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, each request could be handled...

### Multiple Processes - Workers

If you have more clients than what a single process can handle (for example if the virtual machine is not too big) and you have **multiple cores** in the server's CPU, then you could have **multiple processes** running with the same application at the same time, and distribute all the requests among them.

* **Robuste** : Obtenez un code prêt pour la production. Avec une documentation interactive automatique.

   * stable. There is usually no reason to retain a reference of type {@code SetView}; typically,
   * you either use it as a plain {@link Set}, or immediately invoke {@link #immutableCopy} or
   * {@link #copyInto} and forget the {@code SetView} itself.
   *
   * @since 2.0
   */
  public abstract static class SetView<E extends @Nullable Object> extends AbstractSet<E> {
    private SetView() {} // no subclasses but our own

    /**

		if err != nil {
			return wrapSRErr(err)
		}
		return nil
	}

	return nil
}

// PeerBucketMetadataUpdateHandler - merges the bucket metadata, save and ping other nodes
func (c *SiteReplicationSys) PeerBucketMetadataUpdateHandler(ctx context.Context, item madmin.SRBucketMeta) error {
	objectAPI := newObjectLayerFn()
	if objectAPI == nil {

    `float16` for acceleration on Volta and Turing Tensor Cores. This feature
    can be enabled by wrapping an optimizer class with
    `tf.train.experimental.enable_mixed_precision_graph_rewrite()`.
*   Add environment variable `TF_CUDNN_DETERMINISTIC`. Setting to "true" or "1"
    forces the selection of deterministic cuDNN convolution and max-pooling

github.com/cheggaaa/pb v1.0.29/go.mod h1:W40334L7FMC5JKWldsTWbdGjLo0RxUKK73K+TuPxX30=
github.com/chromedp/cdproto v0.0.0-20230802225258-3cf4e6d46a89/go.mod h1:GKljq0VrfU4D5yc+2qA6OVr8pmO/MBbPEWqWQ/oqGEs=
github.com/chromedp/chromedp v0.9.2/go.mod h1:LkSXJKONWTCHAfQasKFUZI+mxqS4tZqhmtGzzhLsnLs=
github.com/chromedp/sysutil v1.0.0/go.mod h1:kgWmDdq8fTzXYcKIBqIYvRRTnYb9aNS9moAV0xufSww=

github.com/cheggaaa/pb/v3 v3.1.5/go.mod h1:CrxkeghYTXi1lQBEI7jSn+3svI3cuc19haAj6jM60XI=
github.com/chromedp/cdproto v0.0.0-20230802225258-3cf4e6d46a89/go.mod h1:GKljq0VrfU4D5yc+2qA6OVr8pmO/MBbPEWqWQ/oqGEs=
github.com/chromedp/chromedp v0.9.2/go.mod h1:LkSXJKONWTCHAfQasKFUZI+mxqS4tZqhmtGzzhLsnLs=
github.com/chromedp/sysutil v1.0.0/go.mod h1:kgWmDdq8fTzXYcKIBqIYvRRTnYb9aNS9moAV0xufSww=