fix(docs): broken link (#3537)

Co-authored-by: Sauyon Lee <[email protected]>
Co-authored-by: Chaoyu <[email protected]>

docs/source/concepts/bento.rst

@@ -456,7 +456,7 @@ which also allow users to add or modify labels at any time.
 Files to include
 ^^^^^^^^^^^^^^^^
 
-In the example :ref:`above </concepts/bento:The Build Command>`, the :code:`*.py` includes every Python files under ``build_ctx``.
+In the example :ref:`above <concepts/bento:The Build Command>`, the :code:`*.py` includes every Python files under ``build_ctx``.
 You can also include other wildcard and directory pattern matching.
 
 .. code-block:: yaml

docs/source/concepts/model.rst

@@ -356,7 +356,7 @@ From the example above, the :code:`iris_clf_runner.predict.run` call will pass t
 the function input to the model's :code:`predict` method, running from a remote runner
 process.
 
-For many :doc:`other ML frameworks <frameworks/index>`, the model object's inference
+For many :doc:`other ML frameworks </frameworks/index>`, the model object's inference
 method may not be called :code:`predict`. Users can customize it by specifying the model
 signature during :code:`save_model`:
 

docs/source/concepts/service.rst

@@ -65,7 +65,7 @@ BentoML provides a convenient way of creating Runner instance from a saved model
     runner = bentoml.sklearn.get("iris_clf:latest").to_runner()
 
 .. tip::
-    Users can also create custom Runners via the :doc:`Runner and Runnable interface <concepts/runner>`.
+    Users can also create custom Runners via the :doc:`Runner and Runnable interface </concepts/runner>`.
 
 
 Runner created from a model will automatically choose the most optimal Runner
@@ -76,7 +76,7 @@ natively, BentoML will create a single global instance of the runner worker and
 all API requests to the global instance; otherwise, BentoML will create multiple
 instances of runners based on the available system resources. We also let advanced users
 to customize the runtime configurations to fine tune the runner performance. To learn
-more, please see the :doc:`concepts/runner` guide.
+more, see the :doc:`introduction to Runners </concepts/runner>`.
 
 Debugging Runners
 ^^^^^^^^^^^^^^^^^
@@ -146,8 +146,7 @@ this URL via the ``route`` option, e.g.:
 .. code-block:: python
 
     @svc.api(
-        input=NumpyNdarray(),
-        output=NumpyNdarray(),
+        input=NumpyNdarray(), output=NumpyNdarray(),
         route="/v2/models/my_model/versions/v0/infer",
     )
     def predict(input_array: np.ndarray) -> np.ndarray:
@@ -243,7 +242,7 @@ The data type and shape of the ``NumpyNdarray`` can be specified with the ``dtyp
 and ``shape`` arguments. By setting the ``enforce_shape`` and ``enforce_dtype`` 
 arguments to `True`, the IO descriptor will strictly validate the input and output data 
 based the specified data type and shape. To learn more, see IO descrptor reference for 
-:ref:`reference/api_io_descriptors:NumPy ndarray`.
+:ref:`reference/api_io_descriptors:NumPy ``ndarray```.
 
 .. code-block:: python
 
@@ -336,7 +335,7 @@ Built-in Types
 ^^^^^^^^^^^^^^
 
 Beside ``NumpyNdarray``, BentoML supports a variety of other built-in IO descriptor
-types under the :doc:`bentoml.io <reference/api_io_descriptors>` module. Each type comes
+types under the :doc:`bentoml.io </reference/api_io_descriptors>` module. Each type comes
 with support of type validation and OpenAPI specification generation. For example:
 
 +-----------------+---------------------+---------------------+-------------------------+

docs/source/frameworks/onnx.rst

@@ -289,8 +289,7 @@ Refer to :ref:`concepts/model:Model Signatures` and :ref:`Batching behaviour <co
 
 .. note::
 
