Framework Integration

class DatasetView:
    """PyTorch integration for dataset views."""
    
    def pytorch(self, transform: Optional[Callable[[Any], Any]] = None) -> Any:
        """
        Create PyTorch Dataset from Deep Lake dataset.
        
        Parameters:
        - transform: Optional transform function to apply to samples
        
        Returns:
        TorchDataset: PyTorch-compatible dataset object
        """

class DatasetView:
    """TensorFlow integration for dataset views."""
    
    def tensorflow(self) -> Any:
        """
        Create TensorFlow Dataset from Deep Lake dataset.
        
        Returns:
        tf.data.Dataset: TensorFlow-compatible dataset object
        """

class DatasetView:
    """Batch processing capabilities."""
    
    def batches(self, batch_size: int = 1) -> Iterator[Dict[str, Any]]:
        """
        Iterate over dataset in batches.
        
        Parameters:
        - batch_size: Number of samples per batch
        
        Returns:
        Iterator[Dict[str, Any]]: Iterator yielding batches as dictionaries
        """

import deeplake
import torch
from torch.utils.data import DataLoader

# Open dataset
dataset = deeplake.open("./ml_dataset")

# Create PyTorch dataset
torch_dataset = dataset.pytorch()

# Use with PyTorch DataLoader
train_loader = DataLoader(
    torch_dataset,
    batch_size=32,
    shuffle=True,
    num_workers=4
)

# Training loop
for batch_idx, batch in enumerate(train_loader):
    images = batch["images"]
    labels = batch["labels"]
    
    # Convert to tensors if needed
    if isinstance(images, list):
        images = torch.stack([torch.tensor(img) for img in images])
    if isinstance(labels, list):
        labels = torch.tensor(labels)
    
    print(f"Batch {batch_idx}: images shape {images.shape}, labels shape {labels.shape}")
    
    if batch_idx >= 2:  # Just show first few batches
        break

import torchvision.transforms as transforms
from PIL import Image
import numpy as np

# Define custom transform pipeline
def custom_transform(sample):
    """Custom transform for image-text pairs."""
    
    # Load and transform image
    if isinstance(sample["images"], str):
        # Load image from path
        image = Image.open(sample["images"])
    else:
        # Convert numpy array to PIL
        image = Image.fromarray(sample["images"])
    
    # Apply torchvision transforms
    transform = transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], 
                           std=[0.229, 0.224, 0.225])
    ])
    
    transformed_image = transform(image)
    
    # Process text label
    label = sample["labels"]
    if isinstance(label, str):
        # Convert string label to integer (example mapping)
        label_map = {"cat": 0, "dog": 1, "bird": 2}
        label = label_map.get(label, -1)
    
    return {
        "image": transformed_image,
        "label": torch.tensor(label, dtype=torch.long)
    }

# Apply custom transform
torch_dataset = dataset.pytorch(transform=custom_transform)

# Use in training
train_loader = DataLoader(torch_dataset, batch_size=16, shuffle=True)

for batch in train_loader:
    images = batch["image"]  # Already tensor from transform
    labels = batch["label"]  # Already tensor from transform
    
    print(f"Transformed batch - Images: {images.shape}, Labels: {labels.shape}")
    break

# Multi-modal dataset with complex transforms
def multimodal_transform(sample):
    """Transform for vision-language model training."""
    
    # Process image
    image = Image.open(sample["image_path"])
    image_transform = transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
    ])
    image_tensor = image_transform(image)
    
    # Process text
    text = sample["description"]
    # Tokenize text (example with simple word-level tokenization)
    tokens = text.lower().split()[:50]  # Limit to 50 tokens
    
    # Convert to embedding indices (simplified)
    vocab = {"<pad>": 0, "<unk>": 1}  # Simplified vocab
    vocab.update({word: i+2 for i, word in enumerate(set(tokens))})
    
    token_ids = [vocab.get(token, vocab["<unk>"]) for token in tokens]
    # Pad to fixed length
    token_ids += [vocab["<pad>"]] * (50 - len(token_ids))
    token_ids = token_ids[:50]
    
