What is GraphPipe?
GraphPipe is a protocol and collection of software designed to simplify machine learning model deployment and decouple it from framework-specific model implementations.
GraphPipe is a tool in the Machine Learning Tools category of a tech stack.
GraphPipe is an open source tool with 718 GitHub stars and 109 GitHub forks. Here’s a link to GraphPipe's open source repository on GitHub
Who uses GraphPipe?
TensorFlow, PyTorch, Caffe2, Torch, and Numba are some of the popular tools that integrate with GraphPipe. Here's a list of all 6 tools that integrate with GraphPipe.
- 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.
GraphPipe Alternatives & Comparisons
What are some alternatives to GraphPipe?
See all alternatives
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.
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.
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.