Flambeau provides Nim bindings to libtorch C++ API and torchvision.

The library is not ready for general use at the moment.

The library installation including:

• auto-downloading and deploying of libtorch
• optional auto-compiling of torchvision into a shared library (if requested)

Initialize submodule : git submodule update --init --recursive

On Ubuntu : sudo apt-get install ffmpeg libpng-dev libjpeg-dev libzip-dev

On OpenSuse : sudo zypper install libpng16-devel libjpeg62-devel libzip-devel ffmpeg

git clone https://github.com/SciNim/flambeau

cd flambeau

nimble install or nimble develop

Note that install and develop will download and build libtorch so it may take a while.

Torchvision can now be built if desired:

nimble build_torchvision

As this library is still in heavy developments, some constructs are a bit brittle.

If you wish to return a RawTensor (the wrapped Torch Tensor type) from a procedure, you need to annotate the procedure with the {.noInit.} pragma, like so:

proc foo(x: float): RawTensor {.noInit.} =
  ...

Otherwise, you will get a segmentation fault due to the implicit initialization of the RawTensor object.

Note: you can use the {.push.} and {.pop.} pragmas at top level in your code, if you wish to write multiple procedures returning RawTensor without having to add this pragma to each procedure.

Note 2: In theory the {.requiresInit.} pragma should mean that the RawTensor type is not implicitly initialized. However, this pragma does not solve the issue at this time.

By default the Torch installation downloaded by this package contains CUDA support. However, by default Nim packages using Flambeau compile without CUDA support. This is controlled by the cuda compile time option. Compile with:

nim cpp -d:cuda <foo>

to compile with CUDA support.

Compared to Numpy and Arraymancer, Flambeau inherits the following PyTorch limitations:

• No string tensors.
• No support for negative step in tensor slice (a[::-1])
  • pytorch/pytorch#229
  • pytorch/pytorch#604

Licensed and distributed under either of

• MIT license: LICENSE-MIT or https://opensource.org/licenses/MIT

or

• Apache License, Version 2.0, (LICENSE-APACHEv2 or https://www.apache.org/licenses/LICENSE-2.0)

at your option. This file may not be copied, modified, or distributed except according to those terms.