plexus-container-default 1.0-alpha-8 org.codehaus.plexus plexus-utils 1.0.4 org.apache.maven.wagon wagon-provider-api 1.0-alpha-5 org.apache.maven.wagon wagon-ssh 1.0-alpha-5 org.apache.maven.wagon wagon-file 1.0-alpha-5 org.apache.maven.wagon wagon-http-lightweight 1.0-alpha-5 repo1 Maven Central Repository scp://repo1.maven.org/home/projects/maven/repository-staging/to-ibiblio/maven2 snapshots Maven Central Development Repository scp://repo1.maven.org/home/projects/maven/repository-staging/snapshots/maven2...

        <artifactId>wagon-file</artifactId>
        <version>1.0-alpha-5</version>
      </dependency>
      <dependency>
        <groupId>org.apache.maven.wagon</groupId>
        <artifactId>wagon-http-lightweight</artifactId>
        <version>1.0-alpha-5</version>
      </dependency>
    </dependencies>
  </dependencyManagement>
  <distributionManagement>
    <repository>
      <id>repo1</id>

  * Explicitly opting into behavioral changes can be acceptable. For example, introducing Istio multi-cluster semantics.
* Ensure traffic between mesh workloads is securely encrypted with an Istio identity.
* Be lightweight enough to not limit adoption.
  * This puts a much tighter budget on CPU, memory, latency, and throughput requirements than traditional Istio sidecars.

Ztunnel was not designed to be a feature-rich data plane.

Containers (mainly Linux containers) are a very **lightweight** way to package applications including all their dependencies and necessary files while keeping them isolated from other containers (other applications or components) in the same system.

    **FastAPI** then takes that JSON Schema data and puts it in OpenAPI, apart from all the other things it does.

### <a href="https://www.starlette.io/" class="external-link" target="_blank">Starlette</a>

Starlette is a lightweight <abbr title="The new standard for building asynchronous Python web applications">ASGI</abbr> framework/toolkit, which is ideal for building high-performance asyncio services.

   * <p>If {@link #interruptTask} is also run during completion, {@link #afterDone} runs after it.
   *
   * <p>The default implementation of this method in {@code AbstractFuture} does nothing. This is
   * intended for very lightweight cleanup work, for example, timing statistics or clearing fields.
   * If your task does anything heavier consider, just using a listener with an executor.
   *
   * @since 20.0
   */
  @ForOverride

*   Added a `model.export(filepath)` API to create a lightweight SavedModel artifact that can be used for inference (e.g. with TF-Serving).