Introducing training optimization features
In the previous chapters, we saw how we could leverage MXNet, GluonCV, and GluonNLP to retrieve pre-trained models in certain datasets (such as ImageNet, MS COCO, or IWSLT2015) and use them for our specific tasks and datasets. Furthermore, we used transfer learning and fine-tuning techniques to improve the performance on those tasks/datasets.
In this recipe, we will introduce (and revisit) several concepts and features that will optimize our training loops, after which we will analyze the trade-offs involved.
Getting ready
Similar to the previous chapters, in this recipe, we will be using some matrix operations and linear algebra, but it will not be hard at all, as you will find lots of examples and code snippets to facilitate your learning.
How to do it...
In this recipe, we will work through the following steps:
- Working with lazy evaluation and automatic parallelization
- Optimizing DataLoaders: GPU preprocessing...
