Google Vertex AI for Streamlining AI Development and model fine – tuning

Google Vertex AI is a comprehensive AI platform by Google Cloud aimed at streamlining the development of AI models. It unifies various machine learning services, offering features like foundation models access, a web-based Vertex AI Studio for prototyping, no-code development tools via Agent Builder, AutoML capabilities, and advanced MLOps tools. This platform enhances productivity, collaboration, and flexibility while reducing operational overhead and costs for AI development.

Key Features of Google Vertex AI:

• Unified Platform: Provides a single interface for all stages of the ML workflow, from data preparation and model training to deployment, monitoring, and management.
• Access to Foundation Models: Offers access to a wide range of Google’s powerful foundation models, including the Gemini family, as well as open-source and third-party models through the Model Garden.
• Vertex AI Studio: A web-based interface for rapid prototyping and experimentation with generative AI models, prompt engineering, and model tuning.
• Agent Builder: Enables developers to build and deploy enterprise-ready generative AI applications with no-code tools and customization options.
• AutoML: Allows users with limited coding experience to train high-quality custom ML models for various data types (tabular, image, text, video).
• Custom Training: Provides complete control over the model training process, allowing the use of preferred ML frameworks (TensorFlow, PyTorch, scikit-learn), custom code, and hyperparameter tuning.
• MLOps Tools: Offers purpose-built MLOps capabilities for automating, standardizing, and managing ML projects, including features for model monitoring, versioning, and continuous integration/continuous delivery (CI/CD).
• Feature Store: A centralized repository for storing, managing, and serving machine learning features, improving data consistency and model performance.
• Explainable AI: Helps understand how different features contribute to model predictions, increasing transparency and trust.
• Model Monitoring: Detects issues like data drift and prediction skew in deployed models, ensuring their continued performance.
• Integration with Google Cloud: Seamlessly integrates with other Google Cloud services like BigQuery for data warehousing, Cloud Storage for data storage, and AI Infrastructure for scalable compute resources.
• Generative AI Support: Provides tools and infrastructure for building and deploying generative AI applications for various modalities like text, code, images, speech, and music.

Benefits of using Google Vertex AI:

• Accelerated Development: Speeds up the AI development lifecycle by providing a unified platform and easy-to-use tools.
• Increased Productivity: Empowers data scientists and ML engineers to focus on model building and innovation rather than managing infrastructure and disparate tools.
• Simplified MLOps: Streamlines the deployment, management, and monitoring of ML models, reducing operational overhead.
• Scalability and Flexibility: Leverages Google Cloud’s robust infrastructure to scale AI workloads based on demand and supports various open-source frameworks.
• Cost Efficiency: Optimizes resource utilization and offers pay-as-you-go pricing.
• Improved Collaboration: Enables teams to work together more effectively using a common toolset.
• Faster Time-to-Market: Helps businesses deploy AI-powered applications more quickly.
• Access to Cutting-Edge AI: Provides access to Google’s latest advancements in AI, including powerful foundation models.

Fine-tuning models with Vertex AI

You can fine-tune a variety of models on Google Vertex AI, like Gemini, Gemma, Mistral AI,…. For more details, visit Vertex AI model garden.

You can fine-tune models with Google Vertex AI through several methods, depending on the type of model and your preferred way of working:

1. Using Vertex AI Studio (Google Cloud Console):

• For Generative AI Models (like Gemini):
  • Go to the Vertex AI Studio page in the Google Cloud console.
  • Click on “Create tuned model”.
  • Choose “Supervised tuning” as the tuning method.
  • Configure the “Model details”, including the tuned model name, base model, and region.
  • Optionally, configure “Advanced Options” like the number of epochs, adapter size, and learning rate multiplier.
  • On the “Tuning dataset” page, upload your training data (in JSONL format) from Cloud Storage or select an existing file. You can also optionally add a validation dataset.
  • Click “Start Tuning”.
  • Your tuned model will appear under the Gemini tuned models section on the Tune and Distill page once the process is complete.
• For other model types (through Vertex AI Pipelines):
  • In the Vertex AI section, go to the “Vertex AI Pipelines” page.
  • Click “Create run” and select “Select from existing pipelines”.
  • Choose the appropriate pipeline for your model type (e.g., “llm-text-embedding” for text embeddings).
  • Configure the pipeline parameters, including the Cloud Storage location for your training data and output artifacts.
  • Click “Submit” to create the pipeline run.

