Pretraining and Fine-tuning a Model with Snowflake Cortex AI: An Example

Overview

This example outlines the general process of pretraining and fine-tuning a model using Snowflake Cortex AI. It assumes you have a basic understanding of Snowflake and Python. Actual code and specific configurations depend on the model type and dataset.

Prerequisites

1. Data Preparation

Your data is the foundation. It needs to be well-structured and appropriately formatted for the model you are using. Example data structure might look like this (this is just a *template* and needs modification):

Example Data Table: `MY_DATABASE.MY_SCHEMA.TRAINING_DATA`

Sample Data:

id | text | label
---|---|---
1 | "This is a great product." | positive
2 | "The service was terrible." | negative
3 | "The food was okay." | neutral

2. Pretraining (Illustrative - Specific code depends on model)

Pretraining involves training the model on a large, general dataset to learn underlying language patterns. Snowflake Cortex AI doesn't directly handle all pretraining tasks, but provides tools to orchestrate the process, which often involves external compute. Here’s a conceptual outline:

  1. Define Pretraining Script (Outside Snowflake): Create a Python script that uses a library like Transformers to train your model on a large dataset (potentially loaded from Snowflake or a cloud storage bucket). This script would typically involve:
  2. Create a Snowflake Task (Optional): You can create a Snowflake Task to run this script using a stage. This requires defining a stage to hold the Python script and any dependencies. 
            -- Example (Conceptual - needs adaptation)
        CREATE OR REPLACE TASK pretraining_task
        WAREHOUSE = MY_WAREHOUSE
        SCHEDULE = '10:00 UTC'  -- Example schedule
        AS
        CALL SNOWFLAKE.APP_MAINTENANCE.EXECUTE_STAGE_TASK(
            stage_name = 'MY_STAGE',  -- Your stage to hold the Python script
            procedure_name = 'MY_PYTHON_SCRIPT'
        );
  3. External Compute: Most pretraining requires significant compute resources (GPUs). You'll typically run your training script on a cloud platform like AWS SageMaker, Google Cloud AI Platform, or Azure Machine Learning. Snowflake can orchestrate calling these services through its API or by defining tasks that trigger cloud functions.

3. Fine-tuning

Fine-tuning adapts the pretrained model to your specific task using your labeled dataset. This is where Snowflake Cortex AI shines.

Steps for Fine-tuning in Snowflake Cortex AI

  1. Define a Snowflake Function: Create a Snowflake SQL function that wraps your fine-tuning script. This script will: 
            -- Example (Conceptual - Highly Simplified)
        CREATE OR REPLACE FUNCTION fine_tune_model(model_name VARCHAR, training_table VARCHAR, epochs INTEGER)
        RETURNS VARCHAR  -- Returns a status message
        LANGUAGE PYTHON
        RUNTIME_VERSION = '3.9'
        PACKAGES = ('snowflake-connector-python', 'transformers')
        HANDLER = 'path/to/your/fine_tuning_script.py'
        ;

        -- Example of calling the function:
        SELECT fine_tune_model('my_pretrained_model', 'MY_DATABASE.MY_SCHEMA.TRAINING_DATA', 10);
  2. Fine-tuning Script (Example `fine_tuning_script.py` - Conceptual) 
            import snowflake
        from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments, Dataset

        def fine_tune_model(model_name, training_table, epochs):
            # Connect to Snowflake
            ctx = snowflake.connector.connect(
                account = 'YOUR_SNOWFLAKE_ACCOUNT',
                user = 'YOUR_SNOWFLAKE_USER',
                password = 'YOUR_SNOWFLAKE_PASSWORD',
                database = 'MY_DATABASE',
                schema = 'MY_SCHEMA'
            )

            cursor = ctx.cursor()

            # Load data from Snowflake
            query = f"SELECT text, label FROM {training_table}"
            cursor.execute(query)

            data = []
            for row in cursor:
                data.append({'text': row[0], 'label': row[1]})

            # Convert to a Transformers Dataset
            dataset = Dataset.from_list(data)

            # Load pre-trained model
            model = AutoModelForSequenceClassification.from_pretrained(model_name)

            # Define training arguments
            training_args = TrainingArguments(
                output_dir="./results",
                num_train_epochs=epochs,
                per_device_train_batch_size=8,
                per_device_eval_batch_size=16,
                # other arguments...
            )

            # Define Trainer
            trainer = Trainer(
                model=model,
                args=training_args,
                train_dataset=dataset,
                # eval_dataset=eval_dataset,  # If you have a validation dataset
            )

            # Train the model
            trainer.train()

            # Save the fine-tuned model (e.g., to Snowflake's internal storage)
            # This often requires custom Snowflake integration or using Snowflake Object Storage

            ctx.close()
            return "Fine-tuning complete. Model saved (implementation depends on your Snowflake setup)"
  3. Execute the Function: Run the Snowflake SQL function. Cortex AI will manage the execution of the fine-tuning script.

4. Model Deployment and Inference

After fine-tuning, you can deploy the model and use it for inference. Cortex AI provides features for model deployment and serving, including:

Important Considerations

© Kevin Thomas Luzbetak, MSc