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LostTech.TensorFlow 1.15.0

• .NETStandard 2.0

  • CoreCompat.Portable.Licensing (>= 1.2.4)
  • Gradient.Runtime (>= 0.4.4 && < 0.5.0)
  • JetBrains.Annotations (>= 2020.1.0)
  • LostTech.Cloud.InstanceInfo (>= 0.1.1)
  • LostTech.NumPy (>= 0.2.2)
  • LostTech.Python.Runtime (>= 3.0.4 && < 4.0.0)
  • Nito.Comparers.Core (>= 6.0.0)
  • System.DirectoryServices (>= 4.7.0)
  • System.Text.Json (>= 4.7.2)
  • WhichPython (>= 0.3.3 && < 0.4.0)

This version requires Python 3.x x64 to be installed with tensorflow or tensorflow-gpu. See the official installation instructions in https://www.tensorflow.org/install/pip#older-versions-of-tensorflow (ensure you are installing version 1.15 to avoid hard-to-debug issues).

If your app, that uses LostTech.TensorFlow, targets net4xx (like net472), you need to specify a proper runtime identifier to run and publish. e.g. "dotnet publish -r win" and "dotnet run -r win7-x64". Note, "run" requires specific identifier.

What's new

1.15.0 (Release):
- enhanced Tensor<T>
- added licensing via Azure Subscription (must be admin of subscription): https://portal.azure.com/#create/losttechllc.tensorflow
- fixed off-by-one errors in passing `Index` and `Range` counting from the end
- support more NumPy functions
- added `PythonSlice`
- added .NET types for `ArithmeticError`, `FloatingPointError`, `OverflowError`, and `ZeroDivisionError`
- fixed a few GIL-related crashes

RC1:
- replaced expiration with licenses
- improved typing on many APIs
- fixed inability to access static settings
- strongly-typed wrappers for Tensor
- enhanced ndarray<T>
- improved exception handling and debugging
- core runtime components include source and debug symbols
- LINQ enumerables and 1D .NET arrays are now automatically converted to Python lists for compatibility with bad TensorFlow APIs (can be disabled)

Preview 7:
- TensorFlow 1.15
- strongly-typed accessors for ndarray<T>
- arithmetic, bitwise and comparison operators on Tensors (note, now to check for null `is null` must be used instead of `== null`)
- StartUsing extension on classes like Session, variable_scope, etc to allow simpler `using` blocks in .NET
- improved support for enums
- prepackaged TensorFlow runtime in a NuGet package for easy installation (separate): LostTech.TensorFlow.Python
- minimal wrapper for NumPy is released in a separate package (see dependencies)
- runtime initialization moved to Gradient.Runtime
- bugfixes: https://github.com/losttech/Gradient/milestone/3 + internally reported bugs
- new sample: reinforcement learning with Unity ML agents (see https://github.com/losttech/Gradient-Samples/ after 2020/02/10)

Preview 6:
- feature: enabled inheriting from TensorFlow classes. Now it is possible to build custom Keras layers, callbacks, etc
- feature: automatic marshalling of Gradient types for use with TensorFlow
- fixed an ability to modify collections belonging to TensorFlow objects
- fixed crash when enumerating TensorFlow collections without an explicit lock

Preview 5.1:
- improved passing dictionaries
- setup: optionally specify Conda environment via an environment variable
- setup: fixed Conda environment autodectection on Linux
- improved argument types in many places
- Gradient warnings are now printed to Console.Error by default, instead of Console.Out
- fixed crashes on dynamic interop and multithreaded enumeration
- fixed some properties not being exposed https://github.com/losttech/Gradient/issues/4

Preview 5:
- support for indexing Tensor objects via `dynamic`
- allow using specific Python environment via GradientSetup.UsePythonEnvironment

Preview 4:
- support for Ubuntu 18.04 x64 and MacOS with .NET Core; other *nix OS might work too


Preview 2:
- dynamically typed overloads, that enable fallback for tricky signatures
- a common interface for tf.Variable and tf.Tensor
- enabled enumeration over TensorFlow collection types