[AIR-3][AIS-3][BPC-3][RES-3]

AI Model Development Guide

This guide outlines the process for developing and training AI models for the Anya Core platform.

Table of Contents

Overview

This guide covers the end-to-end process of developing AI models for Anya Core, from setting up the development environment to deploying models in production.

Development Environment

Prerequisites

  • Python 3.8+
  • CUDA 11.7+ (for GPU acceleration)
  • Docker (optional)
  • Git LFS (for large model files)

Setup

  1. Clone the repository:

bash git clone https://github.com/anya-org/anya-core.git cd anya-core/ai

  1. Create a virtual environment:

bash python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate

  1. Install dependencies:

bash pip install -r requirements-dev.txt pip install -e .

  1. Install pre-commit hooks:

bash pre-commit install

Project Structure

ai/
├── configs/             # Model configurations
├── data/                # Dataset storage
│   ├── raw/            # Raw data
│   ├── processed/      # Processed data
│   └── splits/         # Train/val/test splits
├── models/             # Model implementations
├── notebooks/          # Jupyter notebooks
├── scripts/            # Utility scripts
├── tests/              # Unit and integration tests
├── training/           # Training pipelines
└── utils/              # Utility functions

Model Architecture

Design Principles

  1. Modularity: Separate model architecture from training logic
  2. Reproducibility: Ensure experiments are reproducible
  3. Scalability: Design for distributed training
  4. Maintainability: Follow clean code principles

Example Model

# models/transformer.py
import torch
import torch.nn as nn

class TransformerModel(nn.Module):
    def __init__(self, vocab_size, d_model, nhead, num_layers, dropout=0.1):
        super().__init__()
        self.embedding = nn.Embedding(vocab_size, d_model)
        self.pos_encoder = PositionalEncoding(d_model, dropout)
        encoder_layers = nn.TransformerEncoderLayer(d_model, nhead, dropout=dropout)
        self.transformer_encoder = nn.TransformerEncoder(encoder_layers, num_layers)
        self.decoder = nn.Linear(d_model, vocab_size)

    def forward(self, src, src_mask=None):
        src = self.embedding(src) * math.sqrt(self.d_model)
        src = self.pos_encoder(src)
        output = self.transformer_encoder(src, src_mask)
        output = self.decoder(output)
        return output

Training Pipeline

Data Preparation

  1. Data Loading
from torch.utils.data import Dataset, DataLoader

class TextDataset(Dataset):
    def __init__(self, texts, labels, tokenizer, max_length):
        self.texts = texts
        self.labels = labels
        self.tokenizer = tokenizer
        self.max_length = max_length

    def __len__(self):
        return len(self.texts)

    def __getitem__(self, idx):
        text = str(self.texts[idx])
        label = self.labels[idx]

        encoding = self.tokenizer(
            text,
            max_length=self.max_length,
            padding='max_length',
            truncation=True,
            return_tensors='pt'
        )

        return {
            'input_ids': encoding['input_ids'].flatten(),
            'attention_mask': encoding['attention_mask'].flatten(),
            'labels': torch.tensor(label, dtype=torch.long)
        }
  1. Training Loop
def train_epoch(model, data_loader, optimizer, device, scheduler=None):
    model.train()
    total_loss = 0

    for batch in tqdm(data_loader, desc="Training"):
        input_ids = batch['input_ids'].to(device)
        attention_mask = batch['attention_mask'].to(device)
        labels = batch['labels'].to(device)

        optimizer.zero_grad()
        outputs = model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            labels=labels
        )

        loss = outputs.loss
        total_loss += loss.item()

        loss.backward()
        torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
        optimizer.step()

        if scheduler:
            scheduler.step()

    return total_loss / len(data_loader)

Hyperparameter Tuning

Use Optuna for hyperparameter optimization:

import optuna

def objective(trial):
    # Define hyperparameters to tune
    lr = trial.suggest_float('lr', 1e-5, 1e-3, log=True)
    batch_size = trial.suggest_categorical('batch_size', [16, 32, 64])
    num_epochs = trial.suggest_int('num_epochs', 3, 10)

    # Initialize model and data loaders
    model = initialize_model()
    train_loader, val_loader = get_data_loaders(batch_size)

    # Training loop
    optimizer = torch.optim.AdamW(model.parameters(), lr=lr)

    for epoch in range(num_epochs):
        train_loss = train_epoch(model, train_loader, optimizer)
        val_loss = evaluate(model, val_loader)

        # Report intermediate results
        trial.report(val_loss, epoch)

        # Handle pruning
        if trial.should_prune():
            raise optuna.TrialPruned()

    return val_loss

study = optuna.create_study(direction='minimize')
study.optimize(objective, n_trials=100)

