"Flame graphs with $profiler for ${performanceScenario.scenario.scenario} | ${performanceScenario.testProject} " +
                        "on ${os.asName()} (bucket $bucketIndex)",
                performanceSubProject = "performance",
                bucketIndex = bucketIndex,
                extraParameters =

const val INDIVIDUAL_PERFORAMCE_TEST_ARTIFACT_RULES = """
testing/*/build/test-results-*.zip => results
testing/*/build/tmp/**/log.txt => failure-logs
testing/*/build/tmp/**/profile.log => failure-logs
testing/*/build/tmp/**/daemon-*.out.log => failure-logs
"""

// to avoid pathname too long error
fun BuildSteps.substDirOnWindows(os: Os) {
    if (os == Os.WINDOWS) {
        script {

        "//tensorflow/core/platform:stringpiece",
        "//tensorflow/core/platform:types",
        "//tensorflow/core/profiler/internal:print_model_analysis",
        "//tensorflow/core/profiler/lib:traceme",
        "//tensorflow/core/profiler/rpc/client:profiler_client_impl",
        "//tensorflow/core/profiler/rpc:profiler_server_impl",
        "//tensorflow/core/util:managed_stack_trace",
        "//tensorflow/core:all_kernels",

[GC guide](/doc/gc-guide#Linux_transparent_huge_pages), or switch to a Linux
distribution that disables transparent huge pages altogether.

Go 1.22 added contention on runtime-internal locks to the [`mutex`
profile](/pkg/runtime/pprof#Profile). Contention on these locks is always
reported at `runtime._LostContendedRuntimeLock`. Complete stack traces of
runtime locks can be enabled with the [`runtimecontentionstacks`

    analyzing input pipeline performance with TF Profiler.

*   [`tf.distribute.TPUStrategy`](https://www.tensorflow.org/api_docs/python/tf/distribute/TPUStrategy)
    is now a stable API and no longer considered experimental for TensorFlow.
    (earlier `tf.distribute.experimental.TPUStrategy`).

*   [TF Profiler](https://www.tensorflow.org/guide/profiler) introduces two new
    tools: a memory profiler to visualize your model’s memory usage over time