In the past few months, we've spoken to quite a few customers that have added Machine Learning (ML) tasks into IronWorker. The problem is, these tasks can take a significant amount of time on a CPU vs a GPU. GPU's were built to handle the parallelization of complex matrix/vector operations that gaming required, and it so happens that deep learning exercises also have similar requirements.
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That said, we thought it was about time to add GPU support to IronWorker. We started with a simple test of doing image recognition via TensorFlow. After hacking the example python script to add the ability to download an image via a URL, we zipped up the script and uploaded it to IronWorker. We went ahead and used the latest Tensorflow docker image.
> zip classify_image.zip classify_image.py
> iron worker upload --zip classify_image.zip --name classify_image tensorflow/tensorflow:latest-gpu python classify_image.py --image_url "https://www.petfinder.com/wp-content/uploads/2012/11/91615172-find-a-lump-on-cats-skin-632x475.jpg"
In this initial push, we released support for the g2, g3 and p2 GPU instances on AWS. Once we fired off that task, here's what things look like on our end:
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It took about 10 seconds of run time for the image classification via IronWorker using a p2.xlarge instance on AWS. We didn't have a chance to run this against a non-GPU instance, but we'll leave that as an exercise for the reader. We're pretty sure it will take a little longer than 10 seconds! The actual output from the script is as follows:
Found device 0 with properties:
name: Tesla K80
major: 3 minor: 7 memoryClockRate (GHz) 0.8235
Total memory: 11.17GiB
Free memory: 11.10GiB
Egyptian cat (score = 0.60871)
tabby, tabby cat (score = 0.12714)
lynx, catamount (score = 0.07766)
tiger cat (score = 0.07641)
cougar, puma, catamount, mountain lion, painter, panther, Felis concolor (score = 0.00148)
We'll be working closely with a few customers in the coming months on some large ML/AI projects, and we'll post as much as we can on their use cases and resulting benchmarks. As ML becomes more and more prominent in Business Intelligence operations, we're expecting to see a big increase in GPU usage. If you have any questions about our GPU support, drop us a line and we'd be happy to chat.
Related Reading: Top 10 uses of ironworker
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