2. Using the Vertex AI SDK for Python:

• This method provides more programmatic control over the fine-tuning process.
• You’ll need to install the Vertex AI SDK: Bashpip install google-cloud-aiplatform
• Initialize the Vertex AI SDK with your project ID and region: Pythonimport vertexai vertexai.init(project="your-project-id", location="your-region")
• Load the pre-trained model you want to fine-tune.
• Prepare your training data, typically as a Pandas DataFrame or a Cloud Storage URI to a JSONL file. The data should be formatted as examples with input prompts and expected response outputs for supervised fine-tuning.
• Use the appropriate tuning function provided by the SDK (e.g., model.tune_model() for text generation models).
• Configure the tuning parameters, such as the training data, learning rate multiplier, number of training steps, and the location for the tuned model.
• Run the tuning job.

Here’s a basic example for fine-tuning a text generation model:

Python

from vertexai.preview.language_models import TextGenerationModel

model = TextGenerationModel.from_pretrained("google/text-bison@002") # Replace with your base model

tuned_model = model.tune_model(
    training_data="gs://your-bucket/your_training_data.jsonl",
    tuned_model_name="my-fine-tuned-model",
    learning_rate_multiplier=0.5,
    train_steps=200,
    tuning_job_location="your-region",
)

print(f"Tuned model name: {tuned_model.name}")

3. Using Vertex AI Workbench (Jupyter Notebooks):

• You can perform fine-tuning within a Vertex AI Workbench notebook instance.
• This provides an interactive environment where you can write and execute code using the Vertex AI SDK or other relevant libraries (like TensorFlow or PyTorch).
• You can load and preprocess your data, define your fine-tuning logic, and track the training process within the notebook.
• For large-scale fine-tuning, you can leverage the integration with Vertex AI Training Jobs to run your notebook code on managed compute resources.

4. Using Custom Training Jobs:

• For more advanced fine-tuning scenarios or when you need greater control over the training environment, you can use Vertex AI Custom Training Jobs.
• This involves defining a training script, specifying the compute resources (including GPUs or TPUs), and configuring the training pipeline.
• You can use your preferred ML frameworks (TensorFlow, PyTorch, etc.) within the custom training job.
• This method is often used for fine-tuning open-source models or implementing specialized fine-tuning techniques like LoRA (Low-Rank Adaptation).

Key Considerations for Fine-Tuning:

• Data Preparation: Your training data should be high-quality, relevant to your specific task, and properly formatted. For supervised fine-tuning of generative models, it typically consists of prompt-response pairs.
• Dataset Size: While you can fine-tune with as few as 20 examples, larger and more diverse datasets generally lead to better performance. Aim for at least 100-500 examples depending on the complexity of your task.
• Hyperparameter Tuning: Experiment with different hyperparameters (e.g., learning rate, number of epochs, batch size) to optimize the performance of your fine-tuned model.
• Evaluation: Use a separate validation dataset to monitor the model’s performance during training and a test dataset to evaluate the final tuned model on unseen data.
• Cost and Time: Fine-tuning can be computationally intensive and time-consuming, especially for large models and datasets. Consider the cost implications and plan accordingly.
• Region Availability: Ensure that the base model you want to fine-tune and the Vertex AI services you are using are available in your chosen Google Cloud region.

Fine-tune models from Hugging Face using Google Vertex AI

You can fine-tune models from Hugging Face using Google Vertex AI. Vertex AI provides several ways to achieve this, leveraging its scalable infrastructure and MLOps capabilities. Here are the primary methods:

