Note that ExistingObjectReplication needs to be enabled in the config via `mc replicate [add|edit]` by passing `existing-objects` as one of the values to `--replicate` flag. Only those objects meeting replication rules and having existing object replication enabled will be re-synced.

### Multi destination replication

Having another process manager inside the container (as would be with multiple workers) would only add **unnecessary complexity** that you are most probably already taking care of with your cluster system.

  const char dev2_name[] = "/job:localhost/replica:0/task:2/device:CPU:0";

  if (inject_error) {
    // Inject a function optimization pass failure when it sees the
    // 'read0_maybe_with_graph_error' op having a requested device `dev2_name`.
    // During execution:
    //   * task:0 processes main function `VariableAddFunctionWithGraphError`
    //     and places the 'read0_maybe_with_graph_error' op on task:2

			if !strings.Contains(v, "create") {
				field.Creatable = false
			}

			if !strings.Contains(v, "update") {
				field.Updatable = false
			}
		}
	}

	// Normal anonymous field or having `EMBEDDED` tag
	if _, ok := field.TagSettings["EMBEDDED"]; ok || (field.GORMDataType != Time && field.GORMDataType != Bytes && !isValuer &&
		fieldStruct.Anonymous && (field.Creatable || field.Updatable || field.Readable)) {

			Endpoint{URL: &url.URL{Scheme: "http", Host: "localhost:9000", Path: "/d4"}, IsLocal: true},
		}, ErasureSetupType, nil},
		// DistErasure Setup with URLEndpointType having mixed naming to local host.

// file support represent sparse files in the sparseDatas form. That is, they
// specify the fragments in the file that has data, and treat everything else as
// having zero bytes. As such, the encoding and decoding logic in this package
// deals with sparseDatas.
//
// However, the external API uses sparseHoles instead of sparseDatas because the

With **FastAPI** you can take advantage of concurrency that is very common for web development (the same main attraction of NodeJS).

But you can also exploit the benefits of parallelism and multiprocessing (having multiple processes running in parallel) for **CPU bound** workloads like those in Machine Learning systems.

		u.Path = path.Clean(u.Path)
		if isEmptyPath(u.Path) {
			return ep, fmt.Errorf("empty or root path is not supported in URL endpoint")
		}

		// On windows having a preceding SlashSeparator will cause problems, if the
		// command line already has C:/<export-folder/ in it. Final resulting
		// path on windows might become C:/C:/ this will cause problems

* **Kubernetes** and other distributed **container systems**
    * Something in the **Kubernetes** layer would listen on the **IP** and **port**. The replication would be by having **multiple containers**, each with **one Uvicorn process** running.
* **Cloud services** that handle this for you

build:short_logs --output_filter=DONT_MATCH_ANYTHING
build:verbose_logs --output_filter=

# Instruction set optimizations
# TODO(gunan): Create a feature in toolchains for avx/avx2 to
#   avoid having to define linux/win separately.
build:avx_linux --copt=-mavx
build:avx_linux --host_copt=-mavx
build:avx_win --copt=/arch:AVX

# Use Clang-cl compiler on Windows
build:win_clang --copt=/clang:-Weverything