GraphPipe vs Lobe: What are the differences?
Developers describe GraphPipe as "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. On the other hand, Lobe is detailed as "Deep learning made simple". An easy-to-use visual tool that lets you build custom deep learning models, quickly train them, and ship them directly in your app without writing any code.
GraphPipe and Lobe can be categorized as "Machine Learning" tools.
Some of the features offered by GraphPipe are:
- A minimalist machine learning transport specification based on flatbuffers
- Simple, efficient reference model servers for Tensorflow, Caffe2, and ONNX.
- Efficient client implementations in Go, Python, and Java.
On the other hand, Lobe provides the following key features:
- Build - Drag in your training data and Lobe automatically builds you a custom deep learning model. Then refine your model by adjusting settings and connecting pre-trained building blocks.
- Train - Monitor training progress in real-time with interactive charts and test results that update live as your model improves. Cloud training lets you get results quickly, without slowing down your computer.
- Ship - Export your trained model to TensorFlow or CoreML and run it directly in your app on iOS and Android. Or use the easy-to-use Lobe Developer API and run your model remotely over the air.
GraphPipe is an open source tool with 643 GitHub stars and 91 GitHub forks. Here's a link to GraphPipe's open source repository on GitHub.