# Model Management

Complete model serialization, checkpointing, and deployment utilities for production and inference. These operations provide comprehensive model lifecycle management capabilities.

## Capabilities

### Model Saving and Loading

Save and load complete models with all weights, architecture, and training configuration.

```python { .api }
def save(obj, export_dir, signatures=None, options=None):
    """
    Exports a tf.Module (and subclasses) obj to SavedModel format.
    
    Parameters:
    - obj: A trackable object (e.g. tf.Module or tf.keras.Model) to export
    - export_dir: A directory in which to write the SavedModel
    - signatures: Optional, either a tf.function with an input signature specified or a dictionary
    - options: Optional, tf.saved_model.SaveOptions object that specifies options for saving
    """

def load(export_dir, tags=None, options=None):
    """
    Load a SavedModel from export_dir.
    
    Parameters:
    - export_dir: The SavedModel directory to load from
    - tags: A tag or sequence of tags identifying the MetaGraph to load
    - options: Optional, tf.saved_model.LoadOptions object that specifies options for loading
    
    Returns:
    A trackable object with a save method
    """

def contains_saved_model(export_dir):
    """
    Checks whether the provided export directory could contain a SavedModel.
    
    Parameters:
    - export_dir: Absolute or relative path to a directory containing the SavedModel
    
    Returns:
    True if the export directory contains SavedModel files, False otherwise
    """
```

### Checkpointing

Save and restore model weights and training state for resuming training.

```python { .api }
class Checkpoint:
    """
    Groups trackable objects, saving and restoring them.
    
    Methods:
    - save(file_prefix): Saves a training checkpoint and provides a context manager
    - restore(save_path): Restore a training checkpoint
    - read(save_path): Returns CheckpointReader for checkpoint inspection
    """
    
    def __init__(self, **kwargs):
        """
        Groups trackable objects, saving and restoring them.
        
        Parameters:
        - **kwargs: Keyword arguments are set as attributes of this object, and are saved with the checkpoint
        """
    
    def save(self, file_prefix, session=None):
        """
        Saves a training checkpoint and provides a context manager.
        
        Parameters:
        - file_prefix: A prefix to use for the checkpoint filenames
        - session: The session to evaluate variables in. Ignored when executing eagerly
        
        Returns:
        The full path to the checkpoint
        """
    
    def restore(self, save_path):
        """
        Restore a training checkpoint.
        
        Parameters:
        - save_path: The path to the checkpoint, as returned by save or tf.train.latest_checkpoint
        
        Returns:
        A load status object, which can be used to make assertions about the status of a checkpoint restoration
        """
    
    def read(self, save_path):
        """
        Returns a CheckpointReader for the checkpoint.
        
        Parameters:
        - save_path: The path to the checkpoint, as returned by save or tf.train.latest_checkpoint
        
        Returns:
        A CheckpointReader object
        """

class CheckpointManager:
    """
    Deletes old checkpoints.
    
    Methods:
    - save(checkpoint_number): Creates a new checkpoint
    """
    
    def __init__(self, checkpoint, directory, max_to_keep=5, keep_checkpoint_every_n_hours=None,
                 checkpoint_name="ckpt", step_counter=None, checkpoint_interval=None,
                 init_fn=None):
        """
        Deletes old checkpoints.
        
        Parameters:
        - checkpoint: The tf.train.Checkpoint instance to save and manage checkpoints for
        - directory: The path to a directory in which to write checkpoints
        - max_to_keep: An integer, the number of checkpoints to keep
        - keep_checkpoint_every_n_hours: Upon removal, keep checkpoints every N hours
        - checkpoint_name: Custom name for the checkpoint file
        - step_counter: A tf.Variable instance for checking the current step counter value
        - checkpoint_interval: An integer, indicates that keep_checkpoint_every_n_hours should be based on checkpoints saved every checkpoint_interval steps
        - init_fn: Callable. Function executed the first time a checkpoint is saved
        """
    
    def save(self, checkpoint_number=None, check_interval=True):
        """
        Creates a new checkpoint and manages deletion of old checkpoints.
        
        Parameters:
        - checkpoint_number: An optional integer, or an integer-dtype Variable or Tensor, used to number the checkpoint
        - check_interval: An optional boolean. The default behaviour is that checkpoint_interval is ignored when checkpoint_number is provided
        
        Returns:
        The path to the new checkpoint. It is also recorded in the checkpoints and latest_checkpoint properties
        """
```

