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Anton Dmitriev updated IGNITE-10202: ------------------------------------ Description: TensorFlow models are represented internally as computation graphs that are saved as a Protobuf objects with some kind of metadata. TensorFlow provides a good API for Python that allows to work with them, but Java API is very poor. TensorFlow allows to import computation graph in Java API and manipulate it using low-level API and it looks like a one way to make model inference in Java. Another way is based on the fact that the most popular way to build a TensorFlow model is using of the Keras API. It allows to save model in _.h5_ format that can be imported into Java using Deeplearning4J. So, the goal of this task is to *prepare a POC that demonstrates*: * How to import TensorFlow model into Java and make inference using TensorFlow Java API. was: TensorFlow models are represented internally as computation graphs that are saved as a Protobuf objects with some kind of metadata. TensorFlow provides a good API for Python that allows to work with them, but Java API is very poor. TensorFlow allows to import computation graph in Java API and manipulate it using low-level API and it looks like a one way to make model inference in Java. Another way is based on the fact that the most popular way to build a TensorFlow model is using of the Keras API. It allows to save model in _.h5_ format that can be imported into Java using Deeplearning4J. So, the goal of this task is to *prepare a POC that demonstrates*: * How to import TensorFlow model into Java and make inference using TensorFlow Java API. * How to import TensorFlow model into Java and make inference using Deeplearning4J. > ML: Create a POC of TensorFlow model inference in Java > ------------------------------------------------------ > > Key: IGNITE-10202 > URL: https://issues.apache.org/jira/browse/IGNITE-10202 > Project: Ignite > Issue Type: Sub-task > Components: ml > Affects Versions: 2.8 > Reporter: Anton Dmitriev > Assignee: Anton Dmitriev > Priority: Major > Fix For: 2.8 > > > TensorFlow models are represented internally as computation graphs that are > saved as a Protobuf objects with some kind of metadata. TensorFlow provides a > good API for Python that allows to work with them, but Java API is very poor. > TensorFlow allows to import computation graph in Java API and manipulate it > using low-level API and it looks like a one way to make model inference in > Java. Another way is based on the fact that the most popular way to build a > TensorFlow model is using of the Keras API. It allows to save model in _.h5_ > format that can be imported into Java using Deeplearning4J. > So, the goal of this task is to *prepare a POC that demonstrates*: > * How to import TensorFlow model into Java and make inference using > TensorFlow Java API. -- This message was sent by Atlassian JIRA (v7.6.3#76005)