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    children: _jsx(_createMdxContent, {})
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    const _components = Object.assign({
      p: "p",
      blockquote: "blockquote",
      h1: "h1",
      ul: "ul",
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      a: "a",
      code: "code",
      h2: "h2",
      strong: "strong",
      pre: "pre",
      span: "span",
      img: "img"
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    return _jsxs(_Fragment, {
      children: [_jsx(_components.p, {
        children: "Running CUDA on the Windows Subsystem for Linux (WSL) is not a trivial task seeing that it requires the GPU to be available on a Virtual Machine. In this article I go in more detail how you can get your GPU working in WSL through something NVIDIA calls \"Paravirtualization\""
      }), "\n", _jsxs(_components.blockquote, {
        children: ["\n", _jsx(_components.p, {
          children: "💡 It is quite straightforward to utilize our Graphic Card, but at this moment of writing, a lot of dependencies are not out of the box for everyone."
        }), "\n"]
      }), "\n", _jsx(_components.h1, {
        children: "Prerequisites"
      }), "\n", _jsxs(_components.ul, {
        children: ["\n", _jsx(_components.li, {
          children: _jsx(_components.a, {
            href: "/post/2021/1/4/coding-python-windows-development-environment-setup",
            children: "A working WSL environment with Python installed"
          })
        }), "\n", _jsx(_components.li, {
          children: _jsx(_components.a, {
            href: "https://docs.microsoft.com/en-us/windows/win32/direct3d12/gpu-cuda-in-wsl?WT.mc_id=AI-MVP-5004263",
            children: "Windows Insider Build> =20150- Dev Channel"
          })
        }), "\n", _jsxs(_components.li, {
          children: ["WSL 2 Kernel> =4.19.121 (check with ", _jsx(_components.code, {
            children: "uname -r"
          }), ")"]
        }), "\n", _jsx(_components.li, {
          children: "An NVIDIA Graphic Card"
        }), "\n"]
      }), "\n", _jsx(_components.h1, {
        children: "Tool Installation"
      }), "\n", _jsx(_components.h2, {
        children: "Installing Docker on WSL"
      }), "\n", _jsxs(_components.p, {
        children: ["Once our WSL environment is setup, we can get started with setting up Docker. Installing Docker is quite trivial, but we should make sure ", _jsx(_components.strong, {
          children: "not"
        }), " to utilize the apt packages, but the installer provided by docker."]
      }), "\n", _jsx(_components.pre, {
        className: "language-bash",
        children: _jsx(_components.code, {
          className: "language-bash code-highlight",
          children: _jsxs(_components.span, {
            className: "code-line",
            children: [_jsx(_components.span, {
              className: "token function",
              children: "curl"
            }), " https://get.docker.com ", _jsx(_components.span, {
              className: "token operator",
              children: "|"
            }), " ", _jsx(_components.span, {
              className: "token function",
              children: "sh"
            }), "\n"]
          })
        })
      }), "\n", _jsx(_components.h2, {
        children: "Installing NVIDIA for Docker"
      }), "\n", _jsx(_components.p, {
        children: "Once Docker is installed, we need to install Nvidia their toolkit for it. Do this by running the commands below"
      }), "\n", _jsx(_components.pre, {
        className: "language-bash",
        children: _jsxs(_components.code, {
          className: "language-bash code-highlight",
          children: [_jsxs(_components.span, {
            className: "code-line",
            children: [_jsx(_components.span, {
              className: "token comment",
              children: "# Enable Nvidia for Docker"
            }), "\n"]
          }), _jsxs(_components.span, {
            className: "code-line",
            children: [_jsx(_components.span, {
              className: "token assign-left variable",
              children: "distribution"
            }), _jsx(_components.span, {
              className: "token operator",
              children: "="
            }), _jsxs(_components.span, {
              className: "token variable",
              children: [_jsx(_components.span, {
                className: "token variable",
                children: "$("
              }), _jsx(_components.span, {
                className: "token builtin class-name",
                children: "."
              }), " /etc/os-release", _jsx(_components.span, {
                className: "token punctuation",
                children: ";"
              }), _jsx(_components.span, {
                className: "token builtin class-name",
                children: "echo"
              }), " $ID$VERSION_ID", _jsx(_components.span, {
                className: "token variable",
                children: ")"
              })]
            }), "\n"]
          }), _jsxs(_components.span, {
            className: "code-line",
            children: [_jsx(_components.span, {
              className: "token function",
              children: "curl"
            }), " -s -L https://nvidia.github.io/nvidia-docker/gpgkey ", _jsx(_components.span, {
              className: "token operator",
              children: "|"
            }), " ", _jsx(_components.span, {
              className: "token function",
              children: "sudo"
            }), " apt-key ", _jsx(_components.span, {
              className: "token function",
