Oracle offers GraphPipe spec for machine learning data transmission | Computing

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has developed an open source specification for transmitting tensor , which the company wants to become a standard for  .

Called , the specification provides a protocol for network data . is intended to bring the efficiency of a binary, memory-mapped format while being simple and light on dependencies. There also are clients and servers for deploying and querying machine learning models from any framework.

It includes:

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  • A set of flatbuffer definitions. Flatbuffers are similar to Google protocol buffers, with an additional benefit of avoiding memory copy during deserialization. Flatbuffer definitions provide a request message that includes input, tensors, input names, and output names.
  • Guidelines for serving models.
  • Examples of serving models from various machine learning frameworks.
  • Client libraries for querying models served through GraphPipe. Clients are available for Python, Go, and Java. There’s a plugin for Google’s TensorFlow library, for including a remote model inside a local TensorFlow graph.

With GraphPipe, a remote model accepts a request message and returns one tensor per output name. The model also provides metadata about types and shapes of inputs and outputs. 

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