# The RBE machine itself has older kernel mode driver, and it requires
# nvidia driver to be installed.
nvidia-driver-580-open
# TODO(b/445248346): The Docker image shouldn't have cuda-compat installed.
# However, hermetic CUDA forward-compatibility mode is still missing some
# libraries.

    execution time and the sharding
    [could create an overhead on the test execution](https://github.com/bazelbuild/bazel/issues/2113#issuecomment-264054799).

2.  Using [Docker](https://www.docker.com) and TensorFlow's CI scripts.

    ```bash
    # Install Docker first, then this will build and run cpu tests
    tensorflow/tools/ci_build/ci_build.sh CPU bazel test //tensorflow/...
    ```

    See

  /**
   * Remove {@code edge} from the set of incoming edges. Returns the former predecessor node.
   *
   * <p>In the undirected case, returns {@code null} if {@code isSelfLoop} is true.
   */
  @CanIgnoreReturnValue
  @Nullable N removeInEdge(E edge, boolean isSelfLoop);

  /** Remove {@code edge} from the set of outgoing edges. Returns the former successor node. */
  @CanIgnoreReturnValue
  N removeOutEdge(E edge);

  /**

	set bool
}

// MarshalXML encodes delete marker boolean into an XML form.
func (b Boolean) MarshalXML(e *xml.Encoder, startElement xml.StartElement) error {
	if !b.set {
		return nil
	}
	return e.EncodeElement(b.val, startElement)
}

// UnmarshalXML decodes delete marker boolean from the XML form.

	config.Metadata.Retry.Backoff = (1 * time.Second)
	config.Metadata.RefreshFrequency = (15 * time.Minute)

	target.config = config

	brokers := []string{}
	for _, broker := range args.Brokers {
		brokers = append(brokers, broker.String())
	}

	client, err := sarama.NewClient(brokers, config)
	if err != nil {
		if !errors.Is(err, sarama.ErrOutOfBrokers) {

TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_cpu
TFCI_BUILD_PIP_PACKAGE_WHEEL_NAME_ARG="--repo_env=WHEEL_NAME=tensorflow_cpu"
TFCI_DOCKER_ENABLE=1
TFCI_DOCKER_IMAGE=us-docker.pkg.dev/ml-oss-artifacts-published/ml-public-container/ml-build:latest
TFCI_DOCKER_PULL_ENABLE=1
TFCI_DOCKER_REBUILD_ARGS="--target=devel ci/official/containers/ml_build"
TFCI_INDEX_HTML_ENABLE=1

	margin: 20px 0;
}

.notification {
	text-align: center;
}

ul.has-error {
	color: #dd4b39 !important;
	list-style-type: none;
	padding: 0;
}

.form-group .has-error + .form-control {
	border-color: #dd4b39 !important;
	box-shadow: none;
}

.sidebar-mini.sidebar-collapse .sidebar-search {
	display: none !important;
}

input[type="file"].form-control:not([type="file"]) {
	height: 0% !important;

	lm.mu.Lock()
	defer lm.mu.Unlock()

	lm.ref = 0
	lm.isWriteLock = false
}

// DRLocker returns a sync.Locker interface that implements
// the Lock and Unlock methods by calling drw.RLock and drw.RUnlock.
func (lm *LRWMutex) DRLocker() sync.Locker {
	return (*drlocker)(lm)
}

type drlocker LRWMutex

func (dr *drlocker) Lock()   { (*LRWMutex)(dr).RLock() }

WIP ML Build Docker container for ML repositories (Tensorflow, JAX and XLA).

This container branches off from
/tensorflow/tools/tf_sig_build_dockerfiles/. However, since
hermetic CUDA and hermetic Python is now available for Tensorflow, a lot of the
requirements installed on the original container can be removed to reduce the
footprint of the container and make it more reusable across different ML

etcd is a distributed key value store that provides a reliable way to store data across a cluster of machines.

## Get started

### 1. Prerequisites

- Docker 18.03 or above, refer here for [installation](https://docs.docker.com/install/).

### 2. Start etcd

etcd uses [gcr.io/etcd-development/etcd](https://console.cloud.google.com/gcr/images/etcd-development/GLOBAL/etcd) as a primary container registry.

```