fix links

Author: rgsl888prabhu

docs/cuopt/source/cuopt-c/lp-milp/lp-example.rst

@@ -5,7 +5,7 @@ LP C API Examples
 Example With Data
 -----------------
 
-This example demonstrates how to use the LP solver in C. More details on the API can be found in `C API <lp-milp-c-api.html>`_.
+This example demonstrates how to use the LP solver in C. More details on the API can be found in :doc:`C API <lp-milp-c-api>`.
 
 Copy the code below into a file called ``lp_example.c``:
 

docs/cuopt/source/cuopt-c/lp-milp/lp-milp-c-api.rst

@@ -48,15 +48,15 @@ Certain constants are needed to define an optimization problem. These constants
 Objective Sense Constants
 -------------------------
 
-These constants are used to define the objective sense in the `cuOptCreateProblem <lp-milp-c-api.html#c.cuOptCreateProblem>`_ and `cuOptCreateRangedProblem <lp-milp-c-api.html#c.cuOptCreateRangedProblem>`_ functions.
+These constants are used to define the objective sense in the :c:func:`cuOptCreateProblem` and :c:func:`cuOptCreateRangedProblem` functions.
 
 .. doxygendefine:: CUOPT_MINIMIZE
 .. doxygendefine:: CUOPT_MAXIMIZE
 
 Constraint Sense Constants
 --------------------------
 
-These constants are used to define the constraint sense in the `cuOptCreateProblem <lp-milp-c-api.html#c.cuOptCreateProblem>`_ and `cuOptCreateRangedProblem <lp-milp-c-api.html#c.cuOptCreateRangedProblem>`_ functions.
+These constants are used to define the constraint sense in the :c:func:`cuOptCreateProblem` and :c:func:`cuOptCreateRangedProblem` functions.
 
 .. doxygendefine:: CUOPT_LESS_THAN
 .. doxygendefine:: CUOPT_GREATER_THAN
@@ -65,15 +65,15 @@ These constants are used to define the constraint sense in the `cuOptCreateProbl
 Variable Type Constants
 -----------------------
 
-These constants are used to define the the variable type in the `cuOptCreateProblem <lp-milp-c-api.html#c.cuOptCreateProblem>`_ and `cuOptCreateRangedProblem <lp-milp-c-api.html#c.cuOptCreateRangedProblem>`_ functions.
+These constants are used to define the the variable type in the :c:func:`cuOptCreateProblem` and :c:func:`cuOptCreateRangedProblem` functions.
 
 .. doxygendefine:: CUOPT_CONTINUOUS
 .. doxygendefine:: CUOPT_INTEGER
 
 Infinity Constant
 -----------------
 
-This constant may be used to represent infinity in the `cuOptCreateProblem <lp-milp-c-api.html#c.cuOptCreateProblem>`_ and `cuOptCreateRangedProblem <lp-milp-c-api.html#c.cuOptCreateRangedProblem>`_ functions.
+This constant may be used to represent infinity in the :c:func:`cuOptCreateProblem` and :c:func:`cuOptCreateRangedProblem` functions.
 
 .. doxygendefine:: CUOPT_INFINITY
 

docs/cuopt/source/cuopt-c/lp-milp/milp-examples.rst

@@ -5,7 +5,7 @@ MILP C API Examples
 Example With Data
 -----------------
 
-This example demonstrates how to use the MILP solver in C. More details on the API can be found in `C API <lp-milp-c-api.html>`_.
+This example demonstrates how to use the MILP solver in C. More details on the API can be found in :doc:`C API <lp-milp-c-api>`.
 
 Copy the code below into a file called ``milp_example.c``:
 

docs/cuopt/source/cuopt-server/csp-guides/csp-aws.rst

@@ -46,7 +46,7 @@ Step 1: Create an AWS VM with NVAIE Image
 Step 2: Activate NVAIE Subscription
 ------------------------------------
 
-Once connected to the VM, generate an identity token. Activate your NVIDIA AI Enterprise subscription using that identity token on NGC. Follow the instructions `here <https://docs.nvidia.com/ai-enterprise/deployment-guide-cloud/0.1.0/azure-ai-enterprise-vmi.html#accessing-the-nc-on-ngc>`__.
+Once connected to the VM, generate an identity token. Activate your NVIDIA AI Enterprise subscription using that identity token on NGC. Follow the instructions `here <https://docs.nvidia.com/ai-enterprise/deployment/cloud/latest/azure-ai-enterprise-vmi.html#accessing-the-ngc-catalog-on-ngc>`__.
 