    # Process embeddings if available
    embeddings = sample.get("embeddings", np.zeros(768))
    embeddings_tensor = torch.tensor(embeddings, dtype=torch.float32)
    
    return {
        "image": image_tensor,
        "text_tokens": torch.tensor(token_ids, dtype=torch.long),
        "embeddings": embeddings_tensor,
        "metadata": sample.get("metadata", {})
    }

# Create multimodal dataset
multimodal_dataset = dataset.pytorch(transform=multimodal_transform)

# Custom collate function for variable-length data
def collate_multimodal(batch):
    """Custom collate function for multimodal data."""
    
    images = torch.stack([item["image"] for item in batch])
    text_tokens = torch.stack([item["text_tokens"] for item in batch])
    embeddings = torch.stack([item["embeddings"] for item in batch])
    
    # Collect metadata
    metadata = [item["metadata"] for item in batch]
    
    return {
        "images": images,
        "text_tokens": text_tokens,
        "embeddings": embeddings,
        "metadata": metadata
    }

# Use with custom collate
multimodal_loader = DataLoader(
    multimodal_dataset,
    batch_size=8,
    shuffle=True,
    collate_fn=collate_multimodal,
    num_workers=2
)

for batch in multimodal_loader:
    print(f"Multimodal batch:")
    print(f"  Images: {batch['images'].shape}")
    print(f"  Text tokens: {batch['text_tokens'].shape}")
    print(f"  Embeddings: {batch['embeddings'].shape}")
    print(f"  Metadata items: {len(batch['metadata'])}")
    break

import tensorflow as tf
import deeplake

# Create TensorFlow dataset
dataset = deeplake.open("./ml_dataset")
tf_dataset = dataset.tensorflow()

# Basic usage with TensorFlow
for batch in tf_dataset.take(3):
    print(f"TensorFlow batch keys: {list(batch.keys())}")
    for key, value in batch.items():
        print(f"  {key}: {value.shape if hasattr(value, 'shape') else type(value)}")

# Apply TensorFlow transformations
def preprocess_tf(sample):
    """TensorFlow preprocessing function."""
    
    # Process image data
    if "images" in sample:
        image = sample["images"]
        # Resize and normalize
        image = tf.image.resize(image, [224, 224])
        image = tf.cast(image, tf.float32) / 255.0
        sample["images"] = image
    
    # Process labels
    if "labels" in sample:
        # Convert string labels to integers
        label_map = tf.lookup.StaticHashTable(
            tf.lookup.KeyValueTensorInitializer(
                keys=["cat", "dog", "bird"],
                values=[0, 1, 2]
            ),
            default_value=-1
        )
        sample["labels"] = label_map.lookup(sample["labels"])
    
    return sample

# Apply preprocessing
processed_tf_dataset = tf_dataset.map(
    preprocess_tf,
    num_parallel_calls=tf.data.AUTOTUNE
)

# Batch and prefetch for training
train_tf_dataset = processed_tf_dataset.batch(32).prefetch(tf.data.AUTOTUNE)

# Use in TensorFlow training
for batch in train_tf_dataset.take(2):
    images = batch["images"]
    labels = batch["labels"]
    print(f"TF Training batch - Images: {images.shape}, Labels: {labels.shape}")

# Complex TensorFlow pipeline with multiple data types
def create_tf_pipeline(dataset_path, batch_size=32):
    """Create optimized TensorFlow data pipeline."""
    
    dataset = deeplake.open(dataset_path)
    tf_dataset = dataset.tensorflow()
    
    def process_sample(sample):
        """Process individual sample."""
        processed = {}
        
        # Handle images
        if "images" in sample:
            image = sample["images"]
            # Decode if string path
            if tf.dtypes.string == image.dtype:
                image = tf.io.read_file(image)
                image = tf.image.decode_image(image, channels=3)
            
