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Results 1 - 4 of 4 for parallelization (0.07 seconds)

  1. CONTRIBUTING.md

    > even if you have Gradle or Develocity build caching enabled for the project.
    > The Gradle Build Tool repository is massive, and it will take ages to build on
    > a local machine without necessary parallelization and caching.
    > The full test suites are executed on the CI instance for multiple configurations,
    > and you can rely on it after doing initial sanity check and targeted local testing.
    
    ### Copyright and License
    
    Created: Wed Apr 01 11:36:16 GMT 2026
    - Last Modified: Fri Mar 27 18:43:39 GMT 2026
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  2. docs/en/docs/deployment/docker.md

    normally have **just one 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,...
    Created: Sun Apr 05 07:19:11 GMT 2026
    - Last Modified: Thu Mar 05 18:13:19 GMT 2026
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  3. docs/fr/docs/deployment/docker.md

    processus** (par ex. un processus Uvicorn exécutant votre application FastAPI). Ils seront tous des **conteneurs identiques**, exécutant la même chose, mais chacun avec son propre processus, sa mémoire, etc. De cette façon, vous profiterez de la **parallélisation** sur **différents cœurs** du CPU, voire sur **différentes machines**.
    
    Et le système de conteneurs distribués avec l'**équilibreur de charge** **distribuera les requêtes** à chacun des conteneurs exécutant votre application **à tour...
    Created: Sun Apr 05 07:19:11 GMT 2026
    - Last Modified: Thu Mar 19 18:37:13 GMT 2026
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  4. docs/es/docs/deployment/docker.md

    normalmente tendría **solo un proceso** (e.g., un proceso Uvicorn ejecutando tu aplicación FastAPI). Todos serían **contenedores idénticos**, ejecutando lo mismo, pero cada uno con su propio proceso, memoria, etc. De esa forma, aprovecharías la **paralelización** en **diferentes núcleos** de la CPU, o incluso en **diferentes máquinas**.
    
    Y el sistema de contenedores distribuido con el **load balancer** **distribuiría las requests** a cada uno de los contenedores con tu aplicación **en turnos**....
    Created: Sun Apr 05 07:19:11 GMT 2026
    - Last Modified: Thu Mar 19 18:15:55 GMT 2026
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