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#: ../../source/development/xinference_internals.rst:3
msgid "The internals of Xinference"
msgstr "Estructura interna de Xinference"

#: ../../source/development/xinference_internals.rst:6
msgid "Table of contents:"
msgstr "Tabla de contenido"

#: ../../source/development/xinference_internals.rst:9
msgid "Overview"
msgstr "Resumen"

#: ../../source/development/xinference_internals.rst:10
msgid ""
"Xinference leverages `Xoscar <https://github.com/xorbitsai/xoscar>`_, an "
"actor programming framework we designed, as its core component to manage "
"machines, devices, and model inference processes. Each actor serves as a "
"basic unit for model inference and various inference backends can be "
"integrate into the actor, enabling us to support multiple inference engines "
"and hardware. These actors are hosted and scheduled within actor pools, "
"which are designed to be asynchronous and non-blocking and function as "
"resource pools."
msgstr ""
"Xinference utiliza el marco de programación de actores `Xoscar "
"<https://github.com/xorbitsai/xoscar>`_ que diseñamos como su componente "
"central para gestionar máquinas, dispositivos y procesos de inferencia de "
"modelos. Cada actor es la unidad básica de inferencia de modelos, y varios "
"backends de inferencia pueden integrarse en los actores, lo que nos permite "
"admitir múltiples motores de inferencia y hardware. Estos actores se alojan "
"y programan en un grupo de actores, que funciona como un grupo de recursos. "
"El diseño de los actores es asíncrono y no bloqueante."

#: ../../source/development/xinference_internals.rst:22
msgid ""
"Both supervisor and worker are actor instances. Initially, an actor pool, "
"serving as a resource pool, needs to be created on each server; and each "
"actor can utilize a CPU core or a GPU device. Each server has its own "
"address (IP address or hostname), so actors on different computing nodes can"
" communicate with each other through these addresses. See `Actor`_ for more "
"information."
msgstr ""
"Los supervisores y los trabajadores son instancias de actores. Primero, es "
"necesario crear un grupo de actores que funcione como un grupo de recursos "
"en cada servidor; cada actor puede usar un núcleo de CPU o un dispositivo "
"GPU. Cada servidor tiene su propia dirección (dirección IP o nombre de "
"host), por lo que los actores en diferentes nodos de cómputo pueden "
"comunicarse entre sí a través de estas direcciones. Para más información, "
"consulte `Actor`_."

#: ../../source/development/xinference_internals.rst:27
msgid "RESTful API"
msgstr "RESTful API"

#: ../../source/development/xinference_internals.rst:28
msgid ""
"The RESTful API is implemented using `FastAPI "
"<https://github.com/tiangolo/fastapi>`_, as specified in `api/restful_api.py"
" "
"<https://github.com/xorbitsai/inference/tree/main/xinference/api/restful_api.py>`_."
msgstr ""
"La API RESTful está implementada utilizando `FastAPI "
"<https://github.com/tiangolo/fastapi>`_, y el código específico se encuentra"
" en `api/restful_api.py "
"<https://github.com/xorbitsai/inference/tree/main/xinference/api/restful_api.py>`_."

#: ../../source/development/xinference_internals.rst:35
msgid ""
"This is an example of the API ``/status``, it's corresponding function is "
"``get_status``. You can add connection between RESTful API and the backend "
"function you want in `api/restful_api.py "
"<https://github.com/xorbitsai/inference/tree/main/xinference/api/restful_api.py>`_."
msgstr ""
"Este es un ejemplo de una API. La API ``/status`` corresponde a la función "
"``get_status``. Puede agregar la relación entre la API RESTful y la función "
"de backend correspondiente en `api/restful_api.py "
"<https://github.com/xorbitsai/inference/tree/main/xinference/api/restful_api.py>`_."

#: ../../source/development/xinference_internals.rst:39
msgid "Command Line"
msgstr "Línea de comandos"

#: ../../source/development/xinference_internals.rst:40
msgid ""
"The Command Line is implemented using `Click "
"<https://click.palletsprojects.com/>`_, as specified in `deploy/cmdline.py "
"<https://github.com/xorbitsai/inference/tree/main/xinference/deploy/cmdline.py>`_,"
" allowing users to interact with the Xinference deployment features directly"
" from the terminal."
msgstr ""
"La línea de comandos se implementa mediante `Click "
"<https://click.palletsprojects.com/>`_, y el código específico está en "
"`deploy/cmdline.py "
"<https://github.com/xorbitsai/inference/tree/main/xinference/deploy/cmdline.py>`_,"
" lo que permite a los usuarios interactuar directamente con Xinference en la"
" terminal."

