Gradient Descent Update Rule

Set of Distributed Data Batches in Data Parallelism

Ideal Speed-up in Data Parallelism

A team is training a neural network using a technique where a large batch of data is split equally among 8 machines. Each machine has a full, identical copy of the network model. During a training step, each machine processes its portion of the data and calculates a set of proposed parameter updates. Given this setup, what is the most critical subsequent action to ensure the entire system learns effectively from the full batch of data?

Distributed Gradient Calculation

A single training step is performed using a technique where a mini-batch of data is processed in parallel across multiple machines. Each machine holds a complete copy of the model. Arrange the following events in the correct chronological order for one such training step.

A machine learning team is training a large neural network on a massive dataset. To accelerate the process, they employ a strategy where the training data is split across 16 GPUs. Each GPU holds a complete copy of the model and processes its own subset of the data. After each forward and backward pass, the results from all GPUs are combined before updating the model's parameters. The team observes that while using 8 GPUs provided a nearly 8x speed-up compared to a single GPU, scaling to 16 GPUs only resulted in a 10x total speed-up. Based on the principles of the training strategy described, what is the most likely bottleneck causing this diminishing return in performance when scaling from 8 to 16 GPUs?

Evaluating a Training Strategy

Your team must train a 30B-parameter LLM on a sing...

You are on-call for an internal LLM training platf...

Your team is training a 70B-parameter LLM on 8 GPU...

You’re advising an internal platform team that mus...

Designing a Distributed Training Plan Under Memory, Throughput, and Stability Constraints

Postmortem and Redesign of a Distributed LLM Training Run with Divergence and Low GPU Utilization

Diagnosing a Scaling Regression in Hybrid Parallel LLM Training

Stabilizing and Scaling an LLM Training Job Across Two GPU Clusters

Choosing a Distributed Training Configuration After a Hardware Refresh

Selecting a Hybrid Parallelism + Mixed-Precision Strategy for a Memory-Bound LLM Training Run