load("//tensorflow:tensorflow.bzl", "tf_cc_shared_object")
load("//tensorflow/core/platform:rules_cc.bzl", "cc_library")

package(
    # copybara:uncomment default_applicable_licenses = ["//tensorflow:license"],
    default_visibility = ["//visibility:private"],
    licenses = ["notice"],
)

# Filesystem implementation for POSIX environments: Linux, MacOS, Android, etc.
tf_cc_shared_object(
    name = "libposix_filesystem.so",

plugins {
    id("gradlebuild.kotlin-shared-runtime")
}

description = "Provides Kotlin DSL code that is shared between build-logic and runtime"

dependencies {
    compileOnly(platform("gradlebuild:build-platform"))
    compileOnly(kotlin("stdlib"))
    compileOnly("org.ow2.asm:asm-tree")
    compileOnly("com.google.code.findbugs:jsr305")

  // driver can be mounted separately with different `-o context` options. This is
  // typical for storage backends that provide volumes as filesystems on block
  // devices or as independent shared volumes.
  // Kubernetes will call NodeStage / NodePublish with "-o context=xyz" mount
  // option when mounting a ReadWriteOncePod volume used in Pod that has

          "legendFormat": "{{server}}",
          "refId": "A"
        }
      ],
      "thresholds": [],
      "timeRegions": [],
      "title": "Avg. Active Workers",
      "tooltip": {
        "shared": true,
        "sort": 0,
        "value_type": "individual"
      },
      "type": "graph",
      "xaxis": {
        "mode": "time",
        "show": true,
        "values": []
      },

				"parallelizationMethod":{
					"name":"TestDistribution"
				},
				"subprojects":[
					"launcher",
					"time",
					"logging-api",
					"kotlin-dsl-plugins",
					"serialization",
					"toolchains-jvm-shared",
					"docs-asciidoctor-extensions-base",
					"internal-instrumentation-processor",
					"security",
					"input-tracking",
					"base-ide-plugins"
				]
			},
			{
				"parallelizationMethod":{

title: TensorFlow, Large-scale machine learning on heterogeneous systems

# Patch to allow non-glibc 2.12 compatible builds to work.
sed -i '54i#define TCP_USER_TIMEOUT 18' "/${TARGET}/usr/include/netinet/tcp.h"

# Download specific version of libstdc++ shared library based on the value of
# the `VERSION` parameter
  # Download binary libstdc++ 4.8 shared library release

Erasure coding used by MinIO is [Reed-Solomon](https://github.com/klauspost/reedsolomon) erasure coding scheme, which has a total shard maximum of 256 i.e 128 data and 128 parity. MinIO design goes beyond this limitation by doing some practical architecture choices.

- Erasure set is a single erasure coding unit within a MinIO deployment. An object is sharded within an erasure set. Erasure set size is automatically calculated based on the number of drives. MinIO supports unlimited number...

- MinIO distributed mode requires **fresh directories**. If required, the drives can be shared with other applications. You can do this by using a sub-directory exclusive to MinIO. For example, if you have mounted your volume under `/export`, pass `/export/data` as arguments to MinIO server.

  [[ ! -s $BATS_TEST_TMPDIR/out ]]
}

@test "Pip package doesn't depend on CUDA for static builds (i.e. Windows)" {
  bazel cquery \
    --experimental_cc_shared_library \
    --@local_config_cuda//:enable_cuda \
    --define framework_shared_object=false \
    "somepath(//tensorflow/tools/pip_package:build_pip_package, " \
    "@local_config_cuda//cuda:cudart + "\
    "@local_config_cuda//cuda:cudart + "\