Distributed Training in PyTorch QUIZ (MCQ QUESTIONS AND ANSWERS)

Data shuffling

Gradient compression

Task parallelism

Ensemble parallelism

To reduce the model's learning rate

To improve model generalization performance

To minimize the communication overhead between nodes

To synchronize model updates across nodes

Reducing the number of training samples

Minimizing the communication overhead between nodes

Increasing the depth of the neural network

Using a smaller batch size during training

Data parallelism

Model parallelism

Task parallelism

Ensemble parallelism

It helps reduce model overfitting

It increases the communication overhead between nodes

It randomizes the order of training samples to improve convergence

It coordinates model updates across nodes

Increased memory requirements on each node

Limited scalability to a large number of nodes

Slower convergence compared to model parallelism

Inability to handle large models with multiple layers

Increasing the number of nodes

Reducing the communication overhead between nodes

Balancing model bias and variance

Efficient utilization of available memory on each node

Improved model interpretability

Efficient handling of large datasets

Centralized management of model parameters

Faster convergence compared to other synchronization methods

Data parallelism

Model parallelism

Task parallelism

Hybrid parallelism

Limited scalability to a large number of nodes

Increased communication overhead between nodes

Inability to handle deep neural networks

Difficulty in coordinating model updates

Reduce the model's learning rate

Improve model generalization performance

Compensate for limited communication bandwidth between nodes

Enhance the model's ability to handle large datasets

Data parallelism

Model parallelism

Task parallelism

Ensemble parallelism

Manage model parameters and coordinate updates

Execute training computations on the client side

Generate synthetic training data

Validate model predictions during training

Increased memory requirements on each node

Limited scalability to a large number of nodes

Slower convergence compared to data parallelism

Inability to handle large models with multiple layers

Increase the model's learning rate

Improve model generalization performance

Enhance model interpretability

Compensate for limited communication bandwidth between nodes

Improve model interpretability

Accelerate training by leveraging multiple resources

Reduce the number of features in the model

Enhance the model's ability to generalize

Improved model interpretability

Increased scalability

Communication overhead between nodes

Reduced computation power

A gradient descent variant that uses a fixed learning rate

A training approach where model parameters are updated independently without synchronization

A method to balance model bias and variance

A technique for preventing overfitting during training

It manages the training data distribution

It coordinates model updates across nodes

It preprocesses input features for the model

It performs model validation during training

Data parallelism

Model parallelism

Task parallelism

Hybrid parallelism

Improved model interpretability

Faster convergence compared to other parallelism strategies

Enhanced generalization performance

Increased model robustness through ensemble averaging

Improved model interpretability

Increased scalability

Communication overhead between nodes

Reduced computation power

Data parallelism

Model parallelism

Task parallelism

Ensemble parallelism

To prevent model overfitting

To synchronize the learning rates across nodes

To ensure all nodes have completed their computations before moving to the next training iteration

To randomly initialize model parameters

Data parallelism

Model parallelism

Task parallelism

Ensemble parallelism

Data parallelism

Model parallelism

Task parallelism

Ensemble parallelism

Improved training speed on a single node

Simpler implementation compared to data parallelism

Efficient handling of large datasets

Ability to train large models that do not fit into a single device's memory

Gradient accumulation

Model checkpointing

Task parallelism

Ensemble parallelism

Manage model parameters and coordinate updates

Execute training computations on the client side

Generate synthetic training data

Validate model predictions during training

Multiple neural network layers

Multiple training epochs

Multiple computing devices or servers

Multiple training samples