Distributed training basics in MLOps - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is distributed training in machine learning?

Distributed training is a method where the training of a machine learning model is split across multiple computers or devices to speed up the process and handle larger datasets.

beginner

Name two common strategies used in distributed training.

Two common strategies are data parallelism, where data is split across devices but the model is the same, and model parallelism, where the model itself is split across devices.

intermediate

Why is synchronization important in distributed training?

Synchronization ensures that all devices update the model parameters consistently, preventing conflicts and ensuring the model learns correctly.

intermediate

What role does a parameter server play in distributed training?

A parameter server manages and updates the shared model parameters during training, coordinating between different devices to keep the model consistent.

beginner

How does distributed training help with large datasets?

It splits the dataset across multiple devices, allowing parallel processing which speeds up training and makes it possible to handle data too big for one machine.

What is the main goal of distributed training?

A Reduce the size of the model

B Speed up training by using multiple devices

C Simplify the code

D Avoid using GPUs

Distributed training uses multiple devices to speed up the training process.

Which strategy splits the data across devices but keeps the model the same?

A Data parallelism

B Model parallelism

C Parameter server

D Batch normalization

Data parallelism splits the data but each device has a full copy of the model.

What is a key challenge in distributed training?

A Synchronizing model updates

B Writing more code

C Reducing dataset size

D Avoiding GPUs

Synchronizing updates ensures all devices keep the model consistent.

What does a parameter server do?

A Stores training data

B Runs the training code

C Manages model parameters during training

D Visualizes results

The parameter server manages and updates model parameters across devices.

Why use distributed training for large datasets?

A To simplify the model

B To reduce model size

C To avoid using GPUs

D To speed up training and handle big data

Distributed training splits data and uses multiple devices to speed up training and handle large datasets.

Explain the difference between data parallelism and model parallelism in distributed training.

Think about what is divided: data or model.

Describe why synchronization is necessary in distributed training and how it affects model accuracy.

Consider what happens if devices update model differently.