            # Resize and normalize
            image = tf.image.resize(image, [224, 224])
            image = tf.cast(image, tf.float32) / 255.0
            
            # Data augmentation
            image = tf.image.random_flip_left_right(image)
            image = tf.image.random_brightness(image, 0.1)
            
            processed["image"] = image
        
        # Handle embeddings
        if "embeddings" in sample:
            embeddings = tf.cast(sample["embeddings"], tf.float32)
            # L2 normalize embeddings
            embeddings = tf.nn.l2_normalize(embeddings, axis=-1)
            processed["embeddings"] = embeddings
        
        # Handle text
        if "text" in sample:
            # Simple text processing (in practice, use proper tokenizer)
            text = sample["text"]
            processed["text"] = text
        
        # Handle labels
        if "labels" in sample:
            processed["label"] = tf.cast(sample["labels"], tf.int32)
        
        return processed
    
    # Apply transformations
    processed_dataset = (tf_dataset
                        .map(process_sample, num_parallel_calls=tf.data.AUTOTUNE)
                        .batch(batch_size)
                        .prefetch(tf.data.AUTOTUNE))
    
    return processed_dataset

# Create optimized pipeline
tf_pipeline = create_tf_pipeline("./complex_dataset", batch_size=16)

# Use in model training
for epoch in range(2):
    print(f"Epoch {epoch + 1}")
    for step, batch in enumerate(tf_pipeline.take(5)):
        print(f"  Step {step + 1}: {list(batch.keys())}")
        # Here you would pass batch to your model
        # loss = model.train_step(batch)

# Direct batch processing using Deep Lake's batches method
dataset = deeplake.open("./large_dataset")

# Process data in batches without ML framework
for batch_data in dataset.batches(batch_size=64):
    # batch_data is a dictionary with column names as keys
    # and lists of values as values
    
    images = batch_data["images"]
    labels = batch_data["labels"]
    
    print(f"Processing batch with {len(images)} samples")
    
    # Custom processing logic
    for i, (image_path, label) in enumerate(zip(images, labels)):
        # Process individual sample
        # This could be feature extraction, validation, etc.
        pass
    
    # Break after first batch for demo
    break

# Batch processing with filtering
high_confidence_data = deeplake.query(
    "SELECT * FROM dataset WHERE confidence > 0.9"
)

for batch in high_confidence_data.batches(batch_size=32):
    confidence_scores = batch["confidence"]
    avg_confidence = sum(confidence_scores) / len(confidence_scores)
    print(f"Batch average confidence: {avg_confidence:.3f}")

# PyTorch distributed training setup
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data.distributed import DistributedSampler

def setup_distributed_training(dataset_path, world_size, rank):
    """Setup distributed training with Deep Lake."""
    
    # Initialize distributed training
    dist.init_process_group("nccl", rank=rank, world_size=world_size)
    
    # Open dataset
    dataset = deeplake.open(dataset_path)
    torch_dataset = dataset.pytorch(transform=custom_transform)
    
    # Create distributed sampler
    sampler = DistributedSampler(
        torch_dataset,
        num_replicas=world_size,
        rank=rank,
        shuffle=True
    )
    
    # Create distributed data loader
    train_loader = DataLoader(
        torch_dataset,
        batch_size=32,
        sampler=sampler,
        num_workers=4,
        pin_memory=True
    )
    
    return train_loader, sampler

# Usage in distributed training script
def train_distributed():
    """Distributed training example."""
    
    world_size = torch.cuda.device_count()
    rank = int(os.environ.get("LOCAL_RANK", 0))
    
    train_loader, sampler = setup_distributed_training(
        "./distributed_dataset", world_size, rank
    )
    
    # Training loop with distributed sampler
    for epoch in range(10):
        sampler.set_epoch(epoch)  # Important for proper shuffling
        
        for batch_idx, batch in enumerate(train_loader):
            # Distributed training step
            # model_output = model(batch)
            # loss = criterion(model_output, batch["labels"])
            # loss.backward()
            # optimizer.step()
            
            if batch_idx % 100 == 0 and rank == 0:
                print(f"Epoch {epoch}, Batch {batch_idx}")

