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GraphPipe vs TensorFlow.js: What are the differences?
GraphPipe: Machine Learning Model Deployment Made Simple, by Oracle. GraphPipe is a protocol and collection of software designed to simplify machine learning model deployment and decouple it from framework-specific model implementations; TensorFlow.js: Machine Learning in JavaScript. Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API.
GraphPipe and TensorFlow.js can be categorized as "Machine Learning" tools.
GraphPipe and TensorFlow.js are both open source tools. It seems that TensorFlow.js with 11.2K GitHub stars and 816 forks on GitHub has more adoption than GraphPipe with 643 GitHub stars and 91 GitHub forks.
Pros of GraphPipe
Pros of TensorFlow.js
- Open Source6
- NodeJS Powered5
- Deploy python ML model directly into javascript2
- Cost - no server needed for inference1
- Privacy - no data sent to server1
- Runs Client Side on device1
- Can run TFJS on backend, frontend, react native, + IOT1
- Easy to share and use - get more eyes on your research1