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A Docker image for serving fastai models, mimicking the API of Tensorflow Serving. It is designed for running batch inference at scale. It is not optimized for performance (but it's not that slow).
The main features of developmentseed/fastai-serving are: Cloud Infrastructure, Orchestration and Pipelines.
Projects with overlapping indexed features include: developmentseed/chip-n-scale-queue-arranger — chip-n-scale-queue-arranger helps you run machine learning models over satellite imagery at scale. It is a collection… c6fc/npk. cosmiq/solaris — CosmiQ Works Geospatial Machine Learning Analysis Toolkit. awslabs/aws-data-wrangler — This project is an AWS pandas integration library and data pipeline framework designed to simplify the movement and… donnemartin/awesome-aws — This project serves as a comprehensive directory of open-source libraries, tools, and technical documentation designed… aws/aws-sdk-ruby — The official AWS SDK for Ruby.
chip-n-scale-queue-arranger helps you run machine learning models over satellite imagery at scale. It is a collection of AWS CloudFormation templates deployed by kes, lambda functions, and utility scripts for monitoring and managing the project.
This project is an AWS pandas integration library and data pipeline framework designed to simplify the movement and transformation of data between local memory and AWS storage and analytics services. It functions as a cloud data lake toolkit and storage file manager, allowing users to read, write, and transform structured data across various cloud environments. The library distinguishes itself as a distributed compute orchestrator capable of managing clusters in environments such as EMR to process datasets that exceed the memory limits of a single machine. It also provides specialized capabil
