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Propel vs Neptune: What are the differences?
Propel: Machine learning for JavaScript. Propel provides a GPU-backed numpy-like infrastructure for scientific computing in JavaScript; Neptune: The most lightweight experiment tracking tool for machine learning. It brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed, reproduced and shared with others. Works with all common technologies and integrates with other tools.
Propel and Neptune can be primarily classified as "Machine Learning" tools.
Some of the features offered by Propel are:
- Run anywhere, in the browser or natively from Node
- Target multiple GPUs and make TCP connections
- PhD optional
On the other hand, Neptune provides the following key features:
- Experiment tracking
- Experiment versioning
- Experiment comparison
Propel is an open source tool with 2.79K GitHub stars and 80 GitHub forks. Here's a link to Propel's open source repository on GitHub.
Pros of Neptune
- Aws managed services1
- Supports both gremlin and openCypher query languages1
Pros of Propel
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Cons of Neptune
- Doesn't have much support for openCypher clients1
- Doesn't have proper clients for different lanuages1
- Doesn't have much community support1
