What’s Inside the Box? Comparing Data Storage Across Frameworks @ray_deck

Code as Data

PyTorch Keras TensorFlow TensorFlow Lite CoreML

Serialization pytorch.org/ docs/ stable/ notes/ serialization.html

Pickle docs.python.org/3/library/pickle.html

JSON + HDF5

HDFView portal.hdfgroup.org/display/support/Download+HDFView Panoply www.giss.nasa.gov/tools/panoply/

JSON in HDF5

https://sparktoro.com/fake-followers/ray_deck

developers.google.com/protocol-buffers

https://www.slideshare.net/SergeyPodolsky/google-protocol-buffers-56085699

github.com/ tensorflow/ tensorflow/ tree/ master/ tensorflow/ core/ protobuf

Protobuf Viewer MacOS App Store Protobuf Editor sourceforge.net/projects/protobufeditor/

FlatBuf google.github.io/ flatbuffers/

FlatBuf github.com/ tensorflow/ tensorflow/ blob/ master/ tensorflow/ contrib/ lite/ schema/ schema_v3.fbs

github.com/ apple/ coremltools/ tree/ master/ mlmodel/ format

let c = MLModel.compile(url: u) let m = MLModel(url: c) let d:[String:Any] = ["input": image] let dfpin = MLDictionaryFeatureProvider(dictionary: d) let fp = m.predict(dfp) let dfpout = fp.featureValue(for: "labels") let dic = dfpout.dictionaryValue let topResult = dic.first()

Quo Vadis?

Thank You @ray_deck github.com/rhdeck/papis-2018