#: ../../source/development/xinference_internals.rst:45
msgid "Entry Points"
msgstr "Punto de entrada"

#: ../../source/development/xinference_internals.rst:46
msgid "Take the command-lines we implemented as examples:"
msgstr "Usando la línea de comandos que implementamos como ejemplo:"

#: ../../source/development/xinference_internals.rst:48
msgid ""
"``xinference``: Provides commands for model management, including "
"registering/unregistering models, listing all registered/running models, and"
" launching or terminating specific models. It also features interactive "
"commands like generate and chat for testing and interacting with deployed "
"models in real-time."
msgstr ""
"``xinference``: proporciona comandos para la gestión de modelos, incluyendo "
"registrar/anular el registro de modelos, listar todos los modelos "
"registrados/en ejecución, e iniciar o detener un modelo específico. También "
"ofrece comandos interactivos como generación de lenguaje y chat para probar "
"o interactuar con modelos implementados."

#: ../../source/development/xinference_internals.rst:52
msgid "``xinference-local``: Starts a local Xinference service."
msgstr "``xinference-local``: Inicia un servicio local de Xinference."

#: ../../source/development/xinference_internals.rst:54
msgid ""
"``xinference-supervisor``: Initiates a supervisor process that manages and "
"monitors worker actors within a distributed setup."
msgstr ""
"``xinference-supervisor``: inicia el proceso supervisor, gestiona y "
"monitorea los actores trabajadores en un entorno distribuido."

#: ../../source/development/xinference_internals.rst:56
msgid ""
"``xinference-worker``: Starts a worker process that executes tasks assigned "
"by the supervisor, utilizing available computational resources effectively."
msgstr ""
"``xinference-worker``: Inicia el proceso worker, utilizando los recursos "
"computacionales disponibles para ejecutar las tareas asignadas por el "
"supervisor."

#: ../../source/development/xinference_internals.rst:59
msgid ""
"Each command is equipped with ``options`` and ``flags`` to customize its "
"behavior, such as specifying log levels, host addresses, port numbers, and "
"other relevant settings."
msgstr ""
"Cada comando viene con ``option`` y ``flag`` para personalizar su "
"comportamiento, como especificar el nivel de registro, la dirección del "
"host, el número de puerto y otras configuraciones relacionadas."

#: ../../source/development/xinference_internals.rst:62
msgid ""
"Python projects define command-line console entry points in "
"``pyproject.toml``."
msgstr ""
"Los proyectos de Python definen puntos de entrada de consola en "
"``pyproject.toml``."

#: ../../source/development/xinference_internals.rst:72
msgid ""
"The command-line ``xinference`` can be referred to code in "
"``xinference.deploy.cmdline:cli``."
msgstr ""
"La línea de comandos ``xinference`` puede consultar el código en "
"``xinference.deploy.cmdline:cli``."

#: ../../source/development/xinference_internals.rst:75
msgid "Click"
msgstr "Click"

#: ../../source/development/xinference_internals.rst:76
msgid "We use Click to implement a specific command-line:"
msgstr "Usamos Click para implementar una línea de comandos específica:"

#: ../../source/development/xinference_internals.rst:95
msgid ""
"For example, the ``xinference-local`` command allows you to define the host "
"address and port."
msgstr ""
"Por ejemplo, el comando ``xinference-local`` le permite definir la dirección"
" del host y el puerto."

#: ../../source/development/xinference_internals.rst:98
msgid "Actor"
msgstr "Actor"

#: ../../source/development/xinference_internals.rst:99
msgid ""
"Xinference is fundamentally based on `Xoscar "
"<https://github.com/xorbitsai/xoscar>`_, our actor framework, which can "
"manage computational resources and Python processes to support scalable and "
"concurrent programming. The following is a pseudocode demonstrating how our "
"Worker Actor works, the actual Worker Actor is more complex than this."
msgstr ""
"Xinference se basa en `Xoscar <https://github.com/xorbitsai/xoscar>`_, un "
"framework de actores que gestiona recursos computacionales y procesos de "
"Python, soportando programación concurrente escalable. El siguiente "
"pseudocódigo demuestra el principio de funcionamiento del Worker Actor, "
"aunque el Worker Actor real es mucho más complejo."

