/settings.gradle*                           @gradle/bt-developer-productivity
gradle/shared-with-buildSrc/                @gradle/bt-developer-productivity
packaging/internal-build-reports/           @gradle/bt-developer-productivity
testing/distributions-basics/               @gradle/bt-developer-productivity
testing/distributions-core/                 @gradle/bt-developer-productivity

To install:

1) chrome://extensions/
2) click "[+] Developer Mode" in top right
3) "Load unpacked extension..."
4) pick $GOROOT/misc/chrome/gophertool

export DEBIAN_FRONTEND=noninteractive

# Fetch the NVIDIA key.
apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub;

# Set up sources for NVIDIA CUDNN.
cat >/etc/apt/sources.list.d/nvidia.list <<SOURCES
# NVIDIA
deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/ /

the bundle id before installing a new app. If the uninstalled app is the last app by
the developer identity, the device might also remove the permission to run apps from
that developer, and the exec wrapper will fail to install the new app. To avoid that,
install another app with the same developer identity but with a different bundle id.
That way, the permission to install apps is held on to while the primary app is

		<name>CodeLibs</name>
		<url>https://fess.codelibs.org/</url>
	</organization>
	<developers>
		<developer>
			<id>shinsuke</id>
			<name>Shinsuke Sugaya</name>
			<email>******@****.***</email>
			<organization>CodeLibs Inc.</organization>
			<organizationUrl>https://codelibs.co</organizationUrl>
		</developer>
	</developers>
	<issueManagement>
		<system>GitHub</system>

		<name>CodeLibs</name>
		<url>https://fess.codelibs.org/</url>
	</organization>
	<developers>
		<developer>
			<id>shinsuke</id>
			<name>Shinsuke Sugaya</name>
			<email>******@****.***</email>
			<organization>CodeLibs Inc.</organization>
			<organizationUrl>https://codelibs.co</organizationUrl>
		</developer>
	</developers>
	<scm>
		<connection>scm:git:******@****.***:codelibs/fess-suggest.git</connection>

nvidia-cuda-nvcc-cu12>=12.5.82,<13.0
nvidia-cuda-nvrtc-cu12>=12.5.82,<13.0
nvidia-cuda-runtime-cu12>=12.5.82,<13.0
# The upper bound is set for the CUDNN API compatibility.
# See
# https://docs.nvidia.com/deeplearning/cudnn/backend/latest/developer/forward-compatibility.html#cudnn-api-compatibility
nvidia-cudnn-cu12>=9.3.0.75,<10.0
nvidia-cufft-cu12>=11.2.3.61,<12.0
nvidia-curand-cu12>=10.3.6.82,<11.0
nvidia-cusolver-cu12>=11.6.3.83,<12.0

1.  Using tools and libraries installed directly on your system.

    Refer to the
    [CPU-only developer Dockerfile](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/dockerfiles/dockerfiles/devel-cpu.Dockerfile)
    and
    [GPU developer Dockerfile](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/dockerfiles/dockerfiles/devel-gpu.Dockerfile)

- [ ] Make sure that all commits are [signed off](https://git-scm.com/docs/git-commit#Documentation/git-commit.txt---signoff) to indicate that you agree to the terms of [Developer Certificate of Origin](https://developercertificate.org/).

devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer, whereas in previous “parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several...