1. Using Vertex AI Custom Training Jobs:

• This is the most flexible and common approach for fine-tuning Hugging Face models on Vertex AI.
• Process:
  1. Containerize your training script: You’ll need to create a Docker container that includes your Python training script (using libraries like Transformers, Datasets, and potentially Accelerate from Hugging Face), your training data, and any necessary dependencies.
  2. Push the container to Google Container Registry (GCR) or Artifact Registry: Vertex AI will pull this container to run your training job.
  3. Define and submit a Vertex AI Custom Training Job: In your job definition, you’ll specify:
     • The container image you created.
     • The command to execute your training script within the container.
     • The compute resources you need (CPU or GPU instances, number of nodes).
     • The location of your training data in Google Cloud Storage (GCS).
     • The GCS location where you want to save the fine-tuned model.
  4. Your training script will:
     • Download the pre-trained model from Hugging Face Hub using transformers.AutoModelForSequenceClassification.from_pretrained(), transformers.AutoModelForCausalLM.from_pretrained(), etc.
     • Load your training data using datasets.load_dataset() or by reading files from GCS.
     • Implement your fine-tuning logic using the Transformers Trainer API or your own custom training loop (potentially using Accelerate for distributed training).
     • Save the fine-tuned model to the specified GCS location.
• Benefits:
  • Full control over the training process.
  • Ability to use the latest Hugging Face libraries and features.
  • Scalability with Vertex AI’s managed compute infrastructure (including GPUs and TPUs).
  • Integration with Vertex AI’s MLOps features for tracking, monitoring, and deploying your fine-tuned model.

2. Leveraging Pre-built Containers (if available):

• Google Cloud sometimes provides pre-built containers optimized for specific ML tasks, which might include support for Hugging Face libraries. Check the Vertex AI documentation for available pre-built containers that could simplify the process. If a suitable container exists, you can skip the containerization step.

3. Integrating with Vertex AI Pipelines:

• You can incorporate the fine-tuning of a Hugging Face model as a component within a Vertex AI Pipeline. This allows you to automate the entire ML workflow, including data preprocessing, fine-tuning, evaluation, and deployment. You would still likely use a custom container for the fine-tuning step within the pipeline.

Steps Involved (General Outline):

1. Choose a Pre-trained Model: Select the desired model from the Hugging Face Hub (e.g., bert-base-uncased, gpt-2).
2. Prepare Your Dataset: Ensure your training data is in a format compatible with the Hugging Face datasets library or can be easily loaded. Upload your data to Google Cloud Storage.
3. Write Your Training Script: Create a Python script that:
   • Imports necessary libraries from transformers and datasets.
   • Loads the pre-trained model.
   • Loads and preprocesses your training data.
   • Defines your training arguments (learning rate, epochs, batch size, etc.).
   • Initializes the Trainer (or implements a custom training loop).
   • Runs the training process.
   • Saves the fine-tuned model to a specified path (which will be mapped to GCS in the container).
4. Create a Dockerfile: Define the environment for your training script, including Python version, installed libraries (install transformers, datasets, torch or tensorflow, accelerate, etc.), and any other dependencies.
5. Build and Push the Docker Image: Build your Docker image and push it to GCR or Artifact Registry.
6. Define and Submit the Vertex AI Custom Training Job: Use the Google Cloud CLI or the Vertex AI SDK for Python to define and submit your training job, specifying the container image, compute resources, and data paths.
7. Monitor Your Training Job: Track the progress of your fine-tuning job in the Vertex AI console.
8. Deploy Your Fine-Tuned Model: Once training is complete, you can deploy the model stored in GCS using Vertex AI Endpoints for online predictions or Vertex AI Batch Predictions for offline inference.

Example (Conceptual with Vertex AI SDK):

Python

from google.cloud import aiplatform

# Initialize Vertex AI
aiplatform.init(project="your-project-id", location="your-region")

# Define container specification
container_spec = {
    "image_uri": "your-gcr-or-artifact-registry-image:latest",
    "command": [
        "python",
        "/app/train.py",  # Your training script inside the container
        "--model_name", "bert-base-uncased",
        "--train_data_path", "gs://your-bucket/train.csv",
        "--output_dir", "/gcs/your-bucket/fine-tuned-model", # Map to GCS
        "--epochs", "3"
        # ... other training arguments
    ],
}

# Define compute resources
worker_pool_specs = [
    {
        "machine_spec": {
            "machine_type": "n1-standard-4",
            "accelerator_type": "NVIDIA_TESLA_T4",
            "accelerator_count": 1,
        },
        "replica_count": 1,
        "container_spec": container_spec,
    }
]

# Create and run the custom training job
job = aiplatform.CustomJob(
    display_name="fine-tune-huggingface-bert",
    worker_pool_specs=worker_pool_specs,
    staging_bucket="gs://your-staging-bucket",
)

job.run()

print(f"Training job name: {job.name}")

By following these steps, you can effectively leverage the vast collection of pre-trained models on Hugging Face Hub and fine-tune them for your specific tasks using the powerful infrastructure and MLOps capabilities of Google Vertex AI.