 Step 3: Run cuOpt
 ------------------

docs/cuopt/source/cuopt-server/csp-guides/csp-azure.rst

@@ -56,12 +56,12 @@ Step 1: Create an Azure VM with NVAIE Image
 Step 2: Activate NVAIE Subscription
 ------------------------------------
 
-Once connected to the VM, generate an identity token. Activate your NVIDIA AI Enterprise subscription using that identity token on NGC. Follow the instructions `here <https://docs.nvidia.com/ai-enterprise/deployment-guide-cloud/0.1.0/azure-ai-enterprise-vmi.html#accessing-the-nc-on-ngc>`__.
+Once connected to the VM, generate an identity token. Activate your NVIDIA AI Enterprise subscription using that identity token on NGC. Follow the instructions `here <https://docs.nvidia.com/ai-enterprise/deployment/cloud/latest/azure-ai-enterprise-vmi.html#accessing-the-ngc-catalog-on-ngc>`__.
 
 Step 3: Run cuOpt
 ------------------
 
-To run cuOpt, you will need to log in to the NVIDIA Container Registry, pull the cuOpt container, and then run it. To test that it is successfully running, you can run a sample cuOpt request. This process is the same for deploying cuOpt on your own infrastructure. Refer `Self-Hosted Service Quickstart Guide </cuopt-server/quick-start.html#container-from-nvidia-ngc>`__.
+To run cuOpt, you will need to log in to the NVIDIA Container Registry, pull the cuOpt container, and then run it. To test that it is successfully running, you can run a sample cuOpt request. This process is the same for deploying cuOpt on your own infrastructure. Refer :ref:`Self-Hosted Service Quickstart Guide <container-from-nvidia-ngc>`.
 
 
 Step 4: Mapping Visualization with Azure

docs/cuopt/source/cuopt-server/examples/lp-examples.rst

@@ -15,6 +15,8 @@ If you want to run server locally, please run the following command in a termina
     export port=5000
     python -m cuopt_server.cuopt_service --ip $ip --port $port
 
+.. _generic-example-with-normal-and-batch-mode:
+
 Genric Example With Normal Mode and Batch Mode
 ------------------------------------------------
 
@@ -225,6 +227,8 @@ Batch mode response:
 .. note::
     Warm start is only applicable to LP and not for MILP.
 
+.. _warm-start:
+
 Warm Start
 ----------
 

docs/cuopt/source/cuopt-server/quick-start.rst

@@ -104,7 +104,7 @@ After installation, you can verify that cuOpt Server is working correctly by run
    The following example is for running the server locally. If you are using the container approach, you should comment out the server start and kill commands in the script below since the server is already running in the container.
 
 The following example is testing with a simple routing problem constuctured as Json request and sent over HTTP to the server using ``curl``.This example is running server with few configuration options such as ``--ip`` and ``--port``.
-Additional configuration options for server can be found in `Server CLI <server-api/server-cli.html>`_
+Additional configuration options for server can be found in :doc:`Server CLI <server-api/server-cli>`.
 
 
 Install jq and curl for basic HTTP requests and parsing JSON responses

docs/cuopt/source/faq.rst

@@ -45,11 +45,11 @@ General FAQ
 
 .. dropdown:: Do I need a GPU to use cuOpt?
 
-    Yes, please refer to `system requirements <system-requirements.html>`_ for GPU specifications. You can acquire a cloud instance with a supported GPU and launch cuOpt; alternatively, you can launch it in your local machine if it meets the requirements.
+    Yes, please refer to :doc:`system requirements <system-requirements>` for GPU specifications. You can acquire a cloud instance with a supported GPU and launch cuOpt; alternatively, you can launch it in your local machine if it meets the requirements.
 