-   BentoML internally use `onnxruntime.InferenceSession
-   <https://onnxruntime.ai/docs/api/python/api_summary.html#inferencesession>`_
+   BentoML internally uses |onnxruntime_inferencesession|_
    to run inference. When the original model is converted to ONNX
    format and loaded by ``onnxruntime.InferenceSession``, the
    inference method of the original model is converted to the ``run``
@@ -394,7 +393,7 @@ Building a Service for **ONNX**
 
    In the aboved example, notice there are both ``run`` and ``async_run``  in ``runner.run.async_run(input_data)`` inside inference code. The distinction between ``run`` and ``async_run`` is as follow:
 
-   1.  The ``run`` refers  to `onnxruntime.InferenceSession <https://github.com/microsoft/onnxruntime/blob/master/onnxruntime/core/session/inference_session.cc>`_'s ``run`` method, which is ONNX Runtime API to run `inference <https://onnxruntime.ai/docs/api/python/api_summary.html#data-inputs-and-outputs>`_.
+   1.  The ``run`` refers  to |onnxruntime_inferencesession|_'s ``run`` method, which is the ONNX Runtime API to run `inference <https://onnxruntime.ai/docs/api/python/api_summary.html#data-inputs-and-outputs>`_.
    2. The ``async_run`` refers to BentoML's runner inference API for invoking a model's signature. In the case of ONNX, it happens to have a similar name like the ``InferenceSession`` endpoint.
 
 
@@ -578,3 +577,7 @@ override the default setting using ``with_options`` when creating a runner:
 .. seealso::
 
    `Execution Providers' documentation <https://onnxruntime.ai/docs/execution-providers/>`_
+
+.. _onnxruntime_inferencesession: https://onnxruntime.ai/docs/api/python/api_summary.html#inferencesession
+
+.. |onnxruntime_inferencesession| replace:: ``onnxruntime.InferenceSession``

docs/source/frameworks/pytorch.rst

@@ -7,7 +7,7 @@ BentoML provides native support for serving and deploying models trained from Py
 Preface
 -------
 
-If you have already compiled your PyTorch model to TorchScript, you might consider to use :doc:`bentoml.torchscript </reference/frameworks/torchscript>`. BentoML provides first-class support for TorchScript, hence using ``bentoml.torchscript`` is less prone to compatibility issues during production.
+If you have already compiled your PyTorch model to TorchScript, you should consider using BentoML's first-class module :doc:`bentoml.torchscript </reference/frameworks/torchscript>` instead, as it is less likely to cause compatibility issues during production.
 
 .. note::
 
@@ -165,7 +165,6 @@ The signatures used for creating a Runner is ``{"__call__": {"batchable": False}
     bentoml.pytorch.save(model, "my_model", signatures={"__call__": {"batch_dim": 0, "batchable": True}})
 
 
-
 Building a Service
 ------------------
 

docs/source/frameworks/sklearn.rst

@@ -33,4 +33,4 @@ Below is a simple example of using scikit-learn with BentoML:
 
 .. note::
 
-   You can find more examples for **scikit-learn** in our `bentoml/examples https://github.com/bentoml/BentoML/tree/main/examples`_ directory.
+   You can find more examples for **scikit-learn** in our :github:`bentoml/examples <bentoml/BentoML/tree/main/examples>` directory.

docs/source/frameworks/tensorflow.rst

@@ -8,7 +8,7 @@ serving and deploying models trained from TensorFlow.
 Preface
 -------
 
-Even though ``bentoml.tensorflow`` supports Keras model, we recommend our users to use :ref:`bentoml.keras <frameworks/keras>` for better development experience. 
+Even though ``bentoml.tensorflow`` supports Keras model, we recommend our users to use :doc:`bentoml.keras </frameworks/keras>` for better development experience. 
 
 If you must use TensorFlow for your Keras model, make sure that your Keras model inference callback (such as ``predict``) is decorated with :obj:`~tf.function`.
 