#: ../../source/development/xinference_internals.rst:126
msgid ""
"We use the ``WorkerActor`` as an example to illustrate how we build the "
"Xinference. Each actor class is a standard Python class that inherits from "
"``xoscar.Actor``. An instance of this class is a specific actor within the "
"actor pool."
msgstr ""
"Tomamos ``WorkerActor`` como ejemplo para explicar cómo construir "
"Xinference. Cada clase de actor es una clase estándar de Python que hereda "
"de ``xoscar.Actor``. Las instancias de esta clase son actores específicos "
"dentro del pool de actores."

#: ../../source/development/xinference_internals.rst:130
msgid ""
"**Define Actor Actions**: Each actor needs to define certain actions or "
"behaviors to accomplish specific tasks. For instance, the model inference "
"``WorkerActor`` needs to launch the model (``launch_model``), list the "
"models in this actor (``list_models``), terminate a model "
"(``terminate_model``). There are two special methods worth noting. The "
"``__post_create__`` is invoked before the actor is created, allowing for "
"necessary initializations. The ``__pre_destroy__`` is called after the actor"
" is destroyed, allowing for cleanup or finalization tasks."
msgstr ""
"**Definir el comportamiento del Actor**: cada actor necesita definir ciertas"
" acciones o comportamientos para completar tareas específicas. Por ejemplo, "
"el ``WorkerActor`` de inferencia de modelos debe iniciar el modelo "
"(``launch_model``), listar los modelos en ese actor (``list_models``) y "
"finalizar el modelo (``termininate_model``). Hay dos métodos especiales que "
"vale la pena destacar. ``__post_create__`` se invoca antes de crear el actor"
" para realizar la inicialización necesaria. Mientras que "
"``__pre_destroy____`` se llama después de que el actor sea destruido para "
"ejecutar tareas de limpieza."

#: ../../source/development/xinference_internals.rst:136
msgid ""
"**Reference Actor and Invoke Methods**: When an actor is created, it yields "
"a reference variable so that other actors can reference it. The actor "
"reference can also be referenced with the address. Suppose the "
"``WorkerActor`` is created and the reference variable is ``worker_ref``,  "
"the ``launch_model`` method of this actor class can be invoked by calling "
"``worker_ref.launch_model()``. Even if the actor's method is originally a "
"synchronized method, when called with an actor reference, it will become as "
"an asynchronous method."
msgstr ""
"**Referencia al Actor e invocación de métodos**: Cuando se crea un Actor, se"
" genera una variable de referencia para que otros Actores puedan "
"referenciarlo. Un Actor también se puede referenciar mediante una dirección "
"IP. Suponiendo que se ha creado ``WorkerActor`` y la variable de referencia "
"es ``worker_ref``, entonces se puede invocar ``launch_model`` de la clase "
"Actor mediante ``worker_ref.launch_model()``. Incluso si el método en el "
"actor era originalmente un método tradicional de bloqueo, cuando lo "
"invocamos usando la variable de referencia, se convierte en un método "
"asíncrono."

#: ../../source/development/xinference_internals.rst:143
msgid ""
"**Inference Engine**: The actor can manage the process, and the inference "
"engine is also a process. In the launch model part of the ``WorkerActor``, "
"we can initialize different inference engines according to the user's need. "
"Therefore, Xinference can support multiple inference engines and can easily "
"adapt to new inference engines in the future."
msgstr ""
"**Motor de inferencia**: Un Actor puede gestionar procesos, y un motor de "
"inferencia también es un tipo de proceso. En la parte del modelo de inicio "
"de `WorkerActor`, podemos inicializar diferentes motores de inferencia según"
" las necesidades del usuario. Por lo tanto, Xinference puede soportar "
"múltiples motores de inferencia y adaptarse fácilmente a nuevos motores de "
"inferencia en el futuro."

#: ../../source/development/xinference_internals.rst:148
msgid ""
"See `Xoscar document <https://xoscar.dev/en/latest/getting_started/llm-"
"inference.html>`_ for more actor use cases."
msgstr ""
"Consulte la `documentación de Xoscar "
"<https://xoscar.dev/en/latest/getting_started/llm-inference.html>`_ para "
"obtener más casos de uso de Actor."

