Introducing Lit-GPT: Hackable implementation of open-source large language models released under Apache 2.0 →

Log in or create a free account to track your progress and access additional course materials  

Unit 9.5 Increasing Batch Sizes to Increase Throughput



What we covered in this video lecture

In this lecture, we discussed the topic of increasing batch sizes to boost throughput in machine learning model training. The batch size, or the number of training samples processed before the model is updated, plays a critical role in the efficiency and effectiveness of model training. By increasing the batch size, we can process more data simultaneously, leading to higher computational efficiency and increased throughput, particularly on hardware like GPUs which excel in parallel processing.

However, in practice, throughput is not always everything, and we have to make sure to strike a careful balance between batch size, learning rate, computational resources, and the potential impact on model performance, which are all crucial considerations in machine learning training pipelines.

Additional resources if you want to learn more

I highly recommend checking out the various papers referenced in the lecture and in the reference section above if you want to learn more about the impact of batch sizes on the computational and predictive performance.

Log in or create a free account to access:

  • Quizzes
  • Completion badges
  • Progress tracking
  • Additional downloadable content
  • Additional AI education resources
  • Notifications when new units are released
  • Free cloud computing credits

Quiz: 9.5 Increasing Batch Sizes to Increase Throughput (PART 1)

Why might using a very large batch size in deep learning training sometimes lead to less accurate results?

Incorrect. If typically leads to less noise.

Correct. More smaller updates can lead to more small noise, which can sometimes be beneficial. However, often large batch sizes work just as well if we use a learning rate scheduler.

Incorrect. The batch size doesn’t make the learning rate larger, but it often requires larger learning rates.

Please answer all questions to proceed.

Quiz: 9.5 Increasing Batch Sizes to Increase Throughput (PART 2)

What is the primary memory-related concern when using large batch sizes in deep learning training?

Incorrect. The model parameters stay exactly the same.

Correct. Larger batch sizes lead to larger matrix multiplications, for example.

Incorrect. Although this could also happen, this is usually not a primary concern due to parallelism in the data loader, as discussed in an earlier lecture.

Please answer all questions to proceed.
Watch Video 1

Unit 9.5