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Roadmap.rst

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Pre-release

  • Come up with a name!

  • Support all ONNX operators supported by onnxruntime

  • Design how classes are registered

  • Handle functions that return multiple (potentially optional) arrays (e.g. GRU/LSTM return last state and/or all hidden states)

  • Support all ONNX operator type/shape constraints

  • Design the minimum required utility functions to perform checks

  • Support all ONNX types that make sense with python

  • Support @jit decorator for graph caching

    # the correponding graph is generated once and cached
@jit
def linear(X, w, b):
  return X @ w + b

# expression is evaluated in every function call
def linear_slow(X, w, b):
  return X @ w + b

  • Minimal (does not have to be efficient) grad and jacobian support

  • PRNG

  • Have clear strategy on how to align with onnxruntime releases

  • Should this library only support a single onnxruntime version in each release?

    • If not:
      • define how to register functions + version

  • Define to what extent and how to interact with onnxruntime

    • how to set the provider? Maybe try GPU, if not possible use CPU?
    • custom operators possible? if so how? (not required for pre-release, but should know if makes sense to accomodate this possibility)
    • how to set graph optimisation? always extended?
    • should it handle minimal builds? i.e. certain ops may not be available

  • Sort out CI

    • Azure pipelines?
    • Which platforms to support? Build against all of these?

  • Add ONNX graph visualisation. Maybe something like: ```python import onnxruntime_numpy as onp def add(x, y): return x + y

    # current design would require at least some dummy inputs here onp.display(add, x, y) ```

Post-release

  • Support more operations composed of ONNX operators
  • I am looking at you obscure bessel functions needed for Matern kernel