#: ../../source/development/xinference_internals.rst:151
msgid "Asynchronous Programming"
msgstr "Programación asíncrona"

#: ../../source/development/xinference_internals.rst:153
msgid ""
"Both Xinference and Xoscar highly utilize asynchronous programming of "
"``asyncio``. Asynchronous programming is a programming paradigm that does "
"not block. Instead, requests and function calls are issued and executed in "
"the background and results are returned in the future. This enables us to "
"perform activities concurrently."
msgstr ""
"Xinference y Xoscar dependen en gran medida de la biblioteca de programación"
" asíncrona ``asyncio``. La programación asíncrona es un paradigma de "
"programación sin bloqueo. En comparación con las llamadas a funciones "
"bloqueantes tradicionales, las solicitudes o llamadas a funciones en la "
"programación asíncrona se ejecutan en segundo plano y los resultados se "
"devuelven en un momento futuro. La ventaja de la programación asíncrona es "
"que permite realizar muchas actividades o tareas diferentes de manera "
"concurrente."

#: ../../source/development/xinference_internals.rst:159
msgid ""
"If you're not familiar with Pythons's ``asyncio``, you can see more "
"tutorials for help:"
msgstr ""
"Si no está familiarizado con ``asyncio`` de Python, puede consultar más "
"tutoriales para obtener ayuda:"

#: ../../source/development/xinference_internals.rst:161
msgid ""
"`Python Asyncio Tutorial <https://bbc.github.io/cloudfit-public-"
"docs/asyncio/asyncio-part-1.html>`__"
msgstr ""
"`Tutorial de Python Asyncio <https://bbc.github.io/cloudfit-public-"
"docs/asyncio/asyncio-part-1.html>`__"

#: ../../source/development/xinference_internals.rst:163
msgid ""
"`Real Python's asyncio Tutorial <https://realpython.com/async-io-python/>`__"
msgstr ""
"`Tutorial de asyncio de Real Python <https://realpython.com/async-io-"
"python/>`__"

#: ../../source/development/xinference_internals.rst:165
msgid ""
"`Python Official Documentation "
"<https://docs.python.org/3/library/asyncio.html>`__"
msgstr ""
"`Documentación oficial de Python "
"<https://docs.python.org/3/library/asyncio.html>`__"

#: ../../source/development/xinference_internals.rst:169
msgid "Model"
msgstr "model"

#: ../../source/development/xinference_internals.rst:171
msgid ""
"Xinference supports different types of models including large language "
"models (LLMs), image models, audio models, embedding models, etc. All models"
" are implemented in `model/ "
"<https://github.com/xorbitsai/inference/tree/main/xinference/model>`_."
msgstr ""
"Xinference admite diferentes tipos de modelos, incluidos modelos de lenguaje"
" grande (LLM), modelos de imagen, modelos de audio, modelos de embedding, "
"entre otros. Todos los modelos se implementan en la carpeta `model/ "
"<https://github.com/xorbitsai/inference/tree/main/xinference/model>`_."

#: ../../source/development/xinference_internals.rst:175
msgid "LLM"
msgstr ""

#: ../../source/development/xinference_internals.rst:177
msgid ""
"Take `model/llm/ "
"<https://github.com/xorbitsai/inference/tree/main/xinference/model/llm>`_ "
"for example, it focuses on the management and instantiation of LLMs. It "
"includes detailed implementations for loading, configuring, and deploying "
"LLMs."
msgstr ""
"Con `model/llm/ "
"<https://github.com/xorbitsai/inference/tree/main/xinference/model/llm>`_ "
"como ejemplo, este principalmente gestiona e inicia LLM, incluyendo la "
"carga, configuración y ejecución de modelos de lenguaje grandes."

#: ../../source/development/xinference_internals.rst:181
msgid ""
"We support many backends such as GGML, PyTorch, and vLLM. Our generated "
"content is compatible with the format of OpenAI, supporting features such as"
" streaming output and returning chat completion format (for chat models "
"only). Therefore, there is a lot of adaptation work to be done after the "
"model generate content. These tasks are not difficult, but they do require "
"some time. When writing this part of the code, please refer to the `OpenAI "
"API documentation <https://platform.openai.com/docs/introduction>`_ and the "
"documentation of various inference backends, and make the necessary "
"adaptations."
msgstr ""
"Soportamos diferentes backends de inferencia, como GGML, PyTorch y vLLM. El "
"contenido que generamos es compatible con el formato de OpenAI, por ejemplo,"
" soportamos salida en streaming (stream), y el modelo de diálogo devuelve "
"resultados en formato chat completion. Por lo tanto, después de que el "
"modelo genere contenido, se deben realizar muchas adaptaciones. Estas tareas"
" no son difíciles, pero requieren algo de tiempo. Al escribir esta parte del"
" código, consulta la `documentación de la API de OpenAI "
"<https://platform.openai.com/docs/introduction>`_ y la documentación de cada"
" backend de inferencia para realizar las adaptaciones necesarias."

