upload_data('train.csv', bucket=bucket, key_prefix=prefix)
test_location = sagemaker_session.upload_data('test.csv', bucket=bucket, key_prefix=prefix)
# Define the SageMaker estimator
from sagemaker import get_execution_role
role = get_execution_role()
container = sagemaker.image_uris.retrieve('xgboost', boto3.Session().region_name, 'latest')
xgb = sagemaker.estimator.Estimator(container,
role,
instance_count=1,
instance_type='ml.m4.xlarge',
output_path='s3://{}/{}/output'.format(bucket, prefix),
sagemaker_session=sagemaker_session)
# Set hyperparameters and fit the model
xgb.set_hyperparameters(max_depth=5,
eta=0.2,
gamma=4,
min_child_weight=6,
subsample=0.8,
objective='binary:logistic',
early_stopping_rounds=10,
num_round=200)
s3_input_train = sagemaker.inputs.TrainingInput(s3_data=train_location, content_type='csv')
s3_input_test = sagemaker.inputs.TrainingInput(s3_data=test_location, content_type='csv')
xgb.fit({'train': s3_input_train, 'validation': s3_input_test})
# Deploy the model
xgb_predictor = xgb.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')# Convert DataFrame to CSV and save
train_df.to_csv('train.csv', index=False, header=False)
test_df.to_csv('test.csv', index=False, header=False)
# Upload the dataset to S3
sagemaker_session = sagemaker.Session()
bucket = sagemaker_session.default_bucket()
prefix = 'gpt-3.5-demo'
train_location = sagemaker_session.upload_data('train.csv', bucket=bucket, key_prefix=prefix)
test_location = sagemaker_session.upload_data('test.csv', bucket=bucket, key_prefix=prefix)
# Define the SageMaker estimator
from sagemaker import get_execution_role
role = get_execution_role()
container = sagemaker.image_uris.retrieve('xgboost', boto3.Session().region_name, 'latest')
xgb = sagemaker.estimator.Estimator(container,
role,
instance_count=1,
instance_type='ml.m4.xlarge',
output_path='s3://{}/{}/output'.format(bucket, prefix),
sagemaker_session=sagemaker_session)
# Set hyperparameters and fit the model
xgb.set_hyperparameters(max_depth=5,
eta=0.2,
gamma=4,
min_child_weight=6,
subsample=0.8,
objective='binary:logistic',
early_stopping_rounds=10,
num_round=200)
s3_input_train = sagemaker.inputs.TrainingInput(s3_data=train_location, content_type='csv')
s3_input_test = sagemaker.inputs.TrainingInput(s3_data=test_location, content_type='csv')
xgb.fit({'train': s3_input_train, 'validation': s3_input_test})
# Deploy the model
xgb_predictor = xgb.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')In this section, we save our training and test datasets as CSV files, upload them to an S3 bucket, and utilize the power of AWS SageMaker to train an XGBoost model on this data. After the training process, we deploy the model, which provides us with an endpoint. This endpoint can be used by various applications to make predictions.
Conclusion
In conclusion, this comprehensive guide has given a detailed walkthrough of creating an end-to-end ML pipeline using GPT-3.5 and AWS SageMaker. It provides a novel way of automating several tasks in ML projects, showcasing the power of GPT-3.5 in ML pipeline automation. There might be challenges and edge cases not covered in this guide, but it provides a solid foundation to start using GPT-3.5 in your ML projects.
Happy experimenting!
Originally published at https://www.machinelearningmindset.com on June 3, 2023.