@@ -20,7 +20,7 @@ If you must use TensorFlow for your Keras model, make sure that your Keras model
 
 .. note::
 
-    :bdg-info:`Remarks:` We recommend users apply model optimization techniques such as **distillation** or **quantization**. Alternatively, Keras models can also be converted to :ref:`ONNX <frameworks/onnx>` models and leverage different runtimes.
+    :bdg-info:`Remarks:` We recommend users apply model optimization techniques such as **distillation** or **quantization**. Alternatively, Keras models can also be converted to :doc:`ONNX </frameworks/onnx>` models and leverage different runtimes.
 
 Compatibility
 -------------

docs/source/guides/client.rst

@@ -54,6 +54,7 @@ For multipart requests, all arguments to the function must currently be keyword
 For example, for the service API function:
 
 .. code-block:: python
+
     @svc.api(input=Multipart(a=Text(), b=Text()), output=JSON())
     def combine(a, b) -> dict[typing.Any, typing.Any]:
         return {a: b}

docs/source/guides/graph.rst

@@ -51,7 +51,7 @@ Create :ref:`Runners <concepts/runner:Using Runners>` for the three text generat
 Create Service
 ##############
 
-Create a :ref:`Service <concept/service:Service and APIs>` named ``inference_graph`` and specify the runners created earlier in the ``runners`` argument.
+Create a :doc:`Service </concepts/service>` named ``inference_graph`` and specify the runners created earlier in the ``runners`` argument.
 
 .. code-block:: python
 

docs/source/guides/logging.rst

@@ -51,7 +51,7 @@ Logging Configuration
 
 Access logs can be configured by setting the appropriate flags in the bento configuration file for
 both web requests and model serving requests. Read more about how to use a bento configuration file
-here in the - :ref:`Configuration Guide <guides/configuration>`
+here in the - :doc:`Configuration Guide </guides/configuration>`
 
 To configure other logs, please use the `default Python logging configuration <https://docs.python.org/3/howto/logging.html>`_. All BentoML logs are logged under the ``bentoml`` namespace.
 

docs/source/guides/metrics.rst

@@ -2,9 +2,11 @@
 Metrics
 =======
 
+*time expected: 6 minutes*
+
 Metrics are measurements of statistics about your service, which can provide information about the usage and performance of your bentos in production.
 
-BentoML allows users to define custom metrics with `Prometheus <https://prometheus.io/docs/introduction/overview/>`_ to easily enable monitoring for their Bentos.
+BentoML allows users to define custom metrics with |prometheus|_ to easily enable monitoring for their Bentos.
 
 This article will dive into the default metrics and how to add custom metrics for
 either a :ref:`concepts/runner:Custom Runner` or :ref:`Service <concepts/service:Service and APIs>`.
@@ -255,8 +257,6 @@ Visit `http://localhost:9090/graph <http://localhost:9090/graph>`_ and use the f
     to get started.
 
 
-----
-
-.. rubric:: Notes
+.. _prometheus: https://prometheus.io/
 
-.. [#prometheus] `Prometheus <https://prometheus.io/>`_
+.. |prometheus| replace:: Prometheus

docs/source/guides/migration.rst

@@ -113,8 +113,7 @@ Next, we will transform the service definition module and breakdown each section
 Environment
 ~~~~~~~~~~~
 
-BentoML version 0.13.1 relies on the :code:`@env`
-`decorator API <https://docs.bentoml.org/en/0.13-lts/concepts.html#defining-service-environment>`_ for defining the
+BentoML version 0.13.1 relies on the :code:`@env` decorator API for defining the
 environment settings and dependencies of the service. Typical arguments of the environment decorator includes Python
 dependencies (e.g. :code:`pip_packages`, :code:`pip_index_url`), Conda dependencies (e.g. :code:`conda_channels`,
 :code:`conda_dependencies`), and Docker options (e.g. :code:`setup_sh`, :code:`docker_base_image`).
@@ -148,8 +147,9 @@ Artifacts
 ~~~~~~~~~
 
