apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: nvidia-gpu-device-plugin
  namespace: kube-system
  labels:
    k8s-app: nvidia-gpu-device-plugin
    addonmanager.kubernetes.io/mode: EnsureExists
spec:
  selector:
    matchLabels:
      k8s-app: nvidia-gpu-device-plugin
  template:
    metadata:
      labels:
        k8s-app: nvidia-gpu-device-plugin
    spec:
      priorityClassName: system-node-critical
      affinity:

# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation
libcudnn9-dev-cuda-12=9.1.1.17-1
libcudnn9-cuda-12=9.1.1.17-1

# This can be removed once NVIDIA publishes a cuda-12.3.2 Docker image.
# For now it ensures that we install at least version 12.3.107 of PTXAS,
# since 12.3.103 has a bug.

    assignees = apple_silicon_assignees;
  if (lowercased_title.includes('tf-trt') && domain.includes('nvidia.com')) {
    assignees.push(
        'DEKHTIARJonathan', 'meena-at-work', 'nluehr', 'pjannaty', 'poulsbo');
  } else if (
      lowercased_title.includes('nvidia') && domain.includes('nvidia.com')) {
    if (lowercased_title.includes('jax')) {
      assignees.push('hawkinsp', 'yashk2810', 'skye');
    }

    srcs = [
        "device_compiler_serialize_test.cc",
    ],
    tags = [
        "config-cuda-only",
        "no_oss",  # This test only runs with GPU.
        "requires-gpu-nvidia",
        "xla",
    ],
    deps = [
        ":device_compiler_test_helper",
        "//tensorflow/compiler/jit:compilation_passes",
        "//tensorflow/compiler/jit:flags",
        "//tensorflow/core:test",

#   container and start fresh, run "docker rm -f tf". Removing the container
#   destroys some temporary bazel data and causes longer builds.
#
#   You will need the NVIDIA Container Toolkit for GPU testing:
#   https://github.com/NVIDIA/nvidia-container-toolkit
#
#   Note: if you interrupt a bazel command on docker (ctrl-c), you
#   will need to run `docker exec tf pkill bazel` to quit bazel.
#

TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_arm64
# Note: this is not set to "--cpu", because that changes the package name
# to tensorflow_cpu. These ARM builds are supposed to have the name "tensorflow"
# despite lacking Nvidia CUDA support.
TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow"
TFCI_DOCKER_ENABLE=1
TFCI_DOCKER_IMAGE=us-central1-docker.pkg.dev/tensorflow-sigs/tensorflow/build-arm64:tf-2-16-multi-python
TFCI_DOCKER_PULL_ENABLE=1

# ==============================================================================
source "${BASH_SOURCE%/*}/utilities/setup.sh"

# Record GPU count and CUDA version status
if [[ "$TFCI_NVIDIA_SMI_ENABLE" == 1 ]]; then
  tfrun nvidia-smi
fi

# Update the version numbers for Nightly only
if [[ "$TFCI_NIGHTLY_UPDATE_VERSION_ENABLE" == 1 ]]; then
  tfrun python3 tensorflow/tools/ci_build/update_version.py --nightly
fi

					},
				},
			},
			{
				Resources: v1.ResourceRequirements{
					Requests: v1.ResourceList{
						v1.ResourceCPU:    resource.MustParse("2000m"),
						v1.ResourceMemory: resource.MustParse("3000"),
						"nvidia.com/gpu":  resource.MustParse("3"),
					},
				},
			},
		},
		NodeName: "node1",
	}
	labels1 := map[string]string{
		"foo": "bar",
		"baz": "blah",
	}
	labels2 := map[string]string{

# ==============================================================================
source "${BASH_SOURCE%/*}/utilities/setup.sh"

# Record GPU count and CUDA version status
if [[ "$TFCI_NVIDIA_SMI_ENABLE" == 1 ]]; then
  tfrun nvidia-smi
fi

# Update the version numbers for Nightly only
if [[ "$TFCI_NIGHTLY_UPDATE_VERSION_ENABLE" == 1 ]]; then
  tfrun python3 tensorflow/tools/ci_build/update_version.py --nightly

[ -z "$PS1" ] && return

# Set up attractive prompt
export PS1="\[\e[31m\]tf-docker\[\e[m\] \[\e[33m\]\w\[\e[m\] > "
export TERM=xterm-256color
alias grep="grep --color=auto"
alias ls="ls --color=auto"
# Fix nvidia-docker