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  1. docs/es/docs/deployment/server-workers.md

    ///
    
    ## Múltiples Workers { #multiple-workers }
    
    Puedes iniciar múltiples workers con la opción de línea de comando `--workers`:
    
    //// tab | `fastapi`
    
    Si usas el comando `fastapi`:
    
    <div class="termy">
    
    ```console
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  2. docs/de/docs/deployment/server-workers.md

    ///
    
    ## Mehrere Worker { #multiple-workers }
    
    Sie können mehrere Worker mit der `--workers`-Befehlszeilenoption starten:
    
    //// tab | `fastapi`
    
    Wenn Sie den `fastapi`-Befehl verwenden:
    
    <div class="termy">
    
    ```console
    $ <font color="#4E9A06">fastapi</font> run --workers 4 <u style="text-decoration-style:solid">main.py</u>
    
    Registered: Sun Dec 28 07:19:09 UTC 2025
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  3. docs/en/docs/deployment/server-workers.md

    ///
    
    ## Multiple Workers { #multiple-workers }
    
    You can start multiple workers with the `--workers` command line option:
    
    //// tab | `fastapi`
    
    If you use the `fastapi` command:
    
    <div class="termy">
    
    ```console
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  4. docs/es/llm-prompt.md

    * mount (verb): montar
    * statement (as in code statement): statement (do not translate to "declaración" or "sentencia")
    * worker process: worker process (do not translate to "proceso trabajador" or "proceso de trabajo")
    * worker processes: worker processes (do not translate to "procesos trabajadores" or "procesos de trabajo")
    * worker: worker (do not translate to "trabajador")
    * load balancer: load balancer (do not translate to "balanceador de carga")
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  5. docs/de/docs/deployment/docker.md

    Und wenn Sie mehrere Worker benötigen, können Sie einfach die `--workers` Befehlszeilenoption verwenden.
    
    /// note | Technische Details
    
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  6. docs/en/docs/_llm-test.md

    * the task
    * the background task
    * the task function
    
    * the template
    * the template engine
    
    * the type annotation
    * the type hint
    
    * the server worker
    * the Uvicorn worker
    * the Gunicorn Worker
    * the worker process
    * the worker class
    * the workload
    
    * the deployment
    * to deploy
    
    * the SDK
    * the software development kit
    
    * the `APIRouter`
    * the `requirements.txt`
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  7. docs/en/docs/deployment/concepts.md

    In this example, there's a **Manager Process** that starts and controls two **Worker Processes**.
    
    This Manager Process would probably be the one listening on the **port** in the IP. And it would transmit all the communication to the worker processes.
    
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  8. cmd/bucket-lifecycle.go

    		workers = workers[:len(workers)-1]
    		worker <- expiryOp(nil)
    		es.stats.workers.Add(-1)
    	}
    	// Atomically replace workers.
    	es.workers.Store(&workers)
    }
    
    // Worker handles 4 types of expiration tasks.
    // 1. Expiry of objects, includes regular and transitioned objects
    // 2. Expiry of noncurrent versions due to NewerNoncurrentVersions
    Registered: Sun Dec 28 19:28:13 UTC 2025
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  9. docs/en/docs/deployment/docker.md

    /// note | Technical Details
    
    The Docker image was created when Uvicorn didn't support managing and restarting dead workers, so it was needed to use Gunicorn with Uvicorn, which added quite some complexity, just to have Gunicorn manage and restart the Uvicorn worker processes.
    
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  10. docs/es/docs/deployment/concepts.md

    En este ejemplo, hay un **Proceso Administrador** que inicia y controla dos **Worker Processes**.
    
    Este Proceso Administrador probablemente sería el que escuche en el **puerto** en la IP. Y transmitirá toda la comunicación a los worker processes.
    
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