I'm much more familiar with MATLAB than I am with TensorFlow so I might be wrong about the lowest-level computation stuff. Part of it is an architecture issue.

I'm much more familiar with MATLAB than I am with TensorFlow so I might be wrong about the lowest-level computation stuff. Part of it is an architecture issue. I can imagine writing a transpiler from MATLAB that automatically dices up the matrix calls to work over a distributed system, maybe with something like Go or Elixir running under the hood and sending the jobs in batches to other processors, then joining all of those results, which abstracts away most concurrency issues. I find it more difficult to think about TensorFlow this way, because to me it seems more similar to OpenCL or OpenGL, where you are dealing with multiple cores reading from a single memory space and transpiling the TensorFlow code to something like a shader. That type of code works more like SIMD or VLIW and has trouble with things like branching or dynamic codepaths or even reading/writing from/to a computer's main memory. So what I'm looking for is a way to write at the high-level abstraction of MATLAB syntax and then have the runtime dice that up internally as TensorFlow or Elixir or whatever (that part doesn't matter to me as much). Maybe Julia can do that but I haven't learned it yet. But I want to stay away from the boilerplate - things like binding buffers or carrying the mental burden of having separate vector and system memories.