#: ../../source/development/xinference_internals.rst:185
msgid "JSON"
msgstr ""

#: ../../source/development/xinference_internals.rst:187
msgid ""
"In `model/llm/llm_family.json "
"<https://github.com/xorbitsai/inference/blob/main/xinference/model/llm/llm_family.json>`_,"
" we utilize JSON files to manage the metadata of emerging open-source "
"models. Adding a new model does not necessitate writing new code, it merely "
"requires appending new metadata to the existing JSON file."
msgstr ""
"En `model/llm/llm_family.json "
"<https://github.com/xorbitsai/inference/blob/main/xinference/model/llm/llm_family.json>`_"
" , utilizamos archivos JSON para gestionar los metadatos de los nuevos "
"modelos de código abierto que surgen. Agregar un nuevo modelo no requiere "
"escribir código nuevo, solo es necesario añadir los nuevos metadatos al "
"archivo JSON existente."

#: ../../source/development/xinference_internals.rst:214
msgid ""
"This is an example of how to define the Llama-2 chat model. The "
"``model_specs`` define the information of the model, as one model family "
"usually comes with various sizes, quantization methods, and file formats. "
"For instance, the ``model_format`` could be ``pytorch`` (using Hugging Face "
"Transformers or vLLM as backend), ``ggmlv3`` (a tensor library associated "
"with llama.cpp), or ``gptq`` (a post-training quantization framework). The "
"``model_id`` defines the repository of the model hub from which Xinference "
"downloads the checkpoint files. Furthermore, due to distinct instruction-"
"tuning processes, different model families have varying prompt styles. The "
"``prompt_style`` in the JSON file specifies how to format prompts for this "
"particular model. For example, ``system_prompt`` and ``roles`` are used to "
"specify the instructions and personality of the model."
msgstr ""
"Aquí hay un ejemplo de cómo definir el modelo de chat Llama-2. "
"``model_specs`` define la información del modelo, ya que una familia de "
"modelos generalmente tiene diferentes tamaños, métodos de cuantificación y "
"formatos de archivo. Por ejemplo, ``model_format`` puede ser ``pytorch`` "
"(usando Hugging Face Transformers o vLLM como backend), ``ggmlv3`` "
"(biblioteca de tensores relacionada con llama.cpp) o ``gptq`` (marco de "
"cuantificación posterior al entrenamiento). ``model_id`` define el "
"repositorio del centro de modelos, desde donde Xinference descarga los "
"archivos de punto de control. Además, debido a diferentes procesos de ajuste"
" de instrucciones, distintas familias de modelos tienen diferentes estilos "
"de avisos. El ``prompt_style`` en el archivo JSON especifica el formato de "
"aviso para ese modelo en particular. Por ejemplo, ``system_prompt`` y "
"``roles`` se utilizan para especificar las instrucciones y la personalidad "
"del modelo."

#: ../../source/development/xinference_internals.rst:224
msgid "Code Walkthrough"
msgstr "Guía de código"

#: ../../source/development/xinference_internals.rst:226
msgid ""
"The main code is located in the `xinference/ "
"<https://github.com/xorbitsai/inference/tree/main/xinference>`_:"
msgstr ""
"El código principal se encuentra en `xinference/ "
"<https://github.com/xorbitsai/inference/tree/main/xinference>`_："

#: ../../source/development/xinference_internals.rst:228
msgid ""
"`api/ <https://github.com/xorbitsai/inference/tree/main/xinference/api>`_: "
"`restful_api.py "
"<https://github.com/xorbitsai/inference/tree/main/xinference/api/restful_api.py>`_"
" is the core part that sets up and runs the RESTful APIs. It integrates an "
"authentication service (the specific code is located in `oauth2/ "
"<https://github.com/xorbitsai/inference/tree/main/xinference/api/oauth2>`_),"
" as some or all endpointsrequire user authentication."
msgstr ""
"`api/ "
"<https://github.com/xorbitsai/inference/tree/main/xinference/api>`_：`restful_api.py"
" "
"<https://github.com/xorbitsai/inference/tree/main/xinference/api/restful_api.py>`_"
" es la parte central para configurar y ejecutar la API RESTful. Integra un "
"servicio de autenticación (código específico ubicado en `oauth2/ "
"<https://github.com/xorbitsai/inference/tree/main/xinference/api/oauth2>`_),"
" ya que algunos o todos los puertos requieren autenticación de usuario."

