'Scale Vision Transformers (ViT) Beyond Hugging Face' by MaziyarPanahi apachespark databricks
If we compare Spark NLP and Hugging Face side by side for downloading and loading a pre-trained ViT model for an Image Classification prediction, apart from loading images and using post calculations like argmax outside the Hugging Face library, they are both pretty straightforward. Also, they both can be saved and serve later as a pipeline to reduce these lines into only 1 line of code:Since Apache Spark has a concept calledis called. Actions in Apache Spark can be .count or .show or .
Now that we know all the ViT models from Hugging Face are also available in Spark NLP and how to use them in a pipeline, we will repeat our previous benchmarks on the bare-metal Dell server to compare CPU vs. GPU. Let’s have a look at the results of Spark NLP’s image classification pipeline on CPUs over our sample ImageNet dataset:It took around 2.1 minutes from our sample dataset.
Let’s have a look at our Spark NLP image classification pipeline on a GPU device over the sample ImageNet dataset :Out of curiosity to see whether my crusade to find a good batch size on a smaller dataset was correct I ran the same pipeline with GPU on a larger dataset to see if the batch size 32 will have the best result:Thankfully, it is batch size 32 that yields the best time. So it took around 4 and a half minutes .
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