Protocol Buffer. HDF5. TFLite. Pickle. They all hold model weights and/or code, and in many cases they can and do hold the same ones. Understanding how they work and how to unpack them outside of our tools gives us insights into how the tools themselves work, and how we can pipe our models around, and make them more useful for our customers - often app developers!

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

3. Code as Data

4. PyTorch
Keras
TensorFlow
TensorFlow Lite
CoreML

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

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

9. JSON + HDF5

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

13. JSON in HDF5

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

17. developers.google.com/protocol-buffers

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

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

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

24. FlatBuf
google.github.io/
flatbuffers/

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

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

33. 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()

39. Quo Vadis?

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