Workshop: How to Optimize Deep Learning Models for Production
Topics covered:
1. What model pruning is, including benefits and downsides;
2. SOTA pruning algorithms and techniques that you can implement today;
3. SparseML, an open-source tool that makes pruning easy and successful;
4. Guaranteed ways to get production performance out of a pruned model.
After watching this video, you’ll be able to optimize your NLP and/or computer vision model, apply your own data with a few lines of code, and deploy it on commodity CPUs at GPU-level speeds.
*Accompanying slides are linked below.
Timeline:
00:00 - Intro & agenda
02:45 - Advantages and disadvantages of model optimizations
05:00 - SOTA optimization algorithms and techniques
09:25 - Ways to apply optimizations to a model
11:25 - Available model optimization tools
13:27 - Intro to SparseML and SparseZoo, tools we’ll be using during the workshop
15:25 - NLP example using BERT - how to apply optimizations to NLP models with ease using recipes
35:25 - Computer vision example using YOLOv5 - how to apply optimizations to com
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