### Checkpoint Utilities

Utility functions for working with checkpoints.

```python { .api }
def list_variables(checkpoint_dir):
    """
    Returns list of all variables in the checkpoint.
    
    Parameters:
    - checkpoint_dir: Directory with checkpoint file or path to checkpoint
    
    Returns:
    List of tuples (name, shape) for all variables in the checkpoint
    """

def load_checkpoint(checkpoint_dir):
    """
    Returns CheckpointReader for checkpoint found in checkpoint_dir.
    
    Parameters:
    - checkpoint_dir: Directory with checkpoint file or path to checkpoint
    
    Returns:
    CheckpointReader instance
    """

def load_variable(checkpoint_dir, name):
    """
    Returns the tensor value of the given variable in the checkpoint.
    
    Parameters:
    - checkpoint_dir: Directory with checkpoint file or path to checkpoint  
    - name: Name of the variable to return
    
    Returns:
    A numpy ndarray with a copy of the value of this variable
    """

def latest_checkpoint(checkpoint_dir, latest_filename=None):
    """
    Finds the filename of latest saved checkpoint file.
    
    Parameters:
    - checkpoint_dir: Directory where the variables were saved
    - latest_filename: Optional name for the protocol buffer file that contains the list of most recent checkpoint filenames
    
    Returns:
    The full path to the latest checkpoint or None if no checkpoint was found
    """
```

### SavedModel Utilities

Additional utilities for working with SavedModel format.

```python { .api }
class SaveOptions:
    """
    Options for saving to SavedModel.
    
    Parameters:
    - namespace_whitelist: List of strings containing op namespaces to whitelist when saving a model
    - save_debug_info: Boolean indicating whether debug information is saved
    - function_aliases: Optional dictionary of string -> string of function aliases
    - experimental_io_device: string. Applies in a distributed setting
    - experimental_variable_policy: The policy to apply to variables when saving
    """

class LoadOptions:
    """
    Options for loading a SavedModel.
    
    Parameters:
    - allow_partial_checkpoint: Boolean. Defaults to False. When enabled, allows the SavedModel checkpoint to be missing variables
    - experimental_io_device: string. Loads SavedModel and variables on the specified device
    - experimental_skip_checkpoint: boolean. If True, the checkpoint will not be loaded, and the SavedModel will be loaded with randomly initialized variable values
    """

class Asset:
    """
    Represents a file asset to copy into the SavedModel.
    
    Parameters:
    - path: A path, or a 0-D tf.string Tensor with path to the asset
    """
```

## Usage Examples

```python
import tensorflow as tf
import os

# Create a simple model
model = tf.keras.Sequential([
    tf.keras.layers.Dense(64, activation='relu', input_shape=(10,)),
    tf.keras.layers.Dense(32, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Save entire model to SavedModel format
tf.saved_model.save(model, 'my_saved_model')

# Load the saved model
loaded_model = tf.saved_model.load('my_saved_model')

# For Keras models, use keras save/load for full functionality
model.save('my_keras_model.h5')
loaded_keras_model = tf.keras.models.load_model('my_keras_model.h5')

# Checkpoint example
checkpoint_dir = './training_checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")

# Create checkpoint object
checkpoint = tf.train.Checkpoint(optimizer=tf.keras.optimizers.Adam(),
                                model=model)

# Save checkpoint
checkpoint.save(file_prefix=checkpoint_prefix)

# Restore from checkpoint
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))

# Using CheckpointManager for automatic cleanup
manager = tf.train.CheckpointManager(
    checkpoint, directory=checkpoint_dir, max_to_keep=3
)

# Save with automatic cleanup
save_path = manager.save()
print(f"Saved checkpoint for step {step}: {save_path}")

# Training loop with checkpointing
optimizer = tf.keras.optimizers.Adam()
checkpoint = tf.train.Checkpoint(optimizer=optimizer, model=model)
manager = tf.train.CheckpointManager(checkpoint, './checkpoints', max_to_keep=3)

# Restore if checkpoint exists
checkpoint.restore(manager.latest_checkpoint)
if manager.latest_checkpoint:
    print(f"Restored from {manager.latest_checkpoint}")
else:
    print("Initializing from scratch.")

# Training step function
@tf.function
def train_step(x, y):
    with tf.GradientTape() as tape:
        predictions = model(x, training=True)
        loss = tf.keras.losses.binary_crossentropy(y, predictions)
    
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    
    return loss

# Training loop
for epoch in range(10):
    # Training code here...
    # x_batch, y_batch = get_batch()
    # loss = train_step(x_batch, y_batch)
    
    # Save checkpoint every few epochs
    if epoch % 2 == 0:
        save_path = manager.save()
        print(f"Saved checkpoint for epoch {epoch}: {save_path}")

# Inspect checkpoint contents
checkpoint_path = tf.train.latest_checkpoint(checkpoint_dir)
if checkpoint_path:
    variables = tf.train.list_variables(checkpoint_path)
    for name, shape in variables:
        print(f"Variable: {name}, Shape: {shape}")
    
    # Load specific variable
    specific_var = tf.train.load_variable(checkpoint_path, 'model/dense/kernel/.ATTRIBUTES/VARIABLE_VALUE')
    print(f"Loaded variable shape: {specific_var.shape}")

# Check if directory contains SavedModel
if tf.saved_model.contains_saved_model('my_saved_model'):
    print("Directory contains a valid SavedModel")

# Advanced SavedModel with custom signatures
@tf.function(input_signature=[tf.TensorSpec(shape=[None, 10], dtype=tf.float32)])
def inference_func(x):
    return model(x)

# Save with custom signature
tf.saved_model.save(
    model, 
    'model_with_signature',
    signatures={'serving_default': inference_func}
)
```