              children: "add"
            }), " -\n"]
          }), _jsxs(_components.span, {
            className: "code-line",
            children: [_jsx(_components.span, {
              className: "token function",
              children: "curl"
            }), " -s -L https://nvidia.github.io/nvidia-docker/", _jsx(_components.span, {
              className: "token variable",
              children: "$distribution"
            }), "/nvidia-docker.list ", _jsx(_components.span, {
              className: "token operator",
              children: "|"
            }), " ", _jsx(_components.span, {
              className: "token function",
              children: "sudo"
            }), " ", _jsx(_components.span, {
              className: "token function",
              children: "tee"
            }), " /etc/apt/sources.list.d/nvidia-docker.list\n"]
          }), _jsxs(_components.span, {
            className: "code-line",
            children: [_jsx(_components.span, {
              className: "token function",
              children: "curl"
            }), " -s -L https://nvidia.github.io/libnvidia-container/experimental/", _jsx(_components.span, {
              className: "token variable",
              children: "$distribution"
            }), "/libnvidia-container-experimental.list ", _jsx(_components.span, {
              className: "token operator",
              children: "|"
            }), " ", _jsx(_components.span, {
              className: "token function",
              children: "sudo"
            }), " ", _jsx(_components.span, {
              className: "token function",
              children: "tee"
            }), " /etc/apt/sources.list.d/libnvidia-container-experimental.list\n"]
          }), _jsxs(_components.span, {
            className: "code-line",
            children: [_jsx(_components.span, {
              className: "token function",
              children: "sudo"
            }), " ", _jsx(_components.span, {
              className: "token function",
              children: "apt"
            }), " update \n"]
          }), _jsxs(_components.span, {
            className: "code-line",
            children: [_jsx(_components.span, {
              className: "token function",
              children: "sudo"
            }), " ", _jsx(_components.span, {
              className: "token function",
              children: "apt"
            }), " ", _jsx(_components.span, {
              className: "token function",
              children: "install"
            }), " -y nvidia-docker2\n"]
          }), _jsxs(_components.span, {
            className: "code-line",
            children: [_jsx(_components.span, {
              className: "token function",
              children: "sudo"
            }), " ", _jsx(_components.span, {
              className: "token function",
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              className: "token function",
              children: "install"
            }), " nvidia-container-toolkit\n"]
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            className: "code-line",
            children: "\n"
          }), _jsxs(_components.span, {
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            children: [_jsx(_components.span, {
              className: "token comment",
              children: "# Finally restart the docker service"
            }), "\n"]
          }), _jsxs(_components.span, {
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              className: "token function",
              children: "sudo"
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              className: "token function",
              children: "service"
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              className: "token function",
              children: "docker"
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      }), "\n", _jsx(_components.h1, {
        children: "Testing"
      }), "\n", _jsx(_components.p, {
        children: "If the above was done correctly, we should now be able to test our graphic card by starting a benchmarking container:"
      }), "\n", _jsx(_components.pre, {
        className: "language-bash",
        children: _jsx(_components.code, {
          className: "language-bash code-highlight",
          children: _jsxs(_components.span, {
            className: "code-line",
            children: [_jsx(_components.span, {
              className: "token function",
              children: "sudo"
            }), " ", _jsx(_components.span, {
              className: "token function",
              children: "docker"
            }), " run --rm -it --gpus", _jsx(_components.span, {
              className: "token operator",
              children: "="
            }), "all nvcr.io/nvidia/k8s/cuda-sample:nbody nbody -benchmark\n"]
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      }), "\n", _jsx(_components.p, {
        children: "Which should show something like the below:"
      }), "\n", _jsx(_components.p, {
        children: _jsx(_components.img, {
          src: "./nvidia-nbody.png",
          alt: ""
        })
      }), "\n", _jsx(_components.h1, {
        children: "Conclusion"
      }), "\n", _jsx(_components.p, {
        children: "It's quite trivial to run NVIDIA with its paravirtualization support, but we should make sure that we meet the prerequisites."
      })]
    });
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Xavier Geerinck

Running CUDA on Windows Subsystem for Linux (WSL)

Are you struggling with testing and integrating AI into your product?

Are you struggling with testing and integrating AI into your product?