#: ../../source/development/xinference_internals.rst:233
msgid ""
"`client/ "
"<https://github.com/xorbitsai/inference/tree/main/xinference/client>`_: This"
" is the client of Xinference."
msgstr ""
"`client/ "
"<https://github.com/xorbitsai/inference/tree/main/xinference/client>`_：Este "
"es el cliente de Xinference."

#: ../../source/development/xinference_internals.rst:235
msgid ""
"`oscar/ "
"<https://github.com/xorbitsai/inference/tree/main/xinference/client/oscar>`_"
" defines the Actor Client which acts as a client interface for interacting "
"with models deployed in a Xinference cluster."
msgstr ""
"`oscar/ "
"<https://github.com/xorbitsai/inference/tree/main/xinference/client/oscar>`_"
" define el cliente Actor, que es una interfaz de cliente utilizada para "
"interactuar con los modelos en Xinference."

#: ../../source/development/xinference_internals.rst:238
msgid ""
"`restful/ "
"<https://github.com/xorbitsai/inference/tree/main/xinference/client/restful>`_"
" implements a RESTful client for interacting with a Xinference service."
msgstr ""
"`restful/ "
"<https://github.com/xorbitsai/inference/tree/main/xinference/client/restful>`_"
" implementa el cliente RESTful para interactuar con el servicio Xinference."

#: ../../source/development/xinference_internals.rst:241
msgid ""
"`core/ <https://github.com/xorbitsai/inference/tree/main/xinference/core>`_:"
" This is the core part of Xinference."
msgstr ""
"`core/ <https://github.com/xorbitsai/inference/tree/main/xinference/core>`_ "
": Esta es la parte central de Xinference."

#: ../../source/development/xinference_internals.rst:243
msgid ""
"`metrics.py "
"<https://github.com/xorbitsai/inference/tree/main/xinference/core/metrics.py>`_"
" and `resource.py "
"<https://github.com/xorbitsai/inference/tree/main/xinference/core/resource.py>`_"
" defines a set of tools for collecting and reporting metrics and the status "
"of node resources, including model throughput, latency, the usage of CPU and"
" GPU, memory usage, and more."
msgstr ""
"`metrics.py "
"<https://github.com/xorbitsai/inference/tree/main/xinference/core/metrics.py>`_"
" y `resource.py "
"<https://github.com/xorbitsai/inference/tree/main/xinference/core/resource.py>`_"
" definen un conjunto de herramientas para recopilar e informar métricas y el"
" estado de los recursos del nodo, incluyendo el rendimiento del modelo, la "
"latencia, el uso de CPU y GPU, el uso de memoria, etc."

#: ../../source/development/xinference_internals.rst:248
msgid ""
"`image_interface.py "
"<https://github.com/xorbitsai/inference/tree/main/xinference/core/image_interface.py>`_"
" and `chat_interface.py "
"<https://github.com/xorbitsai/inference/tree/main/xinference/core/chat_interface.py>`_"
" implement `Gradio <https://github.com/gradio-app/gradio>`_ interfaces for "
"image and chat models, respectively. These interfaces allow users to "
"interact with models through a Web UI, such as generating images or engaging"
" in chat. They build user interfaces using the gradio package and "
"communicate with backend models through our RESTful APIs."
msgstr ""
"`image_interface.py "
"<https://github.com/xorbitsai/inference/tree/main/xinference/core/image_interface.py>`_"
" y `chat_interface.py "
"<https://github.com/xorbitsai/inference/tree/main/xinference/core/chat_interface.py>`_"
" implementan respectivamente la interfaz de `Gradio "
"<https://github.com/gradio-app/gradio>`_ para modelos de imagen y chat. "
"Estas interfaces permiten a los usuarios interactuar con los modelos a "
"través de una interfaz web, por ejemplo, generando imágenes o manteniendo "
"conversaciones. El código utiliza el paquete Gradio para construir la "
"interfaz de usuario y se comunica con los modelos del backend a través de "
"nuestra API RESTful."

