# limitations under the License.
# ==============================================================================
source ci/official/envs/linux_arm64

build:mkl -c opt

# config to build OneDNN backend with a user specified threadpool.
build:mkl_threadpool --define=build_with_mkl=true --define=enable_mkl=true
build:mkl_threadpool --define=tensorflow_mkldnn_contraction_kernel=0
build:mkl_threadpool --define=build_with_mkl_opensource=true
build:mkl_threadpool -c opt

# Config setting to build oneDNN with Compute Library for the Arm Architecture (ACL).

### Major Features and Improvements

*   [oneDNN CPU performance optimizations](https://github.com/tensorflow/community/blob/master/rfcs/20210930-enable-onednn-ops.md)
    Windows x64 & x86.

    *   **Windows x64 & x86 packages:**
        *   oneDNN optimizations are *enabled by default* on X86 CPUs

        'adding "--config=<>" to your build command. See .bazelrc for more '
        'details.')
  config_info_line('mkl', 'Build with MKL support.')
  config_info_line(
      'mkl_aarch64',
      'Build with oneDNN and Compute Library for the Arm Architecture (ACL).')
  config_info_line('monolithic', 'Config for mostly static monolithic build.')
  config_info_line('numa', 'Build with NUMA support.')
  config_info_line(

    "@mkl_dnn_acl_compatible//:__subpackages__",
    "@ml_dtypes//:__subpackages__",
    "@nccl_archive//:__subpackages__",
    "@nvtx_archive//:__subpackages__",
    "@onednn//:__subpackages__",
    "@org_sqlite//:__subpackages__",
    "@platforms//:__subpackages__",
    "@snappy//:__subpackages__",
    "@upb//:__subpackages__",
    "@zlib//:__subpackages__",

        cb.query().addOrderBy_EndTime_Desc();

        // search

    }

    public void deleteBefore(final int days) {
        final long oneday = 24 * 60 * 60 * 1000L;
        final long targetTime = ComponentUtil.getSystemHelper().getCurrentTimeAsLong() - days * oneday;
        jobLogBhv.queryDelete(cb -> {
            cb.query().setEndTime_LessThan(targetTime);
        });
    }

    * Eğer Starlette'i karşılaştırıyorsanız, Sanic, Flask, Django, vb. frameworkler (veya mikroframeworkler) ile karşılaştırın.
* **FastAPI**:
    * Starlette'in Uvicorn'u kullandığı ve ondan daha hızlı olamayacağı gibi, **FastAPI**'da Starlette'i kullanır, dolayısıyla ondan daha hızlı olamaz.

Starlette ve **FastAPI** için tavsiye edilen sunucu Uvicorndur.

!!! check "**FastAPI** neden tavsiye ediyor?"
    **FastAPI** uygulamalarını çalıştırmak için ana web sunucusu Uvicorn!

    Gunicorn ile birleştirdiğinizde asenkron ve çoklu işlem destekleyen bir sunucu elde ediyorsunuz!

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