Deep-learning and autograd package in D.
Work in progress.
- No mandatory external dependencies.
- Utilizing this library in your project should be as simple as including it like any other D library.
- Once your project is built, it can be deployed anywhere like any other D application, with no additional runtime dependencies.
- Future additions (e.g. for hardware acceleration) may include optional dependencies.
nothrow,@nogc.- Memory is pre-allocated statically, there is no dynamic memory allocation.
- Utilize the full strength of the D programming language where appropriate.
- Instead of copying an existing API (such as TensorFlow or Keras), the library API is designed around D features (metaprogramming / templates / template constraints).
- Take advantage of powerful optimizations provided by optimizing D compilers such as LDC.
- The library may use several layers of abstraction to facilitate expressiveness and code quality.
- Debug/DMD builds are much slower that LDC/release builds, however DMD performance is a non-goal.
- Minimize runtime overhead.
- Indirections are avoided when possible. Instead, memory requirements are pre-computed for entire graphs and are made available to implementations.