#: ../../source/development/xinference_internals.rst:254
msgid ""
"`worker.py "
"<https://github.com/xorbitsai/inference/tree/main/xinference/core/worker.py>`_"
" and `supervisor.py "
"<https://github.com/xorbitsai/inference/tree/main/xinference/core/supervisor.py>`_"
" respectively define the logic for worker actors and supervisor actor. "
"Worker actors are responsible for carrying out specific model computation "
"tasks, while supervisor actors manage the lifecycle of worker nodes, "
"schedule tasks, and monitor system states."
msgstr ""
"`worker.py "
"<https://github.com/xorbitsai/inference/tree/main/xinference/core/worker.py>`_"
" y `supervisor.py "
"<https://github.com/xorbitsai/inference/tree/main/xinference/core/supervisor.py>`_"
" definen la lógica del actor worker y del actor supervisor, respectivamente."
" El actor worker se encarga de ejecutar tareas específicas de cómputo de "
"modelos, mientras que el actor supervisor gestiona el ciclo de vida de los "
"nodos Worker y la programación de tareas, además de monitorear el estado del"
" sistema."

#: ../../source/development/xinference_internals.rst:259
msgid ""
"`status_guard.py "
"<https://github.com/xorbitsai/inference/tree/main/xinference/core/status_guard.py>`_"
" implements a status monitor to track the status of models (like creating, "
"updating, terminating, etc.). It allows querying status information of model"
" instances and managing these statuses based on the model's UID."
msgstr ""
"`status_guard.py "
"<https://github.com/xorbitsai/inference/tree/main/xinference/core/status_guard.py>`_"
" implementa un monitor de estado para rastrear el estado del modelo (como "
"creación, actualización, terminación, etc.). Permite consultar la "
"información de estado de una instancia de modelo según su UID."

#: ../../source/development/xinference_internals.rst:263
msgid ""
"`cache_tracker.py "
"<https://github.com/xorbitsai/inference/tree/main/xinference/core/cache_tracker.py>`_"
" defines a cache tracker for recording and managing cache status and "
"information of model versions. It supports recording cache locations and "
"statuses of model versions and querying model version information based on "
"model names."
msgstr ""
"`cache_tracker.py "
"<https://github.com/xorbitsai/inference/tree/main/xinference/core/cache_tracker.py>`_"
" define un rastreador de caché utilizado para registrar y gestionar el "
"estado de la caché y la información de la versión del modelo. Permite "
"registrar el estado de la ubicación de la caché y la versión del modelo, y "
"consultar la información de la versión del modelo según el nombre del "
"modelo."

#: ../../source/development/xinference_internals.rst:267
msgid ""
"`event.py "
"<https://github.com/xorbitsai/inference/tree/main/xinference/core/event.py>`_"
" defines an event collector for gathering and reporting various runtime "
"events of models, such as information, warnings, and errors. `model.py "
"<https://github.com/xorbitsai/inference/tree/main/xinference/core/model.py>`_"
" defines a Model Actor, the core component for direct model interactions. "
"The Model Actor is responsible for executing model inference requests, "
"handling input and output data streams, and supports various types of model "
"operations."
msgstr ""
"`event.py "
"<https://github.com/xorbitsai/inference/tree/main/xinference/core/event.py>`_"
" define un recolector de eventos, utilizado para recopilar e informar "
"diversos eventos de modelos en tiempo de ejecución, como información, "
"advertencias y errores. `model.py "
"<https://github.com/xorbitsai/inference/tree/main/xinference/core/model.py>`_"
" define un actor de modelo, que es un componente central que interactúa "
"directamente con el modelo. El actor de modelo se encarga de ejecutar "
"solicitudes de inferencia del modelo, gestionar los flujos de entrada y "
"salida de datos, y admitir diversas operaciones del modelo. Ambas partes "
"utilizan `Xoscar <https://github.com/xorbitsai/xoscar>`_ para la ejecución "
"concurrente y distribuida."