 .. dropdown:: Does cuOpt use multiple GPUs/multi-GPUs/multi GPUs?
 
-    #. Yes, in cuOpt self-hosted server, a solver process per GPU can be configured to run multiple solvers. Requests are accepted in a round-robin queue. More details are available in `server api <cuopt-server/server-api/server-cli.html>`_.
+    #. Yes, in cuOpt self-hosted server, a solver process per GPU can be configured to run multiple solvers. Requests are accepted in a round-robin queue. More details are available in :doc:`server api <cuopt-server/server-api/server-cli>`.
     #. There is no support for leveraging multiple GPUs to solve a single problem or oversubscribing a single GPU for multiple solvers.
 
 .. dropdown:: The cuOpt Service is not starting: Issue with port?
@@ -249,7 +249,7 @@ Routing FAQ
 
     #. cuOpt is stateless and cannot handle dynamic constraints directly, but this can be resolved with modeling.
     #. Dynamic reoptimization is used when there is a change in the conditions of the operation such as a vehicle getting broken, a driver calling in sick, a road block, traffic, or a high-priority order coming in.
-    #. The problem is prepped in such a way that the packages that are already en route are assigned to only those vehicles, and new and old deliveries will be added to this problem. Please refer to example notebooks in `cuOpt Resources <resources.html>`__ to understand more about how to tackle this problem.
+    #. The problem is prepped in such a way that the packages that are already en route are assigned to only those vehicles, and new and old deliveries will be added to this problem. Please refer to example notebooks in :doc:`cuOpt Resources <resources>` to understand more about how to tackle this problem.
 
 .. dropdown:: Does cuOpt take an initial solution?
 

docs/cuopt/source/lp-features.rst

@@ -53,7 +53,7 @@ Warm Start
 
 A warm starts allow a user to provide an initial solution to help PDLP converge faster. The initial ``primal`` and ``dual`` solutions can be specified by the user.
 
-Alternatively, previously run solutions can be used to warm start a new solve to decrease solve time. `Examples <cuopt-server/lp-examples.html#warm-start>`_ are shared on the self-hosted page.
+Alternatively, previously run solutions can be used to warm start a new solve to decrease solve time. :ref:`Examples <warm-start>` are shared on the self-hosted page.
 
 PDLP Solver Mode
 ----------------
@@ -73,12 +73,12 @@ Method
 Crossover
 ---------
 
-Crossover allows you to obtain a high-quality basic solution from the results of a PDLP solve. More details can be found `here <lp-milp-settings.html#crossover>`__.
+Crossover allows you to obtain a high-quality basic solution from the results of a PDLP solve. More details can be found :ref:`here <crossover>`.
 
 
 Logging Callback
 ----------------
-With logging callback, users can fetch server-side logs for additional debugs and to get details on solver process details. `Examples <cuopt-server/examples/lp-examples.html#logging-callback>`__ are shared on the self-hosted page.
+With logging callback, users can fetch server-side logs for additional debugs and to get details on solver process details. :ref:`Examples <generic-example-with-normal-and-batch-mode>` are shared on the self-hosted page.
 
 
 Infeasibility Detection
@@ -102,4 +102,4 @@ The user may specify a time limit to the solver. By default the solver runs unti
 Batch Mode
 ----------
 
-Users can submit a set of problems which will be solved in a batch. Problems will be solved at the same time in parallel to fully utilize the GPU. Checkout `self-hosted client <cuopt-server/examples/lp-examples.html#batch-mode>`_ example in thin client.
+Users can submit a set of problems which will be solved in a batch. Problems will be solved at the same time in parallel to fully utilize the GPU. Checkout :ref:`self-hosted client <generic-example-with-normal-and-batch-mode>` example in thin client.

docs/cuopt/source/lp-milp-settings.rst

@@ -133,6 +133,8 @@ detected as infeasible for PDLP to stop.
 
 Note: the default value is false.
 
+.. _crossover:
+
 Crossover
 ^^^^^^^^^