Code: fastai-serving repo

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We’ve been experimenting with some Fast AI models recently for our remote sensing work. Unfortunately, we ran into a lot of issues when trying to deploy those models on large-scale inference jobs (specifically running land-classification on big satellite imagery datasets). This fastai-serving repo is meant to solve this in a way that mimics the TF Serving approach/API. Namely, it helps you package a trained model within a small Docker image (running a mini server) so you can make prediction requests via REST POST requests.

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We’re working on expanding the functionality (and are very receptive to any help!). For anyone who’s running inference on large image sets, we usually spin up multiple of these these inference-ready images and run large batch predictions with our open chip-n-scale pipeline.