Table of Contents

Class DDPOptimizer<T, TInput, TOutput>

Namespace
AiDotNet.DistributedTraining
Assembly
AiDotNet.dll

Implements true DDP (Distributed Data Parallel) optimizer - industry-standard gradient averaging.

public class DDPOptimizer<T, TInput, TOutput> : ShardedOptimizerBase<T, TInput, TOutput>, IShardedOptimizer<T, TInput, TOutput>, IOptimizer<T, TInput, TOutput>, IModelSerializer

Type Parameters

T

The numeric type

TInput

The input type for the model

TOutput

The output type for the model

Inheritance
ShardedOptimizerBase<T, TInput, TOutput>
DDPOptimizer<T, TInput, TOutput>
Implements
IShardedOptimizer<T, TInput, TOutput>
IOptimizer<T, TInput, TOutput>
Inherited Members
Extension Methods

Remarks

Strategy Overview: True DDP is the industry-standard distributed training approach used by PyTorch, TensorFlow, and JAX. After computing gradients on local data, gradients are averaged across all workers using AllReduce, then the averaged gradients are applied to update model parameters. This ensures all workers stay perfectly synchronized with identical parameter updates at every step.

For Beginners: DDP works by having each worker compute gradients on their local batch of data, then averaging those gradients across all workers before updating the model. It's like a study group where everyone works on different practice problems, shares their solutions, averages the feedback, and everyone applies the same averaged correction to their understanding.

Key Difference from Local SGD: - **True DDP (this class)**: Compute gradients → Average GRADIENTS → Apply averaged gradients - **Local SGD**: Optimize locally → Average PARAMETERS after multiple steps

DDP maintains tighter synchronization but requires more frequent communication.

How It Works: 1. Each worker computes gradients on local data batch 2. Gradients are synchronized via AllReduce (averaging across all workers) 3. Each worker applies the same averaged gradients to their model 4. All workers now have identical parameters 5. Repeat for next iteration

Use Cases: - Standard multi-GPU distributed training (PyTorch DDP, TensorFlow MirroredStrategy) - Fast interconnects (NVLink, InfiniBand) where communication is cheap - Training where tight synchronization is critical - Works with any optimizer (SGD, Adam, RMSprop, etc.) - Default choice for distributed training with good network

Trade-offs: - Memory: Each process stores full model and optimizer state - Communication: Moderate - gradients synchronized every step (can use gradient compression) - Synchronization: Perfect - all workers always have identical parameters - Convergence: Identical to single-GPU training (mathematically equivalent) - Complexity: Low - straightforward gradient averaging - Best for: Fast networks, standard distributed training scenarios

Production Implementation: This implementation uses the gradient access infrastructure (LastComputedGradients, ApplyGradients) to properly average gradients before parameter updates. It reverses local gradient applications to recover original parameters, applies averaged gradients, ensuring true DDP semantics.

Industry Standard: This implementation matches PyTorch's DistributedDataParallel, TensorFlow's MirroredStrategy, and JAX's pmap with gradient averaging. It is the gold standard for distributed training.

Constructors

DDPOptimizer(IOptimizer<T, TInput, TOutput>, IShardingConfiguration<T>)

Creates a true DDP optimizer that averages gradients across workers.

public DDPOptimizer(IOptimizer<T, TInput, TOutput> wrappedOptimizer, IShardingConfiguration<T> config)

Parameters

wrappedOptimizer IOptimizer<T, TInput, TOutput>

The base optimizer to wrap (must be gradient-based: SGD, Adam, etc.)

config IShardingConfiguration<T>

Configuration for distributed training communication

Exceptions

ArgumentException

If wrapped optimizer is not gradient-based

Methods

Deserialize(byte[])

Loads a previously serialized model from binary data.

public override void Deserialize(byte[] data)

Parameters

data byte[]

The byte array containing the serialized model data.

Remarks

This method takes binary data created by the Serialize method and uses it to restore a model to its previous state.

For Beginners: This is like opening a saved file to continue your work.

When you call this method:

  • You provide the binary data (bytes) that was previously created by Serialize
  • The model rebuilds itself using this data
  • After deserializing, the model is exactly as it was when serialized
  • It's ready to make predictions without needing to be trained again

For example:

  • You download a pre-trained model file for detecting spam emails
  • You deserialize this file into your application
  • Immediately, your application can detect spam without any training
  • The model has all the knowledge that was built into it by its original creator

This is particularly useful when:

  • You want to use a model that took days to train
  • You need to deploy the same model across multiple devices
  • You're creating an application that non-technical users will use

Think of it like installing the brain of a trained expert directly into your application.

Optimize(OptimizationInputData<T, TInput, TOutput>)

Performs the optimization process to find the best parameters for a model.

public override OptimizationResult<T, TInput, TOutput> Optimize(OptimizationInputData<T, TInput, TOutput> inputData)

Parameters

inputData OptimizationInputData<T, TInput, TOutput>

The data needed for optimization, including the objective function, initial parameters, and any constraints.

Returns

OptimizationResult<T, TInput, TOutput>

The result of the optimization process, including the optimized parameters and performance metrics.

Remarks

This method takes input data and attempts to find the optimal parameters that minimize or maximize the objective function.

For Beginners: This is where the actual "learning" happens. The optimizer looks at your data and tries different parameter values to find the ones that make your model perform best.

The process typically involves:

  1. Evaluating how well the current parameters perform
  2. Calculating how to change the parameters to improve performance
  3. Updating the parameters
  4. Repeating until the model performs well enough or reaches a maximum number of attempts

Serialize()

Converts the current state of a machine learning model into a binary format.

public override byte[] Serialize()

Returns

byte[]

A byte array containing the serialized model data.

Remarks

This method captures all the essential information about a trained model and converts it into a sequence of bytes that can be stored or transmitted.

For Beginners: This is like exporting your work to a file.

When you call this method:

  • The model's current state (all its learned patterns and parameters) is captured
  • This information is converted into a compact binary format (bytes)
  • You can then save these bytes to a file, database, or send them over a network

For example:

  • After training a model to recognize cats vs. dogs in images
  • You can serialize the model to save all its learned knowledge
  • Later, you can use this saved data to recreate the model exactly as it was
  • The recreated model will make the same predictions as the original

Think of it like taking a snapshot of your model's brain at a specific moment in time.

SynchronizeOptimizerState()

Synchronizes optimizer state (like momentum buffers) across all processes.

public override void SynchronizeOptimizerState()

Remarks

For Beginners: Some optimizers (like Adam) keep track of past gradients to make smarter updates. This method makes sure all processes have the same optimizer state, so they stay coordinated. It's like making sure all team members are reading from the same playbook.

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