{ largeElement, factor ->
                List(factor) { SmallSubprojectBucket(largeElement.subProject, parallelization(factor)) }
            },
            { list ->
                SmallSubprojectBucket(list.map { it.subProject }, parallelization(1))
            },
            testCoverage.expectedBucketNumber,
            MAX_PROJECT_NUMBER_IN_BUCKET
        )

                TeamCityParallelTests::class.simpleName -> TeamCityParallelTests(methodJsonObject.getIntValue("numberOfBatches"))
                else -> throw IllegalArgumentException("Unknown parallelization method")
            }
        }
    }
}

class FunctionalTest(
    model: CIBuildModel,
    id: String,
    name: String,
    description: String,
    val testCoverage: TestCoverage,

> even if you have Gradle or Develocity build caching enabled for the project.
> 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

normally have **just one process** (e.g. a Uvicorn process running your FastAPI application). They would all be **identical containers**, running the same thing, but each with its own process, memory, etc. That way you would take advantage of **parallelization** in **different cores** of the CPU, or even in **different machines**.

And the distributed container system with the **load balancer** would **distribute the requests** to each one of the containers with your app **in turns**. So,...

运行你的应用程序的每个容器通常**只有一个进程**（例如，运行 FastAPI 应用程序的 Uvicorn 进程）。 它们都是**相同的容器**，运行相同的东西，但每个容器都有自己的进程、内存等。这样你就可以在 CPU 的**不同核心**， 甚至在**不同的机器**充分利用**并行化(parallelization)**。

具有**负载均衡器**的分布式容器系统将**将请求轮流分配**给你的应用程序的每个容器。 因此，每个请求都可以由运行你的应用程序的多个**复制容器**之一来处理。

通常，这个**负载均衡器**能够处理发送到集群中的*其他*应用程序的请求（例如发送到不同的域，或在不同的 URL 路径前缀下），并正确地将该通信传输到在集群中运行的*其他*应用程序的对应容器。

* Google Compute Engine PD Detach fails if node no longer exists ([29358](https://github.com/kubernetes/kubernetes/issues/29358))
* Mounting (only 'default-token') volume takes a long time when creating a batch of pods  (parallelization issue) ([28616](https://github.com/kubernetes/kubernetes/issues/28616))
* Error while tearing down pod, "device or resource busy" on service account secret ([28750](https://github.com/kubernetes/kubernetes/issues/28750))

    *   Added a new field, `filter_parallelization`, to `tf.data.experimental.OptimizationOptions`. If it is set to `True`, tf.data will run `Filter` transformation with multiple threads. Its default value is `False` if not specified.

*   `tf.keras`: