values my-release > old_values.yaml ``` Then change the field `image.tag` in `old_values.yaml` file with MinIO image tag you want to use. Now update the chart using ```bash helm upgrade -f old_values.yaml my-release minio/minio ``` Default upgrade strategies are specified in the `values.yaml` file. Update these fields if you'd like to use a different strategy. ### Configuration Refer the [Values file](./values.yaml) for all the possible config fields. You can specify each parameter using the `--set...

values my-release > old_values.yaml ``` Then change the field `image.tag` in `old_values.yaml` file with MinIO image tag you want to use. Now update the chart using ```bash helm upgrade -f old_values.yaml my-release minio/minio ``` Default upgrade strategies are specified in the `values.yaml` file. Update these fields if you'd like to use a different strategy. ### Configuration Refer the [Values file](./values.yaml) for all the possible config fields. You can specify each parameter using the `--set...

values my-release > old_values.yaml ``` Then change the field `image.tag` in `old_values.yaml` file with MinIO image tag you want to use. Now update the chart using ```bash helm upgrade -f old_values.yaml my-release minio/minio ``` Default upgrade strategies are specified in the `values.yaml` file. Update these fields if you'd like to use a different strategy. ### Configuration Refer the [Values file](./values.yaml) for all the possible config fields. You can specify each parameter using the `--set...

values my-release > old_values.yaml ``` Then change the field `image.tag` in `old_values.yaml` file with MinIO image tag you want to use. Now update the chart using ```bash helm upgrade -f old_values.yaml my-release minio/minio ``` Default upgrade strategies are specified in the `values.yaml` file. Update these fields if you'd like to use a different strategy. ### Configuration Refer the [Values file](./values.yaml) for all the possible config fields. You can specify each parameter using the `--set...

values my-release > old_values.yaml ``` Then change the field `image.tag` in `old_values.yaml` file with MinIO image tag you want to use. Now update the chart using ```bash helm upgrade -f old_values.yaml my-release minio/minio ``` Default upgrade strategies are specified in the `values.yaml` file. Update these fields if you'd like to use a different strategy. ### Configuration Refer the [Values file](./values.yaml) for all the possible config fields. You can specify each parameter using the `--set...

興味深いことに、各プロセスが使用する**CPU**の割合は時間とともに大きく**変動**する可能性がありますが、**メモリ（RAM）**は通常、多かれ少なかれ**安定**します。

毎回同程度の計算を行うAPIがあり、多くのクライアントがいるのであれば、**CPU使用率**もおそらく**安定**するでしょう（常に急激に上下するのではなく）。

### レプリケーション・ツールと戦略の例 { #examples-of-replication-tools-and-strategies }

これを実現するにはいくつかのアプローチがありますが、具体的な戦略については次の章(Dockerやコンテナの章など)で詳しく説明します。

考慮すべき主な制約は、**パブリックIP**の**ポート**を処理する**単一の**コンポーネントが存在しなければならないということです。

そして、レプリケートされた**プロセス/ワーカー**に通信を**送信**する方法を持つ必要があります。

La **bonne nouvelle**, c'est qu'avec chaque stratégie différente, il existe un moyen de couvrir tous les concepts de déploiement. 🎉

Passons en revue ces **concepts de déploiement** en termes de conteneurs :

* HTTPS

  * Bug fixes

    * Fix some false negatives in detection of meaningful conflicts during strategic merge patch with maps and lists. ([#43469](https://github.com/kubernetes/kubernetes/pull/43469), [@enisoc](https://github.com/enisoc))

		* Fix false positive "meaningful conflict" detection for strategic merge patch with integer values. ([#44788](https://github.com/kubernetes/kubernetes/pull/44788), [@enisoc](https://github.com/enisoc))

- The kubectl --dry-run flag now accepts the values 'client', 'server', and 'none', to support client-side and server-side dry-run strategies. The boolean and unset values for the --dry-run flag are deprecated and a value will be required in a future version. ([#87580](https://github.com/kubernetes/kubernetes/pull/87580), [@julianvmodesto](https://github.com/julianvmodesto)) [SIG CLI]...

        `ResNetRS270`, `ResNetRS350` and `ResNetRS420` model architectures. The
        ResNetRS models are based on the architecture described in
        [Revisiting ResNets: Improved Training and Scaling Strategies](https://arxiv.org/pdf/2103.07579.pdf)
    *   Added `tf.keras.optimizers.experimental.Optimizer`. The reworked
        optimizer gives more control over different phases of optimizer calls,