Evaluation

Metrics

from sklearn.metrics import (
    accuracy_score, precision_score, 
    recall_score, f1_score, roc_auc_score
)

def calculate_metrics(preds, labels):
    return {
        'accuracy': accuracy_score(labels, preds),
        'precision': precision_score(labels, preds, average='weighted'),
        'recall': recall_score(labels, preds, average='weighted'),
        'f1': f1_score(labels, preds, average='weighted'),
        'roc_auc': roc_auc_score(labels, preds, multi_class='ovr')
    }

Cross-Validation

from sklearn.model_selection import cross_val_score, StratifiedKFold

# Define cross-validation strategy
cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)

# Perform cross-validation
scores = cross_val_score(
    model, X, y, 
    cv=cv,
    scoring='f1_weighted',
    n_jobs=-1
)

print(f"Cross-validation scores: {scores}")
print(f"Mean CV score: {scores.mean():.4f} ± {scores.std():.4f}")

Deployment

Model Packaging

import torch
from transformers import AutoModelForSequenceClassification

# Load trained model
model = AutoModelForSequenceClassification.from_pretrained("path/to/model")

# Save model
model.save_pretrained("deploy/model")


# Convert to ONNX
dummy_input = torch.zeros(1, 128, dtype=torch.long)
torch.onnx.export(
    model,
    dummy_input,
    "deploy/model.onnx",
    input_names=["input_ids"],
    output_names=["logits"],
    dynamic_axes={
        "input_ids": {0: "batch", 1: "sequence"},
        "logits": {0: "batch"}
    }
)

API Service

from fastapi import FastAPI
from pydantic import BaseModel
import torch

app = FastAPI()
model = None

def load_model():
    global model
    model = torch.jit.load("deploy/model.pt")
    model.eval()

class TextRequest(BaseModel):
    text: str

@app.post("/predict")
async def predict(request: TextRequest):
    inputs = tokenizer(
        request.text,
        return_tensors="pt",
        padding=True,
        truncation=True,
        max_length=128
    )

    with torch.no_grad():
        outputs = model(**inputs)

    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
    pred = torch.argmax(probs, dim=-1).item()

    return {"prediction": pred, "confidence": probs[0][pred].item()}

Monitoring

Logging

import logging
from logging.handlers import RotatingFileHandler
import json

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        RotatingFileHandler('logs/model.log', maxBytes=10485760, backupCount=5),
        logging.StreamHandler()
    ]
)

logger = logging.getLogger(__name__)

def log_prediction(input_data, prediction, confidence):
    log_entry = {
        'timestamp': datetime.utcnow().isoformat(),
        'input': input_data,
        'prediction': prediction,
        'confidence': confidence,
        'metadata': {
            'model_version': '1.0.0',
            'environment': 'production'
        }
    }
    logger.info(json.dumps(log_entry))

Performance Monitoring

from prometheus_client import start_http_server, Summary, Counter, Gauge

# Create metrics
REQUEST_LATENCY = Summary('request_latency_seconds', 'Request latency')
PREDICTION_COUNTER = Counter('predictions_total', 'Total predictions', ['label'])
MODEL_CONFIDENCE = Gauge('model_confidence', 'Model confidence', ['label'])

@REQUEST_LATENCY.time()
def predict(input_data):
    # Make prediction
    prediction, confidence = model.predict(input_data)

    # Update metrics
    PREDICTION_COUNTER.labels(label=prediction).inc()
    MODEL_CONFIDENCE.labels(label=prediction).set(confidence)

    return prediction, confidence

# Start metrics server
start_http_server(8000)

Best Practices

1. Code Quality

  • Follow PEP 8 style guide
  • Use type hints
  • Write docstrings and comments
  • Maintain high test coverage

2. Model Development

  • Start with a simple baseline
  • Use version control for models
  • Document all experiments
  • Track hyperparameters and metrics

3. Reproducibility

  • Use fixed random seeds
  • Pin dependency versions
  • Save model checkpoints
  • Log all hyperparameters

4. Performance

  • Profile your code
  • Use mixed precision training
  • Optimize data loading
  • Implement gradient accumulation

Troubleshooting

Common Issues

  1. CUDA Out of Memory
  2. Reduce batch size
  3. Use gradient accumulation

  4. Enable gradient checkpointing

  5. Training is Slow

  6. Enable mixed precision training
  7. Use a larger batch size

  8. Profile data loading

  9. Model Not Converging

  10. Check learning rate
  11. Verify data preprocessing
  12. Try a different optimizer

Debugging Tools

# Check for NaNs
torch.autograd.set_detect_anomaly(True)


# Profile code
with torch.profiler.profile() as prof:
    # Your code here
    pass
print(prof.key_averages().table(sort_by="self_cuda_time_total"))

# Debug NaN values
torch.autograd.detect_anomaly()

Conclusion

This guide provides a comprehensive overview of AI model development for the Anya Core platform. For more information, refer to the API documentation and examples.

See Also