In reply to @carlthome:matrix.org
Hello hello! Anybody working with deep learning on audio data here? 🤗

In reply to @carlthome:matrix.org
Hello hello! Anybody working with deep learning on audio data here? 🤗

{
  description = "Pytorch and stuff";

  # Specifies other flakes that this flake depends on.
  inputs = {
    devshell.url = "github:numtide/devshell";
    utils.url = "github:numtide/flake-utils";
    nixpkgs.url = "github:nixos/nixpkgs/nixos-21.11";
  };

  # Function that produces an attribute set.
  # Its function arguments are the flakes specified in inputs.
  # The self argument denotes this flake.
  outputs = inputs@{ self, nixpkgs, utils, ... }:
    (utils.lib.eachSystem [ "x86_64-linux" ] (system:
      let
        pkgs = (import nixpkgs {
          inherit system;
          config = {
            # For CUDA.
            allowUnfree = true;
            # Enables CUDA support in packages that support it.
            cudaSupport = true;
          };
        });
      in rec {
        # Executed by `nix build .#<name>`
        # Ignore this, it was just for testing.
        packages = utils.lib.flattenTree {
          hello = pkgs.hello;
        };

        # Executed by `nix build .`
        defaultPackage = packages.hello;
        # defaultPackage = pkgs.callPackage ./default.nix { };

        # Executed by `nix develop`
        devShell = with pkgs; mkShell {
          buildInputs = [
            python39 # numba-0.54.1 not supported for interpreter python3.10
          ] ++ (with python39.pkgs; [
            inflect
            librosa
            pip
            pytorch-bin
            unidecode
          ]) ++ (with cudaPackages; [
            cudatoolkit
          ]);

          shellHook = ''
            export CUDA_PATH=${pkgs.cudatoolkit}
          '';
        };
      }
    ));
}