• If your project needs only TF C API bindings: $ dub add tfd:deimos
• If your project needs entire tfd packages: $ dub add tfd

see also Requirements

Mir is to tfd what NumPy is to Tensorflow.

/// tensor add

import tfd;
import mir.ndslice : as, iota;

with (newGraph)
{
  auto i = iota(2, 3, 4).as!float;

  Operation x = placeholder!float("x", 2, 3, 4);
  Operation two = constant(i);
  Operation add = x + two;

  Tensor addVal = session.run([add], [x: i.tensor])[0];
  assert(addVal.sliced!(float, 3) == i * 2);
}

And more:

• save/load TF graphs between Python and D

• dpp generated C API bindings
• Support @nogc nothrow.
• Setup CI
• Wrap tensor and session for basic usages (see tfd.session unittests).
• mir.ndslice.Slice s <=> tfd.tensor.Tensor t integration by s.tensor, t.slicedAs(s).
• Example to save/load TF graphs.
• Use pbd to save/load proto files.
• Parse ops.pbtxt to generate typed ops bindings.
• Rewrite C API example with typed bindings.
• Implement autograd, and simple training APIs in D.
• Support cloud TPU.
• Support dip1000.

• libtensorflow. Currently, tfd only supports
  • Linux CPU v1.15.0
  • Linux GPU v1.15.0 (Not tested in CI)
  • Windows CPU v1.15.0

tfd uses dpp to generate bindings from TF C-API. You need libclang to run dpp.

dub fetch dpp
dub run dpp -- --preprocess-only --include-path ./download/include <target dpp file>