 BentoML version 0.13.1 provides the :code:`@artifacts`
-`decorator API <https://docs.bentoml.org/en/0.13-lts/concepts.html#packaging-model-artifacts>`_ for users to specify
-the trained models required by a BentoService. The specified artifacts are automatically serialized and deserialized
+decorator API for users to specify
+the trained models required by a BentoService.
+The specified artifacts are automatically serialized and deserialized
 when saving and loading a BentoService.
 
 .. code-block:: python

docs/source/guides/tracing.rst

@@ -8,7 +8,6 @@ This guide dives into the :wiki:`tracing <Tracing_(software)>` capabilities that
 
 BentoML allows user to export trace with `Zipkin <https://zipkin.io/>`_,
 `Jaeger <https://www.jaegertracing.io/>`_ and `OTLP <https://opentelemetry.io/>`_.
-
 This guide will also provide a simple example of how to use BentoML tracing with `Jaeger <https://www.jaegertracing.io/>`_
 
 Why do you need this?

docs/source/integrations/spark.rst

@@ -27,7 +27,7 @@ be installed in the Spark cluster. Most likely, the service you are hosting Spar
 mechanisms for doing this. If you are using a standalone cluster, you should install those
 dependencies on every node you expect to use.
 
-Finally, we use the quickstart bento from the :ref:`aforementioned tutorial <tutorial>`. If you have
+Finally, we use the quickstart bento from the :doc:`aforementioned tutorial </tutorial>`. If you have
 already followed that tutorial, you should already have that bento. If you have note, simply run the
 following:
 

docs/source/reference/frameworks/index.rst

@@ -10,16 +10,18 @@ Framework APIs
 .. toctree::
     :maxdepth: 1
 
-    catboost
-    fastai
-    keras
-    mlflow
     onnx
     sklearn
     transformers
     flax
     tensorflow
+    torchscript
+    transformers
     xgboost
-    picklable_model
     lightgbm
+    mlflow
+    catboost
+    fastai
+    keras
+    picklable_model
     ray

docs/source/reference/frameworks/picklable_model.rst

@@ -2,9 +2,9 @@
 Pickable Model
 ==============
 
-This is an API reference the :code:`bentoml.picklable_model` module, which can be used for custom
+This is an API reference the ``bentoml.picklable_model`` module, which can be used for custom
 Python-based ML models in BentoML. To learn more, visit
-:doc:`Pickable Model </frameworks/pickable>`.
+:doc:`Pickable Model </frameworks/picklable>`.
 
 
 

docs/source/reference/frameworks/tensorflow.rst

@@ -11,7 +11,7 @@ TensorFlow
 
 .. note::
 
-   You can find more examples for **TensorFlow** in our `bentoml/examples https://github.com/bentoml/BentoML/tree/main/examples`_ directory.
+   You can find more examples for **TensorFlow** in our :github:`bentoml/examples <bentoml/BentoML/tree/main/examples>` directory.
 
 .. currentmodule:: bentoml.tensorflow
 

docs/source/reference/frameworks/torchscript.rst

@@ -0,0 +1,21 @@
+===========
+TorchScript
+===========
+
+.. admonition:: About this page
+
+   This is the API reference for TorchScript in BentoML.
+   You can find more information about TorchScript in the `official documentation <https://pytorch.org/docs/stable/jit.html>`_.
+
+
+.. note::
+
+   You can find more examples for **TorchScript** in our :github:`bentoml/examples <bentoml/BentoML/tree/main/examples>` directory.
+
+.. currentmodule:: bentoml.torchscript
+
+.. autofunction:: bentoml.torchscript.save_model
+
+.. autofunction:: bentoml.torchscript.load_model
+
+.. autofunction:: bentoml.torchscript.get