#: ../../source/development/xinference_internals.rst:273
msgid ""
"`deploy/ "
"<https://github.com/xorbitsai/inference/tree/main/xinference/deploy>`_: It "
"provides a command-line interface (CLI) for interacting with the Xinference "
"framework, allowing users to perform operations by command line. See "
"`Command Line`_ for more information."
msgstr ""
"`deploy/ "
"<https://github.com/xorbitsai/inference/tree/main/xinference/deploy>`_: "
"Proporciona una interfaz de línea de comandos (CLI) para interactuar con el "
"framework Xinference, permitiendo a los usuarios operar mediante comandos. "
"Para más información, consulte `Command Line`_."

#: ../../source/development/xinference_internals.rst:276
msgid ""
"`locale/ "
"<https://github.com/xorbitsai/inference/tree/main/xinference/locale>`_: It "
"supports multi-language localization. By simply adding and updating JSON "
"translation files, it becomes possible to support more languages, improving "
"user experience."
msgstr ""
"`locale/ "
"<https://github.com/xorbitsai/inference/tree/main/xinference/locale>`_: "
"admite la localización multilingüe. Solo necesita agregar y actualizar los "
"archivos de traducción JSON para admitir más idiomas y mejorar la "
"experiencia del usuario."

#: ../../source/development/xinference_internals.rst:279
msgid ""
"`model/ "
"<https://github.com/xorbitsai/inference/tree/main/xinference/model>`_: It "
"provides a structure for model descriptions, creation, and caching. See "
"`Model`_ for more information."
msgstr ""
"`model/ "
"<https://github.com/xorbitsai/inference/tree/main/xinference/model>`_ "
"：proporciona un marco para la descripción, creación y almacenamiento en "
"caché de modelos. Consulte `Model`_ para obtener más información."

#: ../../source/development/xinference_internals.rst:282
msgid ""
"`web/ui/ "
"<https://github.com/xorbitsai/inference/tree/main/xinference/web/ui>`_: The "
"js code of the frontend (Web UI)."
msgstr ""
"`web/ui/ "
"<https://github.com/xorbitsai/inference/tree/main/xinference/web/ui>`_: "
"Código JavaScript del frontend (interfaz de usuario)."

#~ msgid ""
#~ "Xinference supports different types of models including large language "
#~ "models (LLMs), image models, audio models, embedding models, etc"
#~ msgstr ""

#~ msgid ""
#~ "Take `llm/ "
#~ "<https://github.com/xorbitsai/inference/tree/main/xinference/model/llm>`_ "
#~ "for example, it focuses on the management and instantiation of LLMs. It "
#~ "includes detailed implementations for loading, configuring, and deploying "
#~ "LLMs, including handling different types of quantization and model formats. "
#~ "In `llm/ "
#~ "<https://github.com/xorbitsai/inference/tree/main/xinference/model/llm>`_, "
#~ "it supports many backends such as `GGML "
#~ "<https://github.com/xorbitsai/inference/tree/main/xinference/model/llm/ggml>`_,"
#~ " `PyTorch "
#~ "<https://github.com/xorbitsai/inference/tree/main/xinference/model/llm/pytorch>`_,"
#~ " `SGLang "
#~ "<https://github.com/xorbitsai/inference/tree/main/xinference/model/llm/sglang>`_"
#~ " and `vLLM "
#~ "<https://github.com/xorbitsai/inference/tree/main/xinference/model/llm/vllm>`_."
#~ msgstr ""

#~ msgid ""
#~ "All models are implemented in `model/ "
#~ "<https://github.com/xorbitsai/inference/tree/main/xinference/model>`_."
#~ msgstr ""

#~ msgid ""
#~ "In ``model/llm/``, it supports many backends such as GGML, PyTorch, and "
#~ "vLLM."
#~ msgstr ""

#~ msgid ""
#~ "`oscar/ "
#~ "<https://github.com/xorbitsai/inference/tree/main/xinference/client/oscar>`_"
#~ " defines the Actor Client which acts as a client interface for interacting "
#~ "with models deployed in a server environment. It includes functionalities to"
#~ " register/unregister models, launch/terminate models, and interact with "
#~ "different types of models. This part heavily utilizes ``asyncio`` for "
#~ "asynchronous operations. See `Concurrency`_ for more information."
#~ msgstr ""

#~ msgid "Concurrency"
#~ msgstr ""

#~ msgid ""
#~ "Both Xinference and Xoscar highly utilize coroutine programming of "
#~ "``asyncio``."
#~ msgstr ""
