GraphPipe vs Neptune
Overview
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CLI (Node.js)
Manual
Detailed Comparison
GraphPipe is a protocol and collection of software designed to simplify machine learning model deployment and decouple it from framework-specific model implementations. | 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. |
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. | Experiment tracking; Experiment versioning; Experiment comparison; Experiment monitoring; Experiment sharing; Notebook versioning |
Statistics | |
GitHub Stars 718 | GitHub Stars - |
GitHub Forks 103 | GitHub Forks - |
Stacks 2 | Stacks 16 |
Followers 16 | Followers 38 |
Votes 0 | Votes 2 |
Pros & Cons | |
No community feedback yet | Pros
Cons
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Integrations | |
What are some alternatives to GraphPipe, Neptune?
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
PyTorch
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
Kubeflow
The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.
TensorFlow.js
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
Polyaxon
An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.
Streamlit
It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.
MLflow
MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
H2O
H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.
