## How the configuration works

We use Kotlin portable DSL to store TeamCity configuration, which means you can easily create a new pipeline
based on a specific branch. Currently, we have two pipelines: `master` and `release`, but you can easily create
and test another isolated pipeline from any branch. 

We'll explain everything via an example. Let's say you make some changes on your branch `myTestBranch`

    init {
        id("Promotion_AllBranchesStartReleaseCycleTest")
        name = "Start Release Cycle Test"
        description = "Test for Start Release Cycle pipeline"

        steps {
            gradleWrapper {
                name = "PromoteTest"
                tasks = "clean promoteStartReleaseCycle"
                useGradleWrapper = true

configureIde(TestType.INTEGRATION)

createTestTask("integMultiVersionTest", "forking", sourceSet, TestType.INTEGRATION) {
    // This test task runs only multi-version tests and is intended to be used in the late pipeline to sweep up versions not previously tested
    includeSpockAnnotation("org.gradle.integtests.fixtures.compatibility.MultiVersionTestCategory")
    (options as JUnitPlatformOptions).includeEngines("spock")

pkg syscall (openbsd-amd64), const EFER_SCE = 1
pkg syscall (openbsd-amd64), const EFER_SCE ideal-int
pkg syscall (openbsd-amd64), const ImplementsGetwd = false
pkg syscall (openbsd-amd64), const PMC5_PIPELINE_FLUSH = 21
pkg syscall (openbsd-amd64), const PMC5_PIPELINE_FLUSH ideal-int
pkg syscall (openbsd-amd64), const RTF_FMASK = 63496
pkg syscall (openbsd-amd64), const RTM_VERSION = 4
pkg syscall (openbsd-amd64), const SIOCBRDGDADDR = 2150132039

 *
 * Usage: `mvn compile exec:java@update-test-buckets -DinputTestClassDataJson=/path/to/test-class-data.json`.
 * You can get the JSON file as an artifacts of the "autoUpdateTestSplitJsonOnGradleMaster" pipeline in TeamCity.
 */
fun main() {
    val model = CIBuildModel(
        projectId = "Check",
        branch = VersionedSettingsBranch("master"),
        buildScanTags = listOf("Check"),

import org.jetbrains.kotlin.fir.diagnostics.ConeSyntaxDiagnostic
import org.jetbrains.kotlin.fir.languageVersionSettings
import org.jetbrains.kotlin.fir.lazy.AbstractFir2IrLazyDeclaration
import org.jetbrains.kotlin.fir.pipeline.*
import org.jetbrains.kotlin.fir.psi
import org.jetbrains.kotlin.fir.references.FirReference
import org.jetbrains.kotlin.fir.references.FirThisReference
import org.jetbrains.kotlin.fir.references.toResolvedSymbol

filippo.io/edwards25519 v1.1.0 h1:FNf4tywRC1HmFuKW5xopWpigGjJKiJSV0Cqo0cJWDaA=
filippo.io/edwards25519 v1.1.0/go.mod h1:BxyFTGdWcka3PhytdK4V28tE5sGfRvvvRV7EaN4VDT4=
github.com/Azure/azure-pipeline-go v0.2.3 h1:7U9HBg1JFK3jHl5qmo4CTZKFTVgMwdFHMVtCdfBE21U=
github.com/Azure/azure-pipeline-go v0.2.3/go.mod h1:x841ezTBIMG6O3lAcl8ATHnsOPVl2bqk7S3ta6S6u4k=
github.com/Azure/azure-storage-blob-go v0.15.0 h1:rXtgp8tN1p29GvpGgfJetavIG0V7OgcSXPpwp3tx6qk=

*   `tf.data`

    * `tf.data` now has an `autotune_options.initial_parallelism` option to
      control the initial parallelism setting used by autotune before the data
      pipeline has started running. The default is 16. A lower value reduces
      initial memory usage, while a higher value improves startup time.

## Keras

*  `keras.layers.experimental.DynamicEmbedding`

OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

================================================================

github.com/Azure/azure-pipeline-go
https://github.com/Azure/azure-pipeline-go
----------------------------------------------------------------
    MIT License

    Copyright (c) Microsoft Corporation. All rights reserved.

Autoscaler API: the resource metrics API, custom metrics API, and
metrics-server, which is the new replacement for Heapster in the default monitoring
pipeline.

[SIG Instrumentation]: https://github.com/kubernetes/community/tree/master/sig-instrumentation

### SIG Multi-cluster (formerly known as SIG Federation)