Deep Learning

Deep Learning

Production-grade deep learning with PyTorch, neural network architectures, and modern training practices.

Quick Start

# PyTorch Production Training Loop
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.optim import AdamW
from torch.optim.lr_scheduler import CosineAnnealingLR
import wandb

class TransformerClassifier(nn.Module):
    def __init__(self, vocab_size: int, d_model: int = 256, n_heads: int = 8, n_classes: int = 2):
        super().__init__()
        self.embedding = nn.Embedding(vocab_size, d_model)
        self.pos_encoding = nn.Parameter(torch.randn(1, 512, d_model))
        encoder_layer = nn.TransformerEncoderLayer(d_model, n_heads, dim_feedforward=1024, batch_first=True)
        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=6)
        self.classifier = nn.Linear(d_model, n_classes)
        self.dropout = nn.Dropout(0.1)

    def forward(self, x, mask=None):
        x = self.embedding(x) + self.pos_encoding[:, :x.size(1), :]
        x = self.dropout(x)
        x = self.transformer(x, src_key_padding_mask=mask)
        x = x.mean(dim=1)  # Global average pooling
        return self.classifier(x)

# Training configuration
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = TransformerClassifier(vocab_size=30000).to(device)
optimizer = AdamW(model.parameters(), lr=1e-4, weight_decay=0.01)
scheduler = CosineAnnealingLR(optimizer, T_max=10)
criterion = nn.CrossEntropyLoss()

# Training loop with mixed precision
scaler = torch.cuda.amp.GradScaler()

for epoch in range(10):
    model.train()
    for batch in train_loader:
        optimizer.zero_grad()
        with torch.cuda.amp.autocast():
            logits = model(batch["input_ids"].to(device))
            loss = criterion(logits, batch["labels"].to(device))
        scaler.scale(loss).backward()
        scaler.step(optimizer)
        scaler.update()
    scheduler.step()

Core Concepts

1. Modern Neural Network Architectures

import torch
import torch.nn as nn
import torch.nn.functional as F

class ResidualBlock(nn.Module):
    """Residual block with skip connection."""
    def __init__(self, channels: int):
        super().__init__()
        self.conv1 = nn.Conv2d(channels, channels, 3, padding=1)
        self.bn1 = nn.BatchNorm2d(channels)
        self.conv2 = nn.Conv2d(channels, channels, 3, padding=1)
        self.bn2 = nn.BatchNorm2d(channels)

    def forward(self, x):
        residual = x
        x = F.relu(self.bn1(self.conv1(x)))
        x = self.bn2(self.conv2(x))
        return F.relu(x + residual)

class AttentionBlock(nn.Module):
    """Multi-head self-attention."""
    def __init__(self, d_model: int, n_heads: int = 8):
        super().__init__()
        self.attention = nn.MultiheadAttention(d_model, n_heads, batch_first=True)
        self.norm = nn.LayerNorm(d_model)
        self.ffn = nn.Sequential(
            nn.Linear(d_model, d_model * 4),
            nn.GELU(),
            nn.Linear(d_model * 4, d_model)
        )
        self.norm2 = nn.LayerNorm(d_model)

    def forward(self, x, mask=None):
        attn_out, _ = self.attention(x, x, x, attn_mask=mask)
        x = self.norm(x + attn_out)
        return self.norm2(x + self.ffn(x))

2. Training Best Practices

from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import OneCycleLR

# Gradient clipping and accumulation
def train_epoch(model, loader, optimizer, accumulation_steps=4):
    model.train()
    optimizer.zero_grad()

    for i, batch in enumerate(loader):
        with torch.cuda.amp.autocast():
            loss = model(batch) / accumulation_steps

        scaler.scale(loss).backward()

        if (i + 1) % accumulation_steps == 0:
            scaler.unscale_(optimizer)
            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
            scaler.step(optimizer)
            scaler.update()
            optimizer.zero_grad()

# Early stopping
class EarlyStopping:
    def __init__(self, patience: int = 5, min_delta: float = 0.001):
        self.patience = patience
        self.min_delta = min_delta
        self.counter = 0
        self.best_loss = float('inf')

    def __call__(self, val_loss: float) -> bool:
        if val_loss < self.best_loss - self.min_delta:
            self.best_loss = val_loss
            self.counter = 0
        else:
            self.counter += 1
        return self.counter >= self.patience

# Learning rate finder
def find_lr(model, loader, optimizer, start_lr=1e-7, end_lr=10, num_iter=100):
    lrs, losses = [], []
    lr_mult = (end_lr / start_lr) ** (1 / num_iter)

    for i, batch in enumerate(loader):
        if i >= num_iter:
            break

        lr = start_lr * (lr_mult ** i)
        for pg in optimizer.param_groups:
            pg['lr'] = lr

        loss = train_step(model, batch, optimizer)
        lrs.append(lr)
        losses.append(loss)

    return lrs, losses

3. Model Deployment

import torch.onnx
import onnxruntime as ort

# Export to ONNX
def export_to_onnx(model, sample_input, path="model.onnx"):
    model.eval()
    torch.onnx.export(
        model,
        sample_input,
        path,
        export_params=True,
        opset_version=17,
        do_constant_folding=True,
        input_names=['input'],
        output_names=['output'],
        dynamic_axes={'input': {0: 'batch_size'}, 'output': {0: 'batch_size'}}
    )

