Learn Before
Gradient Descent Update Rule
The standard delta rule for gradient descent updates a model's parameters by taking a small step in the direction of the negative loss gradient. The new parameters are obtained according to the formula: . In this equation, represents the latest parameters, is the small step (learning rate), and the fractional term is the gradient of the loss function with respect to , computed on a minibatch of training sample .
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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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
Learn After
A model is being trained using an optimization algorithm where parameters are updated by taking a step in the direction opposite to the gradient of a loss function. For a specific parameter, the calculated gradient of the loss is a large negative value (-10.0). If the learning rate is set to a small positive value (0.01), how will this parameter's value change in the next update step?
Diagnosing Training Instability
Calculating a Parameter Update
Deep Learning Minibatch Training Loop