# Optimized data loading for high-performance training
class OptimizedDataLoader:
    """Optimized data loader for Deep Lake datasets."""
    
    def __init__(self, dataset_path, batch_size=32, num_workers=4):
        self.dataset = deeplake.open(dataset_path)
        self.batch_size = batch_size
        self.num_workers = num_workers
        
        # Pre-compute dataset length
        self.length = len(self.dataset)
        
        # Create optimized transforms
        self.transform = self._create_optimized_transform()
    
    def _create_optimized_transform(self):
        """Create optimized transform pipeline."""
        def fast_transform(sample):
            # Minimize data copying and conversion
            result = {}
            
            # Efficient image processing
            if "images" in sample:
                image = sample["images"]
                # Use optimized image loading
                if isinstance(image, str):
                    # Load image efficiently
                    image = Image.open(image)
                
                # Fast tensor conversion
                image_array = np.array(image)
                result["image"] = torch.from_numpy(image_array).permute(2, 0, 1).float() / 255.0
            
            # Efficient label processing
            if "labels" in sample:
                result["label"] = torch.tensor(sample["labels"], dtype=torch.long)
            
            return result
        
        return fast_transform
    
    def get_dataloader(self):
        """Get optimized PyTorch DataLoader."""
        torch_dataset = self.dataset.pytorch(transform=self.transform)
        
        return DataLoader(
            torch_dataset,
            batch_size=self.batch_size,
            shuffle=True,
            num_workers=self.num_workers,
            pin_memory=True,
            prefetch_factor=2,
            persistent_workers=True
        )

# Usage
optimized_loader = OptimizedDataLoader("./high_perf_dataset", batch_size=64, num_workers=8)
train_loader = optimized_loader.get_dataloader()

# Measure performance
import time

start_time = time.time()
total_samples = 0

for batch_idx, batch in enumerate(train_loader):
    total_samples += batch["image"].shape[0]
    
    if batch_idx >= 100:  # Test first 100 batches
        break

end_time = time.time()
throughput = total_samples / (end_time - start_time)
print(f"Data loading throughput: {throughput:.1f} samples/second")

# Integration with Hugging Face datasets
from datasets import Dataset as HFDataset

def convert_to_huggingface(deeplake_dataset):
    """Convert Deep Lake dataset to Hugging Face format."""
    
    # Export data
    data_dict = {"text": [], "labels": []}
    
    for sample in deeplake_dataset:
        data_dict["text"].append(sample["text"])
        data_dict["labels"].append(sample["labels"])
    
    # Create Hugging Face dataset
    hf_dataset = HFDataset.from_dict(data_dict)
    return hf_dataset

# Integration with Lightning
import pytorch_lightning as pl

class DeepLakeDataModule(pl.LightningDataModule):
    """PyTorch Lightning DataModule for Deep Lake."""
    
    def __init__(self, train_path, val_path, batch_size=32):
        super().__init__()
        self.train_path = train_path
        self.val_path = val_path
        self.batch_size = batch_size
    
    def setup(self, stage=None):
        """Setup datasets."""
        if stage == "fit" or stage is None:
            self.train_dataset = deeplake.open(self.train_path).pytorch(
                transform=custom_transform
            )
            self.val_dataset = deeplake.open(self.val_path).pytorch(
                transform=custom_transform
            )
    
    def train_dataloader(self):
        return DataLoader(self.train_dataset, batch_size=self.batch_size, shuffle=True)
    
    def val_dataloader(self):
        return DataLoader(self.val_dataset, batch_size=self.batch_size)

# Usage with Lightning
datamodule = DeepLakeDataModule("./train_dataset", "./val_dataset", batch_size=32)

# Use with Lightning Trainer
# trainer = pl.Trainer()
# trainer.fit(model, datamodule)