import jetbrains.buildServer.configs.kotlin.BuildSteps
import jetbrains.buildServer.configs.kotlin.buildSteps.script
import java.io.File
import java.nio.charset.StandardCharsets
import java.util.LinkedList
import java.util.Locale

interface PerformanceTestBucketProvider {
    fun createPerformanceTestsFor(stage: Stage, performanceTestCoverage: PerformanceTestCoverage): List<PerformanceTest>
}

import jetbrains.buildServer.configs.kotlin.failureConditions.BuildFailureOnText
import jetbrains.buildServer.configs.kotlin.failureConditions.failOnText
import jetbrains.buildServer.configs.kotlin.ui.add
import java.util.Locale

const val pluginPortalUrlOverride = "-Dorg.gradle.internal.plugins.portal.url.override=%gradle.plugins.portal.url%"

 * limitations under the License.
 */
package okhttp3

import java.util.Calendar
import java.util.Collections
import java.util.Date
import java.util.GregorianCalendar
import java.util.Locale
import java.util.regex.Pattern
import okhttp3.internal.UTC
import okhttp3.internal.canParseAsIpAddress
import okhttp3.internal.delimiterOffset
import okhttp3.internal.http.MAX_DATE

import java.net.Socket
import java.net.SocketException
import java.security.SecureRandom
import java.security.cert.CertificateException
import java.security.cert.X509Certificate
import java.util.Collections
import java.util.Locale
import java.util.concurrent.ConcurrentHashMap
import java.util.concurrent.CountDownLatch
import java.util.concurrent.LinkedBlockingQueue
import java.util.concurrent.TimeUnit
import java.util.concurrent.atomic.AtomicInteger

> The Gradle Build Tool repository is massive, and it will take ages to build on
> a local machine without necessary parallelization and caching.
> The full test suites are executed on the CI instance for multiple configurations,
> and you can rely on it after doing initial sanity check and targeted local testing.

### Submitting Your Change

        .build()
    }

    companion object {
      /** Synthetic response header: the local time when the request was sent. */
      private val SENT_MILLIS = "${Platform.get().getPrefix()}-Sent-Millis"

      /** Synthetic response header: the local time when the response was received. */
      private val RECEIVED_MILLIS = "${Platform.get().getPrefix()}-Received-Millis"
    }
  }

    --@local_config_cuda//:enable_cuda \
    "somepath(//tensorflow/tools/pip_package:wheel, " \
    "@local_config_cuda//cuda:cudart + "\
    "@local_config_cuda//cuda:cudart + "\
    "@local_config_cuda//cuda:cuda_driver + "\
    "@local_config_cuda//cuda:cudnn + "\
    "@local_config_cuda//cuda:curand + "\
    "@local_config_cuda//cuda:cusolver + "\

  * It is critical that this path preserves the Service IP, if the traffic was to a Service.
* All traffic *ingressing* a pod on port 15008 in the mesh is assumed to be HBONE, and should be redirected to the node-local ztunnel on port 15008 (more on this later).

En el output, hay una línea que dice más o menos:

```hl_lines="4"
INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
```

Esa línea muestra la URL dónde se está sirviendo tu app en tu maquina local.

### Revísalo

Abre tu navegador en <a href="http://127.0.0.1:8000" class="external-link" target="_blank">http://127.0.0.1:8000</a>.

Verás la respuesta en JSON:

```JSON
{"message": "Hello World"}
```

Then, in that case, it could be simpler to have **one container** with **multiple processes**, and a local tool (e.g. a Prometheus exporter) on the same container collecting Prometheus metrics for all the internal processes and exposing those metrics on that single container.

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