optional int32 desiredReplicas = 4;

  // currentCPUUtilizationPercentage is the current average CPU utilization over all pods, represented as a percentage of requested CPU,
  // e.g. 70 means that an average pod is using now 70% of its requested CPU.
  // +optional
  optional int32 currentCPUUtilizationPercentage = 5;
}

// MetricSpec specifies how to scale based on a single metric

      panels.timeSeries.bytes('Memory Usage', queries.memUsage, 'Memory usage of each running instance'),
      panels.timeSeries.base('CPU Usage', queries.cpuUsage, 'CPU usage of each running instance'),
    ]),
    row.new('Network')
    + row.withPanels([
      panels.timeSeries.connections('Connections', queries.connections, 'Connections opened and closed per instance'),

      panels.timeSeries.allocations('Memory Allocations', queries.goAllocations, 'Details about memory allocations'),
      panels.timeSeries.base('CPU Usage', queries.cpuUsage, 'CPU usage of each running instance'),
      panels.timeSeries.base('Goroutines', queries.goroutines, 'Goroutine count for each running instance'),
    ]),
  ], panelHeight=10, startY=1)
  + g.util.grid.makeGrid([

TFCI_BAZEL_COMMON_ARGS="--repo_env=HERMETIC_PYTHON_VERSION=$TFCI_PYTHON_VERSION --config release_arm64_linux"
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"

- [PRF](#prf): HMAC-SHA-256
- [AEAD](#aead): AES-256-GCM if the CPU supports AES-NI, ChaCha20-Poly1305 otherwise. More specifically AES-256-GCM is only selected for X86-64 CPUs with AES-NI extension.

if [[ "$TFCI_WHL_NUMPY_VERSION" == 1 ]]; then
  # Uninstall tf nightly wheel built with numpy1.
  "$python" -m pip uninstall -y tf_nightly_numpy1
  # Install tf nightly cpu wheel built with numpy2.x from PyPI in numpy1.x env.
  "$python" -m pip install tf-nightly-cpu
  if [[ "$TFCI_WHL_IMPORT_TEST_ENABLE" == "1" ]]; then
    "$python" -c 'import tensorflow as tf; t1=tf.constant([1,2,3,4]); t2=tf.constant([5,6,7,8]); print(tf.add(t1,t2).shape)'

        processFileDescriptorObj.max = processProbe.getMaxFileDescriptorCount();
        final ProcessCpuObj processCpuObj = new ProcessCpuObj();
        processObj.cpu = processCpuObj;
        processCpuObj.percent = processProbe.getProcessCpuPercent();
        processCpuObj.total = processProbe.getProcessCpuTotalTime();

// ContainerResourceMetricSource indicates how to scale on a resource metric known to
// Kubernetes, as specified in requests and limits, describing each pod in the
// current scale target (e.g. CPU or memory).  The values will be averaged
// together before being compared to the target.  Such metrics are built in to
// Kubernetes, and have special scaling options on top of those available to

streaming compression due to its stability and performance.

This algorithm is specifically optimized for machine generated content.
Write throughput is typically at least 500MB/s per CPU core,
and scales with the number of available CPU cores.
Decompression speed is typically at least 1GB/s.

This means that in cases where raw IO is below these numbers
compression will not only reduce disk usage but also help increase system throughput.

@test "Wheel conforms to upstream size limitations" {
    WHEEL_MEGABYTES=$(stat --format %s "$TF_WHEEL" | awk '{print int($1/(1024*1024))}')
    # Googlers: search for "test_tf_whl_size"
    case "$TF_WHEEL" in
        # CPU:
        *cpu*manylinux*) LARGEST_OK_SIZE=220 ;;
        # GPU:
        *manylinux*)     LARGEST_OK_SIZE=580 ;;
        # Unknown:
        *)
            echo "The wheel's name is in an unknown format."
            exit 1