Oracle has developed an open source specification for transmitting tensor data, which the company wants to become a standard for machine learning.
Called GraphPipe, the specification provides a protocol for network data transmission. GraphPipe 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.
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 TensorFlo..