# ONNX Runtime inference
class ONNXPredictor:
    def __init__(self, model_path: str):
        self.session = ort.InferenceSession(model_path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])

    def predict(self, input_data):
        return self.session.run(None, {'input': input_data})[0]

# TorchScript for production
scripted_model = torch.jit.script(model)
scripted_model.save("model_scripted.pt")

Tools & Technologies

Troubleshooting Guide

Debug Commands

# Check GPU memory
print(torch.cuda.memory_summary())

# Profile training
with torch.profiler.profile(
    activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA]
) as prof:
    train_step(model, batch, optimizer)
print(prof.key_averages().table(sort_by="cuda_time_total"))

# Gradient flow check
for name, param in model.named_parameters():
    if param.grad is not None:
        print(f"{name}: grad_mean={param.grad.mean():.6f}")

Best Practices

# ✅ DO: Use mixed precision training
with torch.cuda.amp.autocast():
    output = model(input)

# ✅ DO: Initialize weights properly
def init_weights(m):
    if isinstance(m, nn.Linear):
        nn.init.xavier_uniform_(m.weight)

# ✅ DO: Use gradient checkpointing for large models
from torch.utils.checkpoint import checkpoint
x = checkpoint(self.layer, x)

# ✅ DO: Freeze base model for fine-tuning
for param in model.base.parameters():
    param.requires_grad = False

# ❌ DON'T: Use dropout during inference
model.eval()

# ❌ DON'T: Forget to move data to device

Resources

• PyTorch Tutorials
• Hugging Face Course
• Fast.ai
• "Deep Learning" by Goodfellow et al.

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Skill Certification Checklist:

• [ ] Can build and train neural networks in PyTorch
• [ ] Can implement attention mechanisms and transformers
• [ ] Can use mixed precision and gradient accumulation
• [ ] Can export models to ONNX/TorchScript
• [ ] Can debug training issues (gradients, memory)

Deep Learning Guide

Overview

This guide provides comprehensive documentation for the deep-learning skill in the custom-plugin-data-engineer plugin.

Category: General

Quick Start

Prerequisites

• Familiarity with general concepts
• Development environment set up
• Plugin installed and configured

Basic Usage

# Invoke the skill
claude "deep-learning - [your task description]"

# Example
claude "deep-learning - analyze the current implementation"

Core Concepts

Key Principles

1. Consistency - Follow established patterns 2. Clarity - Write readable, maintainable code 3. Quality - Validate before deployment

Best Practices

• Always validate input data
• Handle edge cases explicitly
• Document your decisions
• Write tests for critical paths

Common Tasks

Task 1: Basic Implementation

# Example implementation pattern
def implement_deep_learning(input_data):
    """
    Implement deep-learning functionality.

    Args:
        input_data: Input to process

    Returns:
        Processed result
    """
    # Validate input
    if not input_data:
        raise ValueError("Input required")

    # Process
    result = process(input_data)

    # Return
    return result

Task 2: Advanced Usage

For advanced scenarios, consider:

• Configuration customization via assets/config.yaml
• Validation using scripts/validate.py
• Integration with other skills

Troubleshooting

Common Issues

Related Resources

• SKILL.md - Skill specification
• config.yaml - Configuration options
• validate.py - Validation script

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Last updated: 2025-12-30

Deep Learning Patterns

Design Patterns

Pattern 1: Input Validation

Always validate input before processing:

def validate_input(data):
    if data is None:
        raise ValueError("Data cannot be None")
    if not isinstance(data, dict):
        raise TypeError("Data must be a dictionary")
    return True

Pattern 2: Error Handling

Use consistent error handling:

try:
    result = risky_operation()
except SpecificError as e:
    logger.error(f"Operation failed: {e}")
    handle_error(e)
except Exception as e:
    logger.exception("Unexpected error")
    raise

Pattern 3: Configuration Loading

Load and validate configuration:

import yaml

def load_config(config_path):
    with open(config_path) as f:
        config = yaml.safe_load(f)
    validate_config(config)
    return config

Anti-Patterns to Avoid

❌ Don't: Swallow Exceptions

# BAD
try:
    do_something()
except:
    pass

✅ Do: Handle Explicitly

# GOOD
try:
    do_something()
except SpecificError as e:
    logger.warning(f"Expected error: {e}")
    return default_value

Category-Specific Patterns: General

Recommended Approach

1. Start with the simplest implementation 2. Add complexity only when needed 3. Test each addition 4. Document decisions

Common Integration Points

• Configuration: assets/config.yaml
• Validation: scripts/validate.py
• Documentation: references/GUIDE.md

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Pattern library for deep-learning skill