# -----------------------------------------------------------------------------
# Specification
# -----------------------------------------------------------------------------

# Card size and layers
size: { width: 1200, height: 630 }
layers:

  # Background
  - background:
      image: *background_image
      color: *background_color

  # Logo
  - size: { width: 144, height: 144 }
    offset: { x: 992, y: 64 }

will track Kubernetes and be compatible with that repo. As we more cleanly separate the layers, we will review the
compatibility guarantee.


## Where does it come from?

`apimachinery` is synced from https://github.com/kubernetes/kubernetes/blob/master/staging/src/k8s.io/apimachinery.
Code changes are made in that location, merged into `k8s.io/kubernetes` and later synced here.


## Things you should *NOT* do

will track Kubernetes and be compatible with that repo.  As we more cleanly separate the layers, we will review the
compatibility guarantee.


## Where does it come from?

`cli-runtime` is synced from https://github.com/kubernetes/kubernetes/blob/master/staging/src/k8s.io/cli-runtime.
Code changes are made in that location, merged into `k8s.io/kubernetes` and later synced here.


## Things you should *NOT* do

compatibility guarantee. We have a goal to make this easier to use in the future.


## Where does it come from?

`apiserver` is synced from https://github.com/kubernetes/kubernetes/blob/master/staging/src/k8s.io/apiserver.
Code changes are made in that location, merged into `k8s.io/kubernetes` and later synced here.


## Things you should *NOT* do

    // from DEBUGGER_TYPE_FLOAT_PER_LAYER). Each layer in the debugging model
    // has a DumpTensor, and it is used to save the entire value of outputs from
    // both the quantized and unquantized layer.
    DEBUGGER_TYPE_INT_PER_LAYER = 2;
    // DEBUGGER_TYPE_FLOAT_PER_LAYER creates a debugging model with both
    // quantized and unquantized layers. The unquantized layer's input come from

namespace mlir {
namespace quant {
namespace {

using QuantizationUnit =
    tensorflow::quantization::UnitWiseQuantizationSpec::QuantizationUnit;

// Adds QuantizationUnitLoc to quantizable layers.
class AddQuantizationUnitLocPass
    : public PassWrapper<AddQuantizationUnitLocPass,
                         OperationPass<func::FuncOp>> {
 public:

{
  "node": {
    {{ if (index .metadata.Labels "td.networking.gke.io/mesh-name") }}
      "id": "projects/{{ .gcp_project_number }}/networks/mesh:{{ (index .metadata.Labels "td.networking.gke.io/mesh-name") }}/nodes/{{ .nodeID}}",
    {{ else if (index .metadata.Labels "td.networking.gke.io/gateway-name") }}
      "id": "projects/{{ .gcp_project_number }}/networks/scope:{{ (index .metadata.Labels "td.networking.gke.io/gateway-name") }}/nodes/{{ .nodeID}}",
    {{ else }}

#  1) make target calls ./tools/docker
#  2) ./tools/docker calls `make build.docker.x` targets to compute the dependencies required
#  3) ./tools/docker triggers the actual docker commands required
# As a result, there are two layers of make involved.

docker: ## Build all docker images
	./tools/docker

docker.save: ## Build docker images and save to tar.gz
	./tools/docker --save

docker.push: ## Build all docker images and push to

	// fsStatsLogs identifies stats for pod logs.
	fsStatsLogs fsStatsType = "logs"
	// fsStatsRoot identifies stats for pod container writable layers.
	fsStatsRoot fsStatsType = "root"
	// fsStatsContainer identifies stats for pod container read-only layers
	fsStatsImages fsStatsType = "images"
)

// Config holds information about how eviction is configured.
type Config struct {

AR_IMAGE_PATH="us-central1-docker.pkg.dev/tensorflow-sigs/tensorflow/build-arm64"

# Build for both JAX and TF usage.  We do these in one place because they share
# almost all of the same cache layers
export DOCKER_BUILDKIT=1
for target in jax tf; do
  IMAGE="gcr.io/tensorflow-sigs/build-arm64:$target-$TAG"
  AR_IMAGE="$AR_IMAGE_PATH:$target-$TAG"
  docker pull "$IMAGE" || true